Some initial learnings:\n- Generating web app-based reports goes much better if there are explicit templates/pre-defined components for the model to use.\n- Claude can \u201cheal\u201d broken charts if you give it access to chart images and run a separate QA loop.
Would either feedback from the community or to hear from others that have tried similar things!"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Agentic Data Analysis with Claude Code"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://rubenflamshepherd.com/articles/2026-03-09-agentic-data-analysis-with-claude-code"}},"_tags":["story","author_rubenflamshep","story_47325702","show_hn"],"author":"rubenflamshep","created_at":"2026-03-10T16:44:53Z","created_at_i":1773161093,"num_comments":0,"objectID":"47325702","points":6,"story_id":47325702,"story_text":"Hey HN, as a former data analyst, I\u2019ve been tooling around trying to get agents to do my old job. The result is this system that gets you maybe 80% of the way there. I think this is a good data point for what the current frontier models are capable of and where they are still lacking (in this case \u2014 hypothesis generation and general data intuition).
Some initial learnings:\n- Generating web app-based reports goes much better if there are explicit templates/pre-defined components for the model to use.\n- Claude can \u201cheal\u201d broken charts if you give it access to chart images and run a separate QA loop.
Would either feedback from the community or to hear from others that have tried similar things!","title":"Show HN: Agentic Data Analysis with Claude Code","updated_at":"2026-03-14T19:37:44Z","url":"https://rubenflamshepherd.com/articles/2026-03-09-agentic-data-analysis-with-claude-code"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"ramtatatam"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"Hello fellow HN users!
I recently stumbled across the problem where I am given a list of lines and need to join them into shapes. Some lines may overlap with other lines, some lines might be very close (unwanted artifacts), some lines might produce shapes that are not closed and I would need to join beginning and end myself.
Is there any literature which would touch on some more general problem? My intuition tells me I probably should look into graphs theory but maybe there is more intuitive approach? Lines are coming from contour detection algorithm and they may be very very close each to other but not touch each other- human eye would see the shape easily, but from programmatic point of view I feel I'll have to figure out some thresholds that if lines are close enough I consider them part of shape."},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: Is there any CS concept for joining individual lines into shapes"}},"_tags":["story","author_ramtatatam","story_27122422","ask_hn"],"author":"ramtatatam","children":[27122821],"created_at":"2021-05-11T19:29:48Z","created_at_i":1620761388,"num_comments":3,"objectID":"27122422","points":4,"story_id":27122422,"story_text":"Hello fellow HN users!
I recently stumbled across the problem where I am given a list of lines and need to join them into shapes. Some lines may overlap with other lines, some lines might be very close (unwanted artifacts), some lines might produce shapes that are not closed and I would need to join beginning and end myself.
Is there any literature which would touch on some more general problem? My intuition tells me I probably should look into graphs theory but maybe there is more intuitive approach? Lines are coming from contour detection algorithm and they may be very very close each to other but not touch each other- human eye would see the shape easily, but from programmatic point of view I feel I'll have to figure out some thresholds that if lines are close enough I consider them part of shape.","title":"Ask HN: Is there any CS concept for joining individual lines into shapes","updated_at":"2024-09-20T08:31:28Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"dmbaggett"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"A friend in my network recently posted a well-known CS puzzle on Facebook:
You have 12 coins, one of which weighs more than the other 11. You have a balance, and wish to use it to find the heavier coin. You may only use the balance three times. How do you find the heavier coin?
This is a great puzzle for an early CS learning because it gives some intuition into the general strategy of divide-and-conquer. It also nicely extends to harder variants: can you do the same for 27 coins with three weighings? Is this the limit? Why? Can you prove your answer?
My question for HN is: can you recommend some puzzle books that focus on CS puzzles like this one that are suitable for a bright high schooler? (The bright high school in this case is my daughter.)"},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: CS puzzle books for high schoolers?"}},"_tags":["story","author_dmbaggett","story_23379899","ask_hn"],"author":"dmbaggett","created_at":"2020-06-01T15:57:28Z","created_at_i":1591027048,"num_comments":0,"objectID":"23379899","points":1,"story_id":23379899,"story_text":"A friend in my network recently posted a well-known CS puzzle on Facebook:
You have 12 coins, one of which weighs more than the other 11. You have a balance, and wish to use it to find the heavier coin. You may only use the balance three times. How do you find the heavier coin?
This is a great puzzle for an early CS learning because it gives some intuition into the general strategy of divide-and-conquer. It also nicely extends to harder variants: can you do the same for 27 coins with three weighings? Is this the limit? Why? Can you prove your answer?
My question for HN is: can you recommend some puzzle books that focus on CS puzzles like this one that are suitable for a bright high schooler? (The bright high school in this case is my daughter.)","title":"Ask HN: CS puzzle books for high schoolers?","updated_at":"2024-09-20T06:19:12Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"lyavin"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"Sources of Intuitions and Data on Artificial General Intelligence"},"url":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["intuition"],"value":"https://www.lesserwrong.com/posts/BibDWWeo37pzuZCmL/sources-of-intuitions-and-data-on-agi"}},"_tags":["story","author_lyavin","story_16448511"],"author":"lyavin","created_at":"2018-02-23T19:08:22Z","created_at_i":1519412902,"num_comments":0,"objectID":"16448511","points":2,"story_id":16448511,"title":"Sources of Intuitions and Data on Artificial General Intelligence","updated_at":"2024-09-20T02:04:25Z","url":"https://www.lesserwrong.com/posts/BibDWWeo37pzuZCmL/sources-of-intuitions-and-data-on-agi"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"11235813213455"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["general"],"value":"How Hand Dexterity Made Human General Intelligence"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"https://medium.com/intuitionmachine/hand-dexterity-and-human-general-intelligence-af5596e1d36d"}},"_tags":["story","author_11235813213455","story_38754679"],"author":"11235813213455","created_at":"2023-12-24T16:47:46Z","created_at_i":1703436466,"num_comments":0,"objectID":"38754679","points":3,"story_id":38754679,"title":"How Hand Dexterity Made Human General Intelligence","updated_at":"2024-09-20T16:01:31Z","url":"https://medium.com/intuitionmachine/hand-dexterity-and-human-general-intelligence-af5596e1d36d"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"chaitanyabaweja"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"A Century of General Relativity. Part I: History and Intuition"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"https://medium.com/swlh/a-century-of-general-relativity-part-i-history-and-intuition-27bf0c85535a"}},"_tags":["story","author_chaitanyabaweja","story_21090007"],"author":"chaitanyabaweja","created_at":"2019-09-27T09:32:21Z","created_at_i":1569576741,"num_comments":0,"objectID":"21090007","points":2,"story_id":21090007,"title":"A Century of General Relativity. Part I: History and Intuition","updated_at":"2024-09-20T05:02:48Z","url":"https://medium.com/swlh/a-century-of-general-relativity-part-i-history-and-intuition-27bf0c85535a"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"hsikka"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"Hey HN,
I am a graduate student doing ML research, and lately I've been thinking a lot about designing learning systems from the hardware through the software layers.
I have no experience with what is going at the processor levels, and I was wondering what prerequisite subjects or general curricula I should follow to learn and reason at these lower levels of abstraction.
To be clear, I'm doing this to build intuitions about new computational systems and how different chips, from ASIC to neuromorphic, may be designed.
Any resources or advice telling me I'm a fool is welcome!"},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: How to Self-Study Integrated Circuit Design?"}},"_tags":["story","author_hsikka","story_19890949","ask_hn"],"author":"hsikka","children":[19891117,19893447,19893875,19894412,19895605,19896024,19897059,19897283,19897698,19898030,19898132,19899501,19899861,19901273,19910562,19912343,19918930,19945473],"created_at":"2019-05-12T09:26:26Z","created_at_i":1557653186,"num_comments":39,"objectID":"19890949","points":178,"story_id":19890949,"story_text":"Hey HN,
I am a graduate student doing ML research, and lately I've been thinking a lot about designing learning systems from the hardware through the software layers.
I have no experience with what is going at the processor levels, and I was wondering what prerequisite subjects or general curricula I should follow to learn and reason at these lower levels of abstraction.
To be clear, I'm doing this to build intuitions about new computational systems and how different chips, from ASIC to neuromorphic, may be designed.
Any resources or advice telling me I'm a fool is welcome!","title":"Ask HN: How to Self-Study Integrated Circuit Design?","updated_at":"2026-02-23T19:26:40Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"thesephist"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"Hi HN! I've found this visualization tool immensely helpful over the years for getting an intuition for how an LLM "sees" some piece of text, and with a bit of elbow grease decided to move all compute to client side so I could make it publicly available.
I've found it particularly useful for
- Understanding exactly how repetition and patterns affect a small LM's ability to predict correctly
- Understanding different tokenization patterns and how it affects model output
- Getting a general sense of how "hard" different prediction tasks are for GPT-style models
Known problems (that I probably won't fix, since this was a kind of one-off project)
- Doesn't work well with Unicode grapheme clusters that are multiple GPT-2 tokens (e.g. emoji, smart quotes)
- Support for other models (maybe later?)"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Fully client-side GPT2 prediction visualizer"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://perplexity.vercel.app/"}},"_tags":["story","author_thesephist","story_37398812","show_hn"],"author":"thesephist","children":[37398858,37400608,37401157,37401383,37401721,37402196,37402682,37403022,37405847,37439687],"created_at":"2023-09-05T22:42:39Z","created_at_i":1693953759,"num_comments":11,"objectID":"37398812","points":153,"story_id":37398812,"story_text":"Hi HN! I've found this visualization tool immensely helpful over the years for getting an intuition for how an LLM "sees" some piece of text, and with a bit of elbow grease decided to move all compute to client side so I could make it publicly available.
I've found it particularly useful for
- Understanding exactly how repetition and patterns affect a small LM's ability to predict correctly
- Understanding different tokenization patterns and how it affects model output
- Getting a general sense of how "hard" different prediction tasks are for GPT-style models
Known problems (that I probably won't fix, since this was a kind of one-off project)
- Doesn't work well with Unicode grapheme clusters that are multiple GPT-2 tokens (e.g. emoji, smart quotes)
- Support for other models (maybe later?)","title":"Show HN: Fully client-side GPT2 prediction visualizer","updated_at":"2024-09-20T15:06:43Z","url":"https://perplexity.vercel.app/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"beefield"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"I am currently working in a young company (not a software company per se, but we need to e.g. do some IoT type things.) and have ended up having quite a lot of responsibility in developing/designing the information infrastructure for our company. Now, I do not see myself as a professional in most of these things, and I think at some point we would need to hire a person to step up our game. At this point I would like to keep this as a general question, so assuming I want to hire a developer that should be better than I am, how should I approach this problem? How do I distinguish candidates that can do the smooth talk from the ones that can walk the talk and understand the balance between that on the one hand you need to keep the technological debt in reins but in the other hand sometimes you just need to get sh*t done, even if that is ugly?
Or is this just an impossible task and best bet is to trust to dumb luck, intuition and fizzbuzz?"},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: How to hire a better software engineer than I am?"}},"_tags":["story","author_beefield","story_14898865","ask_hn"],"author":"beefield","children":[14900056,14901459,14902332,14903460],"created_at":"2017-08-01T09:00:17Z","created_at_i":1501578017,"num_comments":7,"objectID":"14898865","points":12,"story_id":14898865,"story_text":"I am currently working in a young company (not a software company per se, but we need to e.g. do some IoT type things.) and have ended up having quite a lot of responsibility in developing/designing the information infrastructure for our company. Now, I do not see myself as a professional in most of these things, and I think at some point we would need to hire a person to step up our game. At this point I would like to keep this as a general question, so assuming I want to hire a developer that should be better than I am, how should I approach this problem? How do I distinguish candidates that can do the smooth talk from the ones that can walk the talk and understand the balance between that on the one hand you need to keep the technological debt in reins but in the other hand sometimes you just need to get sh*t done, even if that is ugly?
Or is this just an impossible task and best bet is to trust to dumb luck, intuition and fizzbuzz?","title":"Ask HN: How to hire a better software engineer than I am?","updated_at":"2024-09-20T01:13:30Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"coderunner"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"There's a couple companies in the business of using a 3d simulation for training autonomous cars like Waymo, Cruise, Nvidia, and Applied Intuition. I don't quite understand their product though.
1. Are the trained object detectors in the simulation applied to real world data also or is only the part that makes decisions transferred to the real vehicle (e.g. it's safe to turn left here) while detectors trained on real world images of cars, people, etc. used?
2. Tangentially, I thought that in general detectors trained on computer generated images was not very applicable to real world images. eg training on a bunch of images of 3d modeled humans won't work well with testing on pictures of real humans. Is this not true?"},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: Autonomous Cars Simulation"}},"_tags":["story","author_coderunner","story_19702040","ask_hn"],"author":"coderunner","children":[19702923,19703272,19704556],"created_at":"2019-04-19T18:57:06Z","created_at_i":1555700226,"num_comments":6,"objectID":"19702040","points":12,"story_id":19702040,"story_text":"There's a couple companies in the business of using a 3d simulation for training autonomous cars like Waymo, Cruise, Nvidia, and Applied Intuition. I don't quite understand their product though.
1. Are the trained object detectors in the simulation applied to real world data also or is only the part that makes decisions transferred to the real vehicle (e.g. it's safe to turn left here) while detectors trained on real world images of cars, people, etc. used?
2. Tangentially, I thought that in general detectors trained on computer generated images was not very applicable to real world images. eg training on a bunch of images of 3d modeled humans won't work well with testing on pictures of real humans. Is this not true?","title":"Ask HN: Autonomous Cars Simulation","updated_at":"2024-09-20T04:00:10Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"starchild3001"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"(A counterpoint to the prevailing narrative that singularity will be highly discontinuous and disruptive)
The concept of the technological singularity, a hypothetical future event where artificial superintelligence surpasses human intelligence and leads to unforeseeable changes in human civilization, has been a topic of fascination and concern for many years. However, there are several reasons to believe that the singularity may not be as disruptive or as significant as some predict. In this essay, we will explore five key reasons why the technological singularity might be a "big nothing."
1. Resistance to Adopting Superintelligence One of the main reasons why the singularity may not have a profound impact on daily life is that people will likely be unwilling to recognize and listen to a "so-called" superintelligent being. Humans have a natural tendency to be skeptical of authority figures, especially those that claim to have superior knowledge or abilities. Even if a superintelligent AI were to emerge, many people would likely question its credibility and resist its influence in their daily decision-making processes.
History has shown that people often prefer to rely on their own judgment and intuition rather than blindly following the advice of experts or authority figures. This tendency is likely to be even more pronounced when it comes to an artificial intelligence, as people may view it as a threat to their autonomy and way of life. As a result, the impact of superintelligence on society may be limited by people's willingness to accept and integrate its guidance into their lives.
2. Difficulty in Identifying Superintelligence Another reason why the singularity may not be as significant as some believe is that it will be very challenging to define and recognize superintelligence. Intelligence is a complex and multifaceted concept that encompasses a wide range of abilities, including reasoning, problem-solving, learning, and creativity. Even among humans, there is no universally accepted definition or measure of intelligence, and it is often difficult to compare the intelligence of individuals across different domains or contexts.
Given this complexity, it will be even more challenging to determine whether an artificial intelligence has truly achieved superintelligence. Even if an AI system demonstrates remarkable abilities in specific tasks or domains, it may not necessarily be considered superintelligent by everyone. There will likely be ongoing debates and disagreements among experts and the general public about whether a particular AI system qualifies as superintelligent, which could limit its impact and influence on society.
3. Limitations of Artificial Intelligence Fundamental differences between machine intelligence and human intelligence may permanently limit the scope and applicability of AI. This can be understood through analogies. For example planes can fly, but they aren't a replacement for birds. Similarly, submarines can swim, but they aren't a replacement for fish. Likewise a machine built to mimic human intelligence may never be a perfect replacement of human intelligence or intellect, due to structural incompatibilities and differences (biological organism vs silicon machine). The gap may remain this way for the foreseeable future.
Contemporary AI systems predominantly rely on narrow, domain-specific algorithms trained on vast datasets. They lack the general intelligence and versatility that humans possess, which enables us to learn from experience, apply knowledge across diverse domains, and navigate novel scenarios. The degree to which silicon machines can emulate human capabilities remains uncertain, even if they eventually surpass us in specific areas such as information retrieval, logical reasoning, textual Q&A, analysis, scientific research and discovery.
<To be continued>"},"title":{"matchLevel":"none","matchedWords":[],"value":"Why the Technological Singularity May Be a \"Big Nothing\""}},"_tags":["story","author_starchild3001","story_45154950","ask_hn"],"author":"starchild3001","children":[45154951,45155520,45157588,45157740,45159783,45160118,45161604],"created_at":"2025-09-07T02:48:03Z","created_at_i":1757213283,"num_comments":8,"objectID":"45154950","points":8,"story_id":45154950,"story_text":"(A counterpoint to the prevailing narrative that singularity will be highly discontinuous and disruptive)
The concept of the technological singularity, a hypothetical future event where artificial superintelligence surpasses human intelligence and leads to unforeseeable changes in human civilization, has been a topic of fascination and concern for many years. However, there are several reasons to believe that the singularity may not be as disruptive or as significant as some predict. In this essay, we will explore five key reasons why the technological singularity might be a "big nothing."
1. Resistance to Adopting Superintelligence One of the main reasons why the singularity may not have a profound impact on daily life is that people will likely be unwilling to recognize and listen to a "so-called" superintelligent being. Humans have a natural tendency to be skeptical of authority figures, especially those that claim to have superior knowledge or abilities. Even if a superintelligent AI were to emerge, many people would likely question its credibility and resist its influence in their daily decision-making processes.
History has shown that people often prefer to rely on their own judgment and intuition rather than blindly following the advice of experts or authority figures. This tendency is likely to be even more pronounced when it comes to an artificial intelligence, as people may view it as a threat to their autonomy and way of life. As a result, the impact of superintelligence on society may be limited by people's willingness to accept and integrate its guidance into their lives.
2. Difficulty in Identifying Superintelligence Another reason why the singularity may not be as significant as some believe is that it will be very challenging to define and recognize superintelligence. Intelligence is a complex and multifaceted concept that encompasses a wide range of abilities, including reasoning, problem-solving, learning, and creativity. Even among humans, there is no universally accepted definition or measure of intelligence, and it is often difficult to compare the intelligence of individuals across different domains or contexts.
Given this complexity, it will be even more challenging to determine whether an artificial intelligence has truly achieved superintelligence. Even if an AI system demonstrates remarkable abilities in specific tasks or domains, it may not necessarily be considered superintelligent by everyone. There will likely be ongoing debates and disagreements among experts and the general public about whether a particular AI system qualifies as superintelligent, which could limit its impact and influence on society.
3. Limitations of Artificial Intelligence Fundamental differences between machine intelligence and human intelligence may permanently limit the scope and applicability of AI. This can be understood through analogies. For example planes can fly, but they aren't a replacement for birds. Similarly, submarines can swim, but they aren't a replacement for fish. Likewise a machine built to mimic human intelligence may never be a perfect replacement of human intelligence or intellect, due to structural incompatibilities and differences (biological organism vs silicon machine). The gap may remain this way for the foreseeable future.
Contemporary AI systems predominantly rely on narrow, domain-specific algorithms trained on vast datasets. They lack the general intelligence and versatility that humans possess, which enables us to learn from experience, apply knowledge across diverse domains, and navigate novel scenarios. The degree to which silicon machines can emulate human capabilities remains uncertain, even if they eventually surpass us in specific areas such as information retrieval, logical reasoning, textual Q&A, analysis, scientific research and discovery.
<To be continued>","title":"Why the Technological Singularity May Be a \"Big Nothing\"","updated_at":"2026-03-05T22:40:17Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"SonicsLegs"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"Hey guys,
I'm a mid-twenties software engineer who plays a lot of video games in my spare time. Games of choice are mostly FPS titles (e.g. Overwatch, CS:GO) and I tend to be competitive when I play. In case it's relevant, I also have a particularly vicious case of ADHD.
The nature of the games I play make a headset effectively mandatory, as sound cues are particularly important. That being the case, I've noticed that, for some reason that I can't pin down, I tend to perform better overall in-game when I'm not wearing a headset. Specifically, when I don't wear a headset, I:
- Auto-pilot less (I think more about my objectives, how I want to execute, and what I'm doing in general)\n- Tense up less during "stressful situations" (I'm generally overall calmer)\n- Seem to aim/track targets better (likely a result of the above; my mouse arm is more relaxed)\n- Am overall more focused\n- Simply win more games
As a result, I frequently opt to put my headset around my neck and simply turn up the volume, however this is far from ideal.
Why would just wearing a headset impact my performance? Is this a known problem?
