For the past 6 months or so I have been totally working with nodejs and its eco-system. I do agree its blazing fast and has a very good eco-system. But I really don't believe that it is that much strong in web-development. I guess the only stable web framework is Express. But you have to do alot in it to achieve things. After researching I found Sails.js is gaining popularity and has many features out of the box. But after trying it for a month or so, I found that its very much buggy. The thing that works on my local environment does not work in production, however it has the same technology stack with same versions.
npm packages are outdated, dependencies not updated. I wonder why there is so much hype about nodejs and what it is really good at?
What would you say?"},"title":{"matchLevel":"none","matchedWords":[],"value":"Why Node.js?"},"url":{"matchLevel":"none","matchedWords":[],"value":""}},"_tags":["story","author_mahadazad","story_10008803","ask_hn"],"author":"mahadazad","children":[10008833,10008859,10008861,10009501,10009719,10009848,10010590,10011797,10015449,10021591],"created_at":"2015-08-05T10:23:16Z","created_at_i":1438770196,"num_comments":14,"objectID":"10008803","points":13,"story_id":10008803,"story_text":"I am a developer with a very strong PHP background. Experience over 5-6 years in PHP and related technologies.
For the past 6 months or so I have been totally working with nodejs and its eco-system. I do agree its blazing fast and has a very good eco-system. But I really don't believe that it is that much strong in web-development. I guess the only stable web framework is Express. But you have to do alot in it to achieve things. After researching I found Sails.js is gaining popularity and has many features out of the box. But after trying it for a month or so, I found that its very much buggy. The thing that works on my local environment does not work in production, however it has the same technology stack with same versions.
npm packages are outdated, dependencies not updated. I wonder why there is so much hype about nodejs and what it is really good at?
What would you say?","title":"Why Node.js?","updated_at":"2024-09-19T22:07:10Z","url":""},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"AbhinavX"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["sail","research"],"value":"Hi HN, we\u2019re Abhinav, Andy, and Jeremy, and we\u2019re building Lucidic AI (https://dashboard.lucidic.ai), an AI agent interpretability tool to help observe/debug AI agents.
Here is a demo: https://youtu.be/Zvoh1QUMhXQ.
Getting started is easy with just one line of code. You just call lai.init() in your agent code and log into the dashboard. You can see traces of each run, cumulative trends across sessions, built-in or custom evals, and grouped failure modes. Call lai.create_step() with any metadata you want, memory snapshots, tool outputs, stateful info, and we'll index it for debugging.
We did NLP research at Stanford AI Lab (SAIL), where we worked on creating an AI agent (w/ fine-tuned models and DSPy) to solve math olympiad problems (focusing on AIME/USAMO); and we realized debugging these agents was hard. But the last straw was when we built an e-commerce agent that could buy items online. It kept failing at checkout, and every one-line change, tweaking a prompt, switching to Llama, adjusting tool logic, meant another 10-minute rerun just to see if we hit the same checkout page.
At this point, we were all like, this sucks, so we improved agent interpretability with better debugging, monitoring, and evals.
We started by listening to users who told us traditional LLM observability platforms don't capture the complexity of agents. Agents have tools, memories, events, not just input/output pairs. So we automatically transform OTel (and/or regular) agent logs into interactive graph visualizations that cluster similar states based on memory and action patterns.\nWe heard that people wanted to test small changes even with the graphs, so we created \u201ctime traveling,\u201d where you can modify any state (memory contents, tool outputs, context), then re-simulate 30\u201340 times to see outcome distributions. We embed the responses, cluster by similarity, and show which modifications lead to stable vs. divergent behaviors.
Then we saw people running their agent 10 times on the same task, watching each run individually, and wasting hours looking at mostly repeated states. So we built trajectory clustering on similar state embeddings (like similar tools or memories) to surface behavioral patterns across mass simulations.
We then use that to create a force-directed layout that automatically groups similar paths your agent took, which displays states as nodes, actions as edges, and failure probability as color intensity. The clusters make failure patterns obvious; you see trends across hundreds of runs, not individual traces.
Finally, when people saw our observability features, they naturally wanted evaluation capabilities. So we developed a concept for people to make their own evals called "rubrics," which lets you define specific criteria, assign weights to each criterion, and set score definitions, giving you a structured way to measure agent performance against your exact requirements.
To evaluate these criteria, we used our own platform to build an investigator agent that reviews your criteria and evaluates performance much more effectively than traditional LLM-as-a-judge approaches.
To get started visit dashboard.lucidic.ai and https://docs.lucidic.ai/getting-started/quickstart. You can use it for free for 1,000 event and step creations.
Look forward to your thoughts! And don\u2019t hesitate to reach out at team@lucidic.ai"},"title":{"matchLevel":"none","matchedWords":[],"value":"Launch HN: Lucidic (YC W25) \u2013 Debug, test, and evaluate AI agents in production"}},"_tags":["story","author_AbhinavX","story_44735843","launch_hn"],"author":"AbhinavX","children":[44736020,44736243,44736389,44736461,44737009,44737110,44737185,44737435,44737824,44738655,44739338,44739456,44740539,44741816,44742393,44773750],"created_at":"2025-07-30T15:54:02Z","created_at_i":1753890842,"num_comments":39,"objectID":"44735843","points":116,"story_id":44735843,"story_text":"Hi HN, we\u2019re Abhinav, Andy, and Jeremy, and we\u2019re building Lucidic AI (https://dashboard.lucidic.ai), an AI agent interpretability tool to help observe/debug AI agents.
Here is a demo: https://youtu.be/Zvoh1QUMhXQ.
Getting started is easy with just one line of code. You just call lai.init() in your agent code and log into the dashboard. You can see traces of each run, cumulative trends across sessions, built-in or custom evals, and grouped failure modes. Call lai.create_step() with any metadata you want, memory snapshots, tool outputs, stateful info, and we'll index it for debugging.
We did NLP research at Stanford AI Lab (SAIL), where we worked on creating an AI agent (w/ fine-tuned models and DSPy) to solve math olympiad problems (focusing on AIME/USAMO); and we realized debugging these agents was hard. But the last straw was when we built an e-commerce agent that could buy items online. It kept failing at checkout, and every one-line change, tweaking a prompt, switching to Llama, adjusting tool logic, meant another 10-minute rerun just to see if we hit the same checkout page.
At this point, we were all like, this sucks, so we improved agent interpretability with better debugging, monitoring, and evals.
We started by listening to users who told us traditional LLM observability platforms don't capture the complexity of agents. Agents have tools, memories, events, not just input/output pairs. So we automatically transform OTel (and/or regular) agent logs into interactive graph visualizations that cluster similar states based on memory and action patterns.\nWe heard that people wanted to test small changes even with the graphs, so we created \u201ctime traveling,\u201d where you can modify any state (memory contents, tool outputs, context), then re-simulate 30\u201340 times to see outcome distributions. We embed the responses, cluster by similarity, and show which modifications lead to stable vs. divergent behaviors.
