I have tried out two different Aikido dojos this week. Emerald City Aikido and Puget Sound Aikikai (I live in Seattle WA). Both differ from one another a fair bit in terms of the \"feel\" of the classroom environment and instruction. Emerald City Aikido was much more casual and had a comfortable enough environment that I had a good dialog forming between myself and the sensei. The other dojo was much more intense. It and its students seemed much more focused and serious and I never really felt comfortable enough with the teacher to really discuss my form or the techniques. Also Puget Sound Aikikai is affiliated with the USAF whereas the other is an independent dojo.
At this point I'm heavily confused. Both seem great but in different ways. Any suggestions?"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Ask HN: Choosing an Aikido Dojo (Seattle)"},"url":{"matchLevel":"none","matchedWords":[],"value":""}},"_tags":["story","author_darkxanthos","story_1892844","ask_hn"],"author":"darkxanthos","children":[1892866,1892867],"created_at":"2010-11-11T04:04:57Z","created_at_i":1289448297,"num_comments":3,"objectID":"1892844","points":2,"story_id":1892844,"story_text":"I am learning Aikido for exercise, self-defense, and as a study in philosophy.
I have tried out two different Aikido dojos this week. Emerald City Aikido and Puget Sound Aikikai (I live in Seattle WA). Both differ from one another a fair bit in terms of the \"feel\" of the classroom environment and instruction. Emerald City Aikido was much more casual and had a comfortable enough environment that I had a good dialog forming between myself and the sensei. The other dojo was much more intense. It and its students seemed much more focused and serious and I never really felt comfortable enough with the teacher to really discuss my form or the techniques. Also Puget Sound Aikikai is affiliated with the USAF whereas the other is an independent dojo.
At this point I'm heavily confused. Both seem great but in different ways. Any suggestions?","title":"Ask HN: Choosing an Aikido Dojo (Seattle)","updated_at":"2024-09-19T17:24:36Z","url":""},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"booffa"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"Meowth GBA Translator is an open-source, AI-powered tool that automates translation of Pok\u00e9mon GBA ROMs (including binary hacks like FireRed, Emerald, Ruby/Sapphire, and Mystery Dungeon). Powered by LLMs (supports OpenAI, DeepSeek, Gemini, Claude, Groq, and 10+ others), it extracts text, translates intelligently while preserving codes and context, then rebuilds the ROM \u2014 all in one click via a friendly GUI or simple CLI command.\nSupports 6+ languages (Chinese, English, French, German, Italian, Spanish) with optimized prompts and smart font patching. Focus on gameplay mods, let AI handle the words. Free, MIT-licensed, cross-platform."},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Show HN: AI-powered one-click translator for Pok\u00e9mon GBA ROM hacks"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/Olcmyk/Meowth-GBA-Translator"}},"_tags":["story","author_booffa","story_47347680","show_hn"],"author":"booffa","children":[47347747,47347798,47348943,47385420],"created_at":"2026-03-12T07:45:32Z","created_at_i":1773301532,"num_comments":4,"objectID":"47347680","points":4,"story_id":47347680,"story_text":"Meowth GBA Translator is an open-source, AI-powered tool that automates translation of Pok\u00e9mon GBA ROMs (including binary hacks like FireRed, Emerald, Ruby/Sapphire, and Mystery Dungeon). Powered by LLMs (supports OpenAI, DeepSeek, Gemini, Claude, Groq, and 10+ others), it extracts text, translates intelligently while preserving codes and context, then rebuilds the ROM \u2014 all in one click via a friendly GUI or simple CLI command.\nSupports 6+ languages (Chinese, English, French, German, Italian, Spanish) with optimized prompts and smart font patching. Focus on gameplay mods, let AI handle the words. Free, MIT-licensed, cross-platform.","title":"Show HN: AI-powered one-click translator for Pok\u00e9mon GBA ROM hacks","updated_at":"2026-03-20T08:08:09Z","url":"https://github.com/Olcmyk/Meowth-GBA-Translator"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"kmgrassi"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"Hey HN folks!
Emerald founder here - I\u2019m a dev, pediatrician and father. I\u2019ve been thinking about this idea for a long time. I recently sold my last company [0] and this seems like a good time to try something a little more risky but that I\u2019m passionate about.
In the distant past most kids worked on farms from a young age (I live in upstate NY and families with farms still have their kids work from a young age). With industrialization we got away from child labor because children were being exploited and exposed to dangerous factory environments.
With Emerald, I\u2019d like to challenge the notion that \u201cchild labor\u201d is necessarily a bad thing. As a pediatrician and parent I can tell you that American children could benefit from a dose of the real world. There are many important benefits to having the responsibilities of a job at a young age. I outlined several of these below.
The idea behind Emerald is to use the loophole that kids can work for their parents. We are going to create LLCs and bank accounts (will probably use Stipe Atlas and Mercury bank) for each family and then contract work through that LLC. This way kids can work on projects, earn money and gain experience.
Emerald will be a marketplace similar to Fiverr or Upwork. Companies or people who need work done can post the spec and then will be matched with a workforce who can complete the work.
The main difference will be that Emerald will be much more hands on during the work completion. Instead of individual freelancers bidding for work Emerald will construct teams of youth (prob 2-5 per team - maybe more?) with an adult mentor. The mentor will be paid and will oversee the work. The youth team will then collaborate to complete work. Lots of unknowns here and we will have to iterate on the best process.
There will also be extensive safety tooling and oversight. We plan to require all conversations to occur on-platform (Slack like chat), set time limits and give parents dashboards/controls.
The obvious first type of work to be done are things that teens are good at - content editing, content creation and software dev. Although I\u2019d like to hear other ideas for work that teens might be well suited for.
Benefits:\n- Earn money\n- Learn time management\n- Learn how to work with a team\n- Learn real world software development or tech skills\n- Start creating a resume and list of references
Risks:\n- Time commitment \n- Increase screen time. Kids already have too much screen time\n- Shorten childhood - we should let \u201ckids be kids\u201d for longer\n- Exploitation - companies taking advantage of inexperienced workers (paying less, etc)\n- Cause more work for parents (monitoring, etc)\n- Parents masquerading as their kids and doing the work (I don\u2019t know why they\u2019d do this but figure I\u2019d list it)
Questions:\n- What are some additional risks?\n- What are additional benefits?\n- Are there examples of programs like this out there? If so, what are the learnings?
Side note on AI\nI\u2019d like to avoid the \u201cAI will take all these jobs anyway\u201d rabbit hole. I\u2019d like to assume we can find something productive for kids (or any of us) to do.
Who I\u2019m looking for\n- Educators or people who have worked with teens on longer-term group projects - learnings from these projects\n- People interested in being mentors for teens on Emerald - will be paid positions\n- People with children who are interested in the initial pilot - no dates or commitment are set yet. Just if you are remotely interested.\n- Companies/people who are interested in submitting projects that they need completed. We can help you scope the project so it can be amorphous.
This is VERY early for Emerald. We haven\u2019t built any tech yet (aside from the very basic landing page). If anyone has interest in this idea feel free to reach out - my HN handle at gmail.
Thanks for reading this far!
[0]: PlentiAI acquired by Digiphy - https://www.digiphy.it/"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["emerald"],"value":"Emerald \u2013 Work marketplace for teens/youth"},"url":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["emerald"],"value":"https://www.emerald.new/"}},"_tags":["story","author_kmgrassi","story_42844176"],"author":"kmgrassi","children":[42844187,42844190,42844476],"created_at":"2025-01-27T18:35:50Z","created_at_i":1738002950,"num_comments":2,"objectID":"42844176","points":2,"story_id":42844176,"story_text":"Hey HN folks!
Emerald founder here - I\u2019m a dev, pediatrician and father. I\u2019ve been thinking about this idea for a long time. I recently sold my last company [0] and this seems like a good time to try something a little more risky but that I\u2019m passionate about.
In the distant past most kids worked on farms from a young age (I live in upstate NY and families with farms still have their kids work from a young age). With industrialization we got away from child labor because children were being exploited and exposed to dangerous factory environments.
With Emerald, I\u2019d like to challenge the notion that \u201cchild labor\u201d is necessarily a bad thing. As a pediatrician and parent I can tell you that American children could benefit from a dose of the real world. There are many important benefits to having the responsibilities of a job at a young age. I outlined several of these below.
The idea behind Emerald is to use the loophole that kids can work for their parents. We are going to create LLCs and bank accounts (will probably use Stipe Atlas and Mercury bank) for each family and then contract work through that LLC. This way kids can work on projects, earn money and gain experience.
Emerald will be a marketplace similar to Fiverr or Upwork. Companies or people who need work done can post the spec and then will be matched with a workforce who can complete the work.
The main difference will be that Emerald will be much more hands on during the work completion. Instead of individual freelancers bidding for work Emerald will construct teams of youth (prob 2-5 per team - maybe more?) with an adult mentor. The mentor will be paid and will oversee the work. The youth team will then collaborate to complete work. Lots of unknowns here and we will have to iterate on the best process.
There will also be extensive safety tooling and oversight. We plan to require all conversations to occur on-platform (Slack like chat), set time limits and give parents dashboards/controls.
The obvious first type of work to be done are things that teens are good at - content editing, content creation and software dev. Although I\u2019d like to hear other ideas for work that teens might be well suited for.
Benefits:\n- Earn money\n- Learn time management\n- Learn how to work with a team\n- Learn real world software development or tech skills\n- Start creating a resume and list of references
Risks:\n- Time commitment \n- Increase screen time. Kids already have too much screen time\n- Shorten childhood - we should let \u201ckids be kids\u201d for longer\n- Exploitation - companies taking advantage of inexperienced workers (paying less, etc)\n- Cause more work for parents (monitoring, etc)\n- Parents masquerading as their kids and doing the work (I don\u2019t know why they\u2019d do this but figure I\u2019d list it)
Questions:\n- What are some additional risks?\n- What are additional benefits?\n- Are there examples of programs like this out there? If so, what are the learnings?
Side note on AI\nI\u2019d like to avoid the \u201cAI will take all these jobs anyway\u201d rabbit hole. I\u2019d like to assume we can find something productive for kids (or any of us) to do.
Who I\u2019m looking for\n- Educators or people who have worked with teens on longer-term group projects - learnings from these projects\n- People interested in being mentors for teens on Emerald - will be paid positions\n- People with children who are interested in the initial pilot - no dates or commitment are set yet. Just if you are remotely interested.\n- Companies/people who are interested in submitting projects that they need completed. We can help you scope the project so it can be amorphous.
This is VERY early for Emerald. We haven\u2019t built any tech yet (aside from the very basic landing page). If anyone has interest in this idea feel free to reach out - my HN handle at gmail.
Thanks for reading this far!
[0]: PlentiAI acquired by Digiphy - https://www.digiphy.it/","title":"Emerald \u2013 Work marketplace for teens/youth","updated_at":"2025-01-28T14:40:00Z","url":"https://www.emerald.new/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"ajcarpy2005"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"New Futurist Magazine to Teach Implementable AI"},"url":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["emerald"],"value":"https://www.kickstarter.com/projects/coreylemont/the-emerald-ethos"}},"_tags":["story","author_ajcarpy2005","story_20099945"],"author":"ajcarpy2005","created_at":"2019-06-04T22:39:01Z","created_at_i":1559687941,"num_comments":0,"objectID":"20099945","points":1,"story_id":20099945,"title":"New Futurist Magazine to Teach Implementable AI","updated_at":"2024-09-20T04:21:52Z","url":"https://www.kickstarter.com/projects/coreylemont/the-emerald-ethos"},{"_highlightResult":{"author":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["emerald"],"value":"Emerald_dreamer"},"title":{"matchLevel":"none","matchedWords":[],"value":"Humanity Is Coming to an End"},"url":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"https://dearworld.ai/"}},"_tags":["story","author_Emerald_dreamer","story_45578018"],"author":"Emerald_dreamer","children":[45578047,45578050,45578081,45578134,45578155,45578184,45578196,45578212,45578331,45578392,45578553,45578801,45579583],"created_at":"2025-10-14T09:41:44Z","created_at_i":1760434904,"num_comments":9,"objectID":"45578018","points":12,"story_id":45578018,"title":"Humanity Is Coming to an End","updated_at":"2026-03-05T22:52:14Z","url":"https://dearworld.ai/"},{"_highlightResult":{"author":{"fullyHighlighted":true,"matchLevel":"partial","matchedWords":["emerald"],"value":"emeraldd"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"AI company has released an app that lets people converse with avatars of dead"},"url":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"https://old.reddit.com/r/STEW_ScTecEngWorld/comments/1owliqk/an_ai_company_has_released_an_app_that_lets/"}},"_tags":["story","author_emeraldd","story_46440024"],"author":"emeraldd","children":[46440039,46440077,46440236],"created_at":"2025-12-31T00:40:56Z","created_at_i":1767141656,"num_comments":3,"objectID":"46440024","points":2,"story_id":46440024,"title":"AI company has released an app that lets people converse with avatars of 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Decommissioned","updated_at":"2024-09-20T00:20:45Z","url":"http://www.forces.tv/05981801"},{"_highlightResult":{"author":{"fullyHighlighted":true,"matchLevel":"partial","matchedWords":["emerald"],"value":"emeraldd"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Illustrations of Madness: James Tilly Matthews and the Air Loom"},"url":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"https://publicdomainreview.org/2014/11/12/illustrations-of-madness-james-tilly-matthews-and-the-air-loom/"}},"_tags":["story","author_emeraldd","story_12774236"],"author":"emeraldd","created_at":"2016-10-23T16:43:38Z","created_at_i":1477241018,"num_comments":0,"objectID":"12774236","points":1,"story_id":12774236,"title":"Illustrations of Madness: James Tilly Matthews and the Air Loom","updated_at":"2024-09-19T23:54:03Z","url":"https://publicdomainreview.org/2014/11/12/illustrations-of-madness-james-tilly-matthews-and-the-air-loom/"},{"_highlightResult":{"author":{"fullyHighlighted":true,"matchLevel":"partial","matchedWords":["emerald"],"value":"emeraldd"},"title":{"matchLevel":"none","matchedWords":[],"value":"California bans ITT tech from accepting new students"},"url":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"http://www.latimes.com/business/la-fi-itt-tech-aid-20160826-snap-story.html?utm_source=fark&utm_medium=website&utm_content=link&ICID=ref_fark"}},"_tags":["story","author_emeraldd","story_12386777"],"author":"emeraldd","children":[12386940,12386941,12386945,12387005,12387006,12387025,12387034,12387132,12387145,12387177,12387182,12387221,12387298,12387329,12387355,12387387,12387389,12387420,12387485,12387546,12387617,12387637,12387702,12387723,12387945,12387982,12388040,12388246,12388339,12388359,12389642,12390480],"created_at":"2016-08-30T01:30:27Z","created_at_i":1472520627,"num_comments":312,"objectID":"12386777","points":293,"story_id":12386777,"title":"California bans ITT tech from accepting new students","updated_at":"2026-09-18T04:54:08Z","url":"http://www.latimes.com/business/la-fi-itt-tech-aid-20160826-snap-story.html?utm_source=fark&utm_medium=website&utm_content=link&ICID=ref_fark"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"akshaysg"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"Hi HN! We\u2019re Akshay and Jake. We put together a tool called Haystack to make pull requests straightforward to read.
What Haystack does:
-- Builds a clear narrative. Changes in Haystack aren\u2019t just arranged as unordered diffs. Instead, they unfold in a logical order, each paired with an explanation in plain, precise language
-- Focuses attention where it counts. Routine plumbing and refactors are put into skimmable sections so you can spend your time on design and correctness
-- Provides full cross-file context. Every new or changed function/variable is traced across the codebase, showing how it\u2019s used beyond the immediate diff
Here\u2019s a quick demo: https://youtu.be/w5Lq5wBUS-I
If you\u2019d like to give it a spin, head over to haystackeditor.com/review! We set up some demo PRs that you should be able to understand and review even if you\u2019ve never seen the repos before!
We used to work at big companies, where reviewing non-trivial pull requests felt like reading a book with its pages out of order. We would jump and scroll between files, trying to piece together the author\u2019s intent before we could even start reviewing. And, as authors, we would spend time to restructure our own commits just to make them readable.\nAI has made this even trickier. Today it\u2019s not uncommon for a pull request to contain code the author doesn\u2019t fully understand themselves!
So, we built Haystack to help reviewers spend less time untangling code and more time giving meaningful feedback. We would love to hear about whether it gets the job done for you!
How we got here:
Haystack began as (yet another) VS Code fork where we experimented with visualizing code changes on a canvas. At first, it was a neat way to show how pieces of code worked together. But customers started laying out their entire codebase just to make sense of it. That\u2019s when we realized the deeper problem: understanding a codebase is hard, and engineers need better ways to quickly understand unfamiliar code.
As we kept building, another insight emerged: with AI woven into workflows, engineers don\u2019t always need to master every corner of a codebase to ship features. But in code review, deep and continuous context still matters, especially to separate what\u2019s important to review from plumbing and follow-on changes.
So we pivoted. We took what we\u2019d learned and worked closely with engineers to refine the idea. We started with simple code analysis (using language servers, tree-sitter, etc.) to show how changes relate. Then we added AI to explain and organize those changes and to trace how data moves through a pull request. Finally, we fused the two by empowering AI agents to use static analyses. Step by step, that became the Haystack we\u2019re showing today.
We\u2019d love to hear your thoughts, feedback, or suggestions!"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Haystack \u2013 Review pull requests like you wrote them yourself"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://haystackeditor.com"}},"_tags":["story","author_akshaysg","story_45201703","show_hn"],"author":"akshaysg","children":[45202592,45202668,45202692,45202881,45203343,45203664,45203809,45204406,45204414,45204477,45204868,45206297,45206564,45206861,45207872,45209952,45209978,45210506,45212937],"created_at":"2025-09-10T18:21:12Z","created_at_i":1757528472,"num_comments":59,"objectID":"45201703","points":88,"story_id":45201703,"story_text":"Hi HN! We\u2019re Akshay and Jake. We put together a tool called Haystack to make pull requests straightforward to read.
What Haystack does:
-- Builds a clear narrative. Changes in Haystack aren\u2019t just arranged as unordered diffs. Instead, they unfold in a logical order, each paired with an explanation in plain, precise language
-- Focuses attention where it counts. Routine plumbing and refactors are put into skimmable sections so you can spend your time on design and correctness
-- Provides full cross-file context. Every new or changed function/variable is traced across the codebase, showing how it\u2019s used beyond the immediate diff
Here\u2019s a quick demo: https://youtu.be/w5Lq5wBUS-I
If you\u2019d like to give it a spin, head over to haystackeditor.com/review! We set up some demo PRs that you should be able to understand and review even if you\u2019ve never seen the repos before!
We used to work at big companies, where reviewing non-trivial pull requests felt like reading a book with its pages out of order. We would jump and scroll between files, trying to piece together the author\u2019s intent before we could even start reviewing. And, as authors, we would spend time to restructure our own commits just to make them readable.\nAI has made this even trickier. Today it\u2019s not uncommon for a pull request to contain code the author doesn\u2019t fully understand themselves!
So, we built Haystack to help reviewers spend less time untangling code and more time giving meaningful feedback. We would love to hear about whether it gets the job done for you!
How we got here:
Haystack began as (yet another) VS Code fork where we experimented with visualizing code changes on a canvas. At first, it was a neat way to show how pieces of code worked together. But customers started laying out their entire codebase just to make sense of it. That\u2019s when we realized the deeper problem: understanding a codebase is hard, and engineers need better ways to quickly understand unfamiliar code.