At first I thought it was a comfort thing, and tried other headsets to no avail. I'm now leaning towards it being psychological (sound makes the experience more immersive, the increased immersion makes the "virtually stressful" situations actually stressful, and the increased stress impedes my higher-level thinking), however that's just baseless intuition on my part.
I'd really appreciate any insights any of you have. :)"},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: Why do I perform better in video games when I don't wear a headset?"}},"_tags":["story","author_SonicsLegs","story_18696731","ask_hn"],"author":"SonicsLegs","children":[18696816,18696915,18696948,18698617,18700660,18705546,18721569],"created_at":"2018-12-17T03:29:47Z","created_at_i":1545017387,"num_comments":8,"objectID":"18696731","points":6,"story_id":18696731,"story_text":"Hey guys,
I'm a mid-twenties software engineer who plays a lot of video games in my spare time. Games of choice are mostly FPS titles (e.g. Overwatch, CS:GO) and I tend to be competitive when I play. In case it's relevant, I also have a particularly vicious case of ADHD.
The nature of the games I play make a headset effectively mandatory, as sound cues are particularly important. That being the case, I've noticed that, for some reason that I can't pin down, I tend to perform better overall in-game when I'm not wearing a headset. Specifically, when I don't wear a headset, I:
- Auto-pilot less (I think more about my objectives, how I want to execute, and what I'm doing in general)\n- Tense up less during "stressful situations" (I'm generally overall calmer)\n- Seem to aim/track targets better (likely a result of the above; my mouse arm is more relaxed)\n- Am overall more focused\n- Simply win more games
As a result, I frequently opt to put my headset around my neck and simply turn up the volume, however this is far from ideal.
Why would just wearing a headset impact my performance? Is this a known problem?
At first I thought it was a comfort thing, and tried other headsets to no avail. I'm now leaning towards it being psychological (sound makes the experience more immersive, the increased immersion makes the "virtually stressful" situations actually stressful, and the increased stress impedes my higher-level thinking), however that's just baseless intuition on my part.
I'd really appreciate any insights any of you have. :)","title":"Ask HN: Why do I perform better in video games when I don't wear a headset?","updated_at":"2024-09-20T03:35:28Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"haensi"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"My wife (staying at home) and I have a 2yo girl and a 7mo girl and raise them trilingually (English, Spanish, German).\nTo our surprise, the 2yo has a better pronunciation than monolingual peers.\nShe also has a remarkable memory and can remember names of places and people.\nMoreover, she discovers letters on signs and posters. Her favorite book is a visual dictionary. [3]\nIn general, our daughters can have access to all tools after a short introduction and under supervision.\nScissors? Hold em like this. Kitchen knife? Handle with care. China plates? Careful.
Most people in our neighborhood do not have a strategy on how to raise the children.\nThey rather trust in day care to take care of that or follow their intuition.
I feel a strong need to strategically work out an education.\nSo far, I have been studying the Montessori work, namely 'Montessori, the Science Behind the Genius' [2].
I am interested in the following questions:
1. What educational strategy / syllabus do you have for your children?
2. At what age do you introduce smartphones / tablets for their independent usage? ()
1. What do you think of the WHO guidance [4]?\n\n 2. How much screen time do you think is beneficial / detrimental?\n4. How do you teach technological foundations (STEM) without technology?
5. What activities do you perform outside / in nature? (e.g., Usage of animal guides / plant guides)
6. My daughter sees me working a lot with tablet / laptop and wants to do the same. (She's even 'scheduling meetings'). How would you deal with this?
() I am familiar with a related question from 2014 (Ask HN: How much screen time do you let your kids have?[1]).
---
[1]: https://news.ycombinator.com/item?id=8746045
[2]: https://www.amazon.com/dp/019536936X
[3]: https://www.dk.com/us/book/9781465447562-5-language-visual-dictionary/
[4]: https://www.who.int/news/item/24-04-2019-to-grow-up-healthy-children-need-to-sit-less-and-play-more"},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: What is your education and technology strategy for your children?"}},"_tags":["story","author_haensi","story_29105410","ask_hn"],"author":"haensi","children":[29111175],"created_at":"2021-11-04T10:38:54Z","created_at_i":1636022334,"num_comments":1,"objectID":"29105410","points":6,"story_id":29105410,"story_text":"My wife (staying at home) and I have a 2yo girl and a 7mo girl and raise them trilingually (English, Spanish, German).\nTo our surprise, the 2yo has a better pronunciation than monolingual peers.\nShe also has a remarkable memory and can remember names of places and people.\nMoreover, she discovers letters on signs and posters. Her favorite book is a visual dictionary. [3]\nIn general, our daughters can have access to all tools after a short introduction and under supervision.\nScissors? Hold em like this. Kitchen knife? Handle with care. China plates? Careful.
Most people in our neighborhood do not have a strategy on how to raise the children.\nThey rather trust in day care to take care of that or follow their intuition.
I feel a strong need to strategically work out an education.\nSo far, I have been studying the Montessori work, namely 'Montessori, the Science Behind the Genius' [2].
I am interested in the following questions:
1. What educational strategy / syllabus do you have for your children?
2. At what age do you introduce smartphones / tablets for their independent usage? ()
1. What do you think of the WHO guidance [4]?\n\n 2. How much screen time do you think is beneficial / detrimental?\n4. How do you teach technological foundations (STEM) without technology?
5. What activities do you perform outside / in nature? (e.g., Usage of animal guides / plant guides)
6. My daughter sees me working a lot with tablet / laptop and wants to do the same. (She's even 'scheduling meetings'). How would you deal with this?
() I am familiar with a related question from 2014 (Ask HN: How much screen time do you let your kids have?[1]).
---
[1]: https://news.ycombinator.com/item?id=8746045
[2]: https://www.amazon.com/dp/019536936X
[3]: https://www.dk.com/us/book/9781465447562-5-language-visual-dictionary/
[4]: https://www.who.int/news/item/24-04-2019-to-grow-up-healthy-children-need-to-sit-less-and-play-more","title":"Ask HN: What is your education and technology strategy for your children?","updated_at":"2024-09-20T09:43:32Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"jacobedawson"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"I'm trying to process a disconnect between the unemployment crisis brought on by the virus, an incoming housing market implosion and a decreasing Index of Consumer Sentiment with the fact that the S&P 500 is up 500 points in the past month - it's on track for the best month since 1974.
I don't understand monetary policy or macro-economics in general, but I'm struggling to grasp the gap between my intuition and reality.
What's the deal?"},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: Why is the S&P 500 rallying in a crisis?"}},"_tags":["story","author_jacobedawson","story_23027824","ask_hn"],"author":"jacobedawson","children":[23028141,23028208,23028393,23030716,23040707],"created_at":"2020-04-30T05:50:15Z","created_at_i":1588225815,"num_comments":7,"objectID":"23027824","points":5,"story_id":23027824,"story_text":"I'm trying to process a disconnect between the unemployment crisis brought on by the virus, an incoming housing market implosion and a decreasing Index of Consumer Sentiment with the fact that the S&P 500 is up 500 points in the past month - it's on track for the best month since 1974.
I don't understand monetary policy or macro-economics in general, but I'm struggling to grasp the gap between my intuition and reality.
What's the deal?","title":"Ask HN: Why is the S&P 500 rallying in a crisis?","updated_at":"2024-09-20T06:02:39Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"boa00"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"I have no idea what to make of IQ in general, and I try to figure it out. Majority of the books/videos I found which at least try to rely on some scientific methods (e.g. research) are predominantly in favor of IQ and its reliability/importance.
Could you recommend any books/articles/videos which try criticize IQ tests from the scientific standpoint? Like tests being biased/unrepresentative, most experiments are not properly constructed and anything along those lines. Most of the arguments against IQ I found though relied mostly on personal experience/intuition (e.g. it\u2019s too simplistic, intelligence is much more complex than that, etc), but I\u2019d like to hear more \u2018measurable\u2019 criticism.
P.S. I\u2019m not trying to prove or disprove anything; I just want to look at this from different points of view"},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: Any Evidence Against IQ?"}},"_tags":["story","author_boa00","story_27026778","ask_hn"],"author":"boa00","children":[27026805,27027184,27027390,27034246],"created_at":"2021-05-03T16:04:11Z","created_at_i":1620057851,"num_comments":4,"objectID":"27026778","points":4,"story_id":27026778,"story_text":"I have no idea what to make of IQ in general, and I try to figure it out. Majority of the books/videos I found which at least try to rely on some scientific methods (e.g. research) are predominantly in favor of IQ and its reliability/importance.
Could you recommend any books/articles/videos which try criticize IQ tests from the scientific standpoint? Like tests being biased/unrepresentative, most experiments are not properly constructed and anything along those lines. Most of the arguments against IQ I found though relied mostly on personal experience/intuition (e.g. it\u2019s too simplistic, intelligence is much more complex than that, etc), but I\u2019d like to hear more \u2018measurable\u2019 criticism.
P.S. I\u2019m not trying to prove or disprove anything; I just want to look at this from different points of view","title":"Ask HN: Any Evidence Against IQ?","updated_at":"2024-09-20T08:29:31Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"mpmpmp"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"Hello. Currently in the process of looking for new employment, I am curious about the perception in the development community of working at Microsoft versus places like Google, a similarly well-liked software company, or some other fresh, hyped startup.
I understand this is a somewhat shallow question, but employment is closely related to identity and future employment options; and, while I'm experienced in software development, I don't have much experience in the way of socializing in the development community, so I am unfamiliar with the general perceptions on these types of things within much of the SW world.
Let's just assume for a moment one applied to and received offers from Microsoft and Google. My intuition and personal perception is that Microsoft is something of a software dinosaur, bureaucratic and rigid, a bit out of touch with the leading edge of development, whereas places like Google, smaller startups and such are more agile, developer-friendly and forward-thinking.
I have friends who work at these and other similar locations and I reactively tend to esteem the Google employees job more so than those at Microsoft. I'm aware of this personal tendency and it bothers me a bit when considering my own future employment options.
Is this a common feeling, and does it really matter for future career moves?
What is the general opinion in the development world, when meeting someone who works at Microsoft, what's the perception of such a person based on where they work? What's the stereotype? What about Microsoft Research, how does that change the opinion? How does that compare to someone who works at Google?"},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: Working at MS vs. Google: Community opinion/Effect on resume perception?"},"url":{"matchLevel":"none","matchedWords":[],"value":""}},"_tags":["story","author_mpmpmp","story_1884343","ask_hn"],"author":"mpmpmp","children":[1884366],"created_at":"2010-11-08T22:37:59Z","created_at_i":1289255879,"num_comments":4,"objectID":"1884343","points":4,"story_id":1884343,"story_text":"Hello. Currently in the process of looking for new employment, I am curious about the perception in the development community of working at Microsoft versus places like Google, a similarly well-liked software company, or some other fresh, hyped startup.
I understand this is a somewhat shallow question, but employment is closely related to identity and future employment options; and, while I'm experienced in software development, I don't have much experience in the way of socializing in the development community, so I am unfamiliar with the general perceptions on these types of things within much of the SW world.
Let's just assume for a moment one applied to and received offers from Microsoft and Google. My intuition and personal perception is that Microsoft is something of a software dinosaur, bureaucratic and rigid, a bit out of touch with the leading edge of development, whereas places like Google, smaller startups and such are more agile, developer-friendly and forward-thinking.
I have friends who work at these and other similar locations and I reactively tend to esteem the Google employees job more so than those at Microsoft. I'm aware of this personal tendency and it bothers me a bit when considering my own future employment options.
Is this a common feeling, and does it really matter for future career moves?
What is the general opinion in the development world, when meeting someone who works at Microsoft, what's the perception of such a person based on where they work? What's the stereotype? What about Microsoft Research, how does that change the opinion? How does that compare to someone who works at Google?","title":"Ask HN: Working at MS vs. Google: Community opinion/Effect on resume perception?","updated_at":"2024-09-19T17:23:48Z","url":""},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"glenstein"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"http://en.wikipedia.org/wiki/What_Computers_Can't_Do
Full disclosure- my intuition is a strong reaction in the negative but I've only just begun reading his book, and I find it particularly staggering that the introduction frames the debate as open-and-shut, with Dreyfus having won.
Dreyfus's introduction for example spends a lot of time going over the brick walls hit by AI in the mid 20th century. That's compelling for what its worth as it leads to a deeper appreciation for the problems such researchers were trying to solve, but as an argument it's similar to saying that a lack of a man on mars counts as proof that it's impossible for men to get to mars.
Also, as a general note, I find the approach of supplying strictly philosophical arguments suspicious- there is lots of room for vagueness, and for characterizing AI researchers as taking certain philosophical positions without meaningfully engaging with the fruits of their research.
I apologize if that sounds unnecessarily harsh. I do think that I have an obligation to read and seriously mull over these arguments given that I want to disbelieve them so strongly and I'd like to think I'm open to being convinced. So I'm hoping that there is someone in this community who approached this book with a similar mindset who's willing to share their experience with Dreyfus's ideas."},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: Have you read Dreyfus's What Computers Can't Do? How did you react?"},"url":{"matchLevel":"none","matchedWords":[],"value":""}},"_tags":["story","author_glenstein","story_471802","ask_hn"],"author":"glenstein","created_at":"2009-02-08T04:03:44Z","created_at_i":1234065824,"num_comments":0,"objectID":"471802","points":4,"story_id":471802,"story_text":"http://en.wikipedia.org/wiki/What_Computers_Can't_Do
Full disclosure- my intuition is a strong reaction in the negative but I've only just begun reading his book, and I find it particularly staggering that the introduction frames the debate as open-and-shut, with Dreyfus having won.
Dreyfus's introduction for example spends a lot of time going over the brick walls hit by AI in the mid 20th century. That's compelling for what its worth as it leads to a deeper appreciation for the problems such researchers were trying to solve, but as an argument it's similar to saying that a lack of a man on mars counts as proof that it's impossible for men to get to mars.
Also, as a general note, I find the approach of supplying strictly philosophical arguments suspicious- there is lots of room for vagueness, and for characterizing AI researchers as taking certain philosophical positions without meaningfully engaging with the fruits of their research.
I apologize if that sounds unnecessarily harsh. I do think that I have an obligation to read and seriously mull over these arguments given that I want to disbelieve them so strongly and I'd like to think I'm open to being convinced. So I'm hoping that there is someone in this community who approached this book with a similar mindset who's willing to share their experience with Dreyfus's ideas.","title":"Ask HN: Have you read Dreyfus's What Computers Can't Do? How did you react?","updated_at":"2024-09-19T16:33:26Z","url":""},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"SuKefan"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"I've googled around and haven't been able to find good comparisons of using FastAPI vs a gRPC service in Python. My intuition is that assuming long lived clients gRPC should be faster due to binary serialization of data (since they are both using HTTP2) but I'm surprised that nobody has done a comprehensive comparison post.
I'm interested in comparisons for both performance under heavy load as well as general usability/maintainability thoughts."},"title":{"matchLevel":"none","matchedWords":[],"value":"FastAPI vs. gRPC for high volume Python services"}},"_tags":["story","author_SuKefan","story_23911389","ask_hn"],"author":"SuKefan","created_at":"2020-07-21T21:52:46Z","created_at_i":1595368366,"num_comments":0,"objectID":"23911389","points":4,"story_id":23911389,"story_text":"I've googled around and haven't been able to find good comparisons of using FastAPI vs a gRPC service in Python. My intuition is that assuming long lived clients gRPC should be faster due to binary serialization of data (since they are both using HTTP2) but I'm surprised that nobody has done a comprehensive comparison post.
I'm interested in comparisons for both performance under heavy load as well as general usability/maintainability thoughts.","title":"FastAPI vs. gRPC for high volume Python services","updated_at":"2024-09-20T06:33:33Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"kovezd"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"Hi HN! BI-LLM (https://github.com/amVizion/BI-LLM) automates data analysis workflows on semi-structured data. The assumption is that many decisions could be led by data, but instead are made by intuition because doing analysis would be too slow or expensive. The sweet-spot is decisions based on a few hundreds to a couple thousand items: too many to analyze manually, too few to dedicate an analyst to it. I believe that powerful insights are derived from a large number of data-driven decisions.
There are 3 things special about BI-LLM:
1. Web-native: the code is fully developed on TypeScript. A native integration with web, avoids the need for specialized software, or licenses. More importantly, I hope it contributes opening the field of AI to a wider group of developers.
2. Runs-locally: BI-LLM uses a combination of statistical, machine-learning, and GenAI methods to automate the analysis. Embedding models run in TensorflowJS, and LLMs in Ollama. Running locally not only helps keeping costs down, it also contributes to data privacy, and security.
3. No data-background required: all the technical details when pre-processing data are hidden on the implementation. The analysis only requires a simple JSON file, and after a couple CLI commands, it outputs a full report with the conclusions in a language that directly leads to making decisions.
The data analysis flow starts by turning texts into embeddings, and, using LLMs to label, and score the texts. Then, texts are grouped by similarity and the scores used to write an analysis describing each cluster, and explaining the correlation between labels, and attributes in the final report. You can see in YouTube a 2-minute demo (https://youtu.be/RoLU_REypyY) of a sample analysis. In GitHub, the demo directory also has screenshots from a sample analysis. You can also find diagrams with more detailed explanations of the steps taken during analysis on the ReadME.
The project was partially inspired by feedback from the community. A few months ago I posted an analysis that stirred some controversy about whether it was written by AI. There were some encouraging comments including:
> This is the perfect use case for LLMs and Data Analysis in general. I'd legitimately pay money for a similar productivity tool.
The project took shape after speaking with an investor that had a perfect use case for it. The key insight was to move from doing unsupervised analysis (as the original article) to supervised analysis: attempting to predict a defined outcome. The final straw was when exploring some leads data, I clustered the data and so differences in the rate of conversion between 20% to 2%. The analysis required almost no work, and provided useful insights on how to identify the best prospects. I figure it out this could be a great tool for startups, and small teams that cannot afford a data analyst.
I would love to hear your feedback. Thank you!"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: BI-LLM democratize data analysis"}},"_tags":["story","author_kovezd","story_41285543","show_hn"],"author":"kovezd","created_at":"2024-08-18T21:11:39Z","created_at_i":1724015499,"num_comments":0,"objectID":"41285543","points":3,"story_id":41285543,"story_text":"Hi HN! BI-LLM (https://github.com/amVizion/BI-LLM) automates data analysis workflows on semi-structured data. The assumption is that many decisions could be led by data, but instead are made by intuition because doing analysis would be too slow or expensive. The sweet-spot is decisions based on a few hundreds to a couple thousand items: too many to analyze manually, too few to dedicate an analyst to it. I believe that powerful insights are derived from a large number of data-driven decisions.
There are 3 things special about BI-LLM:
1. Web-native: the code is fully developed on TypeScript. A native integration with web, avoids the need for specialized software, or licenses. More importantly, I hope it contributes opening the field of AI to a wider group of developers.
2. Runs-locally: BI-LLM uses a combination of statistical, machine-learning, and GenAI methods to automate the analysis. Embedding models run in TensorflowJS, and LLMs in Ollama. Running locally not only helps keeping costs down, it also contributes to data privacy, and security.
3. No data-background required: all the technical details when pre-processing data are hidden on the implementation. The analysis only requires a simple JSON file, and after a couple CLI commands, it outputs a full report with the conclusions in a language that directly leads to making decisions.
The data analysis flow starts by turning texts into embeddings, and, using LLMs to label, and score the texts. Then, texts are grouped by similarity and the scores used to write an analysis describing each cluster, and explaining the correlation between labels, and attributes in the final report. You can see in YouTube a 2-minute demo (https://youtu.be/RoLU_REypyY) of a sample analysis. In GitHub, the demo directory also has screenshots from a sample analysis. You can also find diagrams with more detailed explanations of the steps taken during analysis on the ReadME.
The project was partially inspired by feedback from the community. A few months ago I posted an analysis that stirred some controversy about whether it was written by AI. There were some encouraging comments including:
> This is the perfect use case for LLMs and Data Analysis in general. I'd legitimately pay money for a similar productivity tool.
The project took shape after speaking with an investor that had a perfect use case for it. The key insight was to move from doing unsupervised analysis (as the original article) to supervised analysis: attempting to predict a defined outcome. The final straw was when exploring some leads data, I clustered the data and so differences in the rate of conversion between 20% to 2%. The analysis required almost no work, and provided useful insights on how to identify the best prospects. I figure it out this could be a great tool for startups, and small teams that cannot afford a data analyst.
I would love to hear your feedback. Thank you!","title":"Show HN: BI-LLM democratize data analysis","updated_at":"2024-09-20T17:42:21Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"brianllamar"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"Last week I sat in the LLM Paper Club (hosted by the latent.space podcast). We shared a paper from DeepMind on "Discovering Multiagent Learning Algorithms with Large Language Models". Linked in the github repo.