Then we saw people running their agent 10 times on the same task, watching each run individually, and wasting hours looking at mostly repeated states. So we built trajectory clustering on similar state embeddings (like similar tools or memories) to surface behavioral patterns across mass simulations.
We then use that to create a force-directed layout that automatically groups similar paths your agent took, which displays states as nodes, actions as edges, and failure probability as color intensity. The clusters make failure patterns obvious; you see trends across hundreds of runs, not individual traces.
Finally, when people saw our observability features, they naturally wanted evaluation capabilities. So we developed a concept for people to make their own evals called "rubrics," which lets you define specific criteria, assign weights to each criterion, and set score definitions, giving you a structured way to measure agent performance against your exact requirements.
To evaluate these criteria, we used our own platform to build an investigator agent that reviews your criteria and evaluates performance much more effectively than traditional LLM-as-a-judge approaches.
To get started visit dashboard.lucidic.ai and https://docs.lucidic.ai/getting-started/quickstart. You can use it for free for 1,000 event and step creations.
Look forward to your thoughts! And don\u2019t hesitate to reach out at team@lucidic.ai","title":"Launch HN: Lucidic (YC W25) \u2013 Debug, test, and evaluate AI agents in production","updated_at":"2025-08-08T04:22:45Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"pseudolus"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["sail"],"value":"Ultrafast transfer of low-mass payloads to Mars using aerographite solar sails"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["sail","research"],"value":"https://www.researchgate.net/publication/373552196_Ultrafast_transfer_of_low-mass_payloads_to_Mars_and_beyond_using_aerographite_solar_sails"}},"_tags":["story","author_pseudolus","story_37771582"],"author":"pseudolus","children":[37772010],"created_at":"2023-10-04T20:50:57Z","created_at_i":1696452657,"num_comments":1,"objectID":"37771582","points":3,"story_id":37771582,"title":"Ultrafast transfer of low-mass payloads to Mars using aerographite solar sails","updated_at":"2024-09-20T15:18:18Z","url":"https://www.researchgate.net/publication/373552196_Ultrafast_transfer_of_low-mass_payloads_to_Mars_and_beyond_using_aerographite_solar_sails"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"yzh"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["research"],"value":"HloEnv: An environment based on XLA for DL compiler optimization research"},"url":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["sail"],"value":"https://github.com/sail-sg/hloenv"}},"_tags":["story","author_yzh","story_33528994"],"author":"yzh","created_at":"2022-11-09T09:10:06Z","created_at_i":1667985006,"num_comments":0,"objectID":"33528994","points":2,"story_id":33528994,"title":"HloEnv: An environment based on XLA for DL compiler optimization research","updated_at":"2024-09-20T12:25:39Z","url":"https://github.com/sail-sg/hloenv"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"ColinWright"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["sail"],"value":"Oumuamua may be alien light sail: Harvard astronomers"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["sail","research"],"value":"https://www.news.com.au/technology/science/space/oumuamua-harvard-researchers-suggest-strange-interstellar-object-may-be-alien-light-sail/news-story/6527afbbd9e1350a77ba8618da2c1b9e"}},"_tags":["story","author_ColinWright","story_19084457"],"author":"ColinWright","children":[19084490],"created_at":"2019-02-05T11:47:16Z","created_at_i":1549367236,"num_comments":1,"objectID":"19084457","points":1,"story_id":19084457,"title":"Oumuamua may be alien light sail: Harvard astronomers","updated_at":"2024-09-20T03:45:29Z","url":"https://www.news.com.au/technology/science/space/oumuamua-harvard-researchers-suggest-strange-interstellar-object-may-be-alien-light-sail/news-story/6527afbbd9e1350a77ba8618da2c1b9e"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"King-Aaron"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["research"],"value":"Australian University researchers light up pathway to another planetary system"},"url":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["sail"],"value":"https://www.abc.net.au/news/2021-06-08/canberra-anu-scientists-laser-sail-interstellar-space-discovery/100198228"}},"_tags":["story","author_King-Aaron","story_27433767"],"author":"King-Aaron","created_at":"2021-06-08T11:37:22Z","created_at_i":1623152242,"num_comments":0,"objectID":"27433767","points":1,"story_id":27433767,"title":"Australian University researchers light up pathway to another planetary system","updated_at":"2024-09-20T08:43:06Z","url":"https://www.abc.net.au/news/2021-06-08/canberra-anu-scientists-laser-sail-interstellar-space-discovery/100198228"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"MargoRooo"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["sail","research"],"value":"Most people think procrastination is a discipline or time management problem. But after analyzing data from 80,000+ program participants and 3,000+ coaching sessions, we discovered something unexpected.
Procrastination Is Emotional Dysregulation, Not Laziness
Key finding: People don\u2019t postpone tasks because of laziness. They postpone due to fear, perfectionism, overwhelm, or uncertainty. These are subcortical brain structures protecting from perceived risk.
When we explained to participants that their \u201cinner saboteur\u201d is an ancient part of the brain, they stopped blaming themselves for weak willpower and started working with resistance constructively.
Agile Approach to Life Works Better Than Long-term Planning
Pattern: 3 weeks of focus + 1 week of reflection give better results than trying to plan months ahead.
Participants said: \u201cFor the first time, planning doesn\u2019t cause stress.\u201d Short sprints allow experimenting and course correction, instead of \u201cpushing through\u201d toward unclear goals.
\u201cBoat Bottom\u201d Is More Important Than \u201cSail\u201d
Key mistake: 70% of participants set ambitious goals (sail) without covering basic needs (boat bottom).
We divided goals into two levels:
- Basic goals \u2014 sleep, energy, emotional state\n- Growth goals \u2014 career, self-actualization, new skills
When people started with the basic level, growth goals were achieved naturally. When they tried to \u201craise the sail\u201d on a leaky boat \u2014 they burned out quickly.
Three Levels of Working with Procrastination
From the data, a system crystallized:
1. Energy \u2014 restoring healthy dopamine cycles\n2. Goals \u2014 turning dreams into clear action plans\n3. Psychology \u2014 working with internal blocks based on psychotype
Attempts to immediately jump to level 3, bypassing 1 and 2, led to sabotage and regression.
Practical Takeaways
- Procrastination is a symptom, not the disease. You need to treat the causes\n- Short sprints (3 weeks) are more effective than long-term plans\n- Basic needs must be met BEFORE ambitious goals\n- Understanding neurobiology reduces self-flagellation and increases effectiveness
Application
Based on this data, we built an AI system that determines what level a person is at and adapts the approach. Early results are encouraging: 200 users, 300+ completed tasks, early sales exceeded $100k.
But most importantly \u2014 we now have a data-driven understanding of how procrastination works. And these principles can be applied regardless of any apps.
Looking for Early Testers
We\u2019re finalizing the full mobile app AJI (AI Journal & Insights) and need people to test the first versions. We\u2019re especially interested in participants with chronic procrastination \u2014 the approach shows best results with them.