As we kept building, another insight emerged: with AI woven into workflows, engineers don\u2019t always need to master every corner of a codebase to ship features. But in code review, deep and continuous context still matters, especially to separate what\u2019s important to review from plumbing and follow-on changes.
So we pivoted. We took what we\u2019d learned and worked closely with engineers to refine the idea. We started with simple code analysis (using language servers, tree-sitter, etc.) to show how changes relate. Then we added AI to explain and organize those changes and to trace how data moves through a pull request. Finally, we fused the two by empowering AI agents to use static analyses. Step by step, that became the Haystack we\u2019re showing today.
We\u2019d love to hear your thoughts, feedback, or suggestions!","title":"Show HN: Haystack \u2013 Review pull requests like you wrote them yourself","updated_at":"2026-05-18T23:28:41Z","url":"https://haystackeditor.com"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"tryggs"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"This week, 1,763 new AI apps emerged, yet GateAI.space can replace all of them"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://www.gateai.space/"}},"_tags":["story","author_tryggs","story_36062927"],"author":"tryggs","children":[36062928],"created_at":"2023-05-24T19:34:17Z","created_at_i":1684956857,"num_comments":1,"objectID":"36062927","points":1,"story_id":36062927,"title":"This week, 1,763 new AI apps emerged, yet GateAI.space can replace all of them","updated_at":"2024-09-20T14:13:32Z","url":"https://www.gateai.space/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"jotbot"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"Anyone have any experience with these container orchestration platforms?"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"Ask HN: Thoughts on HPE Ezmeral/Private Cloud AI"}},"_tags":["story","author_jotbot","story_44499851","ask_hn"],"author":"jotbot","created_at":"2025-07-08T13:38:18Z","created_at_i":1751981898,"num_comments":0,"objectID":"44499851","points":1,"story_id":44499851,"story_text":"Anyone have any experience with these container orchestration platforms?","title":"Ask HN: Thoughts on HPE Ezmeral/Private Cloud AI","updated_at":"2025-07-08T13:43:14Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"natematthew"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"The Support Agent Who Never Burns Out\nHuman-like AI teammates are quietly solving the problem that broke customer service.\nMeet Sarah.\nSarah is your best customer support agent. She knows your product cold, handles difficult customers with patience, and resolves tickets faster than anyone on the team. She also called in sick Monday, runs on fumes by Thursday, and quit last April right after you finished training her replacement.\nThis is the story nobody tells about customer service. The quiet structural collapse underneath the chatbot failures and the CSAT scores.\nThe Math Has Never Worked\nCall center turnover runs 30 to 45% annually, more than double any other industry. Replacing one agent costs $10,000 to $20,000. Across a 100-person team, that's over $1M in churn before you've served anyone well.\n \u2022 87% of contact center workers report high stress on the job\n \u2022 59% are at active risk of burnout\n \u2022 77% say workload has increased compared to the previous year
US businesses risk losing $856 billion annually to poor customer service, not because companies don't care, but because the system is structurally broken.\nWhat Happens at 2 AM\nYour customers don't keep business hours:\n \u2022 A prospect in a different time zone has a pre-sale question\n \u2022 A customer spots a billing error on Sunday evening\n \u2022 A new user is stuck in onboarding at midnight, about to close the tab
78% of customers have abandoned a purchase due to poor service. The problem isn't AI. It's the wrong kind \u2014 built for deflection, not resolution.\nWhat's Actually Changed\nA new category has emerged: conversational video AI teammates. Not animated chatbots with a moving mouth. Digital humans capable of:\n \u2022 Holding full conversations with dynamic facial expressions and natural eye contact\n \u2022 Responding with emotional tone that adapts to the customer in real time\n \u2022 Pulling contextually aware answers from your actual company data
Leading platforms now deliver under 1 second end-to-end latency, under 80ms speech-to-avatar response, and unlimited concurrent sessions.\nSarah and Maya: Side by Side\nIt's 11:47 PM. Maya notices a duplicate charge and visits your support page.\nOld model:\n \u2022 Chatbot asks clarifying questions, fails to pull account data\n \u2022 Offers a help article, tells her to call back during business hours\n \u2022 Maya disputes the charge through her bank. You lose the relationship.
AI teammate model:\n \u2022 Digital human appears immediately, greets Maya by name\n \u2022 Pulls account info in real time, confirms the duplicate charge\n \u2022 Issues the refund and sends confirmation, all within the same session
Total time: under four minutes. First-contact resolution. No ticket. No human agent needed at midnight. AI teammates don't replace Sarah. They protect her from the 80% of tickets that were burning her out.\nWhat the Numbers Say\n \u2022 AI-assisted support improves issues resolved per hour by 14%\n \u2022 Average return of $3.50 for every $1 invested, top performers hitting 8x\n \u2022 Gartner projects $80 billion in global call center labor cost reductions\n \u2022 AI customer service market growing from $12B (2024) to $47.82B (2030)
64% of consumers say they're more likely to trust AI customer service if it exhibits human-like traits. That's the trust gap text chatbots have never closed.\nPlatforms like Trugen AI are building exactly this, where AI teammates see, hear, and respond with genuine presence.\nSarah Gets to Stay\nReal-time AI teammates absorb the volume:\n \u2022 Billing disputes, order status checks, password resets\n \u2022 Product FAQs and after-hours inquiries
Sarah handles what genuinely needs her:\n \u2022 Escalations, complex problems, high-value accounts\n \u2022 Customers in real distress who need a human
Her job improves. Her burnout risk drops. She stays.\nIf you're exploring what this looks like for your team, Trugen AI is worth a look as a starting point.\nTags: Customer Service, AI Agents, Digital Humans, Conversational AI"},"title":{"matchLevel":"none","matchedWords":[],"value":"The Support Agent Who Never Burns Out"}},"_tags":["story","author_natematthew","story_47227826","ask_hn"],"author":"natematthew","children":[47234359,47255197],"created_at":"2026-03-03T03:56:20Z","created_at_i":1772510180,"num_comments":2,"objectID":"47227826","points":2,"story_id":47227826,"story_text":"The Support Agent Who Never Burns Out\nHuman-like AI teammates are quietly solving the problem that broke customer service.\nMeet Sarah.\nSarah is your best customer support agent. She knows your product cold, handles difficult customers with patience, and resolves tickets faster than anyone on the team. She also called in sick Monday, runs on fumes by Thursday, and quit last April right after you finished training her replacement.\nThis is the story nobody tells about customer service. The quiet structural collapse underneath the chatbot failures and the CSAT scores.\nThe Math Has Never Worked\nCall center turnover runs 30 to 45% annually, more than double any other industry. Replacing one agent costs $10,000 to $20,000. Across a 100-person team, that's over $1M in churn before you've served anyone well.\n \u2022 87% of contact center workers report high stress on the job\n \u2022 59% are at active risk of burnout\n \u2022 77% say workload has increased compared to the previous year
US businesses risk losing $856 billion annually to poor customer service, not because companies don't care, but because the system is structurally broken.\nWhat Happens at 2 AM\nYour customers don't keep business hours:\n \u2022 A prospect in a different time zone has a pre-sale question\n \u2022 A customer spots a billing error on Sunday evening\n \u2022 A new user is stuck in onboarding at midnight, about to close the tab
78% of customers have abandoned a purchase due to poor service. The problem isn't AI. It's the wrong kind \u2014 built for deflection, not resolution.\nWhat's Actually Changed\nA new category has emerged: conversational video AI teammates. Not animated chatbots with a moving mouth. Digital humans capable of:\n \u2022 Holding full conversations with dynamic facial expressions and natural eye contact\n \u2022 Responding with emotional tone that adapts to the customer in real time\n \u2022 Pulling contextually aware answers from your actual company data
Leading platforms now deliver under 1 second end-to-end latency, under 80ms speech-to-avatar response, and unlimited concurrent sessions.\nSarah and Maya: Side by Side\nIt's 11:47 PM. Maya notices a duplicate charge and visits your support page.\nOld model:\n \u2022 Chatbot asks clarifying questions, fails to pull account data\n \u2022 Offers a help article, tells her to call back during business hours\n \u2022 Maya disputes the charge through her bank. You lose the relationship.
AI teammate model:\n \u2022 Digital human appears immediately, greets Maya by name\n \u2022 Pulls account info in real time, confirms the duplicate charge\n \u2022 Issues the refund and sends confirmation, all within the same session
Total time: under four minutes. First-contact resolution. No ticket. No human agent needed at midnight. AI teammates don't replace Sarah. They protect her from the 80% of tickets that were burning her out.\nWhat the Numbers Say\n \u2022 AI-assisted support improves issues resolved per hour by 14%\n \u2022 Average return of $3.50 for every $1 invested, top performers hitting 8x\n \u2022 Gartner projects $80 billion in global call center labor cost reductions\n \u2022 AI customer service market growing from $12B (2024) to $47.82B (2030)
64% of consumers say they're more likely to trust AI customer service if it exhibits human-like traits. That's the trust gap text chatbots have never closed.\nPlatforms like Trugen AI are building exactly this, where AI teammates see, hear, and respond with genuine presence.\nSarah Gets to Stay\nReal-time AI teammates absorb the volume:\n \u2022 Billing disputes, order status checks, password resets\n \u2022 Product FAQs and after-hours inquiries
Sarah handles what genuinely needs her:\n \u2022 Escalations, complex problems, high-value accounts\n \u2022 Customers in real distress who need a human
Her job improves. Her burnout risk drops. She stays.\nIf you're exploring what this looks like for your team, Trugen AI is worth a look as a starting point.\nTags: Customer Service, AI Agents, Digital Humans, Conversational AI","title":"The Support Agent Who Never Burns Out","updated_at":"2026-03-09T21:57:11Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"rickbeaver"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"<p><strong>I. The Rise of AI Writing Tools</strong></p>\n<p>AI writing tools, such as the revolutionary Essay Generator from <a href="https://www.perfectessaywriter.ai/" rel="DoFollow">perfectessaywriter.ai</a>, have emerged as game-changers in the writing landscape. These tools leverage advanced algorithms and natural language processing to assist writers in various aspects of the writing process, from generating ideas and creating outlines to crafting well-structured and coherent essays.</p>\n<p style="text-align: center;"><img src="https://custom-images.strikinglycdn.com/res/hrscywv4p/image/upload/c_limit,fl_lossy,h_9000,w_1200,f_auto,q_auto/9519882/61226_858012.png" alt="" /></p>\n<p><strong>1. Press Releases and News Mentions of PerfectEssayWriterAI</strong></p>\n<p>PerfectEssayWriterAI has garnered significant attention in the press, with notable mentions in press releases and news articles. These include publications like <a href="https://www.startupguys.net/best-ai-essay-generator-tools/" target="_new">StartupGuys</a>, <a href="https://markets.businessinsider.com/news/stocks/perfectessaywriter-ai-introduces-the-easy-ai-essay-writing-tool-of-2023-1032351992" target="_new">Markets Insider</a>, <a href="https://www.ilounge.com/articles/best-5-ai-essay-writing-tools-online-review-2023-24" target="_new">iLounge</a>, and <a href="https://goodmenproject.com/technology/top-5-ai-essay-writing-tools-of-2023/" target="_new">Good Men Project</a>, showcasing the recognition and impact of PerfectEssayWriterAI's AI writing tool in the industry. </p>\n<h3>Crafting a Strong Foundation</h3>\n<p>Selecting appropriate <a href="https://www.perfectessaywriter.ai/how-to-write-an-essay/essay-topics" rel="DoFollow">Essay Topics</a> is the first step in creating a compelling and engaging piece of writing. Once you have a topic in mind, it is crucial to establish a well-organized essay structure to effectively convey your ideas. An <a href="https://www.perfectessaywriter.ai/how-to-write-an-essay/essay-structure" rel="DoFollow">Essay Structure</a> typically consists of an introduction, body paragraphs, and a conclusion. Furthermore, creating an <a href="https://www.perfectessaywriter.ai/how-to-write-an-essay/essay-outline" rel="DoFollow">Essay Outline</a> can help you plan and organize your thoughts before diving into the writing process. Outlining provides a roadmap for your essay, ensuring that your arguments flow logically and coherently from one paragraph to another.</p>\n<p>In this dynamic landscape, PerfectEssayWriterAI's AI writing tool and WriteMyessayHelp's custom essay writing services stand out as exemplary providers. PerfectEssayWriterAI's <a href="https://www.perfectessaywriter.ai/" rel="DoFollow">Essay generator</a> empowers writers with efficient content generation, while WriteMyessayHelp's expertise ensures personalized and high-quality essays.</p>\n<h2><strong>I</strong><strong>II</strong><strong>. The Future of custom writing services</strong></h2>\n<p>As AI technology continues to advance, custom writing services will continue to adapt and evolve. The integration of AI <a href="https://medium.com/ai-essay-writer">AI essay writer</a> tools and custom writing services will become even more seamless, resulting in an enhanced writing experience for writers of all backgrounds.</p>"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Evolution of Custom Writing Services Adapting to AI Writing Tools and Technology"}},"_tags":["story","author_rickbeaver","story_36664330","ask_hn"],"author":"rickbeaver","created_at":"2023-07-10T10:29:11Z","created_at_i":1688984951,"num_comments":0,"objectID":"36664330","points":1,"story_id":36664330,"story_text":"<p><strong>I. The Rise of AI Writing Tools</strong></p>\n<p>AI writing tools, such as the revolutionary Essay Generator from <a href="https://www.perfectessaywriter.ai/" rel="DoFollow">perfectessaywriter.ai</a>, have emerged as game-changers in the writing landscape. These tools leverage advanced algorithms and natural language processing to assist writers in various aspects of the writing process, from generating ideas and creating outlines to crafting well-structured and coherent essays.</p>\n<p style="text-align: center;"><img src="https://custom-images.strikinglycdn.com/res/hrscywv4p/image/upload/c_limit,fl_lossy,h_9000,w_1200,f_auto,q_auto/9519882/61226_858012.png" alt="" /></p>\n<p><strong>1. Press Releases and News Mentions of PerfectEssayWriterAI</strong></p>\n<p>PerfectEssayWriterAI has garnered significant attention in the press, with notable mentions in press releases and news articles. These include publications like <a href="https://www.startupguys.net/best-ai-essay-generator-tools/" target="_new">StartupGuys</a>, <a href="https://markets.businessinsider.com/news/stocks/perfectessaywriter-ai-introduces-the-easy-ai-essay-writing-tool-of-2023-1032351992" target="_new">Markets Insider</a>, <a href="https://www.ilounge.com/articles/best-5-ai-essay-writing-tools-online-review-2023-24" target="_new">iLounge</a>, and <a href="https://goodmenproject.com/technology/top-5-ai-essay-writing-tools-of-2023/" target="_new">Good Men Project</a>, showcasing the recognition and impact of PerfectEssayWriterAI's AI writing tool in the industry. </p>\n<h3>Crafting a Strong Foundation</h3>\n<p>Selecting appropriate <a href="https://www.perfectessaywriter.ai/how-to-write-an-essay/essay-topics" rel="DoFollow">Essay Topics</a> is the first step in creating a compelling and engaging piece of writing. Once you have a topic in mind, it is crucial to establish a well-organized essay structure to effectively convey your ideas. An <a href="https://www.perfectessaywriter.ai/how-to-write-an-essay/essay-structure" rel="DoFollow">Essay Structure</a> typically consists of an introduction, body paragraphs, and a conclusion. Furthermore, creating an <a href="https://www.perfectessaywriter.ai/how-to-write-an-essay/essay-outline" rel="DoFollow">Essay Outline</a> can help you plan and organize your thoughts before diving into the writing process. Outlining provides a roadmap for your essay, ensuring that your arguments flow logically and coherently from one paragraph to another.</p>\n<p>In this dynamic landscape, PerfectEssayWriterAI's AI writing tool and WriteMyessayHelp's custom essay writing services stand out as exemplary providers. PerfectEssayWriterAI's <a href="https://www.perfectessaywriter.ai/" rel="DoFollow">Essay generator</a> empowers writers with efficient content generation, while WriteMyessayHelp's expertise ensures personalized and high-quality essays.</p>\n<h2><strong>I</strong><strong>II</strong><strong>. The Future of custom writing services</strong></h2>\n<p>As AI technology continues to advance, custom writing services will continue to adapt and evolve. The integration of AI <a href="https://medium.com/ai-essay-writer">AI essay writer</a> tools and custom writing services will become even more seamless, resulting in an enhanced writing experience for writers of all backgrounds.</p>","title":"Evolution of Custom Writing Services Adapting to AI Writing Tools and Technology","updated_at":"2024-09-20T14:37:19Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"alexrustic"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"Hi HN ! I'm Alex, a tech enthusiast. I am excited to show you a new iteration of Braq (https://github.com/pyrustic/braq), a customizable data format for config files, AI prompts, and more.
With Braq, I'm experimenting to what extent we can interweave human-readable structured data with prose within a single document. The original ideas were first implemented in Jesth [1] which spawned two spin-offs, Paradict [2] and Braq.
So far, I have discovered the usefulness of Braq for creating flexible config files and I am witnessing a potentially interesting proposal [3] that has emerged naturally to better structure AI prompts [4].
The eponymous Python package for tinkering with Braq is available on PyPI (see the Installation section in the README). Feel free to play with examples and code snippets in the README, or clone the repository to explore the source code and the tests package. The functions and classes have been documented with well-structured docstrings (whose format is also based on Braq) which will feed a documentation generator that I am developing.
Let me know what you think of the project.
[1] https://news.ycombinator.com/item?id=35991018 (Show HN: Jesth \u2013 Next-level human-readable data serialization format)
[2] https://news.ycombinator.com/item?id=38684724 (Show HN: Paradict \u2013 Streamable multi-format serialization with schema)
[3] https://github.com/pyrustic/braq?tab=readme-ov-file#ai-promp...
[4] https://news.ycombinator.com/item?id=34988748 (OpenAI's ChatML)"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Show HN: Braq \u2013 Customizable data format for config files, AI prompts, and more"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/pyrustic/braq"}},"_tags":["story","author_alexrustic","story_39698735","show_hn"],"author":"alexrustic","created_at":"2024-03-13T23:19:34Z","created_at_i":1710371974,"num_comments":0,"objectID":"39698735","points":3,"story_id":39698735,"story_text":"Hi HN ! I'm Alex, a tech enthusiast. I am excited to show you a new iteration of Braq (https://github.com/pyrustic/braq), a customizable data format for config files, AI prompts, and more.
With Braq, I'm experimenting to what extent we can interweave human-readable structured data with prose within a single document. The original ideas were first implemented in Jesth [1] which spawned two spin-offs, Paradict [2] and Braq.
So far, I have discovered the usefulness of Braq for creating flexible config files and I am witnessing a potentially interesting proposal [3] that has emerged naturally to better structure AI prompts [4].
The eponymous Python package for tinkering with Braq is available on PyPI (see the Installation section in the README). Feel free to play with examples and code snippets in the README, or clone the repository to explore the source code and the tests package. The functions and classes have been documented with well-structured docstrings (whose format is also based on Braq) which will feed a documentation generator that I am developing.
Let me know what you think of the project.
[1] https://news.ycombinator.com/item?id=35991018 (Show HN: Jesth \u2013 Next-level human-readable data serialization format)
[2] https://news.ycombinator.com/item?id=38684724 (Show HN: Paradict \u2013 Streamable multi-format serialization with schema)
[3] https://github.com/pyrustic/braq?tab=readme-ov-file#ai-promp...