This was my first time sitting in one of these and reading an LLM paper. I thought it was interesting that the AlphaEvolve agent in the paper essentially writes code, runs it, scores the output, and then writes better code, over and over. It's not discovering strategies through play. It's discovering algorithms through code mutation. The LLM proposes changes to how regret is accumulated or how policies are derived, a fitness function scores the result, and the best variants survive to the next generation.
The two algorithms it found (VAD-CFR and SHOR-PSRO) use mechanisms the authors describe as "non-intuitive," things like volatility-sensitive discounting and hard warm-start schedules that a human designer probably wouldn't have tried. That's the interesting part: the LLM isn't constrained by the same design intuitions we are.
To make it concrete for myself, I built a small version of this loop for a Pokemon game agent. The setup is simple: define a fitness function (turns survived, maps visited, stuck events), parameterize the strategy space (door cooldown, stuck threshold, skip distance), and let an LLM propose variants that get evaluated in parallel. Ten agents race through the game, the best parameters survive. I used tapes.dev to collect session telemetry and feed observational memory back into the fitness scoring.
The first run already surfaced something useful: shorter door cooldowns (4 vs 8) reduce stuck events from 16 to 9. Not a breakthrough, but the point is the system found it without me guessing. That's the same dynamic as the paper, just at toy scale.
What I took away from the paper club: the bottleneck in algorithm design isn't computation, it's the search process itself. If you can express your problem as "parameterized code + fitness function," an LLM evolution loop can explore the space faster than manual iteration. The paper proves it works for game theory. The link is in my startup's repo, I want to explore applying this technique for improving future general purpose and coding agent sessions.
Just pointing out Pokemon aren't the only thing evolving in that repo."},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: AlphaEvolve inspired evolution harness for Pokemon"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/papercomputeco/pokemon"}},"_tags":["story","author_brianllamar","story_47327720","show_hn"],"author":"brianllamar","created_at":"2026-03-10T19:24:34Z","created_at_i":1773170674,"num_comments":0,"objectID":"47327720","points":2,"story_id":47327720,"story_text":"Last week I sat in the LLM Paper Club (hosted by the latent.space podcast). We shared a paper from DeepMind on "Discovering Multiagent Learning Algorithms with Large Language Models". Linked in the github repo.
This was my first time sitting in one of these and reading an LLM paper. I thought it was interesting that the AlphaEvolve agent in the paper essentially writes code, runs it, scores the output, and then writes better code, over and over. It's not discovering strategies through play. It's discovering algorithms through code mutation. The LLM proposes changes to how regret is accumulated or how policies are derived, a fitness function scores the result, and the best variants survive to the next generation.
The two algorithms it found (VAD-CFR and SHOR-PSRO) use mechanisms the authors describe as "non-intuitive," things like volatility-sensitive discounting and hard warm-start schedules that a human designer probably wouldn't have tried. That's the interesting part: the LLM isn't constrained by the same design intuitions we are.
To make it concrete for myself, I built a small version of this loop for a Pokemon game agent. The setup is simple: define a fitness function (turns survived, maps visited, stuck events), parameterize the strategy space (door cooldown, stuck threshold, skip distance), and let an LLM propose variants that get evaluated in parallel. Ten agents race through the game, the best parameters survive. I used tapes.dev to collect session telemetry and feed observational memory back into the fitness scoring.
The first run already surfaced something useful: shorter door cooldowns (4 vs 8) reduce stuck events from 16 to 9. Not a breakthrough, but the point is the system found it without me guessing. That's the same dynamic as the paper, just at toy scale.
What I took away from the paper club: the bottleneck in algorithm design isn't computation, it's the search process itself. If you can express your problem as "parameterized code + fitness function," an LLM evolution loop can explore the space faster than manual iteration. The paper proves it works for game theory. The link is in my startup's repo, I want to explore applying this technique for improving future general purpose and coding agent sessions.
Just pointing out Pokemon aren't the only thing evolving in that repo.","title":"Show HN: AlphaEvolve inspired evolution harness for Pokemon","updated_at":"2026-03-10T19:28:29Z","url":"https://github.com/papercomputeco/pokemon"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"Venky1729"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"Hey Folks, I've been building an MCP server that generates beautiful Excalidraw architecture diagrams fully auto-laid-out, no manual positioning. It's open source, works with any AI IDE (Cursor, Windsurf, etc.).I wanted to share it early because the core layout engine is solid and already producing clean diagrams.
What Problem Am I Solving?\nAI IDEs generate architecture diagrams as Mermaid or ASCII art. When they attempt Excalidraw, they hallucinate coordinates and boxes overlap, arrows cross through nodes, and you spend more time fixing the diagram than drawing it yourself. LLMs understand what a system looks like, but they have zero intuition for where things go on a 2D canvas.
How Does This MCP Solve It?\nYou describe the components and connections. The MCP runs a Sugiyama hierarchical layout algorithm to compute positions deterministically -- the AI never touches coordinates. It auto-styles 50+ technologies (say "Kafka" and get a stream-styled node), stretches hub nodes like API Gateways to span their services, and routes arrows around obstacles. The result is a clean .excalidraw file in seconds, with stateful editing so you can say "add a Redis cache" without starting over.
Why Not the Official excalidraw-mcp?\nThe official MCP gives the AI a raw canvas and lets it place elements freely -- great for general sketching, but architecture diagrams still end up with overlapping boxes. excalidraw-architect-mcp takes a structured graph as input and runs a proper layout algorithm, so every diagram is overlap-free by design. It also runs fully offline in your IDE with no API keys, outputs version-controllable .excalidraw files, and includes architecture-aware styling that the general-purpose tool doesn't have.
What's the Future?\nDiagram-from-code (point at a codebase, get an architecture diagram automatically), live sync so diagrams update as code evolves, richer layouts (radial, swimlane, grid), and an expanded component library covering cloud-provider icons and CI/CD tools. The goal: architecture diagrams as a byproduct of building software, not a separate task."},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Excalidraw Architect MCP for AI Based IDEs"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/BV-Venky/excalidraw-architect-mcp"}},"_tags":["story","author_Venky1729","story_47289785","show_hn"],"author":"Venky1729","created_at":"2026-03-07T17:47:49Z","created_at_i":1772905669,"num_comments":0,"objectID":"47289785","points":2,"story_id":47289785,"story_text":"Hey Folks, I've been building an MCP server that generates beautiful Excalidraw architecture diagrams fully auto-laid-out, no manual positioning. It's open source, works with any AI IDE (Cursor, Windsurf, etc.).I wanted to share it early because the core layout engine is solid and already producing clean diagrams.
What Problem Am I Solving?\nAI IDEs generate architecture diagrams as Mermaid or ASCII art. When they attempt Excalidraw, they hallucinate coordinates and boxes overlap, arrows cross through nodes, and you spend more time fixing the diagram than drawing it yourself. LLMs understand what a system looks like, but they have zero intuition for where things go on a 2D canvas.
How Does This MCP Solve It?\nYou describe the components and connections. The MCP runs a Sugiyama hierarchical layout algorithm to compute positions deterministically -- the AI never touches coordinates. It auto-styles 50+ technologies (say "Kafka" and get a stream-styled node), stretches hub nodes like API Gateways to span their services, and routes arrows around obstacles. The result is a clean .excalidraw file in seconds, with stateful editing so you can say "add a Redis cache" without starting over.
Why Not the Official excalidraw-mcp?\nThe official MCP gives the AI a raw canvas and lets it place elements freely -- great for general sketching, but architecture diagrams still end up with overlapping boxes. excalidraw-architect-mcp takes a structured graph as input and runs a proper layout algorithm, so every diagram is overlap-free by design. It also runs fully offline in your IDE with no API keys, outputs version-controllable .excalidraw files, and includes architecture-aware styling that the general-purpose tool doesn't have.
What's the Future?\nDiagram-from-code (point at a codebase, get an architecture diagram automatically), live sync so diagrams update as code evolves, richer layouts (radial, swimlane, grid), and an expanded component library covering cloud-provider icons and CI/CD tools. The goal: architecture diagrams as a byproduct of building software, not a separate task.","title":"Show HN: Excalidraw Architect MCP for AI Based IDEs","updated_at":"2026-03-07T17:57:03Z","url":"https://github.com/BV-Venky/excalidraw-architect-mcp"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"anifow"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"The Aakkozzll app is a substitute for throwing dice. Players can see the probability distribution on each throw (unlike dice which have a similar pattern, but you just can't see it).
The app is great for high-school to college level stats and data-management classes, and simple enough to serve as a primer for elementary-aged students. It gives them a physical / visual way to appreciate the normal curve. Can be used in a classroom activity of Craps or even monopoly, using the Aakkozzll instead of dice. Games like Craps will take on a whole new meaning when students can actually see the chances of getting a 7.
Will give students an intuition for the binomial distribution, frequency graphs, and the normal curve. As the balls fall down, they randomly go left or right (its about 50/50) depending on how they land. Its like having a series of 50/50 coin tosses which determines the final position of the balls. Only the red ball actually determines your \"roll\" (it blinks red).
The original inspiration came from the Galton board, a physical device that was built 150 years ago to demonstrate the central tendency. We've recreated the physics of that device into an easy to use iPhone app so that it's insights can be shared with the world. History of the Bean Machine: (http://en.wikipedia.org/wiki/Bean_machine)
Why the funny name? The Aakkozzll (pronounced \"acausal\") demonstrates a paradox that although the result of any one individual cannot be predicted, the general pattern can be. This extends to the real world. The fact that a person falls into poverty is not entirely predictable, but the fact that a society will have a certain distribution of rich and poor (regardless of their particular paths towards riches and poverty) is quite constant, especially when you have a large population.
Educators, we encourage you to share this with your peers!"},"title":{"matchLevel":"none","matchedWords":[],"value":"New iPhone App encourages Classroom Activity for Stats/Math"},"url":{"matchLevel":"none","matchedWords":[],"value":"http://itunes.apple.com/us/app/aakkozzll/id561069750?mt=8 "}},"_tags":["story","author_anifow","story_4571946"],"author":"anifow","created_at":"2012-09-25T18:42:37Z","created_at_i":1348598557,"num_comments":0,"objectID":"4571946","points":2,"story_id":4571946,"story_text":"The Aakkozzll app is a substitute for throwing dice. Players can see the probability distribution on each throw (unlike dice which have a similar pattern, but you just can't see it).
The app is great for high-school to college level stats and data-management classes, and simple enough to serve as a primer for elementary-aged students. It gives them a physical / visual way to appreciate the normal curve. Can be used in a classroom activity of Craps or even monopoly, using the Aakkozzll instead of dice. Games like Craps will take on a whole new meaning when students can actually see the chances of getting a 7.
Will give students an intuition for the binomial distribution, frequency graphs, and the normal curve. As the balls fall down, they randomly go left or right (its about 50/50) depending on how they land. Its like having a series of 50/50 coin tosses which determines the final position of the balls. Only the red ball actually determines your \"roll\" (it blinks red).
The original inspiration came from the Galton board, a physical device that was built 150 years ago to demonstrate the central tendency. We've recreated the physics of that device into an easy to use iPhone app so that it's insights can be shared with the world. History of the Bean Machine: (http://en.wikipedia.org/wiki/Bean_machine)
Why the funny name? The Aakkozzll (pronounced \"acausal\") demonstrates a paradox that although the result of any one individual cannot be predicted, the general pattern can be. This extends to the real world. The fact that a person falls into poverty is not entirely predictable, but the fact that a society will have a certain distribution of rich and poor (regardless of their particular paths towards riches and poverty) is quite constant, especially when you have a large population.
Educators, we encourage you to share this with your peers!","title":"New iPhone App encourages Classroom Activity for Stats/Math","updated_at":"2024-09-19T18:58:38Z","url":"http://itunes.apple.com/us/app/aakkozzll/id561069750?mt=8 "},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"zerojames"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"Evaluating visual capabilities of language models is hard.
On the one end of the evaluation spectrum, we have vibe checks which, while useful for building intuition, are time-consuming to run across a dozen or more models. On the other end, we have large benchmarks which are so large that they are intractable to most users.
Vision AI Checkup is a new tool for evaluating VLMs. The site is made up of hand-crafted prompts focused on real-world problems: defect detection, understanding how the position of one object relates to another, colour understanding, and more.
Our prompts are especially focused on industrial tasks -- serial number reading, assembly line understanding, and more -- although we're excited to add more general prompts.
The tool lets you see how models do across categories of prompts, and how different models do on a single prompt.
We have open sourced the codebase, with instructions on how to add a prompt to the assessment: https://github.com/roboflow/vision-ai-checkup. You can also add new models.
We'd love feedback and, also, ideas for areas where VLMs struggle that you'd like to see assessed!"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Vision AI Checkup, an Optometrist for VLMs"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://visioncheckup.com"}},"_tags":["story","author_zerojames","story_43971984","show_hn"],"author":"zerojames","created_at":"2025-05-13T12:08:02Z","created_at_i":1747138082,"num_comments":0,"objectID":"43971984","points":2,"story_id":43971984,"story_text":"Evaluating visual capabilities of language models is hard.
On the one end of the evaluation spectrum, we have vibe checks which, while useful for building intuition, are time-consuming to run across a dozen or more models. On the other end, we have large benchmarks which are so large that they are intractable to most users.
Vision AI Checkup is a new tool for evaluating VLMs. The site is made up of hand-crafted prompts focused on real-world problems: defect detection, understanding how the position of one object relates to another, colour understanding, and more.
Our prompts are especially focused on industrial tasks -- serial number reading, assembly line understanding, and more -- although we're excited to add more general prompts.
The tool lets you see how models do across categories of prompts, and how different models do on a single prompt.
We have open sourced the codebase, with instructions on how to add a prompt to the assessment: https://github.com/roboflow/vision-ai-checkup. You can also add new models.
We'd love feedback and, also, ideas for areas where VLMs struggle that you'd like to see assessed!","title":"Show HN: Vision AI Checkup, an Optometrist for VLMs","updated_at":"2025-05-14T12:49:11Z","url":"https://visioncheckup.com"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"Nwt"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"Problem:
We will probably create AGI before we have a formal model of human ethics, which is not an urgent problem but a potentially catastrophic one.
Proposed solution:
We assume we want that AGI (Artificial general intelligence) to be aligned with all life and we want it to produce maximum "good". We then define "good" as whatever any unit of life defines as such. (Interactions between units will produce additional emergent definitions.) Then instead of optimizing for any of those definitions we set AGI's utility function as maximization of the definitions, that is: maximization of the quantity of both units of life and interactions between units, and then to make it all safe we forbid it from interacting with either. Maximizing the quantity of life without interacting with it means both shielding life from cataclysmic events and creating space for life's expansion into places where no life exists (outer space).
More details: https://pni.ai/future
My intuition is that this can be expressed formally, simulated and proven/disproven and if proven may lead to a future with potential for growth that is hard to imagine, yet without any risk to life and without making humans obsolete. There is also no benefit in being the first to create such AGI and all the reasons to cooperate as it produces universal "good".
Can you guys poke holes in this?"},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: Possible solution to AGI alignment and safety?"}},"_tags":["story","author_Nwt","story_23249504","ask_hn"],"author":"Nwt","created_at":"2020-05-20T17:31:38Z","created_at_i":1589995898,"num_comments":0,"objectID":"23249504","points":2,"story_id":23249504,"story_text":"Problem:
We will probably create AGI before we have a formal model of human ethics, which is not an urgent problem but a potentially catastrophic one.
Proposed solution:
We assume we want that AGI (Artificial general intelligence) to be aligned with all life and we want it to produce maximum "good". We then define "good" as whatever any unit of life defines as such. (Interactions between units will produce additional emergent definitions.) Then instead of optimizing for any of those definitions we set AGI's utility function as maximization of the definitions, that is: maximization of the quantity of both units of life and interactions between units, and then to make it all safe we forbid it from interacting with either. Maximizing the quantity of life without interacting with it means both shielding life from cataclysmic events and creating space for life's expansion into places where no life exists (outer space).
More details: https://pni.ai/future
My intuition is that this can be expressed formally, simulated and proven/disproven and if proven may lead to a future with potential for growth that is hard to imagine, yet without any risk to life and without making humans obsolete. There is also no benefit in being the first to create such AGI and all the reasons to cooperate as it produces universal "good".
Can you guys poke holes in this?","title":"Ask HN: Possible solution to AGI alignment and safety?","updated_at":"2024-09-20T06:14:23Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"vdupras"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"First of all: this post isn't about the meaning of "Elon is Snowball". This is an example. No politics underneath.
So, a few minutes ago, I google this phrase to see if someone else out there has the same intuition. Then, I'm surprised by Google's AI overview:
> The statement "Elon is snowball" is a misunderstanding. Elon is likely referring to Elon Musk [...]. The term "snowball" typically refers to a phenomenon, often used metaphorically to describe something growing rapidly in size or importance. [...]
So, the AI despite all its knowledge, can't draw the parallel at all. The question isn't whether the parallel is valid or not, but I think it's obvious to any human that has read a little bit that one could see a parallel.
I don't want to "spoil" the example by being too blunt about what the parallel is, to give AI experts the time to query their favorite models before they index this post and have it too easy, so I intentionally remain vague.
I'm not a AI expert and I frankly don't spend much time thinking about AI in general, but given how intensely AI capabilities are praised all around, I'm rather surprised by this result.
So, my question to HN: Why isn't Google's AI capable of even seeing the link? Is it just their model, or is it something fundamental to LLMs? How long until LLMs can make this link all by themselves?"},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: Can current AI models grok \"Elon is Snowball\"?"}},"_tags":["story","author_vdupras","story_44193801","ask_hn"],"author":"vdupras","children":[44193845,44193861,44193967,44194071,44194083,44194114,44194493,44201379],"created_at":"2025-06-05T17:28:52Z","created_at_i":1749144532,"num_comments":16,"objectID":"44193801","points":1,"story_id":44193801,"story_text":"First of all: this post isn't about the meaning of "Elon is Snowball". This is an example. No politics underneath.
So, a few minutes ago, I google this phrase to see if someone else out there has the same intuition. Then, I'm surprised by Google's AI overview:
> The statement "Elon is snowball" is a misunderstanding. Elon is likely referring to Elon Musk [...]. The term "snowball" typically refers to a phenomenon, often used metaphorically to describe something growing rapidly in size or importance. [...]
So, the AI despite all its knowledge, can't draw the parallel at all. The question isn't whether the parallel is valid or not, but I think it's obvious to any human that has read a little bit that one could see a parallel.
I don't want to "spoil" the example by being too blunt about what the parallel is, to give AI experts the time to query their favorite models before they index this post and have it too easy, so I intentionally remain vague.
I'm not a AI expert and I frankly don't spend much time thinking about AI in general, but given how intensely AI capabilities are praised all around, I'm rather surprised by this result.
So, my question to HN: Why isn't Google's AI capable of even seeing the link? Is it just their model, or is it something fundamental to LLMs? How long until LLMs can make this link all by themselves?","title":"Ask HN: Can current AI models grok \"Elon is Snowball\"?","updated_at":"2025-06-07T21:16:31Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"SeanAnderson"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"I'm going to be taking some time off from work here at the end of the year to pursue some creative software development.
Although I have almost 15 years of industry experience I've never built a game. Up until about six months ago, I didn't think I was /that/ type of software developer, but now, here I am, interest piqued, reading up on all sorts of technology stacks and having my mind blown by the size of the knowledge space. I am especially excited to try and express logic using an ECS/DOTS approach!
I know I am building for desktop web. Leaning towards Rust + WebGPU and hoping to time an MVP around WebGPU general availability. I know I shouldn't be in this space for the money. I know to expect game programming to be significantly more challenging than whatever my intuition believes and that I should continually and aggressively prune away at scope creep. I know there's a LOT of math and that performance is frequently an issue.
What are some joys, or challenges, you unexpectedly encountered when developing your first game which you wish you'd known before breaking ground?"},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: What do you wish you knew before you developed your first video game?"}},"_tags":["story","author_SeanAnderson","story_33801959","ask_hn"],"author":"SeanAnderson","children":[33801974,33802077],"created_at":"2022-11-30T15:23:18Z","created_at_i":1669821798,"num_comments":4,"objectID":"33801959","points":1,"story_id":33801959,"story_text":"I'm going to be taking some time off from work here at the end of the year to pursue some creative software development.
Although I have almost 15 years of industry experience I've never built a game. Up until about six months ago, I didn't think I was /that/ type of software developer, but now, here I am, interest piqued, reading up on all sorts of technology stacks and having my mind blown by the size of the knowledge space. I am especially excited to try and express logic using an ECS/DOTS approach!
I know I am building for desktop web. Leaning towards Rust + WebGPU and hoping to time an MVP around WebGPU general availability. I know I shouldn't be in this space for the money. I know to expect game programming to be significantly more challenging than whatever my intuition believes and that I should continually and aggressively prune away at scope creep. I know there's a LOT of math and that performance is frequently an issue.
