No signup, runs on mobile/desktop.
Loop per round:
1. choose compute capacity\n2. forecast demand\n3. allocate capacity between training and inference\n4. random demand shock resolves outcome
You can end profitable, cash constrained, or bankrupt depending on allocation + forecast error.
Goal was to make the decision surface intuitive in 2\u20133 minutes per run.
It\u2019s a toy model and deliberately omits many real world factors.
Note: this is based on what I learned after listening to Dario on Dwarkesh's podcast - thought it was fascinating."},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"Show HN: A playable toy model of frontier AI lab capex decisions"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://darios-dilemma.up.railway.app/"}},"_tags":["story","author_jimmyechan","story_47012453","show_hn"],"author":"jimmyechan","created_at":"2026-02-14T07:29:45Z","created_at_i":1771054185,"num_comments":0,"objectID":"47012453","points":8,"story_id":47012453,"story_text":"I made a lightweight web game about compute CAPEX tradeoffs: https://darios-dilemma.up.railway.app/
No signup, runs on mobile/desktop.
Loop per round:
1. choose compute capacity\n2. forecast demand\n3. allocate capacity between training and inference\n4. random demand shock resolves outcome
You can end profitable, cash constrained, or bankrupt depending on allocation + forecast error.
Goal was to make the decision surface intuitive in 2\u20133 minutes per run.
It\u2019s a toy model and deliberately omits many real world factors.
Note: this is based on what I learned after listening to Dario on Dwarkesh's podcast - thought it was fascinating.","title":"Show HN: A playable toy model of frontier AI lab capex decisions","updated_at":"2026-03-05T23:32:29Z","url":"https://darios-dilemma.up.railway.app/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"FuturisticLover"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"Reflection AI raises $2B to be America's open frontier AI lab"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"https://techcrunch.com/2025/10/09/reflection-raises-2b-to-be-americas-open-frontier-ai-lab-challenging-deepseek/"}},"_tags":["story","author_FuturisticLover","story_45536055"],"author":"FuturisticLover","created_at":"2025-10-10T06:57:09Z","created_at_i":1760079429,"num_comments":0,"objectID":"45536055","points":8,"story_id":45536055,"title":"Reflection AI raises $2B to be America's open frontier AI lab","updated_at":"2026-03-05T22:49:21Z","url":"https://techcrunch.com/2025/10/09/reflection-raises-2b-to-be-americas-open-frontier-ai-lab-challenging-deepseek/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"Bender"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"Microsoft chief turns hostile on frontier AI labs, warns companies to guard IP"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"https://www.theregister.com/ai-and-ml/2026/07/13/microsoft-chief-turns-hostile-on-frontier-ai-labs-warns-companies-to-guard-their-ip/5270628"}},"_tags":["story","author_Bender","story_48898065"],"author":"Bender","created_at":"2026-07-13T20:07:14Z","created_at_i":1783973234,"num_comments":0,"objectID":"48898065","points":7,"story_id":48898065,"title":"Microsoft chief turns hostile on frontier AI labs, warns companies to guard IP","updated_at":"2026-07-14T01:18:00Z","url":"https://www.theregister.com/ai-and-ml/2026/07/13/microsoft-chief-turns-hostile-on-frontier-ai-labs-warns-companies-to-guard-their-ip/5270628"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"abhaynayar"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"How to Land a Frontier Lab Job"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"https://vladfeinberg.com/2026/05/10/how-to-land-a-job-at-a-frontier-lab.html"}},"_tags":["story","author_abhaynayar","story_48599230"],"author":"abhaynayar","created_at":"2026-06-19T14:47:07Z","created_at_i":1781880427,"num_comments":0,"objectID":"48599230","points":4,"story_id":48599230,"title":"How to Land a Frontier Lab Job","updated_at":"2026-07-03T12:27:07Z","url":"https://vladfeinberg.com/2026/05/10/how-to-land-a-job-at-a-frontier-lab.html"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"sleepyguy"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"Frontier labs don't use most AI compute(yet)"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"https://epoch.ai/gradient-updates/frontier-labs-dont-use-most-ai-compute"}},"_tags":["story","author_sleepyguy","story_48251433"],"author":"sleepyguy","created_at":"2026-05-23T20:55:59Z","created_at_i":1779569759,"num_comments":0,"objectID":"48251433","points":4,"story_id":48251433,"title":"Frontier labs don't use most AI compute(yet)","updated_at":"2026-05-23T22:12:00Z","url":"https://epoch.ai/gradient-updates/frontier-labs-dont-use-most-ai-compute"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"te_ch"},"story_text":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["labs"],"value":"I built this website while exploring job opportunities at AI companies. Coming from an economics & policy background (I've done a good deal of research on the business/economics side gen AI, quantum and other emerging tech but I'm not an AI engineer), I wanted to understand how different roles fit together inside these companies.