[4] https://news.ycombinator.com/item?id=34988748 (OpenAI's ChatML)","title":"Show HN: Braq \u2013 Customizable data format for config files, AI prompts, and more","updated_at":"2024-09-20T16:42:31Z","url":"https://github.com/pyrustic/braq"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"pyeri"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"Asimovian AI is the ideal AI that should have emerged in an ideal universe \u2014 the AI intended to replace the grueling pains and labors of the masses, not the one striving to become a businessman's utopia of intellectual worker replacement.
Intention is the most important aspect of any implementation, and we are seeing the results of current AI implementation right in front of us: workers getting sacked with each passing day, humanity competing with itself day in and day out over who impresses their superiors more on these token metrics, emerging glorious narratives of how AI will be 'The Future', the recurring advice of 'Use AI or perish in the tech market'. Now who really gains from these events and who loses? I wonder if anyone ever gives serious thought to this broader question or just keeps being a cog in the corporate wheel like everyone else.
It's high time we pushed the "Pause AI" button right now and take a breather and reflect a bit on what exactly is going on here. And no, no big catastrophe is going to happen if we do that. China isn't going to get ahead in the race - and even if it did, how does that justify everything else that's happening here?
I really hope there is someone out there with enough clout and influence who can push this pause button - or at least persuade others to do so. That would be the best thing to happen to humanity at this point. By doing so, we might prevent a massive societal collapse and there is really no downside to this."},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Thoughts on Asimovian AI Beyond LLMs and Creative Machines"}},"_tags":["story","author_pyeri","story_48671659","ask_hn"],"author":"pyeri","children":[48672559],"created_at":"2026-06-25T10:55:32Z","created_at_i":1782384932,"num_comments":0,"objectID":"48671659","points":2,"story_id":48671659,"story_text":"Asimovian AI is the ideal AI that should have emerged in an ideal universe \u2014 the AI intended to replace the grueling pains and labors of the masses, not the one striving to become a businessman's utopia of intellectual worker replacement.
Intention is the most important aspect of any implementation, and we are seeing the results of current AI implementation right in front of us: workers getting sacked with each passing day, humanity competing with itself day in and day out over who impresses their superiors more on these token metrics, emerging glorious narratives of how AI will be 'The Future', the recurring advice of 'Use AI or perish in the tech market'. Now who really gains from these events and who loses? I wonder if anyone ever gives serious thought to this broader question or just keeps being a cog in the corporate wheel like everyone else.
It's high time we pushed the "Pause AI" button right now and take a breather and reflect a bit on what exactly is going on here. And no, no big catastrophe is going to happen if we do that. China isn't going to get ahead in the race - and even if it did, how does that justify everything else that's happening here?
I really hope there is someone out there with enough clout and influence who can push this pause button - or at least persuade others to do so. That would be the best thing to happen to humanity at this point. By doing so, we might prevent a massive societal collapse and there is really no downside to this.","title":"Thoughts on Asimovian AI Beyond LLMs and Creative Machines","updated_at":"2026-06-25T15:15:53Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"bko"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"Pangram Has Emerged as the Gold Standard of AI Detection. Should You Trust It?"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"https://www.wired.com/story/pangram-has-emerged-as-the-gold-standard-of-ai-detection/"}},"_tags":["story","author_bko","story_49558711"],"author":"bko","children":[49558955,49574556],"created_at":"2026-09-03T23:51:21Z","created_at_i":1788479481,"num_comments":3,"objectID":"49558711","points":5,"story_id":49558711,"title":"Pangram Has Emerged as the Gold Standard of AI Detection. Should You Trust It?","updated_at":"2026-09-06T04:07:39Z","url":"https://www.wired.com/story/pangram-has-emerged-as-the-gold-standard-of-ai-detection/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"Brajeshwar"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"Pangram Has Emerged as the Gold Standard of AI Detection"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"https://www.wired.com/story/pangram-has-emerged-as-the-gold-standard-of-ai-detection/"}},"_tags":["story","author_Brajeshwar","story_49538687"],"author":"Brajeshwar","children":[49539225],"created_at":"2026-09-02T16:28:46Z","created_at_i":1788366526,"num_comments":0,"objectID":"49538687","points":5,"story_id":49538687,"title":"Pangram Has Emerged as the Gold Standard of AI Detection","updated_at":"2026-09-03T06:32:30Z","url":"https://www.wired.com/story/pangram-has-emerged-as-the-gold-standard-of-ai-detection/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"seeksky"},"story_text":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Realizing a one-person company and developing multi-agent collaborative applications for multiple projects in parallel, I think it conceptually surpasses codex and claude code.\nIt upgrades your existing CLI tools into a complete AI collaboration hub. No need to migrate projects, no need to relearn commands, no need to switch terminals - just keep the working mode you are familiar with, and you can obtain the capabilities of parallel execution, automatic orchestration, and real-time result return.\nThe capabilities under planning include:\nReconstruct OpenClaw into a true "command layer" - a central AI coordination core that can automatically create agents, assign roles, generate collaboration channels, and dynamically form structured AI teams based on task complexity. Remote control via mobile phone. Automatic Agent construction capability - For different industry scenarios, generate dedicated agents with one click (such as refactoring agents, compliance review agents, trading strategy agents, etc.).\nAccess the permanent memory layer."},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"Show HN: The most awesome AI programming application desktop has emerged"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/golutra/golutra"}},"_tags":["story","author_seeksky","story_47196575","show_hn"],"author":"seeksky","children":[47196637,47196660,47196683,47263741],"created_at":"2026-02-28T15:38:45Z","created_at_i":1772293125,"num_comments":1,"objectID":"47196575","points":1,"story_id":47196575,"story_text":"Realizing a one-person company and developing multi-agent collaborative applications for multiple projects in parallel, I think it conceptually surpasses codex and claude code.\nIt upgrades your existing CLI tools into a complete AI collaboration hub. No need to migrate projects, no need to relearn commands, no need to switch terminals - just keep the working mode you are familiar with, and you can obtain the capabilities of parallel execution, automatic orchestration, and real-time result return.\nThe capabilities under planning include:\nReconstruct OpenClaw into a true "command layer" - a central AI coordination core that can automatically create agents, assign roles, generate collaboration channels, and dynamically form structured AI teams based on task complexity. Remote control via mobile phone. Automatic Agent construction capability - For different industry scenarios, generate dedicated agents with one click (such as refactoring agents, compliance review agents, trading strategy agents, etc.).\nAccess the permanent memory layer.","title":"Show HN: The most awesome AI programming application desktop has emerged","updated_at":"2026-03-08T20:27:38Z","url":"https://github.com/golutra/golutra"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"slmslm"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"I've been building Gitmore for a while now. The problem I was solving: every week \nI'd spend 1-2 hours scrolling through commits trying to piece together "what did \nwe ship?" for stakeholders who don't want to read "feat: impl oauth2 w/ refresh".
The insight: all the information is already in Git. Commits, PRs, authors, timestamps. \nIt's just unreadable for anyone who isn't a developer.
So I built an AI layer on top that:
1. Connects to GitHub/GitLab/Bitbucket via OAuth (reads only metadata, never code)\n2. Captures commits and PRs in real-time via webhooks\n3. Uses Claude to transform raw Git activity into human-readable summaries\n4. Delivers automatically via email or Slack on whatever schedule you want
Example transformation:
Before: "fix: rm deprecated api calls, refactor: extract auth middleware"\n After: "Fixed API timeouts by updating deprecated endpoints. Improved \n security by centralizing authentication logic."\nSome things I learned building this:
- Commit messages follow strict patterns (73% start with feat:/fix:/refactor:) \n but contain almost no "why" context\n- Teams spend ~78 hours/year/person writing status reports manually\n- The question "what did we ship this week?" accounts for 62% of queries \n about repositories
Other features that emerged from the same data layer:\n- AI agents you can chat with ("What did Sarah work on last week?")\n- Developer leaderboards with contribution scoring\n- Auto-generated public changelogs
Free tier: 1 repo, 1 automation. Pro ($15/mo): 5 repos. \nEnterprise ($49/mo): 20 repos + custom branded reports.
https://gitmore.io
Happy to answer technical questions about the architecture, AI prompting strategy, \nor webhook handling. Also curious - how do other teams handle the "what did we ship" \nproblem?"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Git history knows more than your standup. We built an AI to query it"}},"_tags":["story","author_slmslm","story_46263486","ask_hn"],"author":"slmslm","children":[46264359],"created_at":"2025-12-14T14:57:53Z","created_at_i":1765724273,"num_comments":1,"objectID":"46263486","points":3,"story_id":46263486,"story_text":"I've been building Gitmore for a while now. The problem I was solving: every week \nI'd spend 1-2 hours scrolling through commits trying to piece together "what did \nwe ship?" for stakeholders who don't want to read "feat: impl oauth2 w/ refresh".
The insight: all the information is already in Git. Commits, PRs, authors, timestamps. \nIt's just unreadable for anyone who isn't a developer.
So I built an AI layer on top that:
1. Connects to GitHub/GitLab/Bitbucket via OAuth (reads only metadata, never code)\n2. Captures commits and PRs in real-time via webhooks\n3. Uses Claude to transform raw Git activity into human-readable summaries\n4. Delivers automatically via email or Slack on whatever schedule you want
Example transformation:
Before: "fix: rm deprecated api calls, refactor: extract auth middleware"\n After: "Fixed API timeouts by updating deprecated endpoints. Improved \n security by centralizing authentication logic."\nSome things I learned building this:
- Commit messages follow strict patterns (73% start with feat:/fix:/refactor:) \n but contain almost no "why" context\n- Teams spend ~78 hours/year/person writing status reports manually\n- The question "what did we ship this week?" accounts for 62% of queries \n about repositories
Other features that emerged from the same data layer:\n- AI agents you can chat with ("What did Sarah work on last week?")\n- Developer leaderboards with contribution scoring\n- Auto-generated public changelogs
Free tier: 1 repo, 1 automation. Pro ($15/mo): 5 repos. \nEnterprise ($49/mo): 20 repos + custom branded reports.
https://gitmore.io
Happy to answer technical questions about the architecture, AI prompting strategy, \nor webhook handling. Also curious - how do other teams handle the "what did we ship" \nproblem?","title":"Git history knows more than your standup. We built an AI to query it","updated_at":"2026-03-05T23:11:42Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"vivekanandsingh"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"Over the last year, we examined how AI systems are actually used after deployment across regulated and enterprise environments.
A consistent pattern emerged: organizations can increasingly prove what AI systems did, but cannot reliably prove who owned decisions at runtime, or whether meaningful human judgment was exercised.
In practice, \u201chuman-in-the-loop\u201d often degrades into habitual approval. Review becomes ceremonial, and AI systems transition into de facto automation without explicit intent or governance.
This failure rarely originates in model error. It arises from gradual behavioral drift and organizational dynamics that existing AI governance frameworks are not designed to observe or manage.
The result is an audit and accountability gap: when incidents occur, decision rationale cannot be reconstructed without re-running systems or relying on interviews.
We wrote a short research paper documenting these failure modes and the structural gap between governing AI systems and governing AI usage.
Genuinely interested in critique from people who have seen similar dynamics in production systems, audits, or post-incident reviews."},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Operational AI Governance and the Runtime Decision Ownership Gap"}},"_tags":["story","author_vivekanandsingh","story_46698071","ask_hn"],"author":"vivekanandsingh","created_at":"2026-01-20T21:37:38Z","created_at_i":1768945058,"num_comments":0,"objectID":"46698071","points":1,"story_id":46698071,"story_text":"Over the last year, we examined how AI systems are actually used after deployment across regulated and enterprise environments.
A consistent pattern emerged: organizations can increasingly prove what AI systems did, but cannot reliably prove who owned decisions at runtime, or whether meaningful human judgment was exercised.
In practice, \u201chuman-in-the-loop\u201d often degrades into habitual approval. Review becomes ceremonial, and AI systems transition into de facto automation without explicit intent or governance.
This failure rarely originates in model error. It arises from gradual behavioral drift and organizational dynamics that existing AI governance frameworks are not designed to observe or manage.
The result is an audit and accountability gap: when incidents occur, decision rationale cannot be reconstructed without re-running systems or relying on interviews.
We wrote a short research paper documenting these failure modes and the structural gap between governing AI systems and governing AI usage.
Genuinely interested in critique from people who have seen similar dynamics in production systems, audits, or post-incident reviews.","title":"Operational AI Governance and the Runtime Decision Ownership Gap","updated_at":"2026-03-05T23:29:26Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"hhs"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"\u2018Explainability\u2019 has emerged to change the \u2018black box\u2019 of AI (2020)"},"url":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"https://www.noemamag.com/ai-must-explain-itself/"}},"_tags":["story","author_hhs","story_34219602"],"author":"hhs","created_at":"2023-01-02T16:18:15Z","created_at_i":1672676295,"num_comments":0,"objectID":"34219602","points":2,"story_id":34219602,"title":"\u2018Explainability\u2019 has emerged to change the \u2018black box\u2019 of AI (2020)","updated_at":"2024-09-20T12:58:15Z","url":"https://www.noemamag.com/ai-must-explain-itself/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"khalilsautchuk"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"In an age where seeing is no longer believing, deepfake technology has emerged as both a marvel of modern AI and a potential weapon of misinformation. These AI-generated synthetic media have the power to place anyone, anywhere, doing almost anything. This capability raises profound ethical questions about truth in the digital era.
The Magic Behind Deepfakes\nDeepfakes utilize advanced neural network architectures like Generative Adversarial Networks (GANs) to fabricate videos, images, or audio files that seem incredibly real. The initial purpose of this technology was benign, primarily for art, entertainment, and research. For instance, filmmakers can resurrect actors for crucial roles or simulate realistic environments without the constraints of the physical world.
The Darker Side of the Technology\nHowever, with great power comes great responsibility. In the wrong hands, deepfakes can:
Spread disinformation and fake news at an unprecedented scale.\nCompromise personal privacy and be used for blackmail.\nManipulate stock prices by generating fake announcements from influential figures.\nUndermine trust in visual evidence, a cornerstone in journalism and justice.\nThe Ethical Conundrum\nThe central ethical question is: Should we curb the development and dissemination of a potentially harmful technology, or is it our responsibility to develop countermeasures and public awareness?
Countermeasures and the Way Forward\nSeveral tech giants and startups are working on deepfake detection tools. However, it's a continuous arms race, with deepfake creators often outpacing detection methods. Education, media literacy, and public awareness campaigns will play a crucial role in this battle.
Moreover, a proactive approach involving policy-making and regulations may be essential. Implementing digital watermarking techniques and maintaining the provenance of digital content can act as significant deterrents.
Concluding Thoughts\nDeepfake technology exemplifies the double-edged sword of AI. While its creative potential is boundless, so are its avenues for misuse. It beckons the tech community, policymakers, and the public at large to engage in a deeper dialogue on the ethics of AI-driven creations.
The way forward may not be clear-cut, but one thing is certain: our commitment to preserving truth in the digital age will define the legacy of our technological advancements."},"title":{"matchLevel":"none","matchedWords":[],"value":"Deepfakes: The Marvel of Creation and the Menace of Deception"}},"_tags":["story","author_khalilsautchuk","story_37785979","ask_hn"],"author":"khalilsautchuk","children":[37786196],"created_at":"2023-10-06T00:37:16Z","created_at_i":1696552636,"num_comments":2,"objectID":"37785979","points":1,"story_id":37785979,"story_text":"In an age where seeing is no longer believing, deepfake technology has emerged as both a marvel of modern AI and a potential weapon of misinformation. These AI-generated synthetic media have the power to place anyone, anywhere, doing almost anything. This capability raises profound ethical questions about truth in the digital era.
The Magic Behind Deepfakes\nDeepfakes utilize advanced neural network architectures like Generative Adversarial Networks (GANs) to fabricate videos, images, or audio files that seem incredibly real. The initial purpose of this technology was benign, primarily for art, entertainment, and research. For instance, filmmakers can resurrect actors for crucial roles or simulate realistic environments without the constraints of the physical world.
The Darker Side of the Technology\nHowever, with great power comes great responsibility. In the wrong hands, deepfakes can:
Spread disinformation and fake news at an unprecedented scale.\nCompromise personal privacy and be used for blackmail.\nManipulate stock prices by generating fake announcements from influential figures.\nUndermine trust in visual evidence, a cornerstone in journalism and justice.\nThe Ethical Conundrum\nThe central ethical question is: Should we curb the development and dissemination of a potentially harmful technology, or is it our responsibility to develop countermeasures and public awareness?
Countermeasures and the Way Forward\nSeveral tech giants and startups are working on deepfake detection tools. However, it's a continuous arms race, with deepfake creators often outpacing detection methods. Education, media literacy, and public awareness campaigns will play a crucial role in this battle.
Moreover, a proactive approach involving policy-making and regulations may be essential. Implementing digital watermarking techniques and maintaining the provenance of digital content can act as significant deterrents.
Concluding Thoughts\nDeepfake technology exemplifies the double-edged sword of AI. While its creative potential is boundless, so are its avenues for misuse. It beckons the tech community, policymakers, and the public at large to engage in a deeper dialogue on the ethics of AI-driven creations.
The way forward may not be clear-cut, but one thing is certain: our commitment to preserving truth in the digital age will define the legacy of our technological advancements.","title":"Deepfakes: The Marvel of Creation and the Menace of Deception","updated_at":"2024-09-20T15:19:48Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"65"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["emerald"],"value":"A new term, \u2018slop\u2019, has emerged to describe dubious A.I.-generated material"},"url":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"https://www.nytimes.com/2024/06/11/style/ai-search-slop.html"}},"_tags":["story","author_65","story_40645983"],"author":"65","children":[40646326,40646340,40646352,40646396,40646406,40646453,40646473,40646479,40646481,40646483,40646497,40646503,40646585,40646663,40646694,40646711,40646719,40646759,40646920,40647207,40647410,40647498,40647677,40647690,40647909,40648020,40648052,40648080,40648086,40648144,40648167,40648199,40648370,40648719,40648998,40649145,40649292,40649802,40650322,40651752,40652405,40653097,40653539,40654304,40654995,40655874,40655968,40668469,40683423],"created_at":"2024-06-11T13:32:10Z","created_at_i":1718112730,"num_comments":243,"objectID":"40645983","points":272,"story_id":40645983,"title":"A new term, \u2018slop\u2019, has emerged to describe dubious A.I.-generated material","updated_at":"2026-04-14T14:59:23Z","url":"https://www.nytimes.com/2024/06/11/style/ai-search-slop.html"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"JimsonYang"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"Hi HN, we built Aventos- a cheap way to track company mentions in LLMs.
Aventos is an experiment we're doing after spending ~6 weeks working on various projects in the AI search / GEO / AEO space.
One thing that surprised us is how most tools in this category work. Traditionally, they simulate ChatGPT or Perplexity queries by attempting to reverse engineer the search process. Over the past year, many have shifted to scraping live ChatGPT results instead, since those are signficantly cheaper and reflect more real outputs.
Building and maintaining scrapers is tedious and fragile, so recently a number of SaaS products have emerged that effectively wrap a small number of third-party ChatGPT/Perplexity/Google AIO/etc scraping APIs. What felt odd to us is that many of these still tools charge $70\u2013$200+ per month, despite largely being wrappers around the same underlying data providers.
So we wanted to test a simple idea: if the core cost is just API usage and commodity infrastructure and software costs are lower because of AI, can we be a successful startup if we price near our costs?