What are some joys, or challenges, you unexpectedly encountered when developing your first game which you wish you'd known before breaking ground?","title":"Ask HN: What do you wish you knew before you developed your first video game?","updated_at":"2024-09-20T12:34:18Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"nDot_io"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"Hi HN,
I\u2019m working on a \u201chacker science\u201d experiment called Ai_home.\nIt\u2019s a cognitive architecture prototype that I designed to explore the current limits of LLMs in terms of persistent identity, long-term memory, and autonomy.
The system is not just a simple chatbot loop, but a multi-threaded architecture:
1. Worker: Handles user interactions and tool use.\n2. Monologue: A background \u201csubconscious\u201d thread that analyzes context and logs intuitions/tips for the Worker.\n3. Memory: Manages vector-based long-term memory (Postgres + pgvector) with emotional weighting.\n4. Mind: This layer is responsible for deeper interpretation of messages and for exploring creative alternatives.
Because of this, it\u2019s not a synchronous question\u2013answer chatbot.\nThe model and the user (the Helper) can communicate in parallel, and the Worker processes this asynchronously.
Technical details:
- Hybrid Multi-LLM: I combine multiple models (Gemini, GPT-4, Groq). I use different models for creative idea generation (\u201ccreative\u201d) and for logical processing (\u201cinterpreter\u201d).
- Modes: I don\u2019t use a single context window. Depending on the operating mode (General, Developer, Analyst), I partition messages into separate contexts. I\u2019ve introduced a transition process between mode switches to ensure that the essential information is preserved across contexts.
- Dynamic Prompt: Based on memories and accumulated experience, I dynamically modify the prompt on every API call so that each conversation can gain a fresh contextual interpretation.
- Incubator: The system has an experimental environment where it can attempt to refactor its own code. The results are mixed so far, but it\u2019s fascinating to watch a model interpret its own code.
- Identity and Laws: For building identity, the system has a \u201cconstitution\u201d (fundamental laws) and tools for modifying them. The content and structure of this are still an active area of experimentation.
Disclaimer:\nThis is an architectural experiment to investigate whether functional patterns of consciousness (global workspace, recurrence) can be mimicked with LLMs in order to create more reliable agents.
I explicitly do not claim that the system is sentient, nor that this is a formal academic research project (We don\u2019t have the personnel or infrastructure for that).
I\u2019m looking for collaborators not only for coding, but also to help define a development methodology for this open, collaborative experimental project.
All feedback is very welcome!"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Cognitive AI architecture prototype with identity, memory, initiative"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://ivanhonis.github.io/ai_home/"}},"_tags":["story","author_nDot_io","story_46097698","show_hn"],"author":"nDot_io","children":[46098146],"created_at":"2025-11-30T15:59:42Z","created_at_i":1764518382,"num_comments":2,"objectID":"46097698","points":1,"story_id":46097698,"story_text":"Hi HN,
I\u2019m working on a \u201chacker science\u201d experiment called Ai_home.\nIt\u2019s a cognitive architecture prototype that I designed to explore the current limits of LLMs in terms of persistent identity, long-term memory, and autonomy.
The system is not just a simple chatbot loop, but a multi-threaded architecture:
1. Worker: Handles user interactions and tool use.\n2. Monologue: A background \u201csubconscious\u201d thread that analyzes context and logs intuitions/tips for the Worker.\n3. Memory: Manages vector-based long-term memory (Postgres + pgvector) with emotional weighting.\n4. Mind: This layer is responsible for deeper interpretation of messages and for exploring creative alternatives.
Because of this, it\u2019s not a synchronous question\u2013answer chatbot.\nThe model and the user (the Helper) can communicate in parallel, and the Worker processes this asynchronously.
Technical details:
- Hybrid Multi-LLM: I combine multiple models (Gemini, GPT-4, Groq). I use different models for creative idea generation (\u201ccreative\u201d) and for logical processing (\u201cinterpreter\u201d).
- Modes: I don\u2019t use a single context window. Depending on the operating mode (General, Developer, Analyst), I partition messages into separate contexts. I\u2019ve introduced a transition process between mode switches to ensure that the essential information is preserved across contexts.
- Dynamic Prompt: Based on memories and accumulated experience, I dynamically modify the prompt on every API call so that each conversation can gain a fresh contextual interpretation.
- Incubator: The system has an experimental environment where it can attempt to refactor its own code. The results are mixed so far, but it\u2019s fascinating to watch a model interpret its own code.
- Identity and Laws: For building identity, the system has a \u201cconstitution\u201d (fundamental laws) and tools for modifying them. The content and structure of this are still an active area of experimentation.
Disclaimer:\nThis is an architectural experiment to investigate whether functional patterns of consciousness (global workspace, recurrence) can be mimicked with LLMs in order to create more reliable agents.
I explicitly do not claim that the system is sentient, nor that this is a formal academic research project (We don\u2019t have the personnel or infrastructure for that).
I\u2019m looking for collaborators not only for coding, but also to help define a development methodology for this open, collaborative experimental project.
All feedback is very welcome!","title":"Show HN: Cognitive AI architecture prototype with identity, memory, initiative","updated_at":"2026-03-05T23:10:49Z","url":"https://ivanhonis.github.io/ai_home/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"anchority"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"DECONSTRUCTING THE MYTH ABOUT VENTURE RISK: A WAY OUT OF DILUTION
VCs are just great. They are there waiting at the finish-line to see startups through the race. Angels are so graceful. They are the proverbial friend who saw the pulloff from the beginning, and even went down with the buddy to jail saying that was darn good! Whatever\u2019s wrong with the gap between money and ideas, it never should play us all dead about the natural forces of the market and what exactly we stand for in the marketplace.
Since Joe left his fine job to join Dick, fresh out of college and so stubborn for not taking that big offer from General Everything, to turn a question into a startup; someone reading their story later in time, like their families and friends now, should see that they were making or breaking it. Stupid or brilliant, only time can heal what reason can\u2019t.
They met an angel through another friend. He falls in love and tears his check leaves. The prototype goes live and the market is made. That wonderful thingumajig is sold for $3 apiece. Where no taxes apply because of tax holiday for startups in the state they live in, all the revenues amount to $450, 000. Figuring out the total cost of production of $65, 000, they are left with $385, 000 in profit.
If only they had paid back in 1 \u00bd years after company launch the $80,000 lent by the angel, they would not have had to convert the loan to 20% shareholding. According to their agreement, a share unit is worth two dollars at pre-money valuation of $800, 000. In fact, the work-out posits that the effort of each of Joe and Dick is worth twice the amount invested by the angel. Therefore, each of them has invested $160, 000 without handing in any money save for all-important brains and brawns.
$160, 000 of Joe + $160, 000 of Dick + $80, 000 of the angel = $400, 000 total investment in the startup. In the same proportion, the goodwill accruing to each of them amounts to an equal $400, 000. Because there\u2019s no established custom or popularity, the goodwill should not count now, but they figure that VCs might come calling once the promising product is launched.
Besides, they feel that they should be really compensated for risking spending and exerting so much means and pain on proof of concept delivered to two such clients without any remuneration and contractual obligation to buy. One of them was provided by the angel.
Transport, everyday office rituals, sleepless nights, involuntary fasting, costly PR and shoestring marketing went with the whole gamut. They played deep for a great amount of work effort and thus, the longer time before they could avoid the 1 \u00bd year loan clause. Just why goodwill came a fair deal.
Press rave is here about the product. Pop culture and the TV people are dying for attention from Joe and Dick. They are reminded by the angel that they could let out some steam motion, yet they should not abandon substance for vanity. They are always back at work. Giving attention to events and the people that matter at every point in time.
Looking at the future before them, sound reasoning points in the direction of more important product features, valuable employees, necessary Intellectual Property tying-here-and-there, more solid collaboration, research and development, due diligence, etc.
It all goes from making the market to meeting the demands of the exploding market. The VCs are getting in touch with one another about this new high-tech star. The shepherd and the sheep are running to graze the bounty grass. By now, everyone in town knows how much more Joe and Dick figure the growing company needs in the way of money.
Before sales, if the share price was $2, the share price after sales takes after this picture:
$385, 000 (profit after tax: recall no taxes apply in this instance)/ $800, 000 worth of existing shares in the company = $0.48125, what is called the market quotient.
Add the pre-sales share price of $2 to the market quotient of $0.48125. And you get $2.48125 for the new share price. As simple as that. This is the market price, and it represents the least rational price any new investor should be paying to own a piece of the company.
In that wise, because the quotient could have been negative as a result of loss instead of profit, such a market price should serve as the lowest extreme of the share price band. If it was so a loss, the same formula applies and the market price should possibly be the highest extreme. And in both scenarios, whatever might count for premium and added value between buyers and sellers of the company\u2019s shares should be taken into consideration in each unique trading case.
Already, some readers would say that the allegory is demonstrating the reality on stock market floors (IPO or trading publicly quoted stocks), or common everyday buying and selling of anything founded on value. In every form of enterprise, startup investing, shipbuilding, fashion house, electric car manufacturing or trivia gamemaking, it indispensably counts to let reward and risk take their natural place in the scheme of things.
We never should attempt to trick the market, if we don\u2019t want to see another market crash or an unfortunate founder suicide. The market should decide how much reward goes to any risk-taker. From above, if any VC would invest in the hypothetical startup, he should be paying at least $2.48125 and nothing less. Doing otherwise is tricking the market, which is the prevalent culture of Series A funding in Silicon Valley.
His later involvement with the company should reward the angel and the founders for all the risks they\u2019ve taken before now: the VC should stop tearing apart previously existing term sheets and creating new ones for the sake of importunely favoring himself and the fund management people he works for. His attitude is mostly driven by irrational fear.
There is no power in arbitrariness. Even if it exists anywhere, such power function never lasts. That\u2019s easily evident in Third World dictatorships and command line communist states. Being in need of more money to run a startup shouldn\u2019t make VCs twist and wriggle angels and founders because he wants to protect investments from risk.
The only optimal and efficient way of protecting investments from risk is identifying its intrinsic value before time, and taking the well-angled dive into the startup just early enough. That\u2019s what separates the best from the worst in managing funds for startup investing.
That viewpoint already puts the angels at vantage position. It explains why nowadays the species of superangels are evolving in the ecosystem of startup investing. Some VCs are realizing that \u201cearly enough means most profitably enough\u201d. Jump on it before time asks for a greater price.
Such VCs know that the absolute power the VC community wield now would not last long. Not in the face of a whole lot of market-determined eventualities: far cheaper servers, ubiquitous open-source tools and libertarian programming languages, to mention just a sliver. Dead seriously, running a startup to profit is increasingly a function of how far along determination gets and an efficient combination of these consequences.
Merely observing the market rules and staying fair to the pricing process, is way enough to autocorrect the market in a way that the levers of demand and supply are made to naturally belong to the right sides of the marketplace. The good ones would remain, and the bad ones would be gone.
Matter-of-factly, to have more of the company, using the vast warchest in his possession, the VC should just buy more units of the shareholding at the market price. It\u2019s the price he pays for coming late to the jamboree, and might as well be his deep goblet when General Everything starts an acquisition party or the stock market invites the startup to the IPO frenzy.
If startup founders would only concentrate on creating a product the market is dying to have, the cost of funding would increasingly lower out; since such an important market factor would signal the early value investors to invest when it\u2019s so cheap to do so.
As a result, should the law of large numbers prevail; as bringing about a fairly good count of value-product startups backed by a broad population of early value investors; stock dilution caused by irrational fear would be eliminated.
That\u2019s because more would-be investors would see that it\u2019s just the way to go. Of course, it would establish the pre-eminence of deciding the value of any startup (any kind of company for that matter) on the benchmark of market price.
It should even give convenient rise to founders building from day one a company meant to make them a lifetime living. It would not matter then if things turn out to be acquisition or IPO for an exit strategy on the part of market forces.
Lean startup, blind resolve to intuition, cost-bursting technology, ready candor to mend mistakes and restiveness for innovating right are raw qualities that count for guiding lights to the typical founder that will build the value-product company of the future. Together they should materialize into traction space and undiminishing liquidity. All for one reason: any semblance of that $2.48125."},"title":{"matchLevel":"none","matchedWords":[],"value":"DECONSTRUCTING THE MYTH ABOUT VENTURE RISK: A WAY OUT OF DILUTION"},"url":{"matchLevel":"none","matchedWords":[],"value":""}},"_tags":["story","author_anchority","story_1937172","ask_hn"],"author":"anchority","children":[1937186],"created_at":"2010-11-24T12:45:40Z","created_at_i":1290602740,"num_comments":1,"objectID":"1937172","points":1,"story_id":1937172,"story_text":"DECONSTRUCTING THE MYTH ABOUT VENTURE RISK: A WAY OUT OF DILUTION
VCs are just great. They are there waiting at the finish-line to see startups through the race. Angels are so graceful. They are the proverbial friend who saw the pulloff from the beginning, and even went down with the buddy to jail saying that was darn good! Whatever\u2019s wrong with the gap between money and ideas, it never should play us all dead about the natural forces of the market and what exactly we stand for in the marketplace.
Since Joe left his fine job to join Dick, fresh out of college and so stubborn for not taking that big offer from General Everything, to turn a question into a startup; someone reading their story later in time, like their families and friends now, should see that they were making or breaking it. Stupid or brilliant, only time can heal what reason can\u2019t.
They met an angel through another friend. He falls in love and tears his check leaves. The prototype goes live and the market is made. That wonderful thingumajig is sold for $3 apiece. Where no taxes apply because of tax holiday for startups in the state they live in, all the revenues amount to $450, 000. Figuring out the total cost of production of $65, 000, they are left with $385, 000 in profit.
If only they had paid back in 1 \u00bd years after company launch the $80,000 lent by the angel, they would not have had to convert the loan to 20% shareholding. According to their agreement, a share unit is worth two dollars at pre-money valuation of $800, 000. In fact, the work-out posits that the effort of each of Joe and Dick is worth twice the amount invested by the angel. Therefore, each of them has invested $160, 000 without handing in any money save for all-important brains and brawns.
$160, 000 of Joe + $160, 000 of Dick + $80, 000 of the angel = $400, 000 total investment in the startup. In the same proportion, the goodwill accruing to each of them amounts to an equal $400, 000. Because there\u2019s no established custom or popularity, the goodwill should not count now, but they figure that VCs might come calling once the promising product is launched.
Besides, they feel that they should be really compensated for risking spending and exerting so much means and pain on proof of concept delivered to two such clients without any remuneration and contractual obligation to buy. One of them was provided by the angel.
Transport, everyday office rituals, sleepless nights, involuntary fasting, costly PR and shoestring marketing went with the whole gamut. They played deep for a great amount of work effort and thus, the longer time before they could avoid the 1 \u00bd year loan clause. Just why goodwill came a fair deal.
Press rave is here about the product. Pop culture and the TV people are dying for attention from Joe and Dick. They are reminded by the angel that they could let out some steam motion, yet they should not abandon substance for vanity. They are always back at work. Giving attention to events and the people that matter at every point in time.
Looking at the future before them, sound reasoning points in the direction of more important product features, valuable employees, necessary Intellectual Property tying-here-and-there, more solid collaboration, research and development, due diligence, etc.
It all goes from making the market to meeting the demands of the exploding market. The VCs are getting in touch with one another about this new high-tech star. The shepherd and the sheep are running to graze the bounty grass. By now, everyone in town knows how much more Joe and Dick figure the growing company needs in the way of money.
Before sales, if the share price was $2, the share price after sales takes after this picture:
$385, 000 (profit after tax: recall no taxes apply in this instance)/ $800, 000 worth of existing shares in the company = $0.48125, what is called the market quotient.
Add the pre-sales share price of $2 to the market quotient of $0.48125. And you get $2.48125 for the new share price. As simple as that. This is the market price, and it represents the least rational price any new investor should be paying to own a piece of the company.
In that wise, because the quotient could have been negative as a result of loss instead of profit, such a market price should serve as the lowest extreme of the share price band. If it was so a loss, the same formula applies and the market price should possibly be the highest extreme. And in both scenarios, whatever might count for premium and added value between buyers and sellers of the company\u2019s shares should be taken into consideration in each unique trading case.
Already, some readers would say that the allegory is demonstrating the reality on stock market floors (IPO or trading publicly quoted stocks), or common everyday buying and selling of anything founded on value. In every form of enterprise, startup investing, shipbuilding, fashion house, electric car manufacturing or trivia gamemaking, it indispensably counts to let reward and risk take their natural place in the scheme of things.
We never should attempt to trick the market, if we don\u2019t want to see another market crash or an unfortunate founder suicide. The market should decide how much reward goes to any risk-taker. From above, if any VC would invest in the hypothetical startup, he should be paying at least $2.48125 and nothing less. Doing otherwise is tricking the market, which is the prevalent culture of Series A funding in Silicon Valley.
His later involvement with the company should reward the angel and the founders for all the risks they\u2019ve taken before now: the VC should stop tearing apart previously existing term sheets and creating new ones for the sake of importunely favoring himself and the fund management people he works for. His attitude is mostly driven by irrational fear.
There is no power in arbitrariness. Even if it exists anywhere, such power function never lasts. That\u2019s easily evident in Third World dictatorships and command line communist states. Being in need of more money to run a startup shouldn\u2019t make VCs twist and wriggle angels and founders because he wants to protect investments from risk.
The only optimal and efficient way of protecting investments from risk is identifying its intrinsic value before time, and taking the well-angled dive into the startup just early enough. That\u2019s what separates the best from the worst in managing funds for startup investing.
That viewpoint already puts the angels at vantage position. It explains why nowadays the species of superangels are evolving in the ecosystem of startup investing. Some VCs are realizing that \u201cearly enough means most profitably enough\u201d. Jump on it before time asks for a greater price.
Such VCs know that the absolute power the VC community wield now would not last long. Not in the face of a whole lot of market-determined eventualities: far cheaper servers, ubiquitous open-source tools and libertarian programming languages, to mention just a sliver. Dead seriously, running a startup to profit is increasingly a function of how far along determination gets and an efficient combination of these consequences.
Merely observing the market rules and staying fair to the pricing process, is way enough to autocorrect the market in a way that the levers of demand and supply are made to naturally belong to the right sides of the marketplace. The good ones would remain, and the bad ones would be gone.
Matter-of-factly, to have more of the company, using the vast warchest in his possession, the VC should just buy more units of the shareholding at the market price. It\u2019s the price he pays for coming late to the jamboree, and might as well be his deep goblet when General Everything starts an acquisition party or the stock market invites the startup to the IPO frenzy.
If startup founders would only concentrate on creating a product the market is dying to have, the cost of funding would increasingly lower out; since such an important market factor would signal the early value investors to invest when it\u2019s so cheap to do so.
As a result, should the law of large numbers prevail; as bringing about a fairly good count of value-product startups backed by a broad population of early value investors; stock dilution caused by irrational fear would be eliminated.
That\u2019s because more would-be investors would see that it\u2019s just the way to go. Of course, it would establish the pre-eminence of deciding the value of any startup (any kind of company for that matter) on the benchmark of market price.
It should even give convenient rise to founders building from day one a company meant to make them a lifetime living. It would not matter then if things turn out to be acquisition or IPO for an exit strategy on the part of market forces.
Lean startup, blind resolve to intuition, cost-bursting technology, ready candor to mend mistakes and restiveness for innovating right are raw qualities that count for guiding lights to the typical founder that will build the value-product company of the future. Together they should materialize into traction space and undiminishing liquidity. All for one reason: any semblance of that $2.48125.","title":"DECONSTRUCTING THE MYTH ABOUT VENTURE RISK: A WAY OUT OF DILUTION","updated_at":"2024-09-19T17:21:11Z","url":""},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"fengjiabo2400"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["general","intuition"],"value":"Hey HN! I built this over a weekend after a frustrating experience with our wedding photographer.
We paid $5,000 and got back a few dozen photos. The colors were off in several shots \u2013 too warm, inconsistent lighting, some faces looked washed out. I wanted to fix them myself, but photo editing is incredibly complicated. Photoshop has hundreds of buttons, layers, masks, adjustment curves. I spent hours watching YouTube tutorials just to adjust colors on one photo. There had to be a better way.
So I built Photowand.
What it does:\nUpload any photo, describe what you want in plain English, and get professional-quality results in ~20 seconds. "Fix the colors and lighting" or "change background to beach sunset" or "make this look like a professional headshot." No Photoshop skills needed.