If you\u2019re ready to try it and share feedback \u2014 write in the comments.
Data collected from productivity programs, coaching sessions, and scientific research from 2020-202"},"title":{"matchLevel":"none","matchedWords":[],"value":"We Studied Procrastination on a Scale. Here's What Works"}},"_tags":["story","author_MargoRooo","story_44267715","ask_hn"],"author":"MargoRooo","children":[44267717,44267867,44267916],"created_at":"2025-06-13T11:57:05Z","created_at_i":1749815825,"num_comments":2,"objectID":"44267715","points":5,"story_id":44267715,"story_text":"Most people think procrastination is a discipline or time management problem. But after analyzing data from 80,000+ program participants and 3,000+ coaching sessions, we discovered something unexpected.
Procrastination Is Emotional Dysregulation, Not Laziness
Key finding: People don\u2019t postpone tasks because of laziness. They postpone due to fear, perfectionism, overwhelm, or uncertainty. These are subcortical brain structures protecting from perceived risk.
When we explained to participants that their \u201cinner saboteur\u201d is an ancient part of the brain, they stopped blaming themselves for weak willpower and started working with resistance constructively.
Agile Approach to Life Works Better Than Long-term Planning
Pattern: 3 weeks of focus + 1 week of reflection give better results than trying to plan months ahead.
Participants said: \u201cFor the first time, planning doesn\u2019t cause stress.\u201d Short sprints allow experimenting and course correction, instead of \u201cpushing through\u201d toward unclear goals.
\u201cBoat Bottom\u201d Is More Important Than \u201cSail\u201d
Key mistake: 70% of participants set ambitious goals (sail) without covering basic needs (boat bottom).
We divided goals into two levels:
- Basic goals \u2014 sleep, energy, emotional state\n- Growth goals \u2014 career, self-actualization, new skills
When people started with the basic level, growth goals were achieved naturally. When they tried to \u201craise the sail\u201d on a leaky boat \u2014 they burned out quickly.
Three Levels of Working with Procrastination
From the data, a system crystallized:
1. Energy \u2014 restoring healthy dopamine cycles\n2. Goals \u2014 turning dreams into clear action plans\n3. Psychology \u2014 working with internal blocks based on psychotype
Attempts to immediately jump to level 3, bypassing 1 and 2, led to sabotage and regression.
Practical Takeaways
- Procrastination is a symptom, not the disease. You need to treat the causes\n- Short sprints (3 weeks) are more effective than long-term plans\n- Basic needs must be met BEFORE ambitious goals\n- Understanding neurobiology reduces self-flagellation and increases effectiveness
Application
Based on this data, we built an AI system that determines what level a person is at and adapts the approach. Early results are encouraging: 200 users, 300+ completed tasks, early sales exceeded $100k.
But most importantly \u2014 we now have a data-driven understanding of how procrastination works. And these principles can be applied regardless of any apps.
Looking for Early Testers
We\u2019re finalizing the full mobile app AJI (AI Journal & Insights) and need people to test the first versions. We\u2019re especially interested in participants with chronic procrastination \u2014 the approach shows best results with them.
If you\u2019re ready to try it and share feedback \u2014 write in the comments.
Data collected from productivity programs, coaching sessions, and scientific research from 2020-202","title":"We Studied Procrastination on a Scale. Here's What Works","updated_at":"2025-06-15T14:10:30Z"},{"_highlightResult":{"author":{"fullyHighlighted":true,"matchLevel":"partial","matchedWords":["sail"],"value":"sails"},"story_text":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["research"],"value":"Hey!
We\u2019ve built a data extraction tool to flexibly automate data and document processing.
You\u2019ve probably seen a few of these, so have we! A few of us have been varyingly stuck trying to automate the extraction of borrower financials for the past 5 years.
We think that there are a few missing features of most data extraction tools.
* They are usually too complex to quickly get up and running
* They are overly constrained in terms of what workflows and documents they support
We\u2019ve always felt like speed and flexibility were sticking points, so we went slightly orthogonal to the alternatives.
It is deliberately very simple, with three key features
1. Generate a custom schema that is the target of the data extraction
2. Logic and \u201cexpert insights\u201d can get added to field level prompts
3. Ability to share with externals via chat to get data and feedback, quickly!
The demo page is at https://go.sea.dev with an explainer and link to demo.
The demo app is constrained, but feel free to get in touch with me with details if you\u2019d like to unlock the full capabilities
Some of the features we are working on:
* Improvements on OCR
* Advanced data management
* Email or WhatsApp to accept responses
* API and embedded data collection copilot for SaaS
* Meeting recording plugin, and streaming audio
* Securely share partial submissions with 3rd party for completion
* Enrichment via website scraping
* Integrations (Hubspot, Sheets etc)
We are a very small team, with a mix of researchers and engineers. We are leaning into improvements in data extraction, conversation evals and data structuring innovation to make the product as powerful and seamless as possible.
Our background is in financial services, so most of our effort is going into that use-case (business risk assessment to be specific), we\u2019ve solved a bunch of our internal use cases as well as general purpose customer problems, so we thought to share it with a wider audience.
Would love to get your feedback and comments!"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Data Extraction with Flexible Schemas"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://go.sea.dev"}},"_tags":["story","author_sails","story_42430490","show_hn"],"author":"sails","created_at":"2024-12-16T12:42:57Z","created_at_i":1734352977,"num_comments":0,"objectID":"42430490","points":5,"story_id":42430490,"story_text":"Hey!
We\u2019ve built a data extraction tool to flexibly automate data and document processing.
You\u2019ve probably seen a few of these, so have we! A few of us have been varyingly stuck trying to automate the extraction of borrower financials for the past 5 years.
We think that there are a few missing features of most data extraction tools.
* They are usually too complex to quickly get up and running
* They are overly constrained in terms of what workflows and documents they support
We\u2019ve always felt like speed and flexibility were sticking points, so we went slightly orthogonal to the alternatives.
It is deliberately very simple, with three key features
1. Generate a custom schema that is the target of the data extraction
2. Logic and \u201cexpert insights\u201d can get added to field level prompts
3. Ability to share with externals via chat to get data and feedback, quickly!
The demo page is at https://go.sea.dev with an explainer and link to demo.
The demo app is constrained, but feel free to get in touch with me with details if you\u2019d like to unlock the full capabilities
Some of the features we are working on:
* Improvements on OCR
* Advanced data management
* Email or WhatsApp to accept responses
* API and embedded data collection copilot for SaaS
* Meeting recording plugin, and streaming audio
* Securely share partial submissions with 3rd party for completion
* Enrichment via website scraping
* Integrations (Hubspot, Sheets etc)
We are a very small team, with a mix of researchers and engineers. We are leaning into improvements in data extraction, conversation evals and data structuring innovation to make the product as powerful and seamless as possible.
Our background is in financial services, so most of our effort is going into that use-case (business risk assessment to be specific), we\u2019ve solved a bunch of our internal use cases as well as general purpose customer problems, so we thought to share it with a wider audience.