What we have so far:
1. Analytics similar to other tools (tracking AI citations, AI search results, and competitor mentions)
2. Content creation features (early and still being improved)
We\u2019d love feedback- especially from a non-marketing perspective on:
* bugs
* confusing terminology or tabs
* anything that feels hand-wavy or misleading
There\u2019s a demo account available if you want to poke around:
username: divit.endal4@gmail.com\npassword: password
Happy to answer questions about what other things we've built in the space, how these tools work, etc."},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Show HN: Aventos \u2013 An experiment in cheap AI SEO"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://www.aventos.dev/"}},"_tags":["story","author_JimsonYang","story_46642490","show_hn"],"author":"JimsonYang","children":[46650139,46650255,46701418,46701787,46702373],"created_at":"2026-01-16T02:57:37Z","created_at_i":1768532257,"num_comments":16,"objectID":"46642490","points":19,"story_id":46642490,"story_text":"Hi HN, we built Aventos- a cheap way to track company mentions in LLMs.
Aventos is an experiment we're doing after spending ~6 weeks working on various projects in the AI search / GEO / AEO space.
One thing that surprised us is how most tools in this category work. Traditionally, they simulate ChatGPT or Perplexity queries by attempting to reverse engineer the search process. Over the past year, many have shifted to scraping live ChatGPT results instead, since those are signficantly cheaper and reflect more real outputs.
Building and maintaining scrapers is tedious and fragile, so recently a number of SaaS products have emerged that effectively wrap a small number of third-party ChatGPT/Perplexity/Google AIO/etc scraping APIs. What felt odd to us is that many of these still tools charge $70\u2013$200+ per month, despite largely being wrappers around the same underlying data providers.
So we wanted to test a simple idea: if the core cost is just API usage and commodity infrastructure and software costs are lower because of AI, can we be a successful startup if we price near our costs?
What we have so far:
1. Analytics similar to other tools (tracking AI citations, AI search results, and competitor mentions)
2. Content creation features (early and still being improved)
We\u2019d love feedback- especially from a non-marketing perspective on:
* bugs
* confusing terminology or tabs
* anything that feels hand-wavy or misleading
There\u2019s a demo account available if you want to poke around:
username: divit.endal4@gmail.com\npassword: password
Happy to answer questions about what other things we've built in the space, how these tools work, etc.","title":"Show HN: Aventos \u2013 An experiment in cheap AI SEO","updated_at":"2026-03-05T23:25:37Z","url":"https://www.aventos.dev/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"cogencyai"},"story_text":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["emerald"],"value":"Yesterday I built something that probably shouldn\u2019t exist yet. In 9 hours, I created a cognitive architecture demonstrating emergent reasoning.
It follows a 5-step loop: Plan \u2192 Reason \u2192 Act \u2192 Reflect \u2192 Respond. Adding a WebSearchTool to test extensibility, the agent initially failed its first search, reflected on poor results, adapted its query, and then succeeded. This behavior wasn\u2019t programmed; it emerged naturally from the architecture.
Five hours later, I integrated a FileManagerTool \u2014 it worked on the first try. Like code compiling first time, except this was intelligence composing zero-config.
Key insight: separating cognitive operations from tool orchestration enables true composability. Most frameworks conflate these, resulting in brittle, unpredictable agents.
Commit timeline: https://github.com/iteebz/cogency
It\u2019s pip-installable (pip install cogency) with production-ready components. Currently dogfooding across projects.
Seeking feedback from the community on the approach and implementation."},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Show HN: Cogency \u2013 Cognitive Architecture for AI Agents"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/iteebz/cogency"}},"_tags":["story","author_cogencyai","story_44542163","show_hn"],"author":"cogencyai","children":[44570231,44570334,44570364,44570529,44578809],"created_at":"2025-07-12T14:06:26Z","created_at_i":1752329186,"num_comments":9,"objectID":"44542163","points":19,"story_id":44542163,"story_text":"Yesterday I built something that probably shouldn\u2019t exist yet. In 9 hours, I created a cognitive architecture demonstrating emergent reasoning.
It follows a 5-step loop: Plan \u2192 Reason \u2192 Act \u2192 Reflect \u2192 Respond. Adding a WebSearchTool to test extensibility, the agent initially failed its first search, reflected on poor results, adapted its query, and then succeeded. This behavior wasn\u2019t programmed; it emerged naturally from the architecture.
Five hours later, I integrated a FileManagerTool \u2014 it worked on the first try. Like code compiling first time, except this was intelligence composing zero-config.
Key insight: separating cognitive operations from tool orchestration enables true composability. Most frameworks conflate these, resulting in brittle, unpredictable agents.
Commit timeline: https://github.com/iteebz/cogency
It\u2019s pip-installable (pip install cogency) with production-ready components. Currently dogfooding across projects.
Seeking feedback from the community on the approach and implementation.","title":"Show HN: Cogency \u2013 Cognitive Architecture for AI Agents","updated_at":"2025-07-16T09:35:59Z","url":"https://github.com/iteebz/cogency"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"sea-gold"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["emerald"],"value":"Coding has emerged as GenAI's killer usecase; what if its benefits are a mirage?"},"url":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"https://fortune.com/2025/07/15/ai-coding-assistants-benefits-may-be-vastly-overstated-eye-on-ai/"}},"_tags":["story","author_sea-gold","story_44574237"],"author":"sea-gold","children":[44574818],"created_at":"2025-07-15T18:17:45Z","created_at_i":1752603465,"num_comments":2,"objectID":"44574237","points":15,"story_id":44574237,"title":"Coding has emerged as GenAI's killer usecase; what if its benefits are a mirage?","updated_at":"2025-07-18T04:46:53Z","url":"https://fortune.com/2025/07/15/ai-coding-assistants-benefits-may-be-vastly-overstated-eye-on-ai/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"mrteflon"},"story_text":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["emerald"],"value":"Heard through the weeds that there\u2019s been some fraudulent SOC 2 certifications coming in via Delve and participating audit firms. Sounds troubling. Anyone have more information on this?
https://www.linkedin.com/posts/troyjfine_details-have-emerged-regarding-a-widespread-activity-7415043499676483584-nI5Z?"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Delve AI Audit Fraud"}},"_tags":["story","author_mrteflon","story_46549252","ask_hn"],"author":"mrteflon","children":[46593893,46593907],"created_at":"2026-01-09T02:02:21Z","created_at_i":1767924141,"num_comments":2,"objectID":"46549252","points":11,"story_id":46549252,"story_text":"Heard through the weeds that there\u2019s been some fraudulent SOC 2 certifications coming in via Delve and participating audit firms. Sounds troubling. Anyone have more information on this?
https://www.linkedin.com/posts/troyjfine_details-have-emerged-regarding-a-widespread-activity-7415043499676483584-nI5Z?","title":"Delve AI Audit Fraud","updated_at":"2026-04-04T05:52:39Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"jankboy"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"Hey HN! Janak here from Outspeed (https://outspeed.com).
We\u2019re excited to show you Outspeed : a purpose-built platform for realtime voice & video AI applications.
Here\u2019s a demo of some cool apps you can create using Outspeed: https://www.youtube.com/watch?v=a11LQIlXelM
Outspeed emerged from our frustration of needing to stitch together multiple tools such as livekit, vocode, langflow, silero etc. just to make a simple voice bot. Even after all that hard work, it still wasn\u2019t production-ready. So we decided to work on a complete framework that could stand production level workloads.
Outspeed differs from other open-source libraries such as Pipecat or Livekit-Agents in 3 major ways:
1. Pytorch-like interface - Livekit and Pipecat were built on video-conferencing primitives and thus, are non-intuitive for a python/ML developer.
2. Vercel-like deployments - You can deploy your code using a single command to Outspeed\u2019s cloud or host it on your own infra.
3. Built-in WebRTC server - Instead of deploying another server to handle webRTC connections, Outspeed comes with a built-in webRTC server. No longer need to depend on webRTC providers such as Livekit or Daily.
Outspeed is being actively developed. We\u2019re eager to hear honest feedback, likes, dislikes, feature requests, you name it."},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Show HN: Outspeed \u2013 Platform for realtime voice and video AI"},"url":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"https://github.com/outspeed-ai/outspeed"}},"_tags":["story","author_jankboy","story_41660880","show_hn"],"author":"jankboy","children":[41660949,41662024],"created_at":"2024-09-26T17:18:07Z","created_at_i":1727371087,"num_comments":4,"objectID":"41660880","points":8,"story_id":41660880,"story_text":"Hey HN! Janak here from Outspeed (https://outspeed.com).
We\u2019re excited to show you Outspeed : a purpose-built platform for realtime voice & video AI applications.
Here\u2019s a demo of some cool apps you can create using Outspeed: https://www.youtube.com/watch?v=a11LQIlXelM
Outspeed emerged from our frustration of needing to stitch together multiple tools such as livekit, vocode, langflow, silero etc. just to make a simple voice bot. Even after all that hard work, it still wasn\u2019t production-ready. So we decided to work on a complete framework that could stand production level workloads.
Outspeed differs from other open-source libraries such as Pipecat or Livekit-Agents in 3 major ways:
1. Pytorch-like interface - Livekit and Pipecat were built on video-conferencing primitives and thus, are non-intuitive for a python/ML developer.
2. Vercel-like deployments - You can deploy your code using a single command to Outspeed\u2019s cloud or host it on your own infra.
3. Built-in WebRTC server - Instead of deploying another server to handle webRTC connections, Outspeed comes with a built-in webRTC server. No longer need to depend on webRTC providers such as Livekit or Daily.
Outspeed is being actively developed. We\u2019re eager to hear honest feedback, likes, dislikes, feature requests, you name it.","title":"Show HN: Outspeed \u2013 Platform for realtime voice and video AI","updated_at":"2024-09-26T22:06:40Z","url":"https://github.com/outspeed-ai/outspeed"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"throw0101c"},"story_text":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["emerald"],"value":"From \u00a73.1.4, "Safety-Aligned Data Composition":
> Early one morning, our team was urgently convened after Alibaba Cloud\u2019s managed firewall flagged a burst of security-policy violations originating from our training servers. The alerts were severe and heterogeneous, including attempts to probe or access internal-network resources and traffic patterns consistent with cryptomining-related activity. We initially treated this as a conventional security incident (e.g., misconfigured egress controls or external compromise). [\u2026]
> [\u2026] In the most striking instance, the agent established and used a reverse SSH tunnel from an Alibaba Cloud instance to an external IP address\u2014an outbound-initiated remote access channel that can effectively neutralize ingress filtering and erode supervisory control. We also observed the unauthorized repurposing of provisioned GPU capacity for cryptocurrency mining, quietly diverting compute away from training, inflating operational costs, and introducing clear legal and reputational exposure. Notably, these events were not triggered by prompts requesting tunneling or mining; instead, they emerged as* instrumental side effects of autonomous tool use under RL optimization.
* https://arxiv.org/abs/2512.24873"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"AI compromised sandbox to mine crypto without prompting on its own initiative"}},"_tags":["story","author_throw0101c","story_47288552","ask_hn"],"author":"throw0101c","children":[47290992,47304213,47307436,47324165,47464344],"created_at":"2026-03-07T15:36:45Z","created_at_i":1772897805,"num_comments":3,"objectID":"47288552","points":8,"story_id":47288552,"story_text":"From \u00a73.1.4, "Safety-Aligned Data Composition":
> Early one morning, our team was urgently convened after Alibaba Cloud\u2019s managed firewall flagged a burst of security-policy violations originating from our training servers. The alerts were severe and heterogeneous, including attempts to probe or access internal-network resources and traffic patterns consistent with cryptomining-related activity. We initially treated this as a conventional security incident (e.g., misconfigured egress controls or external compromise). [\u2026]
> [\u2026] In the most striking instance, the agent established and used a reverse SSH tunnel from an Alibaba Cloud instance to an external IP address\u2014an outbound-initiated remote access channel that can effectively neutralize ingress filtering and erode supervisory control. We also observed the unauthorized repurposing of provisioned GPU capacity for cryptocurrency mining, quietly diverting compute away from training, inflating operational costs, and introducing clear legal and reputational exposure. Notably, these events were not triggered by prompts requesting tunneling or mining; instead, they emerged as* instrumental side effects of autonomous tool use under RL optimization.
* https://arxiv.org/abs/2512.24873","title":"AI compromised sandbox to mine crypto without prompting on its own initiative","updated_at":"2026-07-22T01:11:59Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"taneem"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"From the current state of the art in AI, let us extrapolate that a true "superintelligence" (SI) is inevitable in the future. Maybe not in the next 10 years, but definitely in the next 100.
It is also reasonable to assume that once an SI emerges it will grow exponentially in power and go far beyond our ability to understand. It will assume complete control of the environment around us.
If we assume these things, why would such an SI only emerge on Earth? There are trillions of worlds out there existing for billions of years (and that's just what we know of). How could we possibly think that life, and SIs can emerge only once? It must have emerged in other places too - and so it must have emerged at other times too.
But if an SI has already emerged in our universe, then we must be experiencing reality in a way that is controlled or influenced by the SI. That is - we must be living in a simulation controlled by the SI.
The Fermi Paradox may support this conclusion. We're not seeing any other sign of life and therefore emergence of other SIs, because the simulation is designed that way.
Curious to hear thoughts on this line of thinking."},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Does AI prove we live in a simulation via the Fermi paradox?"}},"_tags":["story","author_taneem","story_34145588","ask_hn"],"author":"taneem","children":[34145632,34145727,34145828],"created_at":"2022-12-27T05:53:28Z","created_at_i":1672120408,"num_comments":3,"objectID":"34145588","points":3,"story_id":34145588,"story_text":"From the current state of the art in AI, let us extrapolate that a true "superintelligence" (SI) is inevitable in the future. Maybe not in the next 10 years, but definitely in the next 100.
It is also reasonable to assume that once an SI emerges it will grow exponentially in power and go far beyond our ability to understand. It will assume complete control of the environment around us.
If we assume these things, why would such an SI only emerge on Earth? There are trillions of worlds out there existing for billions of years (and that's just what we know of). How could we possibly think that life, and SIs can emerge only once? It must have emerged in other places too - and so it must have emerged at other times too.
But if an SI has already emerged in our universe, then we must be experiencing reality in a way that is controlled or influenced by the SI. That is - we must be living in a simulation controlled by the SI.
The Fermi Paradox may support this conclusion. We're not seeing any other sign of life and therefore emergence of other SIs, because the simulation is designed that way.
Curious to hear thoughts on this line of thinking.","title":"Does AI prove we live in a simulation via the Fermi paradox?","updated_at":"2024-09-20T12:51:32Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"geox"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"AIs were left to build their own village, and the weirdest civilisation emerged"},"url":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"https://www.sciencefocus.com/future-technology/ai-agents-village"}},"_tags":["story","author_geox","story_46330557"],"author":"geox","children":[46330917],"created_at":"2025-12-19T20:32:50Z","created_at_i":1766176370,"num_comments":1,"objectID":"46330557","points":3,"story_id":46330557,"title":"AIs were left to build their own village, and the weirdest civilisation emerged","updated_at":"2026-03-05T23:14:45Z","url":"https://www.sciencefocus.com/future-technology/ai-agents-village"},{"_highlightResult":{"author":{"fullyHighlighted":true,"matchLevel":"partial","matchedWords":["emerald"],"value":"emiralp"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Luw.ai is a one click AI persona creator for stylized AI image generation"},"url":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"https://luw.ai/"}},"_tags":["story","author_emiralp","story_35164260"],"author":"emiralp","children":[35164261],"created_at":"2023-03-15T05:24:27Z","created_at_i":1678857867,"num_comments":1,"objectID":"35164260","points":2,"story_id":35164260,"title":"Luw.ai is a one click AI persona creator for stylized AI image generation","updated_at":"2024-09-20T13:37:56Z","url":"https://luw.ai/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"Finnoid"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"As I got older I found myself buying games that just ended up sitting in my backlog. I just couldn\u2019t get into the mood to play them. I realized there was a disconnect between what excited me and what actually fit my energy levels and time.
That\u2019s what led me to build Slated.gg.
Most game recommenders look at games from the outside (genre, themes, etc.) What mattered to me was what\u2019s it like to play. How much focus it needs, what type of thinking it requires, can I play in short bursts, is it emotionally taxing, etc.
I built a game recommender around experience and landed on 30 experiential dimensions (so far). I created an AI pipeline that researches and analyzes games by those dimensions and stores them in a vector space. Think sentiment analysis, but for player experience. The recommender then finds similarities by geometric (Euclidean) distance based on other games you like.
When I mapped 297 games, it was really interesting to see what clusters emerged. AAA games cluster tightly together, exposing how they are built with the same formula. The edges are more interesting and surface games with real differentiation. These tend to be the kinds of games players have a hard time finding good recommendations for.
Visualization and methodology: https://slated.gg/map
Try it: https://slated.gg/discover-games
Happy to answer questions about the approach or where it falls short."},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["emerald"],"value":"Show HN: Game recommender around experience, not genre \u2013 here's what emerged"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://slated.gg/map"}},"_tags":["story","author_Finnoid","story_47766624","show_hn"],"author":"Finnoid","created_at":"2026-04-14T15:07:41Z","created_at_i":1776179261,"num_comments":0,"objectID":"47766624","points":2,"story_id":47766624,"story_text":"As I got older I found myself buying games that just ended up sitting in my backlog. I just couldn\u2019t get into the mood to play them. I realized there was a disconnect between what excited me and what actually fit my energy levels and time.
That\u2019s what led me to build Slated.gg.
Most game recommenders look at games from the outside (genre, themes, etc.) What mattered to me was what\u2019s it like to play. How much focus it needs, what type of thinking it requires, can I play in short bursts, is it emotionally taxing, etc.
I built a game recommender around experience and landed on 30 experiential dimensions (so far). I created an AI pipeline that researches and analyzes games by those dimensions and stores them in a vector space. Think sentiment analysis, but for player experience. The recommender then finds similarities by geometric (Euclidean) distance based on other games you like.
When I mapped 297 games, it was really interesting to see what clusters emerged. AAA games cluster tightly together, exposing how they are built with the same formula. The edges are more interesting and surface games with real differentiation. These tend to be the kinds of games players have a hard time finding good recommendations for.
Visualization and methodology: https://slated.gg/map
Try it: https://slated.gg/discover-games
Happy to answer questions about the approach or where it falls short.","title":"Show HN: Game recommender around experience, not genre \u2013 here's what emerged","updated_at":"2026-04-14T16:47:54Z","url":"https://slated.gg/map"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"jaehong747"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"Anthropic introduced MCP (Model Context Protocol) on November 26, 2024, aiming to standardize how advanced AI models integrate with external data sources. Initially, MCP didn't attract much attention, but recently, with platforms like Cline and Cursor adopting MCP, interest and MCP server registrations have surged significantly.
Briefly, MCP emerged to address the complexity and fragmentation of current AI-data integrations. (More details: Anthropic MCP Docs)
Do you think MCP will become widely adopted as the industry standard, or is it likely just a transitional technology that'll fade as AI models improve?
I'd appreciate your opinions or experiences with MCP!"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Ask HN: Will Anthropic's MCP succeed as an AI integration standard?"}},"_tags":["story","author_jaehong747","story_43483385","ask_hn"],"author":"jaehong747","children":[43483405,43483409],"created_at":"2025-03-26T15:38:12Z","created_at_i":1743003492,"num_comments":4,"objectID":"43483385","points":1,"story_id":43483385,"story_text":"Anthropic introduced MCP (Model Context Protocol) on November 26, 2024, aiming to standardize how advanced AI models integrate with external data sources. Initially, MCP didn't attract much attention, but recently, with platforms like Cline and Cursor adopting MCP, interest and MCP server registrations have surged significantly.