Try it without signup: photowand.ai
How it compares to existing AI tools:
vs ChatGPT:\n- Better photorealistic quality (specialized model, not general-purpose)\n- 4x faster processing (20 seconds vs 80+ seconds)\n- Bulk generation (create 50 variations simultaneously)\n- Iterative editing (make changes to existing photos, not just one-shot generation)
vs Gemini:\n- No watermarks on any images\n- Faster generation (20 seconds vs 40+ seconds)\n- Simpler interface (just describe what you want)\n- Commercial licensing included
Technical approach:\nCustom diffusion models optimized for photorealistic photography. I trained specifically on professional photo datasets (headshots, products, real estate, food) rather than general internet images.
Key features:\n- Natural language editing ("fix the lighting" instead of adjusting curves)\n- Consistency across batches (same person in multiple poses)\n- 400+ photo pack templates (LinkedIn headshots, product photos, etc.)\n- Unlimited revisions (describe changes, apply instantly)
What happened after I launched:\n- Now processing 500k+ photos daily\n- People using it globally in more than 180 countries\n- Turns out lots of people have the same problem (photographers cost $500-$5,000)
Where it still fails:\n- Wedding photography (emotional timing requires human intuition)\n- Complex hand interactions (I flag ~5% for manual review)\n- Extreme artistic vision (brand-specific creative direction)\n- Events requiring physical presence
The irony: I made this to fix my wedding photos. Now people mostly use it for business photography (LinkedIn headshots, product catalogs, real estate listings).
Tech stack: Custom diffusion models, Next.js, R2 storage, Cloudflare
Try the demo: photowand.ai (no signup required)
I'm here all day to answer questions about the architecture, training approach, or where AI photography still needs humans.
What would make this more useful for you?"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Photowand \u2013 Turn selfies into professional photos with AI"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://photowand.ai"}},"_tags":["story","author_fengjiabo2400","story_45493020","show_hn"],"author":"fengjiabo2400","created_at":"2025-10-06T16:22:03Z","created_at_i":1759767723,"num_comments":0,"objectID":"45493020","points":1,"story_id":45493020,"story_text":"Hey HN! I built this over a weekend after a frustrating experience with our wedding photographer.
We paid $5,000 and got back a few dozen photos. The colors were off in several shots \u2013 too warm, inconsistent lighting, some faces looked washed out. I wanted to fix them myself, but photo editing is incredibly complicated. Photoshop has hundreds of buttons, layers, masks, adjustment curves. I spent hours watching YouTube tutorials just to adjust colors on one photo. There had to be a better way.
So I built Photowand.
What it does:\nUpload any photo, describe what you want in plain English, and get professional-quality results in ~20 seconds. "Fix the colors and lighting" or "change background to beach sunset" or "make this look like a professional headshot." No Photoshop skills needed.
Try it without signup: photowand.ai
How it compares to existing AI tools:
vs ChatGPT:\n- Better photorealistic quality (specialized model, not general-purpose)\n- 4x faster processing (20 seconds vs 80+ seconds)\n- Bulk generation (create 50 variations simultaneously)\n- Iterative editing (make changes to existing photos, not just one-shot generation)
vs Gemini:\n- No watermarks on any images\n- Faster generation (20 seconds vs 40+ seconds)\n- Simpler interface (just describe what you want)\n- Commercial licensing included
Technical approach:\nCustom diffusion models optimized for photorealistic photography. I trained specifically on professional photo datasets (headshots, products, real estate, food) rather than general internet images.
Key features:\n- Natural language editing ("fix the lighting" instead of adjusting curves)\n- Consistency across batches (same person in multiple poses)\n- 400+ photo pack templates (LinkedIn headshots, product photos, etc.)\n- Unlimited revisions (describe changes, apply instantly)
What happened after I launched:\n- Now processing 500k+ photos daily\n- People using it globally in more than 180 countries\n- Turns out lots of people have the same problem (photographers cost $500-$5,000)
Where it still fails:\n- Wedding photography (emotional timing requires human intuition)\n- Complex hand interactions (I flag ~5% for manual review)\n- Extreme artistic vision (brand-specific creative direction)\n- Events requiring physical presence
The irony: I made this to fix my wedding photos. Now people mostly use it for business photography (LinkedIn headshots, product catalogs, real estate listings).
Tech stack: Custom diffusion models, Next.js, R2 storage, Cloudflare
Try the demo: photowand.ai (no signup required)
I'm here all day to answer questions about the architecture, training approach, or where AI photography still needs humans.
What would make this more useful for you?","title":"Show HN: Photowand \u2013 Turn selfies into professional photos with AI","updated_at":"2026-03-05T22:51:26Z","url":"https://photowand.ai"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"calebchiam"},"story_text":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["intuition"],"value":"Cleanlab (https://github.com/cleanlab/cleanlab) is a family of algorithms for automatically finding issues in datasets. It might seem surprising that it\u2019s possible to automatically identify label errors and out-of-distribution data; Cleanlab does this using the algorithms published in https://arxiv.org/abs/1911.00068.
Cleanlab\u2019s algorithms, while clever, are actually relatively simple. To help myself (and others!) build intuition for how they work, I built Vizzy, an interactive demo that runs in the browser. Vizzy lets you experiment with an example dataset, tweak the labels, and run Cleanlab to automatically find issues like label errors and out-of-distribution data
Vizzy includes a JavaScript port of (a part of) cleanlab, along with other neat technical nuggets including ML model training in the browser (using features from a pretrained ResNet-18, performing truncated SVD, and using an SVM model for speed). If you\u2019re interested in the details of how Vizzy works, check out this blog post: https://cleanlab.ai/blog/cleanlab-vizzy/
I\u2019m happy to answer any questions related to Vizzy, cleanlab, or confident learning and data-centric AI in general!"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Cleanlab Vizzy \u2013 automatically find label errors and bad data"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://playground.cleanlab.ai/"}},"_tags":["story","author_calebchiam","story_32496807","show_hn"],"author":"calebchiam","created_at":"2022-08-17T14:23:17Z","created_at_i":1660746197,"num_comments":0,"objectID":"32496807","points":1,"story_id":32496807,"story_text":"Cleanlab (https://github.com/cleanlab/cleanlab) is a family of algorithms for automatically finding issues in datasets. It might seem surprising that it\u2019s possible to automatically identify label errors and out-of-distribution data; Cleanlab does this using the algorithms published in https://arxiv.org/abs/1911.00068.
Cleanlab\u2019s algorithms, while clever, are actually relatively simple. To help myself (and others!) build intuition for how they work, I built Vizzy, an interactive demo that runs in the browser. Vizzy lets you experiment with an example dataset, tweak the labels, and run Cleanlab to automatically find issues like label errors and out-of-distribution data
Vizzy includes a JavaScript port of (a part of) cleanlab, along with other neat technical nuggets including ML model training in the browser (using features from a pretrained ResNet-18, performing truncated SVD, and using an SVM model for speed). If you\u2019re interested in the details of how Vizzy works, check out this blog post: https://cleanlab.ai/blog/cleanlab-vizzy/
I\u2019m happy to answer any questions related to Vizzy, cleanlab, or confident learning and data-centric AI in general!","title":"Show HN: Cleanlab Vizzy \u2013 automatically find label errors and bad data","updated_at":"2024-09-20T11:55:05Z","url":"https://playground.cleanlab.ai/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"jules"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"The physics books that are used in my courses often spend much of their time symbolically computing solutions using ancient elaborate hand waving methods. I think that the explanations and examples in these books can become more precise and general and more intuitive by using numerical and symbolic methods done on a computer. Do you know books that do this? The only one I can think of is Structure and interpretation of classical mechanics."},"title":{"matchLevel":"none","matchedWords":[],"value":"Physics books that use computers?"},"url":{"matchLevel":"none","matchedWords":[],"value":""}},"_tags":["story","author_jules","story_1630125","ask_hn"],"author":"jules","created_at":"2010-08-24T15:53:15Z","created_at_i":1282665195,"num_comments":0,"objectID":"1630125","points":4,"story_id":1630125,"story_text":"The physics books that are used in my courses often spend much of their time symbolically computing solutions using ancient elaborate hand waving methods. I think that the explanations and examples in these books can become more precise and general and more intuitive by using numerical and symbolic methods done on a computer. Do you know books that do this? The only one I can think of is Structure and interpretation of classical mechanics.","title":"Physics books that use computers?","updated_at":"2024-09-19T17:09:42Z","url":""},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"akor"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"If I am being honest I have always gone with the flow in terms of my life and had much less directed effort at any one specific goal. I've been reasonably successful but more and more I'm feeling I want "purpose". I put it in quotes mostly because I haven't defined what that means yet. Looking for inspiration / thoughts but if you were asked what you would like to accomplish from a life standpoint in the next decade what would you say? If you had to say this is what I want to put on my resume in the next decade what would it include?
In case it's helpful money has never been a driver for me except as it helps accomplish a goal. I've always liked the idea of starting a "calm company" and while there have been attempts I can't honestly say I've given 100%. I find my desire waning some for starting a business mostly because I realize how complicated it can be and how little you end up doing the thing you truly enjoy.
I'm intentionally keeping it general as I am looking less for directed advice and more anecdata to open my lens to possibilities. Thank you."},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: How do you think about life/career goals?"}},"_tags":["story","author_akor","story_35303584","ask_hn"],"author":"akor","children":[35303818,35303847,35304794],"created_at":"2023-03-25T15:27:07Z","created_at_i":1679758027,"num_comments":11,"objectID":"35303584","points":14,"story_id":35303584,"story_text":"If I am being honest I have always gone with the flow in terms of my life and had much less directed effort at any one specific goal. I've been reasonably successful but more and more I'm feeling I want "purpose". I put it in quotes mostly because I haven't defined what that means yet. Looking for inspiration / thoughts but if you were asked what you would like to accomplish from a life standpoint in the next decade what would you say? If you had to say this is what I want to put on my resume in the next decade what would it include?
In case it's helpful money has never been a driver for me except as it helps accomplish a goal. I've always liked the idea of starting a "calm company" and while there have been attempts I can't honestly say I've given 100%. I find my desire waning some for starting a business mostly because I realize how complicated it can be and how little you end up doing the thing you truly enjoy.
I'm intentionally keeping it general as I am looking less for directed advice and more anecdata to open my lens to possibilities. Thank you.","title":"Ask HN: How do you think about life/career goals?","updated_at":"2025-10-06T20:43:11Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"frudas24"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"DeskSlice is a small Go tool that lets you remotely view and control a VS Code AI agent from a mobile browser.
The problem I wanted to solve was very practical: I wanted to comfortably interact with a local VS Code agent (read outputs, scroll, and type prompts) from my phone, without reimplementing the UI or relying on editor internals or private APIs.
Instead of building a full remote desktop, DeskSlice streams only a calibrated slice of the desktop where the agent UI lives, and maps touch gestures back to mouse and keyboard input on the host.
I originally implemented this using WebRTC, but after hitting reliability and complexity issues (signaling, renegotiation, RTP quirks), I pivoted to MJPEG over HTTP. For LAN use, MJPEG turned out to be much simpler, easier to debug, and reliable enough for UI-driven workflows.
Key ideas:\n- Manual fullscreen calibration to select the exact agent panel, input area, and scroll area\n- Cropped video stream (not the full desktop)\n- Touch-first interaction model (tap, drag-scroll, typing)\n- No UI scraping, no state persistence \u2014 it operates the real VS Code agent UI\n- Simple password gate for LAN use
This is intentionally not a general-purpose remote desktop. It\u2019s a focused control surface for interacting with a local AI agent through its existing UI.
Repo: https://github.com/frudas24/deskslice/"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: DeskSlice \u2013 controlling a VS Code agent from my phone"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/frudas24/deskslice"}},"_tags":["story","author_frudas24","story_46517846","show_hn"],"author":"frudas24","children":[46518345],"created_at":"2026-01-06T20:03:50Z","created_at_i":1767729830,"num_comments":5,"objectID":"46517846","points":3,"story_id":46517846,"story_text":"DeskSlice is a small Go tool that lets you remotely view and control a VS Code AI agent from a mobile browser.
The problem I wanted to solve was very practical: I wanted to comfortably interact with a local VS Code agent (read outputs, scroll, and type prompts) from my phone, without reimplementing the UI or relying on editor internals or private APIs.
Instead of building a full remote desktop, DeskSlice streams only a calibrated slice of the desktop where the agent UI lives, and maps touch gestures back to mouse and keyboard input on the host.
I originally implemented this using WebRTC, but after hitting reliability and complexity issues (signaling, renegotiation, RTP quirks), I pivoted to MJPEG over HTTP. For LAN use, MJPEG turned out to be much simpler, easier to debug, and reliable enough for UI-driven workflows.
Key ideas:\n- Manual fullscreen calibration to select the exact agent panel, input area, and scroll area\n- Cropped video stream (not the full desktop)\n- Touch-first interaction model (tap, drag-scroll, typing)\n- No UI scraping, no state persistence \u2014 it operates the real VS Code agent UI\n- Simple password gate for LAN use
This is intentionally not a general-purpose remote desktop. It\u2019s a focused control surface for interacting with a local AI agent through its existing UI.
Repo: https://github.com/frudas24/deskslice/","title":"Show HN: DeskSlice \u2013 controlling a VS Code agent from my phone","updated_at":"2026-03-05T23:17:38Z","url":"https://github.com/frudas24/deskslice"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"margolis20"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"Built this as a side project to explore a problem I keep running into with agents: they lose useful continuity across threads and resumed work.
Pallium is a local-first memory sidecar. It stores selected evidence, derives compact memory like decisions/findings/checkpoints, and returns evidence-backed memory blocks for follow-up questions.
It is intentionally not a general knowledge base or vector DB. The focus is scoped memory for agent-mediated conversations.
Repo: https://github.com/rore/Pallium"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Pallium, a local-first memory sidecar for agent conversations"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/rore/Pallium"}},"_tags":["story","author_margolis20","story_47506601","show_hn"],"author":"margolis20","children":[47506768],"created_at":"2026-03-24T17:56:02Z","created_at_i":1774374962,"num_comments":0,"objectID":"47506601","points":1,"story_id":47506601,"story_text":"Built this as a side project to explore a problem I keep running into with agents: they lose useful continuity across threads and resumed work.
Pallium is a local-first memory sidecar. It stores selected evidence, derives compact memory like decisions/findings/checkpoints, and returns evidence-backed memory blocks for follow-up questions.
It is intentionally not a general knowledge base or vector DB. The focus is scoped memory for agent-mediated conversations.
Repo: https://github.com/rore/Pallium","title":"Show HN: Pallium, a local-first memory sidecar for agent conversations","updated_at":"2026-03-24T18:09:32Z","url":"https://github.com/rore/Pallium"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"siavosh"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"I've been reading Brian Greene's Fabric of the Cosmos, and I've been trying to get my head wrapped around general relativity, particularly the counter intuitive notions of time and space at high speeds, great distances, and large gravities.
I'm not a game developer, but the thought occurred if anyone knew of a game that can help make some of these notions more intuitive?"},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: A game about spacetime?"},"url":{"matchLevel":"none","matchedWords":[],"value":""}},"_tags":["story","author_siavosh","story_8788614","ask_hn"],"author":"siavosh","created_at":"2014-12-23T15:56:52Z","created_at_i":1419350212,"num_comments":0,"objectID":"8788614","points":1,"story_id":8788614,"story_text":"I've been reading Brian Greene's Fabric of the Cosmos, and I've been trying to get my head wrapped around general relativity, particularly the counter intuitive notions of time and space at high speeds, great distances, and large gravities.
I'm not a game developer, but the thought occurred if anyone knew of a game that can help make some of these notions more intuitive?","title":"Ask HN: A game about spacetime?","updated_at":"2024-09-19T21:29:41Z","url":""},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"pgspaintbrush"},"title":{"matchLevel":"none","matchedWords":[],"value":"Why are LLMs general learners?"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://intuitiveai.substack.com/p/why-are-large-language-models-general"}},"_tags":["story","author_pgspaintbrush","story_36302048"],"author":"pgspaintbrush","children":[36303350,36303586,36303948,36304154,36304319,36304422,36304449,36304841,36306113,36306219,36306238],"created_at":"2023-06-12T22:16:45Z","created_at_i":1686608205,"num_comments":61,"objectID":"36302048","points":64,"story_id":36302048,"title":"Why are LLMs general learners?","updated_at":"2025-11-10T20:42:11Z","url":"https://intuitiveai.substack.com/p/why-are-large-language-models-general"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"kgodey"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"Hi HN!
We just shipped the public beta of the hosted version of Mathesar, our open source \u201cintuitive spreadsheet UI, full PostgreSQL power\u201d project.
Until now, using Mathesar meant installing and running it yourself. Now you can use Mathesar for free without having to maintain your own infrastructure at https://mathesar.cloud.
Mathesar Cloud is run by the Mathesar maintainers through the 501(c)(3) nonprofit that already maintains the open source project. We\u2019re committed to ALWAYS keeping Mathesar 100% open source (no open core), nonprofit, and treat self-hosting as a first-class citizen. We\u2019re launching a hosted service in part to sustain the project in the long term while keeping our principles. We\u2019re also excited to be making Mathesar accessible to users who cannot or do not want to self-host.
Currently, we have a free plan that\u2019s great for solo projects, prototyping, and evaluating Mathesar for larger deployments. It\u2019s scoped to a single database per user.
We are using this early beta to learn what people need, as we work on launching more plans with feature parity with self-hosted Mathesar, including collaboration, control over Postgres roles and permissions, and connecting existing databases.
I'd love your feedback on Mathesar in general, the Cloud version, or anything else!
MORE ABOUT MATHESAR:
Mathesar is a 100% open source project (we just got to 5000 stars!) that gives users a spreadsheet-like interface to PostgreSQL databases. You can connect an existing database or start a new one from scratch, it works the same either way. We aim for the UI to be familiar and intuitive for non-technical users while also using the full power of PostgreSQL without intermediate layers or abstractions. For example, our permissions system uses PostgreSQL roles and permissions directly.
LINKS:
- GitHub: https://github.com/mathesar-foundation/mathesar
- Mathesar Cloud: https://mathesar.cloud
- Announcement blog post: https://mathesar.org/blog/2026/06/17/announcing-mathesar-clo..."},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Hosted version of Mathesar, intuitive open source UI built on Postgres"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://mathesar.cloud/site/"}},"_tags":["story","author_kgodey","story_48572648","show_hn"],"author":"kgodey","created_at":"2026-06-17T16:20:17Z","created_at_i":1781713217,"num_comments":0,"objectID":"48572648","points":6,"story_id":48572648,"story_text":"Hi HN!
We just shipped the public beta of the hosted version of Mathesar, our open source \u201cintuitive spreadsheet UI, full PostgreSQL power\u201d project.
Until now, using Mathesar meant installing and running it yourself. Now you can use Mathesar for free without having to maintain your own infrastructure at https://mathesar.cloud.
Mathesar Cloud is run by the Mathesar maintainers through the 501(c)(3) nonprofit that already maintains the open source project. We\u2019re committed to ALWAYS keeping Mathesar 100% open source (no open core), nonprofit, and treat self-hosting as a first-class citizen. We\u2019re launching a hosted service in part to sustain the project in the long term while keeping our principles. We\u2019re also excited to be making Mathesar accessible to users who cannot or do not want to self-host.
Currently, we have a free plan that\u2019s great for solo projects, prototyping, and evaluating Mathesar for larger deployments. It\u2019s scoped to a single database per user.
We are using this early beta to learn what people need, as we work on launching more plans with feature parity with self-hosted Mathesar, including collaboration, control over Postgres roles and permissions, and connecting existing databases.
I'd love your feedback on Mathesar in general, the Cloud version, or anything else!
MORE ABOUT MATHESAR:
Mathesar is a 100% open source project (we just got to 5000 stars!) that gives users a spreadsheet-like interface to PostgreSQL databases. You can connect an existing database or start a new one from scratch, it works the same either way. We aim for the UI to be familiar and intuitive for non-technical users while also using the full power of PostgreSQL without intermediate layers or abstractions. For example, our permissions system uses PostgreSQL roles and permissions directly.
LINKS:
- GitHub: https://github.com/mathesar-foundation/mathesar
- Mathesar Cloud: https://mathesar.cloud
- Announcement blog post: https://mathesar.org/blog/2026/06/17/announcing-mathesar-clo...","title":"Show HN: Hosted version of Mathesar, intuitive open source UI built on Postgres","updated_at":"2026-06-19T13:20:15Z","url":"https://mathesar.cloud/site/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"kizer"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"I\u2019ve been working on an http server in C as a side project, and while studying the code bases of other C projects, I\u2019ve noticed that there is a tendency to start programming in terms of the project\u2019s own abstractions (structs, functions) at the lowest levels, or \u201cas soon a possible\u201d; e.g., to use a custom file descriptor structs, custom versions of read(2) and write(2) or equivalent \u201cenhanced\u201d functions. What I mean is that I see for example the native FILE or bind/listen/accept once, but then they are either always wrapped or remain encapsulated at a lowest implementation layer.