Would love to get your feedback and comments!","title":"Show HN: Data Extraction with Flexible Schemas","updated_at":"2024-12-16T15:05:32Z","url":"https://go.sea.dev"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"charlycst"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["sail","research"],"value":"Miralis is a RISC-V firmware that virtualizes RISC-V firmware. In other words, it runs firmware in user-space (M-mode software in U-mode).
The fact that this is even possible is interesting: indeed, not all ISAs are virtualizable, and the same applies for their firmware mode. It all boils down to the virtualization requirements [1], which is a great read if you haven't come across it yet. Arm's EL3 cannot be virtualized, for instance, because some instructions, such as `cpsid`, are sensitive but do not trap (`cpsid` is a nop in user-space).
If you have a VisionFive 2 or a HiFive Premier P550, you can try it out, the instructions are in the documentation [2, 3]. Of course, it runs on QEMU too.
As Miralis is a research project, we have also been using it as a vehicle to explore other research ideas, such as automated verification of hypervisors [4]. For instance, we verified instruction emulation by comparing Miralis' implementation with the reference RISC-V executable specification [5], which we translated to Rust.
It has been fun working on Miralis, I hope you'll find it interesting too!
[1]: https://dl.acm.org/doi/pdf/10.1145/361011.361073
[2]: https://miralis-firmware.github.io/docs/platforms/visionfive...
[3]: https://miralis-firmware.github.io/docs/platforms/premierp55...
[4]: https://charlycst.github.io/papers/lightweight-hypervisor-ve...
[5]: https://github.com/riscv/sail-riscv"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Miralis \u2013 a RISC-V virtual firmware monitor"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/CharlyCst/miralis"}},"_tags":["story","author_charlycst","story_43947576","show_hn"],"author":"charlycst","created_at":"2025-05-10T17:58:11Z","created_at_i":1746899891,"num_comments":0,"objectID":"43947576","points":4,"story_id":43947576,"story_text":"Miralis is a RISC-V firmware that virtualizes RISC-V firmware. In other words, it runs firmware in user-space (M-mode software in U-mode).
The fact that this is even possible is interesting: indeed, not all ISAs are virtualizable, and the same applies for their firmware mode. It all boils down to the virtualization requirements [1], which is a great read if you haven't come across it yet. Arm's EL3 cannot be virtualized, for instance, because some instructions, such as `cpsid`, are sensitive but do not trap (`cpsid` is a nop in user-space).
If you have a VisionFive 2 or a HiFive Premier P550, you can try it out, the instructions are in the documentation [2, 3]. Of course, it runs on QEMU too.
As Miralis is a research project, we have also been using it as a vehicle to explore other research ideas, such as automated verification of hypervisors [4]. For instance, we verified instruction emulation by comparing Miralis' implementation with the reference RISC-V executable specification [5], which we translated to Rust.
It has been fun working on Miralis, I hope you'll find it interesting too!
[1]: https://dl.acm.org/doi/pdf/10.1145/361011.361073
[2]: https://miralis-firmware.github.io/docs/platforms/visionfive...
[3]: https://miralis-firmware.github.io/docs/platforms/premierp55...
[4]: https://charlycst.github.io/papers/lightweight-hypervisor-ve...
[5]: https://github.com/riscv/sail-riscv","title":"Show HN: Miralis \u2013 a RISC-V virtual firmware monitor","updated_at":"2025-05-11T18:20:42Z","url":"https://github.com/CharlyCst/miralis"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"vox_mollis"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["sail","research"],"value":"Today I got stopped out at level 5 of tptacek and patio11's excellent Jailbreak Challenge.
I am a professional developer in my dayjob, and I am mostly proud of the systems that I've engineered over the years. There have always been large challenges and tight constraints, but the path to solution has always been straightforward, given a little research. There has never been a time in my career as a developer where I've been in an "I can't see any possible way to do this" scenario.
Enter Jailbreak. I have never been more frustrated in my entire life. Nothing but wild goose chases and dead ends. While others sail through the levels with ease, I eventually ended up having to give up entirely at level 5 due to total exhaustion of all available options.
Having eaten my humble pie, my question to HN is: how do I improve at this? Are there fundamental cognitive constraints or modes of thinking that some people like me will simply never be able to push through? Why are solutions so clear and obvious during normal software development whereas these class of problems do little more than frustrate? Am I simply too dumb (or old, at 36, due to brain plasticity)?
What resources are available to improve at this, if any? Ideally I'd like to improve my skills at this and retry level 5 in whatever timeframe (a year?) is expected to become competent at this. Any help or suggestions would be appreciated. Thanks."},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: How do I improve at reversing/cracking challenges?"}},"_tags":["story","author_vox_mollis","story_12098784","ask_hn"],"author":"vox_mollis","created_at":"2016-07-15T03:27:28Z","created_at_i":1468553248,"num_comments":0,"objectID":"12098784","points":3,"story_id":12098784,"story_text":"Today I got stopped out at level 5 of tptacek and patio11's excellent Jailbreak Challenge.
I am a professional developer in my dayjob, and I am mostly proud of the systems that I've engineered over the years. There have always been large challenges and tight constraints, but the path to solution has always been straightforward, given a little research. There has never been a time in my career as a developer where I've been in an "I can't see any possible way to do this" scenario.
Enter Jailbreak. I have never been more frustrated in my entire life. Nothing but wild goose chases and dead ends. While others sail through the levels with ease, I eventually ended up having to give up entirely at level 5 due to total exhaustion of all available options.
Having eaten my humble pie, my question to HN is: how do I improve at this? Are there fundamental cognitive constraints or modes of thinking that some people like me will simply never be able to push through? Why are solutions so clear and obvious during normal software development whereas these class of problems do little more than frustrate? Am I simply too dumb (or old, at 36, due to brain plasticity)?