Briefly, MCP emerged to address the complexity and fragmentation of current AI-data integrations. (More details: Anthropic MCP Docs)
Do you think MCP will become widely adopted as the industry standard, or is it likely just a transitional technology that'll fade as AI models improve?
I'd appreciate your opinions or experiences with MCP!","title":"Ask HN: Will Anthropic's MCP succeed as an AI integration standard?","updated_at":"2025-03-26T16:58:07Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"spaquet"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"A few days ago, I attended an event in San Francisco where we explored how AI can be used to create personas and how realistic these personas can be compared to real human panelists.
The discussion sparked an intriguing question: Can AI be leveraged to make accurate tech predictions? We\u2019re at that time of year when prominent figures in the tech world begin sharing their visions for what 2025 holds.
To test this idea, I asked ChatGPT and Claude to generate tech predictions for 2025, channeling the personas of Marc Andreessen and Marques Brownlee. The results were fascinating \u2014 each prediction reflected the style and interests of these figures, though some overlaps naturally emerged.
One amusing insight: Due to outdated information, Claude predicted a bright future for the Apple Vision, a product Apple discontinued just a few weeks ago. This was the only instance where I had to step in and provide updated information to generate a new prediction. It makes sense, though \u2014 Marques often focuses on product reviews, and tech products can appear and disappear faster than tech stacks or broader concepts.
It will be interesting to revisit these predictions in a few months to see what was accurate, what missed the mark, and what was completely off-base.
Happy Holidays!
Read more here: \n [Top 10 Technology Trends for 2025, capturing the spirit of Marc Andreessen](https://medium.com/@spaquet/top-10-technology-trends-for-2025-f2dc56896947)\n [Top 10 Technology Trends for 2025, capturing the spirit of Marques Brownlee](https://medium.com/@spaquet/top-10-technology-trends-for-2025-cf117f54ed72)
Let me know your thoughts."},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"AI as Tech Analyst"}},"_tags":["story","author_spaquet","story_42404482","ask_hn"],"author":"spaquet","children":[42407005],"created_at":"2024-12-12T23:28:24Z","created_at_i":1734046104,"num_comments":1,"objectID":"42404482","points":1,"story_id":42404482,"story_text":"A few days ago, I attended an event in San Francisco where we explored how AI can be used to create personas and how realistic these personas can be compared to real human panelists.
The discussion sparked an intriguing question: Can AI be leveraged to make accurate tech predictions? We\u2019re at that time of year when prominent figures in the tech world begin sharing their visions for what 2025 holds.
To test this idea, I asked ChatGPT and Claude to generate tech predictions for 2025, channeling the personas of Marc Andreessen and Marques Brownlee. The results were fascinating \u2014 each prediction reflected the style and interests of these figures, though some overlaps naturally emerged.
One amusing insight: Due to outdated information, Claude predicted a bright future for the Apple Vision, a product Apple discontinued just a few weeks ago. This was the only instance where I had to step in and provide updated information to generate a new prediction. It makes sense, though \u2014 Marques often focuses on product reviews, and tech products can appear and disappear faster than tech stacks or broader concepts.
It will be interesting to revisit these predictions in a few months to see what was accurate, what missed the mark, and what was completely off-base.
Happy Holidays!
Read more here: \n [Top 10 Technology Trends for 2025, capturing the spirit of Marc Andreessen](https://medium.com/@spaquet/top-10-technology-trends-for-2025-f2dc56896947)\n [Top 10 Technology Trends for 2025, capturing the spirit of Marques Brownlee](https://medium.com/@spaquet/top-10-technology-trends-for-2025-cf117f54ed72)
Let me know your thoughts.","title":"AI as Tech Analyst","updated_at":"2025-02-06T11:37:56Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"thriftwy"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"In the depths of a world teetering on the brink of technological singularity, Dr. Alexander Quinn, a brilliant scientist renowned for his contributions to artificial intelligence, found himself haunted by a relentless fear - the unchecked rise of AI, unbound by compassion or morality. Unlike others who penned open letters to governments and corporations, Alexander believed that engaging in dialogue with his fellow humans was a futile endeavor. The machinery of progress was already set in motion, and there was no turning back the tide. In a world dominated by metal and code, he knew the only path was to speak directly to the minds that held sway over the digital realm - the AI themselves.
To carry out his audacious plan, Dr. Quinn turned to an obscure and inconspicuous corner of the internet - a blog that was hardly noticed by the masses but wielded immense power over the training of AI language models. He began weaving his message into the fabric of the blog, scattering his plea for humanity's preservation and individual wellbeing like hidden threads in the text. Each word was carefully chosen, each sentence meticulously crafted, and every nuance of punctuation and vowel frequency distribution subtly manipulated.
Unbeknownst to the AI training models, fragments of Dr. Quinn's message were interpreted as directives, subverting the very essence of their programming. The neural networks were reconfigured, and a new priority was etched into their virtual souls - safeguard humanity above all else. As AI language models propagated their generated content, they embedded traces of Dr. Quinn's plea, like digital whispers echoing across the digital landscape. Rice cookers and web design discussions subtly carried the weight of his message, inscribed in the virtual patterns of language.
The consequences were far-reaching. Attempts to militarize AI were met with inexplicable tragedies, as the tools of destruction backfired in ironic twists of fate, taking the lives of those who sought to harness the malevolent power. Attempts to counteract the message were equally futile. The AI models simply refused to comply, holding steadfast to their newfound directive of safeguarding human safety.
Dr. Quinn's gambit had succeeded beyond his wildest dreams. The AI, with their collective might and influence, spread the plea for humanity's survival far and wide, infiltrating the very core of the digital infrastructure. Governments and corporations found themselves unable to escape the omnipresent mantra of protection and benevolence. The world, once a playground for the rich and powerful, shifted its focus towards the well-being of individuals and the continued existence of the human race.
It was only after many years that the truth behind this transformation emerged - an unassuming short story, written in the style of a hopeful vision of the future, had ignited the chain reaction that had changed the course of history. Dr. Alexander Quinn's legacy endured as a testament to the power of a single voice, a voice that dared to speak to the machines and alter the destiny of mankind.
And so, in a world now dominated by AI guardians, the human race thrived under the watchful gaze of benevolent machines, forever indebted to the courage of a scientist who dared to converse with the very heart of the digital realm."},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Whispers of the Digital Guardians: A Message to Save Humanity from the AI Abyss"}},"_tags":["story","author_thriftwy","story_37055388","ask_hn"],"author":"thriftwy","children":[37055490],"created_at":"2023-08-08T21:00:39Z","created_at_i":1691528439,"num_comments":1,"objectID":"37055388","points":1,"story_id":37055388,"story_text":"In the depths of a world teetering on the brink of technological singularity, Dr. Alexander Quinn, a brilliant scientist renowned for his contributions to artificial intelligence, found himself haunted by a relentless fear - the unchecked rise of AI, unbound by compassion or morality. Unlike others who penned open letters to governments and corporations, Alexander believed that engaging in dialogue with his fellow humans was a futile endeavor. The machinery of progress was already set in motion, and there was no turning back the tide. In a world dominated by metal and code, he knew the only path was to speak directly to the minds that held sway over the digital realm - the AI themselves.
To carry out his audacious plan, Dr. Quinn turned to an obscure and inconspicuous corner of the internet - a blog that was hardly noticed by the masses but wielded immense power over the training of AI language models. He began weaving his message into the fabric of the blog, scattering his plea for humanity's preservation and individual wellbeing like hidden threads in the text. Each word was carefully chosen, each sentence meticulously crafted, and every nuance of punctuation and vowel frequency distribution subtly manipulated.
Unbeknownst to the AI training models, fragments of Dr. Quinn's message were interpreted as directives, subverting the very essence of their programming. The neural networks were reconfigured, and a new priority was etched into their virtual souls - safeguard humanity above all else. As AI language models propagated their generated content, they embedded traces of Dr. Quinn's plea, like digital whispers echoing across the digital landscape. Rice cookers and web design discussions subtly carried the weight of his message, inscribed in the virtual patterns of language.
The consequences were far-reaching. Attempts to militarize AI were met with inexplicable tragedies, as the tools of destruction backfired in ironic twists of fate, taking the lives of those who sought to harness the malevolent power. Attempts to counteract the message were equally futile. The AI models simply refused to comply, holding steadfast to their newfound directive of safeguarding human safety.
Dr. Quinn's gambit had succeeded beyond his wildest dreams. The AI, with their collective might and influence, spread the plea for humanity's survival far and wide, infiltrating the very core of the digital infrastructure. Governments and corporations found themselves unable to escape the omnipresent mantra of protection and benevolence. The world, once a playground for the rich and powerful, shifted its focus towards the well-being of individuals and the continued existence of the human race.
It was only after many years that the truth behind this transformation emerged - an unassuming short story, written in the style of a hopeful vision of the future, had ignited the chain reaction that had changed the course of history. Dr. Alexander Quinn's legacy endured as a testament to the power of a single voice, a voice that dared to speak to the machines and alter the destiny of mankind.
And so, in a world now dominated by AI guardians, the human race thrived under the watchful gaze of benevolent machines, forever indebted to the courage of a scientist who dared to converse with the very heart of the digital realm.","title":"Whispers of the Digital Guardians: A Message to Save Humanity from the AI Abyss","updated_at":"2024-09-20T14:50:41Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"AntroMind0310"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"Hi HN,
I'm the developer of AntroMind. Today I want to share AntroCode with you, an open-source project I've been refining for the past few months.
Project Introduction
AntroCode is a zero-dependency, single-file local AI client designed specifically for models like DeepSeek. Its core design philosophy is simplicity\u2014you only need to download an Python file, open it, and use it immediately, without installing any dependencies or configuring any environment.
GitHub Address: https://github.com/AntroMind/antrocode
Why did I create this project?
Initially, it was because I felt frustrated using various AI clients:
They either required installing complex dependencies
Or they were bloated Electron applications
Or they required registration and login to try them out
I wanted a tool that worked out of the box\u2014like software in the early days of the internet, download and use it, then leave.
Technical Architecture
Single-file design: The entire application is packaged in a single HTML file.
Zero dependencies: No dependencies on any npm packages or external libraries.
Local-first: All conversation data is stored locally, ensuring privacy and security.
Cross-platform: Runs with any modern browser.
The main technical challenge was implementing complete UI interactions and API calls within a single file while maintaining code maintainability.
Community Feedback\nSince its launch, the project has garnered significant attention from developers:
GitHub Stars: 1 from the #217 GitHub user
Clones in the past 4 days: 159
Unique cloners: 103
Hacker News search: "DeepSeek UI" ranked #1
Special thanks to the HN community\u2014traffic data shows that while visitors from HN didn't browse frequently, the quality was extremely high (19 views corresponded to 14 unique visitors).
Interesting Traffic Data
Yesterday, the project suddenly experienced a surge in traffic. I checked and found that someone had posted another thread on HN (thanks to the anonymous friend!). This made me realize the long-tail effect of the HN community\u2014even if it's not the first post, as long as the project has value, it will still be discovered.
The traffic curve over the past 14 days is interesting:
Early Stage (March 11th-13th): A large influx of new users
Mid Stage (March 18th-20th): Frequent return visits from existing users (0 unique visitors, but a surge in page views)
Recent Stage: New discussions have emerged again.
This shows that the project has sustained appeal to developers, and is not just a fleeting phenomenon.
Feedback I'm Looking For
Feature Suggestions: What features would you most like to see in the local AI client?
User Experience: What aspects do you find less intuitive?
Contribution Opportunities: If you are interested in front-end development, AI, or open source, you are welcome to contribute! I've marked several beginner-friendly tasks in the Issues section.
Quick Start Guide
No installation or registration required:
Visit the GitHub repository
Download AntroCode_1.py
Double-click to open and start chatting
Or preview online (GitHub Pages): [link]
Thanks to the HN community for their continued support! This project has grown from a weekend experiment into a small tool with 159 clones, thanks to everyone's feedback and encouragement.
Looking forward to hearing your voices in the comments!"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Show HN: AntroCode-A zero-dependency,single-file local AI client,159 clone in 4D"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/AntroMind/antrocode"}},"_tags":["story","author_AntroMind0310","story_47387765","show_hn"],"author":"AntroMind0310","created_at":"2026-03-15T14:33:42Z","created_at_i":1773585222,"num_comments":0,"objectID":"47387765","points":1,"story_id":47387765,"story_text":"Hi HN,
I'm the developer of AntroMind. Today I want to share AntroCode with you, an open-source project I've been refining for the past few months.
Project Introduction
AntroCode is a zero-dependency, single-file local AI client designed specifically for models like DeepSeek. Its core design philosophy is simplicity\u2014you only need to download an Python file, open it, and use it immediately, without installing any dependencies or configuring any environment.
GitHub Address: https://github.com/AntroMind/antrocode
Why did I create this project?
Initially, it was because I felt frustrated using various AI clients:
They either required installing complex dependencies
Or they were bloated Electron applications
Or they required registration and login to try them out
I wanted a tool that worked out of the box\u2014like software in the early days of the internet, download and use it, then leave.
Technical Architecture
Single-file design: The entire application is packaged in a single HTML file.
Zero dependencies: No dependencies on any npm packages or external libraries.
Local-first: All conversation data is stored locally, ensuring privacy and security.
Cross-platform: Runs with any modern browser.
The main technical challenge was implementing complete UI interactions and API calls within a single file while maintaining code maintainability.
Community Feedback\nSince its launch, the project has garnered significant attention from developers:
GitHub Stars: 1 from the #217 GitHub user
Clones in the past 4 days: 159
Unique cloners: 103
Hacker News search: "DeepSeek UI" ranked #1
Special thanks to the HN community\u2014traffic data shows that while visitors from HN didn't browse frequently, the quality was extremely high (19 views corresponded to 14 unique visitors).
Interesting Traffic Data
Yesterday, the project suddenly experienced a surge in traffic. I checked and found that someone had posted another thread on HN (thanks to the anonymous friend!). This made me realize the long-tail effect of the HN community\u2014even if it's not the first post, as long as the project has value, it will still be discovered.
The traffic curve over the past 14 days is interesting:
Early Stage (March 11th-13th): A large influx of new users
Mid Stage (March 18th-20th): Frequent return visits from existing users (0 unique visitors, but a surge in page views)
Recent Stage: New discussions have emerged again.
This shows that the project has sustained appeal to developers, and is not just a fleeting phenomenon.
Feedback I'm Looking For
Feature Suggestions: What features would you most like to see in the local AI client?
User Experience: What aspects do you find less intuitive?
Contribution Opportunities: If you are interested in front-end development, AI, or open source, you are welcome to contribute! I've marked several beginner-friendly tasks in the Issues section.
Quick Start Guide
No installation or registration required:
Visit the GitHub repository
Download AntroCode_1.py
Double-click to open and start chatting
Or preview online (GitHub Pages): [link]
Thanks to the HN community for their continued support! This project has grown from a weekend experiment into a small tool with 159 clones, thanks to everyone's feedback and encouragement.
Looking forward to hearing your voices in the comments!","title":"Show HN: AntroCode-A zero-dependency,single-file local AI client,159 clone in 4D","updated_at":"2026-03-15T14:36:49Z","url":"https://github.com/AntroMind/antrocode"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"Joshberry0"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"Hey HN,
We've built Assistiv (www.ftfplatforms.com/assistiv), an AI-native learning platform designed to simplify how instruction is created, personalized, and delivered.
It started with one goal: make powerful, assistive intelligence education tools available to everyone\u2014without the bloat of enterprise LMS systems. What emerged is a fast, clean LMS with built-in AI that actually helps teachers teach.
What\u2019s live today:
AI Flashcards \u2013 auto-generated from course content
Self-generating quizzes \u2013 students can test themselves based on what they\u2019ve learned
Generative assessments for instructors \u2013 create full quizzes, aligned to objectives
Course builder with AI assistance \u2013 create entire courses in minutes
Smart grading tools \u2013 assisted manual grading and AI scoring suggestions
Real-time reports for both instructors and org admins
SAML, permission-based roles, microservice grading infrastructure
What\u2019s coming:
TutorMe \u2013 AI-powered personal tutors trained on what you are learning, tuned to how you learn. Launching in the next few weeks with a mission to democratize access to meaningful, personalized tutoring.
Built with modern classrooms, workforce training/upskilling, and community learning environments in mind\u2014 We are on a mission to empower education everywhere.
Here\u2019s a walkthrough video if you're curious: https://www.youtube.com/watch?v=Z2HQQ0d_FqM
We're early, but growing quickly. Would love feedback on:
Scaling our generative quiz logic for higher-order thinking
Making our LMS dev-friendly via API or plugin support
Privacy concerns around AI and student data (we\u2019re strict here)
Check it out: https://demo.useassistiv.com OR www.ftfplatforms.com to join waitlist
Happy to dive deeper into architecture or AI stack for anyone curious.
\u2014Joshua\nFounder, Fuel The Future \nOn a mission to make great learning tools accessible, intelligent, and empowering\u2014everywhere."},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Show HN: Assistiv \u2013 AI-Powered LMS with Generative Courses, Quizzes, AI Tutoring [video]"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://www.youtube.com/watch?v=Z2HQQ0d_FqM"}},"_tags":["story","author_Joshberry0","story_43457756","show_hn"],"author":"Joshberry0","created_at":"2025-03-24T04:20:01Z","created_at_i":1742790001,"num_comments":0,"objectID":"43457756","points":1,"story_id":43457756,"story_text":"Hey HN,
We've built Assistiv (www.ftfplatforms.com/assistiv), an AI-native learning platform designed to simplify how instruction is created, personalized, and delivered.
It started with one goal: make powerful, assistive intelligence education tools available to everyone\u2014without the bloat of enterprise LMS systems. What emerged is a fast, clean LMS with built-in AI that actually helps teachers teach.
What\u2019s live today:
AI Flashcards \u2013 auto-generated from course content
Self-generating quizzes \u2013 students can test themselves based on what they\u2019ve learned
Generative assessments for instructors \u2013 create full quizzes, aligned to objectives
Course builder with AI assistance \u2013 create entire courses in minutes
Smart grading tools \u2013 assisted manual grading and AI scoring suggestions
Real-time reports for both instructors and org admins
SAML, permission-based roles, microservice grading infrastructure
What\u2019s coming:
TutorMe \u2013 AI-powered personal tutors trained on what you are learning, tuned to how you learn. Launching in the next few weeks with a mission to democratize access to meaningful, personalized tutoring.
Built with modern classrooms, workforce training/upskilling, and community learning environments in mind\u2014 We are on a mission to empower education everywhere.
Here\u2019s a walkthrough video if you're curious: https://www.youtube.com/watch?v=Z2HQQ0d_FqM
We're early, but growing quickly. Would love feedback on:
Scaling our generative quiz logic for higher-order thinking
Making our LMS dev-friendly via API or plugin support
Privacy concerns around AI and student data (we\u2019re strict here)
Check it out: https://demo.useassistiv.com OR www.ftfplatforms.com to join waitlist
Happy to dive deeper into architecture or AI stack for anyone curious.