The way I see it, this effectively maximizes the proportion of your code that speaks in your own abstractions, which certainly makes coding easier (working in your vernacular, so to speak).
Is this intentional, and is there a principle or pattern being applied? For reference, I\u2019ve been looking at ngnix, libuv, livevent, redis and SQLite, the latter two a while back so they may not follow my description."},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: In general, prefer your abstractions vs. runtime ones ASAP?"}},"_tags":["story","author_kizer","story_20969289","ask_hn"],"author":"kizer","children":[20972435,20978395],"created_at":"2019-09-14T06:35:02Z","created_at_i":1568442902,"num_comments":1,"objectID":"20969289","points":1,"story_id":20969289,"story_text":"I\u2019ve been working on an http server in C as a side project, and while studying the code bases of other C projects, I\u2019ve noticed that there is a tendency to start programming in terms of the project\u2019s own abstractions (structs, functions) at the lowest levels, or \u201cas soon a possible\u201d; e.g., to use a custom file descriptor structs, custom versions of read(2) and write(2) or equivalent \u201cenhanced\u201d functions. What I mean is that I see for example the native FILE or bind/listen/accept once, but then they are either always wrapped or remain encapsulated at a lowest implementation layer.
The way I see it, this effectively maximizes the proportion of your code that speaks in your own abstractions, which certainly makes coding easier (working in your vernacular, so to speak).
Is this intentional, and is there a principle or pattern being applied? For reference, I\u2019ve been looking at ngnix, libuv, livevent, redis and SQLite, the latter two a while back so they may not follow my description.","title":"Ask HN: In general, prefer your abstractions vs. runtime ones ASAP?","updated_at":"2024-09-20T04:50:46Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"czsun"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"This is the third post in my ongoing series critically examining the Fugue paper's assertions about Operational Transformation (OT). In the previous two posts, I conducted a thorough analysis of the Fugue paper's arguments, meticulously highlighting the flaws in its reasoning and exposing inaccuracies in its depiction of OT algorithms, particularly the adOPTed algorithm and Jupiter-OT.
To recap, my first post titled "What's Wrong with 'The Art of the Fugue' Paper about OT (adOPTed)?" (https://news.ycombinator.com/item?id=36208585) presented a comprehensive analysis showcasing the consistent and non-interleaving outcomes delivered by the adOPTed algorithm, thereby refuting the alleged "char-interleaving" problem. Moreover, I revealed a fundamental flaw in the Fugue paper's portrayal of the adOPTed algorithm\u2014it mistakenly presented a flawed dOPT-like algorithm instead of the authentic adOPTed algorithm, disregarding the resolution of the well-known dOPT-puzzle. It is disheartening to witness the perpetuation of the dOPT-puzzle within the pages of the Fugue paper, despite its long-standing resolution.
In my second post titled "Unveiling Issues with 'The Art of the Fugue' Paper Regarding Jupiter-OT" (https://news.ycombinator.com/item?id=36415068), I provided a comprehensive explanation of why Jupiter-OT consistently produces non-interleaving outcomes, irrespective of whether it is utilized with string-wise or char-wise transformation functions. This effectively debunked the Fugue paper's baseless claims about Jupiter-OT's "char-interleaving" problem. Additionally, I questioned the relevance and value of discussing concepts like "multi-user-backward-relay-interleaving," urging to direct collective efforts towards addressing genuine co-editing challenges for the advancement of the field.
In this third post, I focus on debunking the unfounded assertions made in the Fugue paper regarding the GOT algorithm. Since GOT supports string-wise co-editing, like Jupiter-OT, and can be combined with various transformation functions, it is straightforward to refute the alleged "char-interleaving" problem in GOT using the same reasoning and illustrations from my second post on Jupiter-OT. Therefore, this post aims to address broader issues, dispel misconceptions, and unveil the truth about the GOT algorithm and OT as a whole.
1.Basic Facts and Features of the GOT algorithm
The GOT (Generic Operation Transformation) work was mainly motivated to solve the classic dOPT puzzle. The GOT algorithm was initially designed and published in [1], without reference to any concrete transformation functions. Later, the combination of the GOT algorithm with a set of independently designed string-wise transformation functions was published in [2].
The GOT algorithm possesses the following main features:
a. Functioning as a distributed OT control algorithm, without relying on a central transformation server.
b.Introducing the notion of operation context and context-based transformation conditions for OT correctness.
c.Solving the dOPT puzzle by ensuring the context-equivalence condition.
d.Achieving convergence without requiring the supporting transformation functions to meet CP1 and CP2 transformation properties.
e.Incorporating a state-vector-based garbage collection scheme to remove operations from the history buffer that are no longer necessary for future transformation.
Similar to Jupiter-OT and the adOPTed algorithm, the GOT algorithm satisfies the mandatory context-based conditions required for all OT control algorithms (see Q&A 3.15-3.18 in OTFAQ [4]); and it can be combined with any suitable transformation functions (not limited to those published in [2]) to create a complete OT solution.
Differing from Jupiter-OT and the adOPTed algorithm, the GOT algorithm employs a pair of Inclusion and Exclusion transformation functions, which are obligated to meet a reversibility transformation property. This reversibility requirement increases the complexity of transformation functions and has been eliminated in subsequent OT control algorithms such as NICE, TIBOT, COT, and POT, which exclusively utilize Inclusion transformation functions.
One side-product of the GOT work is the identification of the False-Tie (FT) puzzle in text co-editing, which has influenced subsequent development in OT and the first CRDT (WOOT) in co-editing. The FT puzzle and CP2-voilation issue have been solved in numerous ways under the OT framework. Readers interested in learning more about FT and its solutions can refer to the following Q&A entries in the OTFAQ [4]:
\u20223.24. What is the False-Tie (FT) puzzle?
\u20223.25. Under what circumstances is an FT-solution needed or not needed?
\u20223.26. How to achieve consistency without solving the FT puzzle?
2. Text-Interleaving is Prohibited in String-Wise Transformation Functions
In the Fugue paper, it was claimed that the "interleaving" problem "has gone unnoticed for decades." However, as I highlighted in my first post, the issue of char-interleaving in some CRDT algorithms (e.g., Logoot) had already been reported as early as 2018. Furthermore, it is important to note that the matter of avoiding concurrent insertion interleaving had been explicitly addressed back in 1998 when designing string-wise transformation functions.
Section 9.1.3 "Criteria for Verifying Intention-Preserved Effects" of [2] (pp. 85-86) provides a precise specification for achieving intention-preserved effects during concurrent string-wise insert and delete operations. This specification served as a guiding principle for the design of string-wise transformation functions, which aim to achieve desired combined effects while explicitly preventing the "interleaving" of concurrent insertions. The following excerpt from [2] highlights this point:
"When the above criteria are satisfied, the execution effects of independent Insert/Delete operations will not interfere with each other in the following sense: an Insert operation may never insert a string into the middle of another string inserted by an independent operation, and a Delete operation may never delete characters inserted by independent operations."\n3.Text-Interleaving is Irrelevant to OT Control Algorithms
Text-interleaving is a special concern in text co-editing. It is a common misconception in some co-editing articles to attribute text co-editing issues to generic OT control algorithms.
In the Fugue paper, Jupiter-OT, adOPTed, and GOT are implicated as the cause of text-interleaving problems. However, even if those illustrations used to support such assertions were valid (which, as demonstrated in my previous posts, they are not), assigning the responsibility of text-editing specific issues to OT control algorithms is misguided and highly misleading. The correctness of an OT control algorithm is determined by its adherence to essential context-based transformation conditions. These conditions are entirely unrelated to text-editing and, consequently, text-interleaving.
This further underscores the need for a better understanding of the principles that govern OT control algorithms and their evaluation criteria. Readers interested in learning more about OT correctness can refer to the following Q&A entries in the OTFAQ [4]:
\u20223.15. What are the OT algorithm correctness requirements?
\u20223.16. Which OT components are responsible for meeting specific algorithm correctness requirements?
\u20223.18. Under what conditions is an OT system algorithmically correct?
4.How to Create Correct OT Solutions by Combining Existing Control Algorithms and Transformation Functions?
A well-established approach to constructing a comprehensive OT solution involves the separation of high-level OT control algorithms from low-level transformation functions, with the specification of their interrelationships through transformation properties and conditions.
One significant advantage of this modular OT system structure is the ability to design and validate control algorithms and transformation functions independently, enabling their flexible combination to create new OT solutions tailored to specific applications, as long as they adhere to the required transformation conditions and properties. The separation and flexible combination of control algorithms and transformation functions have greatly contributed to the continuous advancement of OT and its diverse real-world applications.
Last decade has witnessed significant expansion of OT into new co-editing domains through the invention of novel transformation functions for various data types, such as QuillJS OT functions for rich-text co-editing (https://github.com/ottypes/rich-text), JSON OT functions (https://github.com/ottypes/json0), just to mention a few. Many of these novel transformation functions have been developed by open-source contributors and industry practitioners.
On the other hand, numerous OT control algorithms have been designed and most of them are invented by academic researchers [4]. Some control algorithms, like Jupiter-OT, NICE and Google OT, are Sever-based OT (SOT) algorithms that rely on a central transformation server. However, most other OT control algorithms, including adOPTed, GOT, GOTO, COT, SOCT, TIBOT, and POT, are Distributed OT (DOT) algorithms that do not require a transformation server and allow co-editing clients to connect with each other in flexible communication topologies.
With the availability of a range of OT control algorithms and open-source transformation functions, there are ample opportunities to create comprehensive OT solutions for specific applications by flexibly combining suitable control algorithms and transformation functions.
However, there is a prevalent misconception within co-editing communities that OT necessitates a central server to function. This widespread illusion can be attributed to a combination of factors, including the fact that the popular OT-based Google Docs utilizes a transformation server, a general lack of awareness and understanding of distributed OT algorithms, and the spread of misinformation. Even among experienced industrial engineers and open-source practitioners who have developed practical OT-based co-editing products or designed advanced transformation functions, there was a lack of awareness or limited knowledge about the fact that OT can function perfectly without relying on a central server. This lack of awareness and understanding, combined with the prevailing misconception, led them to mistakenly perceive that their OT systems or functions were confined to operating with a central transformation server like Google Docs.
In fact, OT control algorithms (whether SOT or DOT) and transformation functions (for any data types and applications) are independent components. The publicly available transformation functions developed by practitioners have been commonly integrated with different OT control algorithms (SOT or DOT) in various practical co-editing applications. It is worth noting that most co-editing systems adopt a client-server architecture for valid reasons [3]. If necessary, a server-based OT co-editing system can be transformed into a server-less OT-based co-editing system by adopting a distributed OT control algorithm. This conversion does not require modifying the existing transformation functions for the target application, nor does it necessitate the creation of a new OT control algorithm, as there are numerous existing options readily available.
The notion that OT is unsuitable for peer-to-peer co-editing is a false proposition. For further discussion, refer to Section 4 "Myths and Facts about Peer-to-Peer Co-Editing" in [3].
5. How to Avoid Creating Incorrect OT Solutions in Combining Control Algorithms and Transformation Functions?
While the flexible combination of control algorithms and transformation has been instrumental in creating innovative and effective OT solutions, it is important to acknowledge that this power can, and unfortunately has been, misused to generate incorrect solutions, often employed to substantiate unfounded criticisms of OT. Such misuse may arise from a limited knowledge of OT fundamentals, but its repercussions are far-reaching. It perpetuates distorted views of OT, compromises the integrity of the field, and hinders the overall progress of co-editing.
One example of such misuse can be found in the Fugue paper, which I discussed in detail in my first post of this series. The paper attempted to demonstrate the presence of char-interleaving in the adOPTed algorithm by combining it with the Tombstone Transformation Function (TTF). Unfortunately, the adOPTed algorithm was inaccurately portrayed to function similarly to the flawed dOPT algorithm. This combination of TTF with a dOPT-like algorithm resulted in an erroneous solution that generated inconsistent and interleaving outcomes. These outcomes were then used to support the assertion of an interleaving issue in the adOPTed algorithm and TTF.
In fact, TTF has no connection to char-interleaving either. However, other misconceptions surrounding TTF do exist. In some articles and talks, TTF was portrayed as a correct OT solution, while simultaneously labelling OT control algorithms (such as adOPTed) as incorrect in comparison. However, this comparison is fundamentally flawed because TTF merely comprises a set of transformation functions that must be combined with a suitable OT control algorithm to form a complete solution. Even then, TTF alone does not ensure the correctness of the resulting solution. The Fugue paper serves as a prime example of this, where the combination of TTF with a dOPT-like control algorithm yielded a flawed solution.
Another noteworthy case from the Fugue paper involves the combination of the Jupiter-OT control algorithm with a fabricated char-wise transformation function. This combination was used to justify the alleged issue of char-interleaving within the original Jupiter-OT solution.
In contrast, my second post in this series presented an alternative approach by combining the Jupiter-OT control algorithm with string-wise transformation functions, resulting in consistent and non-interleaving outcomes. Additionally, I presented another new OT solution by integrating the Jupiter-OT control algorithm with a different char-wise transformation function. This solution successfully generated consistent and non-interleaving results for concurrent char-wise insertions.
The moral of the story is clear: the power of combining OT control algorithms and transformation functions in the field of co-editing is immense, but it should be used constructively and responsibly. To harness this power effectively, it is crucial to have a better and more comprehensive understanding of the fundamentals of OT. By doing so, we can avoid potential pitfalls and accelerate the development of correct, valuable, and robust co-editing solutions that drive meaningful progress in the field.
References:
[1] C. Sun, X. Jia, Y. Zhang and Y. Yang: \u201cA Generic Operation Transformation Scheme for Consistency Maintenance in Real-time Cooperative Editing Systems,\u201d Proc. of ACM Conf. on Supporting Group Work, pp. 425 \u2013 434, Nov. 16 \u2013 19, 1997.
[2] C. Sun, X. Jia, Y. Zhang, Y. Yang and D. Chen: "Achieving convergence, causality-preservation, and intention-preservation in real-time cooperative editing systems," ACM Transactions on Computer-Human Interaction, Vol. 5, No. 1, pp.63 \u2013 108, Mar., 1998.
[3] D. Sun, C. Sun, Agustina, W. Cai. Real differences between OT and CRDT in building co-editing systems and real-world applications. https://arxiv.org/abs/1905.01517, May 2, 2019.
[4] C. Sun, "OTFAQ: Operational Transformation Frequently Asked Questions and Answers," https://www3.ntu.edu.sg/scse/staff/czsun/projects/otfaq/
Readers are encouraged to contact the author of this post for copies of any articles referred in this series."},"title":{"matchLevel":"none","matchedWords":[],"value":"Dispelling Misconceptions and Unveiling the Truth about GOT and OT in General"}},"_tags":["story","author_czsun","story_36643393","ask_hn"],"author":"czsun","created_at":"2023-07-08T11:27:45Z","created_at_i":1688815665,"num_comments":0,"objectID":"36643393","points":1,"story_id":36643393,"story_text":"This is the third post in my ongoing series critically examining the Fugue paper's assertions about Operational Transformation (OT). In the previous two posts, I conducted a thorough analysis of the Fugue paper's arguments, meticulously highlighting the flaws in its reasoning and exposing inaccuracies in its depiction of OT algorithms, particularly the adOPTed algorithm and Jupiter-OT.
To recap, my first post titled "What's Wrong with 'The Art of the Fugue' Paper about OT (adOPTed)?" (https://news.ycombinator.com/item?id=36208585) presented a comprehensive analysis showcasing the consistent and non-interleaving outcomes delivered by the adOPTed algorithm, thereby refuting the alleged "char-interleaving" problem. Moreover, I revealed a fundamental flaw in the Fugue paper's portrayal of the adOPTed algorithm\u2014it mistakenly presented a flawed dOPT-like algorithm instead of the authentic adOPTed algorithm, disregarding the resolution of the well-known dOPT-puzzle. It is disheartening to witness the perpetuation of the dOPT-puzzle within the pages of the Fugue paper, despite its long-standing resolution.
In my second post titled "Unveiling Issues with 'The Art of the Fugue' Paper Regarding Jupiter-OT" (https://news.ycombinator.com/item?id=36415068), I provided a comprehensive explanation of why Jupiter-OT consistently produces non-interleaving outcomes, irrespective of whether it is utilized with string-wise or char-wise transformation functions. This effectively debunked the Fugue paper's baseless claims about Jupiter-OT's "char-interleaving" problem. Additionally, I questioned the relevance and value of discussing concepts like "multi-user-backward-relay-interleaving," urging to direct collective efforts towards addressing genuine co-editing challenges for the advancement of the field.
In this third post, I focus on debunking the unfounded assertions made in the Fugue paper regarding the GOT algorithm. Since GOT supports string-wise co-editing, like Jupiter-OT, and can be combined with various transformation functions, it is straightforward to refute the alleged "char-interleaving" problem in GOT using the same reasoning and illustrations from my second post on Jupiter-OT. Therefore, this post aims to address broader issues, dispel misconceptions, and unveil the truth about the GOT algorithm and OT as a whole.
1.Basic Facts and Features of the GOT algorithm
The GOT (Generic Operation Transformation) work was mainly motivated to solve the classic dOPT puzzle. The GOT algorithm was initially designed and published in [1], without reference to any concrete transformation functions. Later, the combination of the GOT algorithm with a set of independently designed string-wise transformation functions was published in [2].
The GOT algorithm possesses the following main features:
a. Functioning as a distributed OT control algorithm, without relying on a central transformation server.
b.Introducing the notion of operation context and context-based transformation conditions for OT correctness.
c.Solving the dOPT puzzle by ensuring the context-equivalence condition.
d.Achieving convergence without requiring the supporting transformation functions to meet CP1 and CP2 transformation properties.
e.Incorporating a state-vector-based garbage collection scheme to remove operations from the history buffer that are no longer necessary for future transformation.
Similar to Jupiter-OT and the adOPTed algorithm, the GOT algorithm satisfies the mandatory context-based conditions required for all OT control algorithms (see Q&A 3.15-3.18 in OTFAQ [4]); and it can be combined with any suitable transformation functions (not limited to those published in [2]) to create a complete OT solution.
Differing from Jupiter-OT and the adOPTed algorithm, the GOT algorithm employs a pair of Inclusion and Exclusion transformation functions, which are obligated to meet a reversibility transformation property. This reversibility requirement increases the complexity of transformation functions and has been eliminated in subsequent OT control algorithms such as NICE, TIBOT, COT, and POT, which exclusively utilize Inclusion transformation functions.
One side-product of the GOT work is the identification of the False-Tie (FT) puzzle in text co-editing, which has influenced subsequent development in OT and the first CRDT (WOOT) in co-editing. The FT puzzle and CP2-voilation issue have been solved in numerous ways under the OT framework. Readers interested in learning more about FT and its solutions can refer to the following Q&A entries in the OTFAQ [4]:
\u20223.24. What is the False-Tie (FT) puzzle?
\u20223.25. Under what circumstances is an FT-solution needed or not needed?
\u20223.26. How to achieve consistency without solving the FT puzzle?
2. Text-Interleaving is Prohibited in String-Wise Transformation Functions
In the Fugue paper, it was claimed that the "interleaving" problem "has gone unnoticed for decades." However, as I highlighted in my first post, the issue of char-interleaving in some CRDT algorithms (e.g., Logoot) had already been reported as early as 2018. Furthermore, it is important to note that the matter of avoiding concurrent insertion interleaving had been explicitly addressed back in 1998 when designing string-wise transformation functions.
Section 9.1.3 "Criteria for Verifying Intention-Preserved Effects" of [2] (pp. 85-86) provides a precise specification for achieving intention-preserved effects during concurrent string-wise insert and delete operations. This specification served as a guiding principle for the design of string-wise transformation functions, which aim to achieve desired combined effects while explicitly preventing the "interleaving" of concurrent insertions. The following excerpt from [2] highlights this point:
"When the above criteria are satisfied, the execution effects of independent Insert/Delete operations will not interfere with each other in the following sense: an Insert operation may never insert a string into the middle of another string inserted by an independent operation, and a Delete operation may never delete characters inserted by independent operations."\n3.Text-Interleaving is Irrelevant to OT Control Algorithms
Text-interleaving is a special concern in text co-editing. It is a common misconception in some co-editing articles to attribute text co-editing issues to generic OT control algorithms.
In the Fugue paper, Jupiter-OT, adOPTed, and GOT are implicated as the cause of text-interleaving problems. However, even if those illustrations used to support such assertions were valid (which, as demonstrated in my previous posts, they are not), assigning the responsibility of text-editing specific issues to OT control algorithms is misguided and highly misleading. The correctness of an OT control algorithm is determined by its adherence to essential context-based transformation conditions. These conditions are entirely unrelated to text-editing and, consequently, text-interleaving.