What resources are available to improve at this, if any? Ideally I'd like to improve my skills at this and retry level 5 in whatever timeframe (a year?) is expected to become competent at this. Any help or suggestions would be appreciated. Thanks.","title":"Ask HN: How do I improve at reversing/cracking challenges?","updated_at":"2024-09-19T23:31:10Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"chlor"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["sail","research"],"value":"On the journey of exploring deep learning, the performance of GPU is the key to the speed of progress. I had tried multiple platforms, attempting to find the "computing power engine" that could carry my deep learning dreams. However, the reality often fell short of expectations. Stuttering and latency were like shadows, restricting the speed of model training. When dealing with large-scale datasets and complex network architectures, the computing efficiency of these platforms seemed inadequate, and I was constantly tormented by stuttering and latency. Even more troublesome was that video memory management also became a major obstacle on my way forward. When handling tasks rich in sequential data such as video action recognition, the GPU video memory of common platforms was often stretched to the limit. When processing batches of high-definition video frames in long sequences with 16GB or 24GB of video memory, video memory overflow became a common occurrence. It was not until I encountered Burncloud (https://www.burncloud.com/835.html) that I truly unlocked the door to efficient deep learning.\n Its computing efficiency is truly astonishing. When I trained a conventional convolutional neural network for image classification on a certain commonly used old cloud platform, the average time for a single iteration was 25 minutes. However, the NVIDIA A100 GPU equipped on Burncloud, with its 6,912 CUDA cores combined with an advanced architecture, reduced the iteration time for the same model and dataset to around 4 minutes. The efficiency improvement was more than 5 times, as if a high-speed motor had been installed for model training, rapidly pushing the loss function towards the optimal value.\n Video memory management is also outstanding. When handling tasks rich in sequential data such as video action recognition, the GPU of common platforms usually has 16GB or 24GB of video memory. When processing batches of high-definition video frames in long sequences, there is a more than 70% probability of encountering video memory overflow. Burncloud is different. With large-capacity video memory starting from 40GB and reaching up to 80GB, it can easily accommodate the features of massive video frames. The whole training process is as stable as a mountain, and even complex 3D convolutional long short-term memory networks can set sail smoothly, ensuring that the exploration of deep learning does not run aground.\n Multi-GPU collaboration is also a highlight of Burncloud. In distributed training for language models of the GPT scale, other platforms are limited by internal interconnection bandwidth, and the data synchronization latency between cards exceeds 50 microseconds, with a computing power utilization rate of less than 60%. Burncloud relies on the NVLink ultra-high-speed channel to reduce the latency to within 10 microseconds. The computing power of multiple cards can be expanded almost linearly, and the utilization rate exceeds 90%. It's like a group of well-trained elite teams working together to charge forward and overcome complex model problems. I sincerely recommend it to my fellow professionals. Come to Burncloud and press the accelerator button for your scientific research and projects!"},"title":{"matchLevel":"none","matchedWords":[],"value":"The Ideal \"Computing Power Engine\" for Deep Learning \u2013 Burncloud GPU Platform"}},"_tags":["story","author_chlor","story_42480075","ask_hn"],"author":"chlor","created_at":"2024-12-21T15:03:02Z","created_at_i":1734793382,"num_comments":0,"objectID":"42480075","points":2,"story_id":42480075,"story_text":"On the journey of exploring deep learning, the performance of GPU is the key to the speed of progress. I had tried multiple platforms, attempting to find the "computing power engine" that could carry my deep learning dreams. However, the reality often fell short of expectations. Stuttering and latency were like shadows, restricting the speed of model training. When dealing with large-scale datasets and complex network architectures, the computing efficiency of these platforms seemed inadequate, and I was constantly tormented by stuttering and latency. Even more troublesome was that video memory management also became a major obstacle on my way forward. When handling tasks rich in sequential data such as video action recognition, the GPU video memory of common platforms was often stretched to the limit. When processing batches of high-definition video frames in long sequences with 16GB or 24GB of video memory, video memory overflow became a common occurrence. It was not until I encountered Burncloud (https://www.burncloud.com/835.html) that I truly unlocked the door to efficient deep learning.\n Its computing efficiency is truly astonishing. When I trained a conventional convolutional neural network for image classification on a certain commonly used old cloud platform, the average time for a single iteration was 25 minutes. However, the NVIDIA A100 GPU equipped on Burncloud, with its 6,912 CUDA cores combined with an advanced architecture, reduced the iteration time for the same model and dataset to around 4 minutes. The efficiency improvement was more than 5 times, as if a high-speed motor had been installed for model training, rapidly pushing the loss function towards the optimal value.\n Video memory management is also outstanding. When handling tasks rich in sequential data such as video action recognition, the GPU of common platforms usually has 16GB or 24GB of video memory. When processing batches of high-definition video frames in long sequences, there is a more than 70% probability of encountering video memory overflow. Burncloud is different. With large-capacity video memory starting from 40GB and reaching up to 80GB, it can easily accommodate the features of massive video frames. The whole training process is as stable as a mountain, and even complex 3D convolutional long short-term memory networks can set sail smoothly, ensuring that the exploration of deep learning does not run aground.\n Multi-GPU collaboration is also a highlight of Burncloud. In distributed training for language models of the GPT scale, other platforms are limited by internal interconnection bandwidth, and the data synchronization latency between cards exceeds 50 microseconds, with a computing power utilization rate of less than 60%. Burncloud relies on the NVLink ultra-high-speed channel to reduce the latency to within 10 microseconds. The computing power of multiple cards can be expanded almost linearly, and the utilization rate exceeds 90%. It's like a group of well-trained elite teams working together to charge forward and overcome complex model problems. I sincerely recommend it to my fellow professionals. Come to Burncloud and press the accelerator button for your scientific research and projects!","title":"The Ideal \"Computing Power Engine\" for Deep Learning \u2013 Burncloud GPU Platform","updated_at":"2024-12-21T16:25:52Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"angiosperm"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["sail","research"],"value":"Marshall Eubanks wants to send a swarm of 1000 flyweight probes to the\nnearest star, reaching it in 20 years.
https://www.researchgate.net/publication/373833951_Swarming_Proxima_Centauri_Optical_Communications_Over_Interstellar_Distances
However:
1. Sending just 1000 probes is fatally timid. We should think in terms of millions of identical probes. Produced in volume, their price-per drops to near nothing. The entire probe could have just one chip plus a couple of capacitors and coils.
2. Launching just one probe swarm would be wasteful: having built the solar-pumped launching laser/maser, we have no reason to turn it off: keep launching probes throughout the life of the mission. The cost of a million probes is small vs. the cost of the project overall. Over the next 20 years, launch 1000 probes (or more: 10,000, 50,000) each week. Having accelerated to cruising speed, probes turn to fly edge-on, minimizing collision risk.
3. With the launch apparatus operating throughout the mission, there is no need to accelerate to cruising speed immediately. This allows a much lesser beam intensity, and radically reduces G loading on the probes.
4. There is no value in having probes formed up in a plane at the destination. A dispersed cloud of a million probes flying through continuously for 20 years offers enormously greater value. The first few thousand through the system can identify interesting bodies, and subsequent swarms can tack against starlight and concentrate on those. This eliminates the "predict position of planetary bodies" problem.
5. Probes spend almost all their flight time close to zero K. This creates design opportunities denied to other projects. The sail surface may be an atomically thin wafer of superconductor, providing perfect reflection of any long-enough wavelength, stiffened by its own field.
6. Trying to transmit data home via laser would be a monumental blunder: the starlight behind the probes interferes with any optical signal. Worse, at optical frequency, phased-array transmission by clouds of probes is impossible; but at ordinary radio frequency, a cloud may transmit as a phased array with no difficulty, having triangulated their relative positions and synchronized to a pilot signal from home. Receivers throughout the asteroid belt may remain synchronized to operate as a phased array using atomic clocks (as was used to image M87).
7. There is no need for probes at the destination to transmit their data all the way home. They need only send to the next swarm coming up a light-day or light-week behind, which may relay data to the next.