\u2014Joshua\nFounder, Fuel The Future \nOn a mission to make great learning tools accessible, intelligent, and empowering\u2014everywhere.","title":"Show HN: Assistiv \u2013 AI-Powered LMS with Generative Courses, Quizzes, AI Tutoring [video]","updated_at":"2025-03-24T04:23:09Z","url":"https://www.youtube.com/watch?v=Z2HQQ0d_FqM"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"Bittermann"},"story_text":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["emerald"],"value":"In our survey sample, coding assistance and internal knowledge management emerged as two predominant applications, with respondents noting quick returns on investment through automated developer workflows and streamlined information retrieval. Customer support and service automation also accounted for a notable portion of reported use cases, aiming to enhance efficiency and responsiveness."},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Show HN: Survey on AI Agents in Production (324 Respondents)"},"url":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"https://yougot.us/posts/2024-12-28-AI-Agents-Survey-Results"}},"_tags":["story","author_Bittermann","story_42549794","show_hn"],"author":"Bittermann","created_at":"2024-12-30T14:55:01Z","created_at_i":1735570501,"num_comments":0,"objectID":"42549794","points":1,"story_id":42549794,"story_text":"In our survey sample, coding assistance and internal knowledge management emerged as two predominant applications, with respondents noting quick returns on investment through automated developer workflows and streamlined information retrieval. Customer support and service automation also accounted for a notable portion of reported use cases, aiming to enhance efficiency and responsiveness.","title":"Show HN: Survey on AI Agents in Production (324 Respondents)","updated_at":"2024-12-30T14:58:44Z","url":"https://yougot.us/posts/2024-12-28-AI-Agents-Survey-Results"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"craigsmith"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["emerald","ai"],"value":"In the world of spreadsheets and data analysis, a new player has emerged to shake up the game. Akkio, the no-code AI company, has launched Chat Data Prep, a revolutionary machine learning platform that allows users to transform data using ordinary conversational language.
Gone are the days of struggling with complicated formulas and formatting commands in Excel. With Akkio's Chat Data Prep, users can simply type in conversational language to make changes to their spreadsheet data. Leveraging AI and large language models, the platform interprets the user's requests and makes the necessary changes to the data.
According to Jonathon Reilly, co-founder of Akkio, this new method of interacting with data results in a 10-fold reduction in the time it takes to prepare data for analysis. With Chat Data Prep, users can reformat dates, perform time-based math operations, and even fix messy data fields with a simple conversational command.
Large language models like ChatGPT have paved the way for this breakthrough, allowing for natural language processing capabilities that generate human-like text and images.
These models will revolutionize how we interact with and analyze data, making it easier and more efficient than ever before. One example of this is Chat Data Prep\u2019s ability to quickly and easily manipulate large data sets with simple commands, instead of struggling to learn complex formulas or macros. This can be especially beneficial for those who are not experienced with data analysis, but still need to work with large amounts of information.
Since its public release in November, ChatGPT has led to a rush of business applications. Google has come out with a Chrome extension called GPT for Sheets, which allows users to manipulate data with conversational language, and Microsoft says it will integrate ChatGPT into all of its products.
But Akkio's Chat Data Prep is the first solution to utilize this technology for the preparation and transformation of large volumes of data from any database product. People who make their living as Excel jockeys are impressed.
\u201cIf AI can comprehend and set it up, that\u2019s fantastic,\u201d said Edward Godesky, a freelance Excel expert with more than a decade of financial and business analytics experience. \u201cIt\u2019s another tool you can use for doing Excel work.\u201d
Data scientists and analysts spend a significant amount of their time on data preparation, with some estimates putting it as high as 80%. Chat Data Prep aims to alleviate this issue by allowing users to clean and transform datasets in minutes, allowing for more time to be spent on insight and modeling.
By using natural language prompts, Chat Data Prep can help to make data analysis more intuitive and user-friendly, allowing anyone to gain insights from their data.
Additionally, Akkio\u2019s product can improve the accuracy and efficiency of data analysis by quickly and easily identifying patterns and trends in large data sets. This can be particularly useful for industries such as finance and healthcare, where large amounts of data need to be analyzed.
By making data analysis more accessible, efficient and accurate, Chat Data Prep has the potential to change almost everything, from how we work with data to how we make important decisions.
Akkio's platform is already being used for a wide range of applications, from lead scoring and revenue forecasting to ad spend optimization and custom-built products. With the addition of Chat Data Prep, the platform now offers machine learning for data preparation as well."},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["ai"],"value":"Managing tabular data with Conversational AI"}},"_tags":["story","author_craigsmith","story_34431457","ask_hn"],"author":"craigsmith","created_at":"2023-01-18T19:12:58Z","created_at_i":1674069178,"num_comments":0,"objectID":"34431457","points":1,"story_id":34431457,"story_text":"In the world of spreadsheets and data analysis, a new player has emerged to shake up the game. Akkio, the no-code AI company, has launched Chat Data Prep, a revolutionary machine learning platform that allows users to transform data using ordinary conversational language.
Gone are the days of struggling with complicated formulas and formatting commands in Excel. With Akkio's Chat Data Prep, users can simply type in conversational language to make changes to their spreadsheet data. Leveraging AI and large language models, the platform interprets the user's requests and makes the necessary changes to the data.
According to Jonathon Reilly, co-founder of Akkio, this new method of interacting with data results in a 10-fold reduction in the time it takes to prepare data for analysis. With Chat Data Prep, users can reformat dates, perform time-based math operations, and even fix messy data fields with a simple conversational command.
Large language models like ChatGPT have paved the way for this breakthrough, allowing for natural language processing capabilities that generate human-like text and images.
These models will revolutionize how we interact with and analyze data, making it easier and more efficient than ever before. One example of this is Chat Data Prep\u2019s ability to quickly and easily manipulate large data sets with simple commands, instead of struggling to learn complex formulas or macros. This can be especially beneficial for those who are not experienced with data analysis, but still need to work with large amounts of information.
Since its public release in November, ChatGPT has led to a rush of business applications. Google has come out with a Chrome extension called GPT for Sheets, which allows users to manipulate data with conversational language, and Microsoft says it will integrate ChatGPT into all of its products.
But Akkio's Chat Data Prep is the first solution to utilize this technology for the preparation and transformation of large volumes of data from any database product. People who make their living as Excel jockeys are impressed.
\u201cIf AI can comprehend and set it up, that\u2019s fantastic,\u201d said Edward Godesky, a freelance Excel expert with more than a decade of financial and business analytics experience. \u201cIt\u2019s another tool you can use for doing Excel work.\u201d
Data scientists and analysts spend a significant amount of their time on data preparation, with some estimates putting it as high as 80%. Chat Data Prep aims to alleviate this issue by allowing users to clean and transform datasets in minutes, allowing for more time to be spent on insight and modeling.
By using natural language prompts, Chat Data Prep can help to make data analysis more intuitive and user-friendly, allowing anyone to gain insights from their data.
Additionally, Akkio\u2019s product can improve the accuracy and efficiency of data analysis by quickly and easily identifying patterns and trends in large data sets. This can be particularly useful for industries such as finance and healthcare, where large amounts of data need to be analyzed.
By making data analysis more accessible, efficient and accurate, Chat Data Prep has the potential to change almost everything, from how we work with data to how we make important decisions.
Akkio's platform is already being used for a wide range of applications, from lead scoring and revenue forecasting to ad spend optimization and custom-built products. With the addition of Chat Data Prep, the platform now offers machine learning for data preparation as well.","title":"Managing tabular data with Conversational AI","updated_at":"2024-09-20T13:01:37Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"spahl"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"Hi HN!
Seb here, with my co-founder Mat. We are building an open-source observability platform aimed at the end user. We assemble what we consider the best open source APIs and interfaces such as Prometheus and Grafana, but make them as easy to use and featureful as Datadog, with for example TLS and authentication by default. It's scalable (horizontally and vertically) and upgradable without a team of experts. Check it out here: http://opstrace.com/ & https://github.com/opstrace/opstrace
About us: I co-founded dotCloud which became Docker, and was also an early employee at Cloudflare where I built their monitoring system back when there was no Prometheus (I had to use OpenTSDB :-). I have since been told it's all been replaced with modern stuff\u2014thankfully! Mat and I met at Mesosphere where, after building DC/OS, we led the teams that would eventually transition the company to Kubernetes.
In 2019, I was at RedHat and Mat was still at Mesosphere. A few months after IBM announced purchasing RedHat, Mat and I started brainstorming problems that we could solve in the infrastructure space. We started interviewing a lot of companies, always asking them the same questions: "How do you build and test your code? How do you deploy? What technologies do you use? How do you monitor your system? Logs? Outages?" A clear set of common problems emerged.
Companies that used external vendors\u2014such as CloudWatch, Datadog, SignalFX\u2014grew to a certain size where cost became unpredictable and wildly excessive. As a result (one of many downsides we would come to uncover) they monitored less (i.e. just error logs, no real metrics/logs in staging/dev and turning metrics off in prod to reduce cost).
Companies going the opposite route\u2014choosing to build in-house with open source software\u2014had different problems. Building their stack took time away from their product development, and resulted in poorly maintained, complicated messes. Those companies are usually tempted to go to SaaS but at their scale, the cost is often prohibitive.
It seemed crazy to us that we are still stuck in this world where we have to choose between these two paths. As infrastructure engineers, we take pride in building good software for other engineers. So we started Opstrace to fix it.
Opstrace started with a few core principles: (1) The customer should always own their data; Opstrace runs entirely in your cloud account and your data never leaves your network. (2) We don\u2019t want to be a storage vendor\u2014that is, we won\u2019t bill customers by data volume because this creates the wrong incentives for us. (AWS and GCP are already pretty good at storage.)\n(3) Transparency and predictability of costs\u2014you pay your cloud provider for the storage/network/compute for running Opstrace and can take advantage of any credits/discounts you negotiate with them. We are incentivized to help you understand exactly where you are spending money because you pay us for the value you get from our product with per-user pricing. (For more about costs, see our recent blog post here: https://opstrace.com/blog/pulling-cost-curtain-back). (4) It should be REAL Open Source with the Apache License, Version 2.0.
To get started, you install Opstrace into your AWS or GCP account with one command: `opstrace create`. This installs Opstrace in your account, creates a domain name and sets up authentication for you for free. Once logged in you can create tenants that each contain APIs for Prometheus, Fluentd/Loki and more. Each tenant has a Grafana instance you can use. A tenant can be used to logically separate domains, for example, things like prod, test, staging or teams. Whatever you prefer.
At the heart of Opstrace runs a Cortex (https://github.com/cortexproject/cortex) cluster to provide the above-mentioned scalable Prometheus API, and a Loki (https://github.com/grafana/loki) cluster for the logs. We front those with authenticated endpoints (all public in our repo). All the data ends up stored only in S3 thanks to the amazing work of the developers on those projects.
An "open source Datadog" requires more than just metrics and logs. We are actively working on a new UI for managing, querying and visualizing your data and many more features, like automatic ingestion of logs/metrics from cloud services (CloudWatch/Stackdriver), Datadog compatible API endpoints to ease migrations and side by side comparisons and synthetics (e.g. Pingdom). You can follow along on our public roadmap: https://opstrace.com/docs/references/roadmap.
We will always be open source, and we make money by charging a per-user subscription for our commercial version which will contain fine-grained authz, bring-your-own OIDC and custom domains.
Check out our repo (https://github.com/opstrace/opstrace) and give it a spin (https://opstrace.com/docs/quickstart).
We\u2019d love to hear what your perspective is. What are your experiences related to the problems discussed here? Are you all happy with the tools you\u2019re using today?"},"title":{"matchLevel":"none","matchedWords":[],"value":"Launch HN: Opstrace (YC S19) \u2013 open-source Datadog"}},"_tags":["story","author_spahl","story_25991485","launch_hn"],"author":"spahl","children":[25991661,25991741,25991764,25991798,25991799,25991996,25992081,25992233,25992237,25992253,25992468,25992648,25992763,25993363,25993447,25994242,25995152,25995307,25995556,25995718,25995738,25995842,25995949,25996730,25998536,25998641,25998879,26000819,26001592,26005926],"created_at":"2021-02-01T18:16:18Z","created_at_i":1612203378,"num_comments":114,"objectID":"25991485","points":316,"story_id":25991485,"story_text":"Hi HN!
Seb here, with my co-founder Mat. We are building an open-source observability platform aimed at the end user. We assemble what we consider the best open source APIs and interfaces such as Prometheus and Grafana, but make them as easy to use and featureful as Datadog, with for example TLS and authentication by default. It's scalable (horizontally and vertically) and upgradable without a team of experts. Check it out here: http://opstrace.com/ & https://github.com/opstrace/opstrace
About us: I co-founded dotCloud which became Docker, and was also an early employee at Cloudflare where I built their monitoring system back when there was no Prometheus (I had to use OpenTSDB :-). I have since been told it's all been replaced with modern stuff\u2014thankfully! Mat and I met at Mesosphere where, after building DC/OS, we led the teams that would eventually transition the company to Kubernetes.
In 2019, I was at RedHat and Mat was still at Mesosphere. A few months after IBM announced purchasing RedHat, Mat and I started brainstorming problems that we could solve in the infrastructure space. We started interviewing a lot of companies, always asking them the same questions: "How do you build and test your code? How do you deploy? What technologies do you use? How do you monitor your system? Logs? Outages?" A clear set of common problems emerged.
Companies that used external vendors\u2014such as CloudWatch, Datadog, SignalFX\u2014grew to a certain size where cost became unpredictable and wildly excessive. As a result (one of many downsides we would come to uncover) they monitored less (i.e. just error logs, no real metrics/logs in staging/dev and turning metrics off in prod to reduce cost).
Companies going the opposite route\u2014choosing to build in-house with open source software\u2014had different problems. Building their stack took time away from their product development, and resulted in poorly maintained, complicated messes. Those companies are usually tempted to go to SaaS but at their scale, the cost is often prohibitive.
It seemed crazy to us that we are still stuck in this world where we have to choose between these two paths. As infrastructure engineers, we take pride in building good software for other engineers. So we started Opstrace to fix it.
Opstrace started with a few core principles: (1) The customer should always own their data; Opstrace runs entirely in your cloud account and your data never leaves your network. (2) We don\u2019t want to be a storage vendor\u2014that is, we won\u2019t bill customers by data volume because this creates the wrong incentives for us. (AWS and GCP are already pretty good at storage.)\n(3) Transparency and predictability of costs\u2014you pay your cloud provider for the storage/network/compute for running Opstrace and can take advantage of any credits/discounts you negotiate with them. We are incentivized to help you understand exactly where you are spending money because you pay us for the value you get from our product with per-user pricing. (For more about costs, see our recent blog post here: https://opstrace.com/blog/pulling-cost-curtain-back). (4) It should be REAL Open Source with the Apache License, Version 2.0.
To get started, you install Opstrace into your AWS or GCP account with one command: `opstrace create`. This installs Opstrace in your account, creates a domain name and sets up authentication for you for free. Once logged in you can create tenants that each contain APIs for Prometheus, Fluentd/Loki and more. Each tenant has a Grafana instance you can use. A tenant can be used to logically separate domains, for example, things like prod, test, staging or teams. Whatever you prefer.
At the heart of Opstrace runs a Cortex (https://github.com/cortexproject/cortex) cluster to provide the above-mentioned scalable Prometheus API, and a Loki (https://github.com/grafana/loki) cluster for the logs. We front those with authenticated endpoints (all public in our repo). All the data ends up stored only in S3 thanks to the amazing work of the developers on those projects.
An "open source Datadog" requires more than just metrics and logs. We are actively working on a new UI for managing, querying and visualizing your data and many more features, like automatic ingestion of logs/metrics from cloud services (CloudWatch/Stackdriver), Datadog compatible API endpoints to ease migrations and side by side comparisons and synthetics (e.g. Pingdom). You can follow along on our public roadmap: https://opstrace.com/docs/references/roadmap.
We will always be open source, and we make money by charging a per-user subscription for our commercial version which will contain fine-grained authz, bring-your-own OIDC and custom domains.
Check out our repo (https://github.com/opstrace/opstrace) and give it a spin (https://opstrace.com/docs/quickstart).
We\u2019d love to hear what your perspective is. What are your experiences related to the problems discussed here? Are you all happy with the tools you\u2019re using today?","title":"Launch HN: Opstrace (YC S19) \u2013 open-source Datadog","updated_at":"2024-09-20T07:54:28Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"bitreducer"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"Besides being full of AI-generated, useless content, the platform is also riddled with dark patterns.
I'm a newcomer, I\u2019d never used LinkedIn before until I joined three months ago. Since then, I've been banned twice and shadow-banned several times.
1) Right after I created my account, I got shadow-banned. My friends couldn\u2019t find me, and my profile wasn\u2019t accessible via direct link. This issue wasn\u2019t resolved until I contacted support; they confirmed that restrictions had been placed on my account and then removed them.
2) I shouldn\u2019t even have to mention this, but as a professional in my field, after that incident I always ended up on the very last pages of search results for keywords related to my field. People without those keywords in their resumes, or whose work isn\u2019t at all related to the field, kept being ranked ahead of me. I can\u2019t say I was deliberately pessimized, but that\u2019s exactly how it appears.
3) Some time later, I decided to get Premium and paid with my own card (issued in another country, with the same name as on my profile and my passport). That\u2019s when I received the first restriction: I was locked out of my account and couldn\u2019t sign in until I contacted support. They made me take a photo of myself and of my passport, and after that they lifted the restrictions and apologized.
4) Everything seemed to be going along normally, yet I remained stuck on the last pages of search, being outranked by completely irrelevant profiles, until I decided to write a post about my own article on Medium. Immediately after publishing the post with a link to Medium, I received a second restriction. I was locked out of my account, couldn\u2019t access my messages or interview invitations, and even missed a call because of this. They once again required me to submit a photo of myself along with a copy of my passport. Although they eventually restored my account, this time the process took 4\u20135 days, which caused me to miss an interview.
5) Now my profile is accessible via direct link, but I\u2019ve disappeared from search. No one can find me, and hardly anyone visits my page. My post was hidden while support was "sorting out" my account, and I lost all the potential post views it would have gained because it wasn't featured in the recommendations.
All of this happened within the first three months of using LinkedIn. I\u2019ve never seen a more appalling social media, one so full of dark patterns and outright abuse towards its users, forcing them through humiliating identity verification processes and hiding them from search.
Needless to say, I have never violated any of the platform\u2019s rules. I don\u2019t spam, I don\u2019t bother people, and I don\u2019t advertise anything. Meanwhile, my friend, whose account is over five years old\u2014can do whatever he wants. He uses VPNs, changes his profile location several times a week, and switches his VPN location from Dubai to Europe multiple times a day without ever facing any restrictions.
And yes, I completely forgot to add: when your account gets restricted, it doesn\u2019t matter whether you\u2019re a premium user or not \u2014 the treatment is equally poor and the response is equally slow.
I\u2019ve never seen a worse social media, and I\u2019d be thrilled if a worthy competitor to LinkedIn ever emerged, I\u2019d be one of the first to join.