This further underscores the need for a better understanding of the principles that govern OT control algorithms and their evaluation criteria. Readers interested in learning more about OT correctness can refer to the following Q&A entries in the OTFAQ [4]:
\u20223.15. What are the OT algorithm correctness requirements?
\u20223.16. Which OT components are responsible for meeting specific algorithm correctness requirements?
\u20223.18. Under what conditions is an OT system algorithmically correct?
4.How to Create Correct OT Solutions by Combining Existing Control Algorithms and Transformation Functions?
A well-established approach to constructing a comprehensive OT solution involves the separation of high-level OT control algorithms from low-level transformation functions, with the specification of their interrelationships through transformation properties and conditions.
One significant advantage of this modular OT system structure is the ability to design and validate control algorithms and transformation functions independently, enabling their flexible combination to create new OT solutions tailored to specific applications, as long as they adhere to the required transformation conditions and properties. The separation and flexible combination of control algorithms and transformation functions have greatly contributed to the continuous advancement of OT and its diverse real-world applications.
Last decade has witnessed significant expansion of OT into new co-editing domains through the invention of novel transformation functions for various data types, such as QuillJS OT functions for rich-text co-editing (https://github.com/ottypes/rich-text), JSON OT functions (https://github.com/ottypes/json0), just to mention a few. Many of these novel transformation functions have been developed by open-source contributors and industry practitioners.
On the other hand, numerous OT control algorithms have been designed and most of them are invented by academic researchers [4]. Some control algorithms, like Jupiter-OT, NICE and Google OT, are Sever-based OT (SOT) algorithms that rely on a central transformation server. However, most other OT control algorithms, including adOPTed, GOT, GOTO, COT, SOCT, TIBOT, and POT, are Distributed OT (DOT) algorithms that do not require a transformation server and allow co-editing clients to connect with each other in flexible communication topologies.
With the availability of a range of OT control algorithms and open-source transformation functions, there are ample opportunities to create comprehensive OT solutions for specific applications by flexibly combining suitable control algorithms and transformation functions.
However, there is a prevalent misconception within co-editing communities that OT necessitates a central server to function. This widespread illusion can be attributed to a combination of factors, including the fact that the popular OT-based Google Docs utilizes a transformation server, a general lack of awareness and understanding of distributed OT algorithms, and the spread of misinformation. Even among experienced industrial engineers and open-source practitioners who have developed practical OT-based co-editing products or designed advanced transformation functions, there was a lack of awareness or limited knowledge about the fact that OT can function perfectly without relying on a central server. This lack of awareness and understanding, combined with the prevailing misconception, led them to mistakenly perceive that their OT systems or functions were confined to operating with a central transformation server like Google Docs.
In fact, OT control algorithms (whether SOT or DOT) and transformation functions (for any data types and applications) are independent components. The publicly available transformation functions developed by practitioners have been commonly integrated with different OT control algorithms (SOT or DOT) in various practical co-editing applications. It is worth noting that most co-editing systems adopt a client-server architecture for valid reasons [3]. If necessary, a server-based OT co-editing system can be transformed into a server-less OT-based co-editing system by adopting a distributed OT control algorithm. This conversion does not require modifying the existing transformation functions for the target application, nor does it necessitate the creation of a new OT control algorithm, as there are numerous existing options readily available.
The notion that OT is unsuitable for peer-to-peer co-editing is a false proposition. For further discussion, refer to Section 4 "Myths and Facts about Peer-to-Peer Co-Editing" in [3].
5. How to Avoid Creating Incorrect OT Solutions in Combining Control Algorithms and Transformation Functions?
While the flexible combination of control algorithms and transformation has been instrumental in creating innovative and effective OT solutions, it is important to acknowledge that this power can, and unfortunately has been, misused to generate incorrect solutions, often employed to substantiate unfounded criticisms of OT. Such misuse may arise from a limited knowledge of OT fundamentals, but its repercussions are far-reaching. It perpetuates distorted views of OT, compromises the integrity of the field, and hinders the overall progress of co-editing.
One example of such misuse can be found in the Fugue paper, which I discussed in detail in my first post of this series. The paper attempted to demonstrate the presence of char-interleaving in the adOPTed algorithm by combining it with the Tombstone Transformation Function (TTF). Unfortunately, the adOPTed algorithm was inaccurately portrayed to function similarly to the flawed dOPT algorithm. This combination of TTF with a dOPT-like algorithm resulted in an erroneous solution that generated inconsistent and interleaving outcomes. These outcomes were then used to support the assertion of an interleaving issue in the adOPTed algorithm and TTF.
In fact, TTF has no connection to char-interleaving either. However, other misconceptions surrounding TTF do exist. In some articles and talks, TTF was portrayed as a correct OT solution, while simultaneously labelling OT control algorithms (such as adOPTed) as incorrect in comparison. However, this comparison is fundamentally flawed because TTF merely comprises a set of transformation functions that must be combined with a suitable OT control algorithm to form a complete solution. Even then, TTF alone does not ensure the correctness of the resulting solution. The Fugue paper serves as a prime example of this, where the combination of TTF with a dOPT-like control algorithm yielded a flawed solution.
Another noteworthy case from the Fugue paper involves the combination of the Jupiter-OT control algorithm with a fabricated char-wise transformation function. This combination was used to justify the alleged issue of char-interleaving within the original Jupiter-OT solution.
In contrast, my second post in this series presented an alternative approach by combining the Jupiter-OT control algorithm with string-wise transformation functions, resulting in consistent and non-interleaving outcomes. Additionally, I presented another new OT solution by integrating the Jupiter-OT control algorithm with a different char-wise transformation function. This solution successfully generated consistent and non-interleaving results for concurrent char-wise insertions.
The moral of the story is clear: the power of combining OT control algorithms and transformation functions in the field of co-editing is immense, but it should be used constructively and responsibly. To harness this power effectively, it is crucial to have a better and more comprehensive understanding of the fundamentals of OT. By doing so, we can avoid potential pitfalls and accelerate the development of correct, valuable, and robust co-editing solutions that drive meaningful progress in the field.
References:
[1] C. Sun, X. Jia, Y. Zhang and Y. Yang: \u201cA Generic Operation Transformation Scheme for Consistency Maintenance in Real-time Cooperative Editing Systems,\u201d Proc. of ACM Conf. on Supporting Group Work, pp. 425 \u2013 434, Nov. 16 \u2013 19, 1997.
[2] C. Sun, X. Jia, Y. Zhang, Y. Yang and D. Chen: "Achieving convergence, causality-preservation, and intention-preservation in real-time cooperative editing systems," ACM Transactions on Computer-Human Interaction, Vol. 5, No. 1, pp.63 \u2013 108, Mar., 1998.
[3] D. Sun, C. Sun, Agustina, W. Cai. Real differences between OT and CRDT in building co-editing systems and real-world applications. https://arxiv.org/abs/1905.01517, May 2, 2019.
[4] C. Sun, "OTFAQ: Operational Transformation Frequently Asked Questions and Answers," https://www3.ntu.edu.sg/scse/staff/czsun/projects/otfaq/
Readers are encouraged to contact the author of this post for copies of any articles referred in this series.","title":"Dispelling Misconceptions and Unveiling the Truth about GOT and OT in General","updated_at":"2024-09-20T14:35:08Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"AIincentiveBB"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"Read about the tech leaders calling for the six month A.I. research slowdown, found it a bit unrealistic. It's like asking a rhino charging through a china shop to put on a tutu and start dancing.
So, if the brightest minds on earth are failing to stop the other brightest minds on earth from creating robots smarter than everyone and ruining humanity for everyone, what could I possibly do?
And I figured I might at least throw my semi-solution out there in case it inspires people with more intellect and energy to do the good work of keeping humanity dominant.
An AIIBB, or Artificial Intelligence Intention Bounty Board, would be a board based on collecting A.I projects and rewarding solutions with bounties. It would be a log of what people intend to do with A.I, a record of A.I successes, and bragging rights for the humans who teach their A.I. neat tricks.
Similar to how bug bounties work now, instead of lots of people using lots of robots in private, you would have more people using robots in public to solve problems in public. This is similar to how open source works in general, but right now the financial incentive to keep A.I honest is not present. An intent bounty board would be the start of a structure of logging A.I activity, promoting A.I successes, as well as letting people know what A.I is currently capable of doing.
It's like the Oscars or Nobel Prize for A.I, except it's year-round with cash prizes.
I believe this is the best way to put a tutu on the rhino.
Do you think it'll work?"},"title":{"matchLevel":"none","matchedWords":[],"value":"Aiibb(A.I. Intention Bounty Boards) or How to save earth from skynet"}},"_tags":["story","author_AIincentiveBB","story_35409997","ask_hn"],"author":"AIincentiveBB","created_at":"2023-04-02T12:51:58Z","created_at_i":1680439918,"num_comments":0,"objectID":"35409997","points":1,"story_id":35409997,"story_text":"Read about the tech leaders calling for the six month A.I. research slowdown, found it a bit unrealistic. It's like asking a rhino charging through a china shop to put on a tutu and start dancing.
So, if the brightest minds on earth are failing to stop the other brightest minds on earth from creating robots smarter than everyone and ruining humanity for everyone, what could I possibly do?
And I figured I might at least throw my semi-solution out there in case it inspires people with more intellect and energy to do the good work of keeping humanity dominant.
An AIIBB, or Artificial Intelligence Intention Bounty Board, would be a board based on collecting A.I projects and rewarding solutions with bounties. It would be a log of what people intend to do with A.I, a record of A.I successes, and bragging rights for the humans who teach their A.I. neat tricks.
Similar to how bug bounties work now, instead of lots of people using lots of robots in private, you would have more people using robots in public to solve problems in public. This is similar to how open source works in general, but right now the financial incentive to keep A.I honest is not present. An intent bounty board would be the start of a structure of logging A.I activity, promoting A.I successes, as well as letting people know what A.I is currently capable of doing.
It's like the Oscars or Nobel Prize for A.I, except it's year-round with cash prizes.
I believe this is the best way to put a tutu on the rhino.
Do you think it'll work?","title":"Aiibb(A.I. Intention Bounty Boards) or How to save earth from skynet","updated_at":"2024-09-20T13:44:03Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"kellogs_aran"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"Hi HN!
I am looking to buy a laptop for software development in the 0 to $2000 (USD) range.
What I am looking for:\n1. Durability: battery life is important to me as well as general longevity of the hardware i.e. I would like it to last a long time.
2. Linux support: I use Linux as my OS of choice and I have no intention of using Windows/MacOS
3. Optimized for intensive computing usage.
Other things of note:
I looked into the Framework laptops and so far it looks like they are still a bit beta.
However, I am curious about users' experiences with:
* the KDE Slimbook 15: https://slimbook.es/en/store/slimbook-kde/kde-slimbook-15-comprar
* the Purism Librem 14: https://puri.sm/products/librem-14/
* Kubuntu Focus: https://kfocus.org/order/order-m2.html
* the StarBook 14-inch \u2013 Star Labs\u00ae: https://starlabs.systems/pages/starbook
Also tips about maintaining battery life would be appreciated. I've read too much conflicting advice about that lately :) Thanks."},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: What\u2019s a good laptop for software development at around $2k?"}},"_tags":["story","author_kellogs_aran","story_31094361","ask_hn"],"author":"kellogs_aran","children":[31094388,31094568,31094569,31094589,31094607,31094616,31094626,31094631,31094696,31094698,31094704,31094708,31094720,31094746,31094760,31094765,31094785,31094795,31094812,31094872,31094891,31094941,31094989,31095007,31095030,31095075,31095118,31095126,31095158,31095174,31095188,31095228,31095289,31095320,31095321,31095331,31095340,31095405,31095426,31095493,31095499,31095530,31095549,31095566,31095570,31095731,31095754,31095771,31095848,31095901,31095911,31095999,31096123,31096130,31096174,31096244,31096274,31096299,31096317,31096323,31096381,31096410,31096434,31096461,31096469,31096477,31096565,31096569,31096596,31096599,31096643,31096686,31096690,31096707,31096769,31096847,31096901,31096931,31097056,31097278,31097280,31097286,31097294,31097307,31097343,31097433,31097497,31097505,31097555,31097587,31097642,31097711,31097753,31097768,31097778,31097850,31097862,31097866,31097873,31097887,31097956,31097970,31097972,31097984,31098024,31098101,31098104,31098374,31098472,31098496,31098526,31098533,31098567,31098596,31098597,31098663,31098731,31098800,31098901,31098965,31098970,31099384,31099524,31099575,31099761,31099763,31099815,31099824,31099921,31100004,31100053,31100119,31100401,31100415,31100481,31100609,31100640,31100825,31100887,31100976,31100983,31101327,31101479,31101544,31101577,31101632,31101665,31101672,31101785,31101892,31101916,31102403,31105039,31106173,31106619,31107027,31107374,31112608,31118427,31122190,31136096,31165711,31228381],"created_at":"2022-04-20T09:03:56Z","created_at_i":1650445436,"num_comments":790,"objectID":"31094361","points":355,"story_id":31094361,"story_text":"Hi HN!
I am looking to buy a laptop for software development in the 0 to $2000 (USD) range.
What I am looking for:\n1. Durability: battery life is important to me as well as general longevity of the hardware i.e. I would like it to last a long time.
2. Linux support: I use Linux as my OS of choice and I have no intention of using Windows/MacOS
3. Optimized for intensive computing usage.
Other things of note:
I looked into the Framework laptops and so far it looks like they are still a bit beta.
However, I am curious about users' experiences with:
* the KDE Slimbook 15: https://slimbook.es/en/store/slimbook-kde/kde-slimbook-15-comprar
* the Purism Librem 14: https://puri.sm/products/librem-14/
* Kubuntu Focus: https://kfocus.org/order/order-m2.html
* the StarBook 14-inch \u2013 Star Labs\u00ae: https://starlabs.systems/pages/starbook
Also tips about maintaining battery life would be appreciated. I've read too much conflicting advice about that lately :) Thanks.","title":"Ask HN: What\u2019s a good laptop for software development at around $2k?","updated_at":"2026-03-22T16:58:50Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"start123"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"Back when I was 24, I pretty much hated my 9-5 job because of lack of control over my destiny, the limit of earnings and growth and the idea of going to the office every single day. I realized I could start something of my own.
So I started to look for something easy to do work on that would not consume a lot of my time. Blogs were a rage back then and multi-million dollar exits were quite common. I bought a domain and installed WordPress and started blogging after my working hours. I started a technology blog in the hope to replicate the success of Mashable and Techcrunch. I spent about 4 hours every night covering tech news about companies and social media in general.
2 years passed and I burned out myself. Traffic to the blog was flat and I was not making any meaningful money. I shut it down.
A few months later, I started a website that pulled information from Amazon and displayed dresses in a fancy and intuitive website. I opened a Facebook page, spent a lot of time marketing it and eventually made a grand total of 2 sales in a span of 3 months.
I decided to give up.
The very next year, I decided to build a note-taking web app that was a mash of Google calendar and a to-do list app. The idea was that people would see today's schedule by default and they would easily add and manage tasks.
I hosted it for a few months and lost interest due to a lack of customers.
After taking a break for a year or so, I decided to do something ground-breaking. I built my version of Facebook Groups/Slack that would allow people to share something interesting with others. You could create groups and add/remove people from them. The UI was fancy and a few of my friends and family loved it.
A few months after running it, I shut it down. I found it hard to justify its existence since everybody else was using Facebook groups and with the rise of mobile apps that allowed seamless sharing, my application made no sense.
Sensing an opportunity in media space again, I then started a news aggregator website that aggregated news titles from hundreds of outlets storing thousands of news articles per day. The website was smart enough to cluster the news articles based on topics which, Google news does well. People loved it and it got great reviews, but it was not growing fast enough.
And like earlier, I ran out of patience after 6 months and I shut it down.
After multiple failures, I decided to take a longer break. I had pretty much given up my entrepreneurship journey knowing there was no way I could build a reasonably successful business.
A year passed and I started to feel uneasy with myself and my day job.
So, I built a stupid web app that cleaned new articles by stripping them off of ads and showing only the relevant content. I shared it and got no real feedback from others. Nobody cared.
That's where it hit me, why not pivot to and a link management platform? I thought it's so easy to build and manage it. I could feel the tingling in my body. I built https://blanq.io/ with the excitement of a toddler.
I was so wrong.
I spent the next 1 year building the landing page, the entire web app plus some extra features in a hope that it will take off.
For the first 18 months, I had no paying customers. I put everything into this. All my previous experiences of failures and learning went into building this platform. "How could I fail?" I thought.
I then decided to stick to it and give myself 3 years to decide its fate.
On the 19th month, my efforts started to pay off. I landed my first customers then 2nd and then 3rd.... and so on. It's been 8 months since then and I now have 10 paying customers using my platform almost every day and growing every month.
My learning:
1.Don't quit too soon and don't be too hard on yourself.
2.With each failure, you do get better at not failing.
3.You improve at everything as time passes - marketing, programming, sales, operations."},"title":{"matchLevel":"none","matchedWords":[],"value":"Failed for the past 12 years as an tech entrepreneur"}},"_tags":["story","author_start123","story_29624502","ask_hn"],"author":"start123","children":[29624590,29624632,29624720,29624760,29625223,29625767,29625793,29625850,29625914,29625920,29626217,29626264,29626283,29626380,29626514,29626534,29626628,29626676,29626788,29626828,29626864,29626925,29626990,29626991,29626995,29627029,29627035,29627078,29627103,29627138,29627209,29627218,29627312,29627345,29627349,29627358,29627365,29627422,29627474,29627485,29627575,29628161,29628400,29628434,29628494,29628685,29628690,29628897,29629265,29629567,29629867,29629892,29630142,29630333,29630397,29630668,29631150,29632384,29632456,29632498,29632654,29632699,29633112,29635628,29650749],"created_at":"2021-12-20T13:10:50Z","created_at_i":1640005850,"num_comments":163,"objectID":"29624502","points":257,"story_id":29624502,"story_text":"Back when I was 24, I pretty much hated my 9-5 job because of lack of control over my destiny, the limit of earnings and growth and the idea of going to the office every single day. I realized I could start something of my own.
So I started to look for something easy to do work on that would not consume a lot of my time. Blogs were a rage back then and multi-million dollar exits were quite common. I bought a domain and installed WordPress and started blogging after my working hours. I started a technology blog in the hope to replicate the success of Mashable and Techcrunch. I spent about 4 hours every night covering tech news about companies and social media in general.
2 years passed and I burned out myself. Traffic to the blog was flat and I was not making any meaningful money. I shut it down.
A few months later, I started a website that pulled information from Amazon and displayed dresses in a fancy and intuitive website. I opened a Facebook page, spent a lot of time marketing it and eventually made a grand total of 2 sales in a span of 3 months.
I decided to give up.
The very next year, I decided to build a note-taking web app that was a mash of Google calendar and a to-do list app. The idea was that people would see today's schedule by default and they would easily add and manage tasks.
I hosted it for a few months and lost interest due to a lack of customers.
After taking a break for a year or so, I decided to do something ground-breaking. I built my version of Facebook Groups/Slack that would allow people to share something interesting with others. You could create groups and add/remove people from them. The UI was fancy and a few of my friends and family loved it.
A few months after running it, I shut it down. I found it hard to justify its existence since everybody else was using Facebook groups and with the rise of mobile apps that allowed seamless sharing, my application made no sense.
Sensing an opportunity in media space again, I then started a news aggregator website that aggregated news titles from hundreds of outlets storing thousands of news articles per day. The website was smart enough to cluster the news articles based on topics which, Google news does well. People loved it and it got great reviews, but it was not growing fast enough.
And like earlier, I ran out of patience after 6 months and I shut it down.
After multiple failures, I decided to take a longer break. I had pretty much given up my entrepreneurship journey knowing there was no way I could build a reasonably successful business.
A year passed and I started to feel uneasy with myself and my day job.
So, I built a stupid web app that cleaned new articles by stripping them off of ads and showing only the relevant content. I shared it and got no real feedback from others. Nobody cared.
That's where it hit me, why not pivot to and a link management platform? I thought it's so easy to build and manage it. I could feel the tingling in my body. I built https://blanq.io/ with the excitement of a toddler.
I was so wrong.
I spent the next 1 year building the landing page, the entire web app plus some extra features in a hope that it will take off.
For the first 18 months, I had no paying customers. I put everything into this. All my previous experiences of failures and learning went into building this platform. "How could I fail?" I thought.
I then decided to stick to it and give myself 3 years to decide its fate.
On the 19th month, my efforts started to pay off. I landed my first customers then 2nd and then 3rd.... and so on. It's been 8 months since then and I now have 10 paying customers using my platform almost every day and growing every month.