8. Upon approaching the destination, the swarm may focus incident light (laser/maser/starlight) on a few, selected probes to slow them down enough to enter orbit. Subsequent swarms flying through can provide phased-array communication, and assist altering their orbits, with the occasional additional probe joining them.
9. A launch laser may be a simple laser cavity pumped with sunlight reflected from an array of monochromatic mirrors ranged around it, with no conversion loss. Monochromatic mirrors are easily produced by depositing alternating layers of transparent material with different dielectric constant but identical thickness. Given continuous operation throughout the mission, a much lower power output (10GW? 1GW?) becomes wholly adequate.
10. But consider the alternative of launching with an array of masers, instead. Masers are much more easily constructed and operated. An array of independent masers can be maintained in phase with the aid of atomic clocks.
11. With two swarms sent out in different directions, you get a phased-array radio telescope light-years across that can (e.g.) image the surface of a pulsar, using the pulsar itself to synchronize. The second swarm could get away with many fewer probes than the first, but you might better target another star with it.
Can this be improved upon?"},"title":{"matchLevel":"none","matchedWords":[],"value":"Project \"Breakthrough Starshot\" Improvements"}},"_tags":["story","author_angiosperm","story_39191932","ask_hn"],"author":"angiosperm","created_at":"2024-01-30T16:20:08Z","created_at_i":1706631608,"num_comments":0,"objectID":"39191932","points":2,"story_id":39191932,"story_text":"Marshall Eubanks wants to send a swarm of 1000 flyweight probes to the\nnearest star, reaching it in 20 years.
https://www.researchgate.net/publication/373833951_Swarming_Proxima_Centauri_Optical_Communications_Over_Interstellar_Distances
However:
1. Sending just 1000 probes is fatally timid. We should think in terms of millions of identical probes. Produced in volume, their price-per drops to near nothing. The entire probe could have just one chip plus a couple of capacitors and coils.
2. Launching just one probe swarm would be wasteful: having built the solar-pumped launching laser/maser, we have no reason to turn it off: keep launching probes throughout the life of the mission. The cost of a million probes is small vs. the cost of the project overall. Over the next 20 years, launch 1000 probes (or more: 10,000, 50,000) each week. Having accelerated to cruising speed, probes turn to fly edge-on, minimizing collision risk.
3. With the launch apparatus operating throughout the mission, there is no need to accelerate to cruising speed immediately. This allows a much lesser beam intensity, and radically reduces G loading on the probes.
4. There is no value in having probes formed up in a plane at the destination. A dispersed cloud of a million probes flying through continuously for 20 years offers enormously greater value. The first few thousand through the system can identify interesting bodies, and subsequent swarms can tack against starlight and concentrate on those. This eliminates the "predict position of planetary bodies" problem.
5. Probes spend almost all their flight time close to zero K. This creates design opportunities denied to other projects. The sail surface may be an atomically thin wafer of superconductor, providing perfect reflection of any long-enough wavelength, stiffened by its own field.
6. Trying to transmit data home via laser would be a monumental blunder: the starlight behind the probes interferes with any optical signal. Worse, at optical frequency, phased-array transmission by clouds of probes is impossible; but at ordinary radio frequency, a cloud may transmit as a phased array with no difficulty, having triangulated their relative positions and synchronized to a pilot signal from home. Receivers throughout the asteroid belt may remain synchronized to operate as a phased array using atomic clocks (as was used to image M87).
7. There is no need for probes at the destination to transmit their data all the way home. They need only send to the next swarm coming up a light-day or light-week behind, which may relay data to the next.
8. Upon approaching the destination, the swarm may focus incident light (laser/maser/starlight) on a few, selected probes to slow them down enough to enter orbit. Subsequent swarms flying through can provide phased-array communication, and assist altering their orbits, with the occasional additional probe joining them.
9. A launch laser may be a simple laser cavity pumped with sunlight reflected from an array of monochromatic mirrors ranged around it, with no conversion loss. Monochromatic mirrors are easily produced by depositing alternating layers of transparent material with different dielectric constant but identical thickness. Given continuous operation throughout the mission, a much lower power output (10GW? 1GW?) becomes wholly adequate.
10. But consider the alternative of launching with an array of masers, instead. Masers are much more easily constructed and operated. An array of independent masers can be maintained in phase with the aid of atomic clocks.
11. With two swarms sent out in different directions, you get a phased-array radio telescope light-years across that can (e.g.) image the surface of a pulsar, using the pulsar itself to synchronize. The second swarm could get away with many fewer probes than the first, but you might better target another star with it.
Can this be improved upon?","title":"Project \"Breakthrough Starshot\" Improvements","updated_at":"2024-09-20T16:19:20Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"angiosperm"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["sail","research"],"value":"Marshall Eubanks wants to send a swarm of 1000 flyweight probes to the nearest star, reaching it in 20 years.
https://www.researchgate.net/publication/373833951_Swarming_Proxima_Centauri_Optical_Communications_Over_Interstellar_Distances
However:
1. Sending just 1000 probes is fatally timid. We should think in terms of millions of identical probes. Produced in volume, their price-per drops to near nothing, even when more capable. The entire probe could have just one graphene chip plus a couple of capacitors and coils.
2. Launching just one probe swarm would be wasteful: having built the solar-pumped launching laser/maser, we have no reason to turn it off: keep launching probes throughout the life of the mission. The cost of a million probes is small vs. the cost of the project overall. Over the next 20 years, launch 1000 probes (or more: 10,000, 50,000) each week. Having accelerated to cruising speed, probes turn to fly edge-on, minimizing erosion.
3. With the launch apparatus operating throughout the mission, there is no need to accelerate to cruising speed immediately. This allows a much lesser beam intensity, and radically reduces G loading on the probes.
4. There is no value in having probes form up in a plane at the destination. A dispersed cloud of a million probes flying through continuously for 20 years offers enormously greater value. The first few thousand through the system can identify interesting bodies, and subsequent swarms can tack against starlight and concentrate on those. This eliminates the "predict position of planetary bodies" problem.
5. Probes spend almost all their flight time close to zero K. This creates design opportunities denied to other projects. The sail surface may be an atomically thin wafer of superconductor, providing perfect reflection of any long-enough wavelength, stiffened by its own field.
6. Trying to transmit data home via laser would be a monumental blunder: at optical wavelengths, phased-array transmission by clouds of probes is impossible; but at ordinary RF, a cloud may transmit as a phased array with no difficulty, having triangulated their relative positions and synchronized to a pilot signal from home. Receivers throughout the asteroid belt may remain synchronized to operate as a phased array using atomic clocks (as was used to image M87).
7. There is no need for probes at the destination to transmit their data all the way home. They need only send to the next swarm coming up a light-day or light-week behind, which may relay data to the next.
8. Upon approaching the destination, the swarm may focus incident light (laser/maser/starlight) on a few, selected probes to slow them down enough to enter orbit. Subsequent swarms flying through can provide phased-array communication, and assist altering their orbits, with the occasional additional probe joining them.