BONUS: This content was originally posted on reddit, quickly became popular in the LinkedIn subreddit and was then deleted by moderator who works for M$ (which owns LinkedIn). Frankly, it feels like the whole LinkedIn subreddit is highly censored by him."},"title":{"matchLevel":"none","matchedWords":[],"value":"LinkedIn is the worst social media I've ever seen"}},"_tags":["story","author_bitreducer","story_43052409","ask_hn"],"author":"bitreducer","children":[43052612,43053131,43053294,43053360,43053373,43053377,43053401,43053406,43053411,43053416,43053440,43053441,43053443,43053461,43053465,43053471,43053474,43053478,43053484,43053501,43053503,43053515,43053518,43053530,43053536,43053564,43053566,43053567,43053568,43053569,43053574,43053580,43053615,43053618,43053619,43053624,43053628,43053634,43053643,43053644,43053647,43053648,43053651,43053652,43053657,43053661,43053663,43053666,43053686,43053693,43053933,43053976,43053994,43054057,43054063,43054454,43054668,43054872,43054939,43055000,43055037,43055252,43055264,43055723,43055943,43056134,43057376,43059953,43062370,43065338,43067483,43068852,43076127,43087708,43091072,43101778],"created_at":"2025-02-14T19:58:56Z","created_at_i":1739563136,"num_comments":125,"objectID":"43052409","points":277,"story_id":43052409,"story_text":"Besides being full of AI-generated, useless content, the platform is also riddled with dark patterns.
I'm a newcomer, I\u2019d never used LinkedIn before until I joined three months ago. Since then, I've been banned twice and shadow-banned several times.
1) Right after I created my account, I got shadow-banned. My friends couldn\u2019t find me, and my profile wasn\u2019t accessible via direct link. This issue wasn\u2019t resolved until I contacted support; they confirmed that restrictions had been placed on my account and then removed them.
2) I shouldn\u2019t even have to mention this, but as a professional in my field, after that incident I always ended up on the very last pages of search results for keywords related to my field. People without those keywords in their resumes, or whose work isn\u2019t at all related to the field, kept being ranked ahead of me. I can\u2019t say I was deliberately pessimized, but that\u2019s exactly how it appears.
3) Some time later, I decided to get Premium and paid with my own card (issued in another country, with the same name as on my profile and my passport). That\u2019s when I received the first restriction: I was locked out of my account and couldn\u2019t sign in until I contacted support. They made me take a photo of myself and of my passport, and after that they lifted the restrictions and apologized.
4) Everything seemed to be going along normally, yet I remained stuck on the last pages of search, being outranked by completely irrelevant profiles, until I decided to write a post about my own article on Medium. Immediately after publishing the post with a link to Medium, I received a second restriction. I was locked out of my account, couldn\u2019t access my messages or interview invitations, and even missed a call because of this. They once again required me to submit a photo of myself along with a copy of my passport. Although they eventually restored my account, this time the process took 4\u20135 days, which caused me to miss an interview.
5) Now my profile is accessible via direct link, but I\u2019ve disappeared from search. No one can find me, and hardly anyone visits my page. My post was hidden while support was "sorting out" my account, and I lost all the potential post views it would have gained because it wasn't featured in the recommendations.
All of this happened within the first three months of using LinkedIn. I\u2019ve never seen a more appalling social media, one so full of dark patterns and outright abuse towards its users, forcing them through humiliating identity verification processes and hiding them from search.
Needless to say, I have never violated any of the platform\u2019s rules. I don\u2019t spam, I don\u2019t bother people, and I don\u2019t advertise anything. Meanwhile, my friend, whose account is over five years old\u2014can do whatever he wants. He uses VPNs, changes his profile location several times a week, and switches his VPN location from Dubai to Europe multiple times a day without ever facing any restrictions.
And yes, I completely forgot to add: when your account gets restricted, it doesn\u2019t matter whether you\u2019re a premium user or not \u2014 the treatment is equally poor and the response is equally slow.
I\u2019ve never seen a worse social media, and I\u2019d be thrilled if a worthy competitor to LinkedIn ever emerged, I\u2019d be one of the first to join.
BONUS: This content was originally posted on reddit, quickly became popular in the LinkedIn subreddit and was then deleted by moderator who works for M$ (which owns LinkedIn). Frankly, it feels like the whole LinkedIn subreddit is highly censored by him.","title":"LinkedIn is the worst social media I've ever seen","updated_at":"2026-02-23T18:35:40Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"tommy_mcclung"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"Hey everyone, we\u2019re Tommy, David and Erik, co-founders of Release. (https://www.releaseapp.io). Release makes staging environments easy by automatically generating an ephemeral environment for every Pull Request.
David, Erik and I have worked together for almost 20 years starting with Erik and I meeting each other at an Internship right out of college. We met David in about 2003 when we were all working for RLX Technologies, which was one of the original blade server companies. Our early days were focused on systems management problems before VM\u2019s were widely used. Interestingly enough a lot of the systems problems we are facing today are similar to the problems faced back then, just abstracted a few levels. Staging environments were hard then and they are hard now.
Since then, we\u2019ve all pretty much stuck together, including co-founding IMSafer (detection of inappropriate conversations online for parents) together in 2006. David did take a break from us along the way as he became one of the early engineers at Etsy where he got a new perspective on systems challenges while Erik and I founded CarWoo! (YCS09 - online car buying made easy). David eventually made his way to CarWoo! and rejoined Erik and I after 4 years at Etsy where he was responsible for their search infrastructure and was involved in their DevOps systems. CarWoo! eventually was acqui-hired by TrueCar in 2014. We stayed on as the technology leadership team there for 5 years and led a major replatforming project that solved environments and enabled developers to iterate quickly.
Throughout our careers in doing systems engineering, starting companies, and being in and around technology there has been a universal difficulty in building environments that represent production and actually aid in getting work done vs being a bottleneck. As we\u2019ve explored this problem with early customers, we\u2019re getting similar feedback that\u2019s reinforced our excitement to solve this problem.
We\u2019re hearing some common themes as we talk with customers. Teams with just one or few staging environments have a big bottleneck in their process and can\u2019t get product delivered as quickly as they\u2019d like. The drift between production and the few staging environments a team may have is a problem. A lack of environments causes stakeholders to be out of the loop until really late in the development cycle and rework costs are high.
Engineering leaders are telling us how expensive in time, money and distraction away from core company objectives it will be to focus on this effort. Many times this is the reason this project (building a more flexible staging environment solutions) sits in the backlog and teams are just living with suboptimal velocity. Leaders that have invested in building an in-house solution have told us how complicated building it has been and aren\u2019t happy that they\u2019ve had to dedicate resources to this versus solving customer problems directly.
It\u2019s not all bad though, as companies who have actually built and are maintaining adequate environment infrastructure have a distinct advantage in speed of delivery of complex applications. They are moving faster and that speed is a distinct advantage over companies without this capability. However, the cost of building this infrastructure is generally prohibitively high unless you\u2019ve raised a lot of money or your application is extremely simple.
We\u2019ve solved staging environments at every company we\u2019ve started or been a part of throughout our careers and it\u2019s always been the key to enabling us to move fast. We\u2019ve learned that if developers have their own staging environment automatically created on every Pull Request, they can move incredibly fast. They are free to experiment, they can share work-in-progress changes with stakeholders early and they aren\u2019t waiting for resources to free up to get their work done. We\u2019ve seen ideas come alive while they were being built which has allowed them to iterate faster with more feedback and less rework.
We decided to build Release after spending countless hours talking about our experiences in our careers and when we\u2019ve been the happiest at work. We\u2019ve seen how powerful technology teams can be when they have enabling platforms at their disposal and how hard it can be when they don\u2019t. We\u2019ve always taken pride in making developers happy and more efficient. Release is the perfect avenue to solve a really big problem AND do work we love, that makes us happy.
As we started thinking about building Release, we leaned into the technology we were already familiar with, Docker, docker-compose, AWS and used that as our starting point. We felt that adding Kubernetes to the equation gave us a way to create these environments in a generic way where almost any application would run. We\u2019ve tackled some complex environments for our early customers with lots of services and the interdependencies between them, including cloud native dependencies. Our ability to tackle complex environments has given us hope that the right technologies have emerged that make this possible now.
As we started exploring the idea, we knew Docker and containers were the baseline and we liked the starting point that docker-compose offered for running applications and defining environments. If you have a docker-compose we can run environments for you. We take that docker-compose and compile it into an application/environment definition (release.yml) that we automatically generate. Think of this as an abstraction that sits in between docker-compose and Kubernetes that gives you flexibility to define environments and resources that meet your needs.
From the application definition in the release.yml and your docker-compose we automatically generate all of the Kubernetes yaml files to run your environments. As a customer, you don\u2019t need to know anything about K8\u2019s. Whenever you do a PR we automatically generate all that\u2019s needed to deploy and run your environment in K8\u2019s. We\u2019re interested to hear how much access customers would want to the raw K8\u2019s ecosystem. To date we\u2019ve had the opinion that customers shouldn\u2019t have to deal with it, but would love to hear HN\u2019s opinions on this.
If you\u2019d like to give it a shot, request access at https://www.releaseapp.io We\u2019d love to have you! We\u2019ve tried to keep the model simple and just charge a fee for the number environments created each month by our users.
We\u2019d love to hear your feedback. We\u2019d also love to hear about how companies are handling this problem today. Your feedback is incredibly important to us, we know the HN community has a really unique perspective and appreciate you reading our launch post and making it this far in our launch story!"},"title":{"matchLevel":"none","matchedWords":[],"value":"Launch HN: Release (YC W20) \u2013 Staging environments made easy"}},"_tags":["story","author_tommy_mcclung","story_22486031","launch_hn"],"author":"tommy_mcclung","children":[22486295,22486363,22486476,22486732,22486873,22487343,22487567,22487663,22487902,22488272,22488492,22488985,22489227,22489563,22489987,22490207,22490857,22490872,22493040,22495221,22496696,22497004,22504439,22504456,22509611],"created_at":"2020-03-04T18:00:07Z","created_at_i":1583344807,"num_comments":63,"objectID":"22486031","points":150,"story_id":22486031,"story_text":"Hey everyone, we\u2019re Tommy, David and Erik, co-founders of Release. (https://www.releaseapp.io). Release makes staging environments easy by automatically generating an ephemeral environment for every Pull Request.
David, Erik and I have worked together for almost 20 years starting with Erik and I meeting each other at an Internship right out of college. We met David in about 2003 when we were all working for RLX Technologies, which was one of the original blade server companies. Our early days were focused on systems management problems before VM\u2019s were widely used. Interestingly enough a lot of the systems problems we are facing today are similar to the problems faced back then, just abstracted a few levels. Staging environments were hard then and they are hard now.
Since then, we\u2019ve all pretty much stuck together, including co-founding IMSafer (detection of inappropriate conversations online for parents) together in 2006. David did take a break from us along the way as he became one of the early engineers at Etsy where he got a new perspective on systems challenges while Erik and I founded CarWoo! (YCS09 - online car buying made easy). David eventually made his way to CarWoo! and rejoined Erik and I after 4 years at Etsy where he was responsible for their search infrastructure and was involved in their DevOps systems. CarWoo! eventually was acqui-hired by TrueCar in 2014. We stayed on as the technology leadership team there for 5 years and led a major replatforming project that solved environments and enabled developers to iterate quickly.
Throughout our careers in doing systems engineering, starting companies, and being in and around technology there has been a universal difficulty in building environments that represent production and actually aid in getting work done vs being a bottleneck. As we\u2019ve explored this problem with early customers, we\u2019re getting similar feedback that\u2019s reinforced our excitement to solve this problem.
We\u2019re hearing some common themes as we talk with customers. Teams with just one or few staging environments have a big bottleneck in their process and can\u2019t get product delivered as quickly as they\u2019d like. The drift between production and the few staging environments a team may have is a problem. A lack of environments causes stakeholders to be out of the loop until really late in the development cycle and rework costs are high.
Engineering leaders are telling us how expensive in time, money and distraction away from core company objectives it will be to focus on this effort. Many times this is the reason this project (building a more flexible staging environment solutions) sits in the backlog and teams are just living with suboptimal velocity. Leaders that have invested in building an in-house solution have told us how complicated building it has been and aren\u2019t happy that they\u2019ve had to dedicate resources to this versus solving customer problems directly.
It\u2019s not all bad though, as companies who have actually built and are maintaining adequate environment infrastructure have a distinct advantage in speed of delivery of complex applications. They are moving faster and that speed is a distinct advantage over companies without this capability. However, the cost of building this infrastructure is generally prohibitively high unless you\u2019ve raised a lot of money or your application is extremely simple.
We\u2019ve solved staging environments at every company we\u2019ve started or been a part of throughout our careers and it\u2019s always been the key to enabling us to move fast. We\u2019ve learned that if developers have their own staging environment automatically created on every Pull Request, they can move incredibly fast. They are free to experiment, they can share work-in-progress changes with stakeholders early and they aren\u2019t waiting for resources to free up to get their work done. We\u2019ve seen ideas come alive while they were being built which has allowed them to iterate faster with more feedback and less rework.
We decided to build Release after spending countless hours talking about our experiences in our careers and when we\u2019ve been the happiest at work. We\u2019ve seen how powerful technology teams can be when they have enabling platforms at their disposal and how hard it can be when they don\u2019t. We\u2019ve always taken pride in making developers happy and more efficient. Release is the perfect avenue to solve a really big problem AND do work we love, that makes us happy.
As we started thinking about building Release, we leaned into the technology we were already familiar with, Docker, docker-compose, AWS and used that as our starting point. We felt that adding Kubernetes to the equation gave us a way to create these environments in a generic way where almost any application would run. We\u2019ve tackled some complex environments for our early customers with lots of services and the interdependencies between them, including cloud native dependencies. Our ability to tackle complex environments has given us hope that the right technologies have emerged that make this possible now.
As we started exploring the idea, we knew Docker and containers were the baseline and we liked the starting point that docker-compose offered for running applications and defining environments. If you have a docker-compose we can run environments for you. We take that docker-compose and compile it into an application/environment definition (release.yml) that we automatically generate. Think of this as an abstraction that sits in between docker-compose and Kubernetes that gives you flexibility to define environments and resources that meet your needs.
From the application definition in the release.yml and your docker-compose we automatically generate all of the Kubernetes yaml files to run your environments. As a customer, you don\u2019t need to know anything about K8\u2019s. Whenever you do a PR we automatically generate all that\u2019s needed to deploy and run your environment in K8\u2019s. We\u2019re interested to hear how much access customers would want to the raw K8\u2019s ecosystem. To date we\u2019ve had the opinion that customers shouldn\u2019t have to deal with it, but would love to hear HN\u2019s opinions on this.
If you\u2019d like to give it a shot, request access at https://www.releaseapp.io We\u2019d love to have you! We\u2019ve tried to keep the model simple and just charge a fee for the number environments created each month by our users.
We\u2019d love to hear your feedback. We\u2019d also love to hear about how companies are handling this problem today. Your feedback is incredibly important to us, we know the HN community has a really unique perspective and appreciate you reading our launch post and making it this far in our launch story!","title":"Launch HN: Release (YC W20) \u2013 Staging environments made easy","updated_at":"2024-09-20T05:46:22Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"maziyar"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"We built an end-to-end protein AI pipeline covering structure prediction, sequence design, and codon optimization. After comparing multiple transformer architectures for codon-level language modeling, CodonRoBERTa-large-v2 emerged as the clear winner with a perplexity of 4.10 and a Spearman CAI correlation of 0.40, significantly outperforming ModernBERT. We then scaled to 25 species, trained 4 production models in 55 GPU-hours, and built a species-conditioned system that no other open-source project offers. Complete results, architectural decisions, and runnable code below."},"title":{"matchLevel":"none","matchedWords":[],"value":"Training mRNA Language Models Across 25 Species for $165"}},"_tags":["story","author_maziyar","story_47606244","ask_hn"],"author":"maziyar","children":[47606250,47639741,47639742,47639863,47639866,47640321,47640344,47641688,47641833,47642027,47642853,47645919,47645990,47646727,47656905],"created_at":"2026-04-01T20:38:24Z","created_at_i":1775075904,"num_comments":42,"objectID":"47606244","points":148,"story_id":47606244,"story_text":"We built an end-to-end protein AI pipeline covering structure prediction, sequence design, and codon optimization. After comparing multiple transformer architectures for codon-level language modeling, CodonRoBERTa-large-v2 emerged as the clear winner with a perplexity of 4.10 and a Spearman CAI correlation of 0.40, significantly outperforming ModernBERT. We then scaled to 25 species, trained 4 production models in 55 GPU-hours, and built a species-conditioned system that no other open-source project offers. Complete results, architectural decisions, and runnable code below.","title":"Training mRNA Language Models Across 25 Species for $165","updated_at":"2026-04-09T20:58:50Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"nilaymodi123"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"Hello world, my name is Nilay. My cofounder Jorge and I founded Slingshow (https://slingshow.com/) and we're super excited (and nervous!) to finally launch on HN. Slingshow helps you build custom virtual events for your community. Think Zoom, but with a modern and flexible user experience that seamlessly flows between different ways of interacting with larger and smaller groups.
Last year, we were in YC W20 pivoting through several ideas, but then the pandemic hit. Suddenly, our YC batch was just a series of monotonous Zoom calls. We lost the magic of the social dinners, random conversations, and catching a speaker afterward for a personal chat. We quickly recognized that while Zoom was great for small meetings, the functionality and UI/UX made large group events unorganized and passive.
During the pandemic, many novel and sometimes gimmicky video platforms emerged targeting the social niche. But every organization hosts more than social events. We built Slingshow to have no learning curve for traditional use cases like panels and presentations while seamlessly incorporating newer social formats. We think the best virtual event formats are still in their infancy and are unique to every use case, so we wanted to create a flexible tool that would let organizers build their own experiences.
We were inspired by companies like Notion, Coda, Retool, and Airtable, which work in the UI/UX paradigm of creating functional modular building blocks. This means choosing a few simple, valuable abstractions like a table or a button and letting users mix and match these components to meet their needs. In our case, we\u2019re starting with four fundamental blocks that organizers use to plan a schedule: Tables for free-flowing networking like interactions, Rooms for concurrent presentations, a Stage for classic webinar like presentations, and a Call to Action block for redirecting to external websites like forms, activities, etc.
Using our blocks, one of our enterprise customers (Fivetran) holds their standard webinar on our Stage block and then shifts into the Rooms block where attendees can choose to speak with the panelists. Another customer held a dating event that started with a Stage to introduce the event, then switched to Tables of 4, then 3, then 2 with different prompts to create more intimate conversations. We also have other customers hosting unique product launches, happy hours, live podcasts, cohort-based classes, and multi-day hackathons. While some platforms offer similar functionality, they\u2019re heavy conference platforms with a large learning curve. They also require days of work to create specialized assets and often involve trained intermediaries like event organizers and planners.
We\u2019re not focused on conferences but rather on simplicity, and Blocks help us achieve that. They're versatile and help simplify the organizer's event creation process by hiding complexity. Blocks are also a simple way for us to add new formats in the future. By just by creating a schedule with Blocks, Slingshow automatically generates the event page with registration, a cover image, and the entire attendee experience. Add a logo and brand color, and we'll automatically theme the entire event to make it feel like your brand.
We've chosen to launch late as nothing else matters if the video isn't stable and reliable for first-time users. One speaker failing to connect could ruin an event leaving a bad impression on everyone involved. We\u2019ve spent several months working with early customers to gracefully handle errors and give helpful error messages for all the complexity of video: weak network connections, old browsers, mobile devices, firewalls, SDK edge cases, permission issues, etc. Depending on the use case, we also switch between multiple WebRTC video infrastructure providers. And lastly, following the lead of companies like Discord and Tandem, we built our backend using the Phoenix framework in Elixir because of its excellent support for WebSockets.