My learning:
1.Don't quit too soon and don't be too hard on yourself.
2.With each failure, you do get better at not failing.
3.You improve at everything as time passes - marketing, programming, sales, operations.","title":"Failed for the past 12 years as an tech entrepreneur","updated_at":"2025-10-11T21:03:02Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"blintz"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"Hi everyone! I\u2019m Samir, and my co-founder Neil and I are building Blyss (https://blyss.dev). Blyss is an open source homomorphic encryption SDK, available as a fully managed service.
Fully homomorphic encryption (FHE) enables computation on encrypted data. This is essentially the ultimate privacy guarantee - a server that does work for its users (like fetching emails, tweets, or search results), without ever knowing what its users are doing - who they talk to, who they follow, or even what they search for. Servers using FHE give you cryptographic proof that they aren\u2019t spying on you.
Unfortunately, performing general computation using FHE is notoriously slow. We have focused on solving a simple, specific problem: retrieve an item from a key-value store, without revealing to the server which item was retrieved.
By focusing on retrievals, we achieve huge speedups that make Blyss practical for real-world applications: a password scanner like \u201cHave I Been Pwned?\u201d that checks your credentials against breaches, but never learns anything about your password (https://playground.blyss.dev/passwords), domain name servers that don\u2019t get to see what domains you\u2019re fetching (https://sprl.it/), and social apps that let you find out which of your contacts are already on the platform, without letting the service see your contacts (https://stackblitz.com/edit/blyss-private-contact-intersecti...).
Big companies (Apple, Google, Microsoft) are already using private retrieval: Chrome and Edge use this technology today to check URLs against blocklists of known phishing sites, and check user passwords against hacked credential dumps, without seeing any of the underlying URLs or passwords.
Blyss makes it easy for developers to use homomorphic encryption from a familiar, Firebase-like interface. You can create key-value data buckets, fill them with data, and then make cryptographically private retrievals. No entity, not even the Blyss service itself, can learn which items are retrieved from a Blyss bucket. We handle all the server infrastructure, and maintain robust open source JS clients, with the cryptography written in Rust and compiled to WebAssembly. We also have an open source server you can host yourself.
(Side note: a lot of what drew us to this problem is just how paradoxical the private retrieval guarantee sounds\u2014it seems intuitively like it should be impossible to get data from a server without it learning what you retrieve! The basic idea of how this is actually possible is: the client encrypts a one-hot vector (all 0\u2019s except a single 1) using homomorphic encryption, and the server is able to \u2018multiply\u2019 these by the database without learning anything about the underlying encrypted values. The dot product of the encrypted query and the database yields an encrypted result. The client decrypts this, and gets the database item it wanted. To the server, all the inputs and outputs stay completely opaque. We have a blog post explaining more, with pictures, that was on HN previously: https://news.ycombinator.com/item?id=32987155.)
Neil and I met eight years ago on the first day of freshman year of college; we\u2019ve been best friends (and roommates!) since. We are privacy nerds\u2014before Blyss, I worked at Yubico, and Neil worked at Apple. I\u2019ve had an academic interest in homomorphic encryption for years, but it became a practical interest when a private Wikipedia demo I posted on HN (https://news.ycombinator.com/item?id=31668814) became popular, and people started asking for a simple way to build products using this technology.
Our client and server are MIT open source (https://github.com/blyssprivacy/sdk), and we plan to make money as a hosted server. Since the server is tricky to operate at scale, and is not part of the trust model, we think this makes sense for both us and our customers. People have used Blyss to build block explorers, DNS resolvers, and malware scanners; you can see some highlights in our playground: https://playground.blyss.dev.
We have a generous free tier, and you get an API key as soon as you log in. For production use, our pricing is usage-based: $1 gets you 10k private reads on a 1 GB database (larger databases scale costs linearly). You can also run the server yourself.
Private retrieval is a totally new building block for privacy - we can\u2019t wait to see what you\u2019ll build with it! Let us know what you think, or if you have any questions about Blyss or homomorphic encryption in general."},"title":{"matchLevel":"none","matchedWords":[],"value":"Launch HN: Blyss (YC W23) \u2013 Homomorphic encryption as a service"}},"_tags":["story","author_blintz","story_35153344","launch_hn"],"author":"blintz","children":[35153784,35154054,35154189,35154284,35154417,35154528,35154585,35154939,35155135,35155168,35155315,35155392,35156307,35157203,35157406,35157712,35157784,35157842,35158265,35159155,35159566,35161491,35162173,35162232,35162721,35162784,35164133,35164858,35164923,35165581,35233587,35269775],"created_at":"2023-03-14T15:42:48Z","created_at_i":1678808568,"num_comments":75,"objectID":"35153344","points":206,"story_id":35153344,"story_text":"Hi everyone! I\u2019m Samir, and my co-founder Neil and I are building Blyss (https://blyss.dev). Blyss is an open source homomorphic encryption SDK, available as a fully managed service.
Fully homomorphic encryption (FHE) enables computation on encrypted data. This is essentially the ultimate privacy guarantee - a server that does work for its users (like fetching emails, tweets, or search results), without ever knowing what its users are doing - who they talk to, who they follow, or even what they search for. Servers using FHE give you cryptographic proof that they aren\u2019t spying on you.
Unfortunately, performing general computation using FHE is notoriously slow. We have focused on solving a simple, specific problem: retrieve an item from a key-value store, without revealing to the server which item was retrieved.
By focusing on retrievals, we achieve huge speedups that make Blyss practical for real-world applications: a password scanner like \u201cHave I Been Pwned?\u201d that checks your credentials against breaches, but never learns anything about your password (https://playground.blyss.dev/passwords), domain name servers that don\u2019t get to see what domains you\u2019re fetching (https://sprl.it/), and social apps that let you find out which of your contacts are already on the platform, without letting the service see your contacts (https://stackblitz.com/edit/blyss-private-contact-intersecti...).
Big companies (Apple, Google, Microsoft) are already using private retrieval: Chrome and Edge use this technology today to check URLs against blocklists of known phishing sites, and check user passwords against hacked credential dumps, without seeing any of the underlying URLs or passwords.
Blyss makes it easy for developers to use homomorphic encryption from a familiar, Firebase-like interface. You can create key-value data buckets, fill them with data, and then make cryptographically private retrievals. No entity, not even the Blyss service itself, can learn which items are retrieved from a Blyss bucket. We handle all the server infrastructure, and maintain robust open source JS clients, with the cryptography written in Rust and compiled to WebAssembly. We also have an open source server you can host yourself.
(Side note: a lot of what drew us to this problem is just how paradoxical the private retrieval guarantee sounds\u2014it seems intuitively like it should be impossible to get data from a server without it learning what you retrieve! The basic idea of how this is actually possible is: the client encrypts a one-hot vector (all 0\u2019s except a single 1) using homomorphic encryption, and the server is able to \u2018multiply\u2019 these by the database without learning anything about the underlying encrypted values. The dot product of the encrypted query and the database yields an encrypted result. The client decrypts this, and gets the database item it wanted. To the server, all the inputs and outputs stay completely opaque. We have a blog post explaining more, with pictures, that was on HN previously: https://news.ycombinator.com/item?id=32987155.)
Neil and I met eight years ago on the first day of freshman year of college; we\u2019ve been best friends (and roommates!) since. We are privacy nerds\u2014before Blyss, I worked at Yubico, and Neil worked at Apple. I\u2019ve had an academic interest in homomorphic encryption for years, but it became a practical interest when a private Wikipedia demo I posted on HN (https://news.ycombinator.com/item?id=31668814) became popular, and people started asking for a simple way to build products using this technology.
Our client and server are MIT open source (https://github.com/blyssprivacy/sdk), and we plan to make money as a hosted server. Since the server is tricky to operate at scale, and is not part of the trust model, we think this makes sense for both us and our customers. People have used Blyss to build block explorers, DNS resolvers, and malware scanners; you can see some highlights in our playground: https://playground.blyss.dev.
We have a generous free tier, and you get an API key as soon as you log in. For production use, our pricing is usage-based: $1 gets you 10k private reads on a 1 GB database (larger databases scale costs linearly). You can also run the server yourself.
Private retrieval is a totally new building block for privacy - we can\u2019t wait to see what you\u2019ll build with it! Let us know what you think, or if you have any questions about Blyss or homomorphic encryption in general.","title":"Launch HN: Blyss (YC W23) \u2013 Homomorphic encryption as a service","updated_at":"2024-12-22T04:09:24Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"LiamPrevelige"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"Hey HN \u2014 we\u2019re Liam and Will from CodeViz (https://codeviz.ai). We're building a VS Code extension that generates interactive diagrams of codebases, from system architecture down to function call graphs. Here\u2019s a demo where we analyze OpenHands, uv, and webviz: https://www.youtube.com/watch?v=fgfDXUtWzRk.
The extension is public if you want to try it on your own repos: https://marketplace.visualstudio.com/items?itemName=CodeViz....
Will and I started CodeViz because we wanted more intuitive representations of software. During our time at Tesla, we encountered a common problem: software engineers spend very little time actually typing code. Most development time was spent navigating convoluted files and building a mental map for each task. At the same time, whiteboard sessions were proof that code could be expressed intuitively.
We started with autogenerated technical documentation. Of course, long markdown docs are not a good solution for long files of code. We realized we needed diagrams that (a) help grasp large quantities of code and (b) can be filtered according to the developer\u2019s task. So, we built a graph-based VS Code extension. It generates diagrams directly within VS Code, illustrating connections between functions and providing overviews of system architecture. These visualizations update as code changes.
CodeViz appears as a side panel in VS Code with two views:
(1) Call graph: as you click on functions, we show a chain of upstream and downstream references. You can navigate your codebase using the call stack and see, in one view, everywhere your functions are called. We generate this call graph using the language servers developers have already installed in VS Code
(2) Architecture diagram: we create a C4 diagram of your system, so you can see a top-level view of your codebase and click into the component layer. We were surprised to find that a small fraction of code can generate a very accurate representation of the system. We detect these important files, then use LLMs to build nested architecture diagrams at the container and component level
Developers are mainly using our extension to navigate spaghetti code, onboard new devs, and interpret open source repos. We're still figuring out our pricing. Currently, we offer basic features for free, with a paid tier for more resource-intensive tools like detailed architecture diagrams. Open to suggestions on this approach.
CodeViz is in active development and our main focus over the next couple of days is to make the call graph much easier to view and navigate. We're continuously working to make it better, so your honest feedback, suggestions, and wishes would be very helpful. Looking forward to hearing any and all thoughts, whether about the current extension, general problem space, or something else!"},"title":{"matchLevel":"none","matchedWords":[],"value":"Launch HN: CodeViz (YC S24) \u2013 Visual maps of your codebase in VS Code"}},"_tags":["story","author_LiamPrevelige","story_41393458","launch_hn"],"author":"LiamPrevelige","children":[41393722,41393856,41394000,41394315,41394550,41394775,41395798,41395866,41396276,41396882,41396971,41397057,41397678,41397829,41397865,41398219,41398393,41398424,41398553,41398556,41399948,41401036,41401357,41402337,41404908,41406838],"created_at":"2024-08-29T17:50:23Z","created_at_i":1724953823,"num_comments":89,"objectID":"41393458","points":189,"story_id":41393458,"story_text":"Hey HN \u2014 we\u2019re Liam and Will from CodeViz (https://codeviz.ai). We're building a VS Code extension that generates interactive diagrams of codebases, from system architecture down to function call graphs. Here\u2019s a demo where we analyze OpenHands, uv, and webviz: https://www.youtube.com/watch?v=fgfDXUtWzRk.
The extension is public if you want to try it on your own repos: https://marketplace.visualstudio.com/items?itemName=CodeViz....
Will and I started CodeViz because we wanted more intuitive representations of software. During our time at Tesla, we encountered a common problem: software engineers spend very little time actually typing code. Most development time was spent navigating convoluted files and building a mental map for each task. At the same time, whiteboard sessions were proof that code could be expressed intuitively.
We started with autogenerated technical documentation. Of course, long markdown docs are not a good solution for long files of code. We realized we needed diagrams that (a) help grasp large quantities of code and (b) can be filtered according to the developer\u2019s task. So, we built a graph-based VS Code extension. It generates diagrams directly within VS Code, illustrating connections between functions and providing overviews of system architecture. These visualizations update as code changes.
CodeViz appears as a side panel in VS Code with two views:
(1) Call graph: as you click on functions, we show a chain of upstream and downstream references. You can navigate your codebase using the call stack and see, in one view, everywhere your functions are called. We generate this call graph using the language servers developers have already installed in VS Code
(2) Architecture diagram: we create a C4 diagram of your system, so you can see a top-level view of your codebase and click into the component layer. We were surprised to find that a small fraction of code can generate a very accurate representation of the system. We detect these important files, then use LLMs to build nested architecture diagrams at the container and component level
Developers are mainly using our extension to navigate spaghetti code, onboard new devs, and interpret open source repos. We're still figuring out our pricing. Currently, we offer basic features for free, with a paid tier for more resource-intensive tools like detailed architecture diagrams. Open to suggestions on this approach.
CodeViz is in active development and our main focus over the next couple of days is to make the call graph much easier to view and navigate. We're continuously working to make it better, so your honest feedback, suggestions, and wishes would be very helpful. Looking forward to hearing any and all thoughts, whether about the current extension, general problem space, or something else!","title":"Launch HN: CodeViz (YC S24) \u2013 Visual maps of your codebase in VS Code","updated_at":"2026-02-11T10:47:41Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"murb"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"Hey HN,
We\u2019re Rahul and Max, co-founders of AgentHub.dev (https://www.agenthub.dev/). We automate repetitive workflows for businesses using LLM-powered automations. Our platform lets you build and host these automations to emulate employee workflows in a scalable way. Here\u2019s a demo video: https://www.youtube.com/watch?v=BD9aoyKPOjs
We started 9 months ago while lurking in the Auto-GPT discord and seeing thousands of non-technical users struggle to clone the repo or set up their environments. We were excited by the concepts of Agents so we built and deployed a (very ugly) web app within a few days so anyone could experiment. We started to see people literally begging the Agents to complete simple tasks and giving up due to cost/frustration. Seeing the type of relatively simple work people were trying to automate with AI was the catalyst for what we ended up building.
We decided to make a drag-and-drop automation builder so these users could piece together their ideal automations instead of begging the agent to do that same task and failing. V1 was a borderline un-usable series of drop down menus but evolved into the canvas based workflow builder it is now.
It\u2019s somewhat similar in concept to Zapier or Make.com except we\u2019re aiming to automate much more complex work end to end instead of just speeding up simple tasks. We originally described it as Zapier on crack but as it's gotten more complex, some people compare it to existing RPA platforms like UI Path. We like to call it an 'LLM-based Intelligent Automation Platform'.
Our biggest challenge from the very beginning has been balancing usability and complexity. We wanted anyone to be able to understand it while still being powerful enough for people to get creative. Building the framework has been an extremely iterative process of users getting confused (for good reason) and us tweaking our approach. We still have a ways to go in terms of usability but are proud of where it\u2019s at. Eager to hear your feedback!
Here are 3 template automations we built to give people a starting point. I think the real beauty of the platform is how personalized the automations users create are but these general templates give a nice idea of how it works.
https://www.agenthub.dev/templates/hr_hiring/linkedin_profil...\nhttps://www.agenthub.dev/templates/media_news/autonomous_twi...\nhttps://www.agenthub.dev/templates/sales_crm/automated_sales...
These templates are on the simpler side. Our power users nest automations, trigger them via webhook and have them running at a pretty surprising scale. The highest we\u2019ve seen was last Friday with a single user running 5k automations within a few hours. The unofficial record before that for most automations runs was one of our users who discovered infinite-recursion by accident, but that doesn't count.
We have two main types of users at the moment, people automating their existing businesses work and people using the no-code builder to build new ideas. The first was our original intention, letting any semi technical person in a company spot inefficiency and quickly get a solution deployed to address it. The second and more unexpected type of user has been non-technical founders spotting problems and being able to build APIs to serve niches they\u2019ve found without needing to code.
It\u2019s called AgentHub because I bought the domain for 10 dollars on day 2 of building when I thought we\u2019d be a hub to host and share agents and never bothered rebranding. If anyone wants to take a crack at a better name, we\u2019d be interested!(I speak kind of quickly and people think I\u2019m saying \u2018asian-hub\u2019 pretty often\u2026
We\u2019re really excited to share the platform with you all and look forward to your feedback!"},"title":{"matchLevel":"none","matchedWords":[],"value":"Launch HN: AgentHub (YC W24) \u2013 A no-code automation platform"}},"_tags":["story","author_murb","story_39302870","launch_hn"],"author":"murb","children":[39303366,39303392,39303399,39303423,39303512,39303548,39303581,39304304,39304379,39304396,39305082,39305441,39305477,39305635,39307155,39307753,39309479,39310508,39311577,39311604,39312231,39312650,39312978,39313013,39314178,39314609,39315476,39315657,39316290,39318367,39374946],"created_at":"2024-02-08T15:17:05Z","created_at_i":1707405425,"num_comments":85,"objectID":"39302870","points":162,"story_id":39302870,"story_text":"Hey HN,
We\u2019re Rahul and Max, co-founders of AgentHub.dev (https://www.agenthub.dev/). We automate repetitive workflows for businesses using LLM-powered automations. Our platform lets you build and host these automations to emulate employee workflows in a scalable way. Here\u2019s a demo video: https://www.youtube.com/watch?v=BD9aoyKPOjs
We started 9 months ago while lurking in the Auto-GPT discord and seeing thousands of non-technical users struggle to clone the repo or set up their environments. We were excited by the concepts of Agents so we built and deployed a (very ugly) web app within a few days so anyone could experiment. We started to see people literally begging the Agents to complete simple tasks and giving up due to cost/frustration. Seeing the type of relatively simple work people were trying to automate with AI was the catalyst for what we ended up building.
We decided to make a drag-and-drop automation builder so these users could piece together their ideal automations instead of begging the agent to do that same task and failing. V1 was a borderline un-usable series of drop down menus but evolved into the canvas based workflow builder it is now.
It\u2019s somewhat similar in concept to Zapier or Make.com except we\u2019re aiming to automate much more complex work end to end instead of just speeding up simple tasks. We originally described it as Zapier on crack but as it's gotten more complex, some people compare it to existing RPA platforms like UI Path. We like to call it an 'LLM-based Intelligent Automation Platform'.
Our biggest challenge from the very beginning has been balancing usability and complexity. We wanted anyone to be able to understand it while still being powerful enough for people to get creative. Building the framework has been an extremely iterative process of users getting confused (for good reason) and us tweaking our approach. We still have a ways to go in terms of usability but are proud of where it\u2019s at. Eager to hear your feedback!
Here are 3 template automations we built to give people a starting point. I think the real beauty of the platform is how personalized the automations users create are but these general templates give a nice idea of how it works.
https://www.agenthub.dev/templates/hr_hiring/linkedin_profil...\nhttps://www.agenthub.dev/templates/media_news/autonomous_twi...\nhttps://www.agenthub.dev/templates/sales_crm/automated_sales...
These templates are on the simpler side. Our power users nest automations, trigger them via webhook and have them running at a pretty surprising scale. The highest we\u2019ve seen was last Friday with a single user running 5k automations within a few hours. The unofficial record before that for most automations runs was one of our users who discovered infinite-recursion by accident, but that doesn't count.
We have two main types of users at the moment, people automating their existing businesses work and people using the no-code builder to build new ideas. The first was our original intention, letting any semi technical person in a company spot inefficiency and quickly get a solution deployed to address it. The second and more unexpected type of user has been non-technical founders spotting problems and being able to build APIs to serve niches they\u2019ve found without needing to code.
It\u2019s called AgentHub because I bought the domain for 10 dollars on day 2 of building when I thought we\u2019d be a hub to host and share agents and never bothered rebranding. If anyone wants to take a crack at a better name, we\u2019d be interested!(I speak kind of quickly and people think I\u2019m saying \u2018asian-hub\u2019 pretty often\u2026
We\u2019re really excited to share the platform with you all and look forward to your feedback!","title":"Launch HN: AgentHub (YC W24) \u2013 A no-code automation platform","updated_at":"2025-11-05T17:40:33Z"}],"hitsPerPage":50,"nbHits":149,"nbPages":3,"page":0,"params":"query=General+Intuition&tags=story&hitsPerPage=50&advancedSyntax=true&analyticsTags=backend","processingTimeMS":21,"processingTimingsMS":{"_request":{"roundTrip":23},"afterFetch":{"format":{"highlighting":5,"total":6},"merge":{"mergeLoop":{"prepareNextHit":6,"total":6},"total":6},"total":6},"fetch":{"query":11,"scanning":3,"total":15},"total":22},"query":"General Intuition","serverTimeMS":29}