9. A launch laser may be a simple laser cavity pumped with sunlight reflected from an array of monochromatic mirrors ranged around it, with no conversion loss. Monochromatic mirrors are easily produced by depositing alternating layers of transparent material with different dielectric constant but identical thickness. Given continuous operation throughout the mission, a much lower power output (10GW? 1GW?) becomes wholly adequate.
10. But consider the alternative of launching with an array of masers, instead. Masers are much more easily constructed and operated. An array of independent masers can be maintained in phase with the aid of atomic clocks.
11. With two swarms sent out in different directions, you get a phased-array radio telescope light-years across that can (e.g.) image the surface of a pulsar, using the pulsar itself to synchronize. The second swarm could get away with many fewer probes than the first, but you might better target another star with it.
Can this be improved upon?"},"title":{"matchLevel":"none","matchedWords":[],"value":"Project Starswarm"}},"_tags":["story","author_angiosperm","story_39252584","ask_hn"],"author":"angiosperm","created_at":"2024-02-04T17:58:08Z","created_at_i":1707069488,"num_comments":0,"objectID":"39252584","points":1,"story_id":39252584,"story_text":"Marshall Eubanks wants to send a swarm of 1000 flyweight probes to the nearest star, reaching it in 20 years.
https://www.researchgate.net/publication/373833951_Swarming_Proxima_Centauri_Optical_Communications_Over_Interstellar_Distances
However:
1. Sending just 1000 probes is fatally timid. We should think in terms of millions of identical probes. Produced in volume, their price-per drops to near nothing, even when more capable. The entire probe could have just one graphene chip plus a couple of capacitors and coils.
2. Launching just one probe swarm would be wasteful: having built the solar-pumped launching laser/maser, we have no reason to turn it off: keep launching probes throughout the life of the mission. The cost of a million probes is small vs. the cost of the project overall. Over the next 20 years, launch 1000 probes (or more: 10,000, 50,000) each week. Having accelerated to cruising speed, probes turn to fly edge-on, minimizing erosion.
3. With the launch apparatus operating throughout the mission, there is no need to accelerate to cruising speed immediately. This allows a much lesser beam intensity, and radically reduces G loading on the probes.
4. There is no value in having probes form up in a plane at the destination. A dispersed cloud of a million probes flying through continuously for 20 years offers enormously greater value. The first few thousand through the system can identify interesting bodies, and subsequent swarms can tack against starlight and concentrate on those. This eliminates the "predict position of planetary bodies" problem.
5. Probes spend almost all their flight time close to zero K. This creates design opportunities denied to other projects. The sail surface may be an atomically thin wafer of superconductor, providing perfect reflection of any long-enough wavelength, stiffened by its own field.
6. Trying to transmit data home via laser would be a monumental blunder: at optical wavelengths, phased-array transmission by clouds of probes is impossible; but at ordinary RF, a cloud may transmit as a phased array with no difficulty, having triangulated their relative positions and synchronized to a pilot signal from home. Receivers throughout the asteroid belt may remain synchronized to operate as a phased array using atomic clocks (as was used to image M87).
7. There is no need for probes at the destination to transmit their data all the way home. They need only send to the next swarm coming up a light-day or light-week behind, which may relay data to the next.
8. Upon approaching the destination, the swarm may focus incident light (laser/maser/starlight) on a few, selected probes to slow them down enough to enter orbit. Subsequent swarms flying through can provide phased-array communication, and assist altering their orbits, with the occasional additional probe joining them.
9. A launch laser may be a simple laser cavity pumped with sunlight reflected from an array of monochromatic mirrors ranged around it, with no conversion loss. Monochromatic mirrors are easily produced by depositing alternating layers of transparent material with different dielectric constant but identical thickness. Given continuous operation throughout the mission, a much lower power output (10GW? 1GW?) becomes wholly adequate.
10. But consider the alternative of launching with an array of masers, instead. Masers are much more easily constructed and operated. An array of independent masers can be maintained in phase with the aid of atomic clocks.
11. With two swarms sent out in different directions, you get a phased-array radio telescope light-years across that can (e.g.) image the surface of a pulsar, using the pulsar itself to synchronize. The second swarm could get away with many fewer probes than the first, but you might better target another star with it.
Can this be improved upon?","title":"Project Starswarm","updated_at":"2024-09-20T16:17:51Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"dariusmonsef"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["sail","research"],"value":"I think agent-first chat interfaces will be a primary software modality and busy dashboard/UI will go away. I\u2019m not sure who exactly wins it, but I want my knowledge to grow/go with me.
A lot of the \u201cknowledge\u201d ie research, analysis, reasoning will be done by agents as the primary user. Our current notes tools & tasks management systems were built for humans\u2026 I don\u2019t care what the 17th thing on my bug backlog is. I want to conduct agents that can execute for me and do great work.
What I built OzBrain to do:\n+ Create a central place for agent reasoned knowledge to live\n+ Be agnostic about what apps/agents connect to it\n+ Capture everything and track it so I can audit it\n+ Enable teams, collaborators or partners to share brains\n+ Handle conflicts so many agents in the same article doesn\u2019t blow up\n+ Refactor knowledge into more token friendly chunks and map the index well\n+ Close the knowledge loop so new thinking supersedes old thinking across the corpus. Don\u2019t erase, depreciate and link\n+ Keep user data safe and secure\n++ Be easy enough to use that you don\u2019t have to have any technical knowledge
Some among us will always build their own custom solutions, but there are millions of tech professionals and small business owners that will use agents heavily and need a solution. So I\u2019m trying to build that.
Isn\u2019t this like gBrain? Yes, similar. I think it\u2019s like AWS vs Vercel. AWS is very powerful, configurable, and useful if you\u2019re technical and want to invest the time into really fine tuning your system\u2026 but if you just want your web deploy/hosting to just work and be easy to deal with you use Vercel.
// WHY I MADE IT
I\u2019ve been enjoying getting back to my technical roots, as I lost my coding skills more than a decade ago, but with AI I can focus on the system and the product in partnership with agent coding workflows.
I recently built a Voice AI for older people. To build it I created an agentic engineering workflow (feel free to rip that up as I\u2019m always looking to improve systems: https://ozbrain.com/resources/eng-flow) My approach with coding agents is trust but verify, and I\u2019m trying to replace the parts where a human would review with an adversarial or specialized agent who would give a better answer/review.
I have workflows that will go high level task to shipped PR running in Claude cloud sessions. I use Claude Code locally and Cursor when I want a tighter loop on doing visual work like UI or layout. And Codex to either load balance usage for TokenThriffting or when I want a different llm to think thru something.
It was a pain in the ass passing .md files around and keep track of which version was the most recent, so I built a hosted .md storage right in Supabase and any of my agents already have Supabase access. This let me build a solid, scalable, secure voice AI from my phone at the gym. All my agents have access to our knowledge, can write to it, update and refer to it as we build and improve the product and the systems we use.