We still have a long way to go, but we feel confident with what we have and are ready to open up to a broader audience. We'd love to hear your feedback and experiences with the many virtual events you've probably experienced over this pandemic. Thanks! :\u2013)
You can try out Slingshow for free here \u2013 https://slingshow.com/."},"title":{"matchLevel":"none","matchedWords":[],"value":"Launch HN: Slingshow (YC W20) \u2013 Easily build custom virtual events and webinars"}},"_tags":["story","author_nilaymodi123","story_27474808","launch_hn"],"author":"nilaymodi123","children":[27475345,27475409,27475627,27475728,27476126,27476615,27476656,27477274,27477375,27478186,27478522,27478911,27480263,27483101],"created_at":"2021-06-11T15:41:01Z","created_at_i":1623426061,"num_comments":34,"objectID":"27474808","points":99,"story_id":27474808,"story_text":"Hello world, my name is Nilay. My cofounder Jorge and I founded Slingshow (https://slingshow.com/) and we're super excited (and nervous!) to finally launch on HN. Slingshow helps you build custom virtual events for your community. Think Zoom, but with a modern and flexible user experience that seamlessly flows between different ways of interacting with larger and smaller groups.
Last year, we were in YC W20 pivoting through several ideas, but then the pandemic hit. Suddenly, our YC batch was just a series of monotonous Zoom calls. We lost the magic of the social dinners, random conversations, and catching a speaker afterward for a personal chat. We quickly recognized that while Zoom was great for small meetings, the functionality and UI/UX made large group events unorganized and passive.
During the pandemic, many novel and sometimes gimmicky video platforms emerged targeting the social niche. But every organization hosts more than social events. We built Slingshow to have no learning curve for traditional use cases like panels and presentations while seamlessly incorporating newer social formats. We think the best virtual event formats are still in their infancy and are unique to every use case, so we wanted to create a flexible tool that would let organizers build their own experiences.
We were inspired by companies like Notion, Coda, Retool, and Airtable, which work in the UI/UX paradigm of creating functional modular building blocks. This means choosing a few simple, valuable abstractions like a table or a button and letting users mix and match these components to meet their needs. In our case, we\u2019re starting with four fundamental blocks that organizers use to plan a schedule: Tables for free-flowing networking like interactions, Rooms for concurrent presentations, a Stage for classic webinar like presentations, and a Call to Action block for redirecting to external websites like forms, activities, etc.
Using our blocks, one of our enterprise customers (Fivetran) holds their standard webinar on our Stage block and then shifts into the Rooms block where attendees can choose to speak with the panelists. Another customer held a dating event that started with a Stage to introduce the event, then switched to Tables of 4, then 3, then 2 with different prompts to create more intimate conversations. We also have other customers hosting unique product launches, happy hours, live podcasts, cohort-based classes, and multi-day hackathons. While some platforms offer similar functionality, they\u2019re heavy conference platforms with a large learning curve. They also require days of work to create specialized assets and often involve trained intermediaries like event organizers and planners.
We\u2019re not focused on conferences but rather on simplicity, and Blocks help us achieve that. They're versatile and help simplify the organizer's event creation process by hiding complexity. Blocks are also a simple way for us to add new formats in the future. By just by creating a schedule with Blocks, Slingshow automatically generates the event page with registration, a cover image, and the entire attendee experience. Add a logo and brand color, and we'll automatically theme the entire event to make it feel like your brand.
We've chosen to launch late as nothing else matters if the video isn't stable and reliable for first-time users. One speaker failing to connect could ruin an event leaving a bad impression on everyone involved. We\u2019ve spent several months working with early customers to gracefully handle errors and give helpful error messages for all the complexity of video: weak network connections, old browsers, mobile devices, firewalls, SDK edge cases, permission issues, etc. Depending on the use case, we also switch between multiple WebRTC video infrastructure providers. And lastly, following the lead of companies like Discord and Tandem, we built our backend using the Phoenix framework in Elixir because of its excellent support for WebSockets.
We still have a long way to go, but we feel confident with what we have and are ready to open up to a broader audience. We'd love to hear your feedback and experiences with the many virtual events you've probably experienced over this pandemic. Thanks! :\u2013)
You can try out Slingshow for free here \u2013 https://slingshow.com/.","title":"Launch HN: Slingshow (YC W20) \u2013 Easily build custom virtual events and webinars","updated_at":"2024-09-20T08:47:00Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"mustafabal"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"Hey HN! Mustafa, Lizzie, and Varun here from NomadicML (https://nomadicml.com). We\u2019re excited to show you Nomadic (https://github.com/nomadic-ml/nomadic): a platform focused on parameter search to continuously optimize AI systems.
Here\u2019s a simple demo notebook where you get the best-performing, statistically significant configurations for your RAG \u2014 and improve hallucination metrics by 4X in just 5 minutes \u2014 with a single Nomadic experiment: https://tinyurl.com/4xmaryyw
Our lightweight library is now live on PyPI (`pip install nomadic`). Try one of the README examples :) Input your model, define an evaluation metric, specify the dataset, and choose which parameters to test.
Nomadic emerged from our frustration with existing HPO (hyperparameter optimization) solutions. We heard over and over that for the sake of deploying fast, folks resort to setting HPs through a single, expensive grid search or better yet, intuition-based \u201cvibes\u201d. From fine-tuning to inference, small tweaks to HPs can have a huge impact on performance.
We wanted a tool to make that \u201cdrunken wander\u201d systematic, quick, and interpretable. So we started building Nomadic - our goal is to create the best parameter search platform out there for your ML systems to keep your hyperparameters, prompts, and all aspects of your AI system production-grade. We started aggregating top parameter search techniques from popular tools and research (Bayesian Optimizations, cost-frugal flavors).
Among us: Built Lyft\u2019s driver earnings platform, automated Snowflake\u2019s just-in-time compute resource allocation, became a finalist for the INFORMS Wagner Prize (top prize in industrial optimization), and developed a fintech fraud screening system for half a million consumers. You might say we love optimization.
If you\u2019re building AI agents / applications across LLM safety, fintech, support, or especially compound AI systems (multiple components > monolithic models), and want to deeply understand your ML system\u2019s best levers to boost performance as it scales - get in touch.
Nomadic is being actively developed. Up next: Supporting text-to-SQL pipelines (TAG) and a Workspace UI (preview it at https://demo.nomadicml.com). We\u2019re eager to hear honest feedback, likes, dislikes, feature requests, you name it. If you\u2019re also a optimization junkie, we\u2019d love for you to join our community here https://discord.gg/PF869aGM"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Nomadic \u2013 Minimize RAG Hallucinations with 1 Hyperparameter Experiment"}},"_tags":["story","author_mustafabal","story_41459121","show_hn"],"author":"mustafabal","children":[41459194,41459225,41459305,41459314,41459326,41459398,41459534,41459539,41459785,41459805,41459916,41459995,41460124,41460182,41460507,41460560,41460579,41460648,41460715,41460809,41461314,41461358,41462104,41462721,41464091,41468712,41468926,41469402,41472556,41498176],"created_at":"2024-09-05T18:14:46Z","created_at_i":1725560086,"num_comments":19,"objectID":"41459121","points":95,"story_id":41459121,"story_text":"Hey HN! Mustafa, Lizzie, and Varun here from NomadicML (https://nomadicml.com). We\u2019re excited to show you Nomadic (https://github.com/nomadic-ml/nomadic): a platform focused on parameter search to continuously optimize AI systems.
Here\u2019s a simple demo notebook where you get the best-performing, statistically significant configurations for your RAG \u2014 and improve hallucination metrics by 4X in just 5 minutes \u2014 with a single Nomadic experiment: https://tinyurl.com/4xmaryyw
Our lightweight library is now live on PyPI (`pip install nomadic`). Try one of the README examples :) Input your model, define an evaluation metric, specify the dataset, and choose which parameters to test.
Nomadic emerged from our frustration with existing HPO (hyperparameter optimization) solutions. We heard over and over that for the sake of deploying fast, folks resort to setting HPs through a single, expensive grid search or better yet, intuition-based \u201cvibes\u201d. From fine-tuning to inference, small tweaks to HPs can have a huge impact on performance.
We wanted a tool to make that \u201cdrunken wander\u201d systematic, quick, and interpretable. So we started building Nomadic - our goal is to create the best parameter search platform out there for your ML systems to keep your hyperparameters, prompts, and all aspects of your AI system production-grade. We started aggregating top parameter search techniques from popular tools and research (Bayesian Optimizations, cost-frugal flavors).
Among us: Built Lyft\u2019s driver earnings platform, automated Snowflake\u2019s just-in-time compute resource allocation, became a finalist for the INFORMS Wagner Prize (top prize in industrial optimization), and developed a fintech fraud screening system for half a million consumers. You might say we love optimization.
If you\u2019re building AI agents / applications across LLM safety, fintech, support, or especially compound AI systems (multiple components > monolithic models), and want to deeply understand your ML system\u2019s best levers to boost performance as it scales - get in touch.
Nomadic is being actively developed. Up next: Supporting text-to-SQL pipelines (TAG) and a Workspace UI (preview it at https://demo.nomadicml.com). We\u2019re eager to hear honest feedback, likes, dislikes, feature requests, you name it. If you\u2019re also a optimization junkie, we\u2019d love for you to join our community here https://discord.gg/PF869aGM","title":"Show HN: Nomadic \u2013 Minimize RAG Hallucinations with 1 Hyperparameter Experiment","updated_at":"2025-01-31T04:05:35Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"isusmelj"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"Hi HackerNews! We\u2019re Matt and Igor from Lightly (https://www.lightly.ai/). Most companies that do machine learning at scale label only 1% of their data because it's too expensive to label all of it. We built Lightly to help companies pick the most valuable 1% to be labeled.
If you wonder what data labeling looks like for images then think about these captchas that want you to tag images in the web containing objects such as a bus or person. When we were working on training machine learning (ML) models from scratch, we often had to do this labeling ourselves. But there was always far too much data for us to be able to label all of it. We talked with more than 250 ML teams ranging from small groups of 2-3 people to large teams at Apple and Google, and they all face the same problem: they have too much data to label.
Not only that, but there wouldn\u2019t be a lot of value in labeling everything. For example, if you have billions of images, it's a waste of time to get humans to label every one of them, because most of those labels wouldn't add useful information to the model you\u2019re hoping to train. Most of the images are probably similar enough to other images that have already been labeled and they have nothing new to tell your model. Spending more labeling effort on those would be a bit like labeling the same image over and over again\u2014quite wasteful.
As soon as your ML model surpasses the initial prototype stage, you\u2019re most interested in the edge cases in your dataset \u2014 the ones that represent rare events. For example, a few days ago, there was a Twitter thread about failure cases for Tesla vehicles. One Tesla has mistaken a yellow moon for a yellow traffic light: https://twitter.com/JordanTeslaTech/status/14184133078625853.... Another edge case is a truck full of traffic lights: https://twitter.com/haltakov/status/1400797882891091970. Finding and labeling such rare cases is key to having a robust system that will work in difficult situations.
Rather than labeling everything, a better approach is to first discard all the redundant images and keep only the ones that it's worth spending time/money to label. Let's call those "interesting" images. If you could spend labeling effort only on the "interesting" images, you'd get the same value for a fraction of the cost.
Many ML companies in a more advanced stage have had to tackle this problem. One approach is to pay people to go through the images and discard the "boring" (nothing-new-to-tell-me) images, leaving the "interesting" (worth-spending-resources-to-label) ones. That can save you money if it's on average cheaper to answer the question "boring or interesting?" about an image than it is to label it. This solution scales as long as you have an increasing human labeling workforce every year. However, ML data doubles every year on average, and therefore the labeling capacity would need to double too.
Much better than that \u2014 the holy grail \u2014 would be for a computer to do the work of discarding the "boring" images. Compared to paying humans to do it, you'd get the "interesting" subset of your billion images almost for free. You would have much less work to do (or money to spend) on labeling, and you'd get just as good a model after training. You could split the savings with whoever knew how to make a computer do this for you, and you'd both come out ahead. That\u2019s basically our intention with Lightly.
My co-founder Matt and I worked on many machine learning projects ourselves, where we also had to manage tooling and annotation budgets. Dealing with data in a production environment is different from academia. In academia, we have well-balanced and manually curated datasets. It is, as some of you know, a huge pain. The solution of the problem boils down to working with unlabeled data.
Luckily, in recent years, a new subfield of deep learning has emerged called self-supervised learning. It\u2019s a technique to train models to understand data without any labels. In natural language processing (NLP), modern models like BERT or GPT all rely on it. In computer vision, we have had a similar breakthrough in the last year with models such as SimCLR or MoCo. Back in 2020, we started experimenting with self-supervised learning to better understand unlabeled data and improve our software. However, there was no easy-to-use framework available to work with the latest models. To solve that problem, we built our own framework to make the power of self-supervised learning easily accessible. Since we want to foster research in this domain and grow a bigger community around this topic we decided to open-source the framework in fall 2020 (https://github.com/lightly-ai/lightly). It is now used by universities and research labs all over the world.\nWe realized that the ability to understand and visualize unlabeled data is also valuable to other ML teams and decided to offer our solution as a SaaS platform. The platform builds on the open-source framework and helps you work with the most valuable data.
Here are some examples where Lightly can help you:
Analyze the quality and diversity of your datasets. Our platform can also use metadata or labels if available. Uncover class distributions, dataset gaps, and representation biases before labeling to save time and money. You can do it manually or automated through our data selection algorithms which ensure the most diverse subset of your dataset is chosen. (https://docs.lightly.ai/).
Once you have a labeled dataset and trained your model, our active-learning algorithms allow you to gradually select the next data to be added to your training set. Only label the best data for model training until you reach your target accuracy. https://docs.lightly.ai/getting_started/active_learning.html
Check it out yourself with this quick demo video https://www.youtube.com/watch?v=38kwv0xEIz4
Lightly integrates with an API directly into your pipeline, is available on-prem, and can process up to 100M samples within hours.
We're excited we get to show Lightly to you all. Thank you for reading! Please let us know your thoughts and questions in the comments."},"title":{"matchLevel":"none","matchedWords":[],"value":"Launch HN: Lightly (YC S21): Label only the data which improves your ML model"}},"_tags":["story","author_isusmelj","story_28116371","launch_hn"],"author":"isusmelj","children":[28117072,28117258,28117632,28117676,28117854,28123524,28125500,28125712,28126415,28128573],"created_at":"2021-08-09T13:17:17Z","created_at_i":1628515037,"num_comments":25,"objectID":"28116371","points":88,"story_id":28116371,"story_text":"Hi HackerNews! We\u2019re Matt and Igor from Lightly (https://www.lightly.ai/). Most companies that do machine learning at scale label only 1% of their data because it's too expensive to label all of it. We built Lightly to help companies pick the most valuable 1% to be labeled.
If you wonder what data labeling looks like for images then think about these captchas that want you to tag images in the web containing objects such as a bus or person. When we were working on training machine learning (ML) models from scratch, we often had to do this labeling ourselves. But there was always far too much data for us to be able to label all of it. We talked with more than 250 ML teams ranging from small groups of 2-3 people to large teams at Apple and Google, and they all face the same problem: they have too much data to label.
Not only that, but there wouldn\u2019t be a lot of value in labeling everything. For example, if you have billions of images, it's a waste of time to get humans to label every one of them, because most of those labels wouldn't add useful information to the model you\u2019re hoping to train. Most of the images are probably similar enough to other images that have already been labeled and they have nothing new to tell your model. Spending more labeling effort on those would be a bit like labeling the same image over and over again\u2014quite wasteful.
As soon as your ML model surpasses the initial prototype stage, you\u2019re most interested in the edge cases in your dataset \u2014 the ones that represent rare events. For example, a few days ago, there was a Twitter thread about failure cases for Tesla vehicles. One Tesla has mistaken a yellow moon for a yellow traffic light: https://twitter.com/JordanTeslaTech/status/14184133078625853.... Another edge case is a truck full of traffic lights: https://twitter.com/haltakov/status/1400797882891091970. Finding and labeling such rare cases is key to having a robust system that will work in difficult situations.
Rather than labeling everything, a better approach is to first discard all the redundant images and keep only the ones that it's worth spending time/money to label. Let's call those "interesting" images. If you could spend labeling effort only on the "interesting" images, you'd get the same value for a fraction of the cost.
Many ML companies in a more advanced stage have had to tackle this problem. One approach is to pay people to go through the images and discard the "boring" (nothing-new-to-tell-me) images, leaving the "interesting" (worth-spending-resources-to-label) ones. That can save you money if it's on average cheaper to answer the question "boring or interesting?" about an image than it is to label it. This solution scales as long as you have an increasing human labeling workforce every year. However, ML data doubles every year on average, and therefore the labeling capacity would need to double too.
Much better than that \u2014 the holy grail \u2014 would be for a computer to do the work of discarding the "boring" images. Compared to paying humans to do it, you'd get the "interesting" subset of your billion images almost for free. You would have much less work to do (or money to spend) on labeling, and you'd get just as good a model after training. You could split the savings with whoever knew how to make a computer do this for you, and you'd both come out ahead. That\u2019s basically our intention with Lightly.
My co-founder Matt and I worked on many machine learning projects ourselves, where we also had to manage tooling and annotation budgets. Dealing with data in a production environment is different from academia. In academia, we have well-balanced and manually curated datasets. It is, as some of you know, a huge pain. The solution of the problem boils down to working with unlabeled data.
Luckily, in recent years, a new subfield of deep learning has emerged called self-supervised learning. It\u2019s a technique to train models to understand data without any labels. In natural language processing (NLP), modern models like BERT or GPT all rely on it. In computer vision, we have had a similar breakthrough in the last year with models such as SimCLR or MoCo. Back in 2020, we started experimenting with self-supervised learning to better understand unlabeled data and improve our software. However, there was no easy-to-use framework available to work with the latest models. To solve that problem, we built our own framework to make the power of self-supervised learning easily accessible. Since we want to foster research in this domain and grow a bigger community around this topic we decided to open-source the framework in fall 2020 (https://github.com/lightly-ai/lightly). It is now used by universities and research labs all over the world.\nWe realized that the ability to understand and visualize unlabeled data is also valuable to other ML teams and decided to offer our solution as a SaaS platform. The platform builds on the open-source framework and helps you work with the most valuable data.
Here are some examples where Lightly can help you:
Analyze the quality and diversity of your datasets. Our platform can also use metadata or labels if available. Uncover class distributions, dataset gaps, and representation biases before labeling to save time and money. You can do it manually or automated through our data selection algorithms which ensure the most diverse subset of your dataset is chosen. (https://docs.lightly.ai/).
Once you have a labeled dataset and trained your model, our active-learning algorithms allow you to gradually select the next data to be added to your training set. Only label the best data for model training until you reach your target accuracy. https://docs.lightly.ai/getting_started/active_learning.html
Check it out yourself with this quick demo video https://www.youtube.com/watch?v=38kwv0xEIz4
Lightly integrates with an API directly into your pipeline, is available on-prem, and can process up to 100M samples within hours.
We're excited we get to show Lightly to you all. Thank you for reading! Please let us know your thoughts and questions in the comments.","title":"Launch HN: Lightly (YC S21): Label only the data which improves your ML model","updated_at":"2025-09-19T08:01:29Z"}],"hitsPerPage":50,"nbHits":72,"nbPages":2,"page":0,"params":"query=Emerald+AI&tags=story&hitsPerPage=50&advancedSyntax=true&analyticsTags=backend","processingTimeMS":11,"processingTimingsMS":{"_request":{"roundTrip":13},"afterFetch":{"format":{"highlighting":4,"total":5},"merge":{"mergeLoop":{"prepareNextHit":2,"total":2},"total":2},"total":2},"fetch":{"query":5,"scanning":2,"total":8},"total":11},"query":"Emerald AI","serverTimeMS":17}