Out of 75 founder friends I asked about how they manage shared knowledge, 26 built their own custom knowledge systems\u2026 Obsidian vaults with 7k files synced through a VPS, markdown repos behind their own MCP servers, cron jobs stitching Supabase to a skills file\u2026 each a different Frankenstein they have to maintain. 32 said they felt the pain of moving static files around but didn\u2019t have any solution for it.
So I rebuilt my brain better and used it to build it.
// HOW YOU CAN HELP
Would love to have you try it out. The maintenance loop is still in alpha so not running it on customer data yet.
If you built your own brain I\u2019d love to hear how you did it. What criteria was most important for you in its design & function.
If you are tired of shuffling .md files around I\u2019d love to have you try out OzBrain and to give feedback, just ask your agent to put it in the shared bugs & features brain!
Cheers!\nBubs.co"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: OzBrain, a shared brain for knowledge between agents and your team"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://ozbrain.com"}},"_tags":["story","author_dariusmonsef","story_49394827","show_hn"],"author":"dariusmonsef","children":[49394952,49395220,49395240,49395359,49395706,49395739,49395778,49395910,49395966,49396028,49396207,49396347,49396370,49396430,49396627,49396746,49396766,49396788,49396873,49396898,49397071,49397451,49397453,49397565,49397747,49398021,49398400,49399271,49400833,49405658,49406323,49408070,49413054,49415238,49429905,49432522,49436555,49457636,49504400],"created_at":"2026-08-21T23:09:06Z","created_at_i":1787353746,"num_comments":59,"objectID":"49394827","points":92,"story_id":49394827,"story_text":"I think agent-first chat interfaces will be a primary software modality and busy dashboard/UI will go away. I\u2019m not sure who exactly wins it, but I want my knowledge to grow/go with me.
A lot of the \u201cknowledge\u201d ie research, analysis, reasoning will be done by agents as the primary user. Our current notes tools & tasks management systems were built for humans\u2026 I don\u2019t care what the 17th thing on my bug backlog is. I want to conduct agents that can execute for me and do great work.
What I built OzBrain to do:\n+ Create a central place for agent reasoned knowledge to live\n+ Be agnostic about what apps/agents connect to it\n+ Capture everything and track it so I can audit it\n+ Enable teams, collaborators or partners to share brains\n+ Handle conflicts so many agents in the same article doesn\u2019t blow up\n+ Refactor knowledge into more token friendly chunks and map the index well\n+ Close the knowledge loop so new thinking supersedes old thinking across the corpus. Don\u2019t erase, depreciate and link\n+ Keep user data safe and secure\n++ Be easy enough to use that you don\u2019t have to have any technical knowledge
Some among us will always build their own custom solutions, but there are millions of tech professionals and small business owners that will use agents heavily and need a solution. So I\u2019m trying to build that.
Isn\u2019t this like gBrain? Yes, similar. I think it\u2019s like AWS vs Vercel. AWS is very powerful, configurable, and useful if you\u2019re technical and want to invest the time into really fine tuning your system\u2026 but if you just want your web deploy/hosting to just work and be easy to deal with you use Vercel.
// WHY I MADE IT
I\u2019ve been enjoying getting back to my technical roots, as I lost my coding skills more than a decade ago, but with AI I can focus on the system and the product in partnership with agent coding workflows.
I recently built a Voice AI for older people. To build it I created an agentic engineering workflow (feel free to rip that up as I\u2019m always looking to improve systems: https://ozbrain.com/resources/eng-flow) My approach with coding agents is trust but verify, and I\u2019m trying to replace the parts where a human would review with an adversarial or specialized agent who would give a better answer/review.
I have workflows that will go high level task to shipped PR running in Claude cloud sessions. I use Claude Code locally and Cursor when I want a tighter loop on doing visual work like UI or layout. And Codex to either load balance usage for TokenThriffting or when I want a different llm to think thru something.
It was a pain in the ass passing .md files around and keep track of which version was the most recent, so I built a hosted .md storage right in Supabase and any of my agents already have Supabase access. This let me build a solid, scalable, secure voice AI from my phone at the gym. All my agents have access to our knowledge, can write to it, update and refer to it as we build and improve the product and the systems we use.
Out of 75 founder friends I asked about how they manage shared knowledge, 26 built their own custom knowledge systems\u2026 Obsidian vaults with 7k files synced through a VPS, markdown repos behind their own MCP servers, cron jobs stitching Supabase to a skills file\u2026 each a different Frankenstein they have to maintain. 32 said they felt the pain of moving static files around but didn\u2019t have any solution for it.
So I rebuilt my brain better and used it to build it.
// HOW YOU CAN HELP
Would love to have you try it out. The maintenance loop is still in alpha so not running it on customer data yet.
If you built your own brain I\u2019d love to hear how you did it. What criteria was most important for you in its design & function.
If you are tired of shuffling .md files around I\u2019d love to have you try out OzBrain and to give feedback, just ask your agent to put it in the shared bugs & features brain!
Cheers!\nBubs.co","title":"Show HN: OzBrain, a shared brain for knowledge between agents and your team","updated_at":"2026-08-31T01:54:51Z","url":"https://ozbrain.com"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"astatine"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["sail","research"],"value":"I have emails from 1997-98 onwards all in one massive inbox, currently on gmail with emclient on Mac as the mail client (there have been probably a dozen services starting from self-hosted in the beginning). I would like to archive the historic mails for the first 25 years, but in a way that's actually searchable and usable in the future (and potentially importable to a new mail client/service) and not in any particular mailbox format that may go obsolete. There is no good reason to hoard them, other than "I feel like doing so". What are the best options, and tools?"},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: Best way to archive 25 years of emails"}},"_tags":["story","author_astatine","story_49489306","ask_hn"],"author":"astatine","children":[49489493,49489546,49493647,49495137,49497116,49500754],"created_at":"2026-08-29T12:19:07Z","created_at_i":1788005947,"num_comments":9,"objectID":"49489306","points":5,"story_id":49489306,"story_text":"I have emails from 1997-98 onwards all in one massive inbox, currently on gmail with emclient on Mac as the mail client (there have been probably a dozen services starting from self-hosted in the beginning). I would like to archive the historic mails for the first 25 years, but in a way that's actually searchable and usable in the future (and potentially importable to a new mail client/service) and not in any particular mailbox format that may go obsolete. There is no good reason to hoard them, other than "I feel like doing so". What are the best options, and tools?","title":"Ask HN: Best way to archive 25 years of emails","updated_at":"2026-08-31T01:29:51Z"}],"hitsPerPage":50,"nbHits":25,"nbPages":1,"page":0,"params":"query=Sail+Research&tags=story&hitsPerPage=50&advancedSyntax=true&analyticsTags=backend","processingTimeMS":11,"processingTimingsMS":{"_request":{"roundTrip":29},"afterFetch":{"format":{"highlighting":2,"total":2},"merge":{"mergeLoop":{"prepareNextHit":1,"total":1},"total":1},"total":1},"fetch":{"query":6,"scanning":2,"total":9},"total":11},"query":"Sail Research","serverTimeMS":15}