So I started building a taxonomy to map job postings into departments and functions that is consistent across labs, making it easier for me to understand where each lab is hiring and for what. Out of curiosity, I expanded to see what's hot, etc. in technical areas too. It ended up being both job search and personal research project.
Coming up with a standard taxonomy is challenging. I'm still figuring out matching jobs to functions/departments, looking at other labs, and thinking of alt ways to organize this, eg., I see that companies associate job postings to specific teams, but I still don't have good descriptions of the scope of those teams to create crosswalks. I'm also looking at other companies.
So, sharing it here for others who may find it useful or want to comment. It'd be great to have feedback from those who have insight into how these companies organize their departments/functions, and from technical job seekers too, if this is something that helps them better understand job opportunities. If people find it useful, it could become something I maintain over the longer term (particularly if I don't end up finding a job at one of these labs)."},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"Show HN: Frontier AI Lab Jobs \u2013 Open Jobs by Function at OpenAI, Anthropic"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://frontierjobs.org/"}},"_tags":["story","author_te_ch","story_48738278","show_hn"],"author":"te_ch","children":[48738828],"created_at":"2026-06-30T19:48:55Z","created_at_i":1782848935,"num_comments":1,"objectID":"48738278","points":3,"story_id":48738278,"story_text":"I built this website while exploring job opportunities at AI companies. Coming from an economics & policy background (I've done a good deal of research on the business/economics side gen AI, quantum and other emerging tech but I'm not an AI engineer), I wanted to understand how different roles fit together inside these companies.
So I started building a taxonomy to map job postings into departments and functions that is consistent across labs, making it easier for me to understand where each lab is hiring and for what. Out of curiosity, I expanded to see what's hot, etc. in technical areas too. It ended up being both job search and personal research project.
Coming up with a standard taxonomy is challenging. I'm still figuring out matching jobs to functions/departments, looking at other labs, and thinking of alt ways to organize this, eg., I see that companies associate job postings to specific teams, but I still don't have good descriptions of the scope of those teams to create crosswalks. I'm also looking at other companies.
So, sharing it here for others who may find it useful or want to comment. It'd be great to have feedback from those who have insight into how these companies organize their departments/functions, and from technical job seekers too, if this is something that helps them better understand job opportunities. If people find it useful, it could become something I maintain over the longer term (particularly if I don't end up finding a job at one of these labs).","title":"Show HN: Frontier AI Lab Jobs \u2013 Open Jobs by Function at OpenAI, Anthropic","updated_at":"2026-07-09T00:28:58Z","url":"https://frontierjobs.org/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"tworats"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"The Bear Case for Frontier AI Labs"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"https://www.parand.com/the-bear-case-for-frontier-ai-labs.html"}},"_tags":["story","author_tworats","story_48504435"],"author":"tworats","created_at":"2026-06-12T14:15:54Z","created_at_i":1781273754,"num_comments":0,"objectID":"48504435","points":3,"story_id":48504435,"title":"The Bear Case for Frontier AI Labs","updated_at":"2026-06-15T15:51:15Z","url":"https://www.parand.com/the-bear-case-for-frontier-ai-labs.html"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"achllies"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"The GPU Moat Has a Side Door: AI Research Outside the Frontier Labs"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://mangeshgupte.substack.com/p/the-gpu-moat-has-a-side-door"}},"_tags":["story","author_achllies","story_47754752"],"author":"achllies","created_at":"2026-04-13T16:48:32Z","created_at_i":1776098912,"num_comments":0,"objectID":"47754752","points":3,"story_id":47754752,"title":"The GPU Moat Has a Side Door: AI Research Outside the Frontier Labs","updated_at":"2026-04-14T01:39:35Z","url":"https://mangeshgupte.substack.com/p/the-gpu-moat-has-a-side-door"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"pranay01"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"Reflection AI raises $2B to be America's open frontier AI lab"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"https://techcrunch.com/2025/10/09/reflection-raises-2b-to-be-americas-open-frontier-ai-lab-challenging-deepseek/"}},"_tags":["story","author_pranay01","story_45545939"],"author":"pranay01","created_at":"2025-10-11T02:01:20Z","created_at_i":1760148080,"num_comments":0,"objectID":"45545939","points":3,"story_id":45545939,"title":"Reflection AI raises $2B to be America's open frontier AI lab","updated_at":"2026-03-05T22:49:58Z","url":"https://techcrunch.com/2025/10/09/reflection-raises-2b-to-be-americas-open-frontier-ai-lab-challenging-deepseek/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"ocean_moist"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"What I Would Tell the Frontier Labs"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://rohan.ga/blog/critique/"}},"_tags":["story","author_ocean_moist","story_42679328"],"author":"ocean_moist","created_at":"2025-01-13T02:15:14Z","created_at_i":1736734514,"num_comments":0,"objectID":"42679328","points":3,"story_id":42679328,"title":"What I Would Tell the Frontier Labs","updated_at":"2025-01-13T07:09:02Z","url":"https://rohan.ga/blog/critique/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"ainthusiast"},"story_text":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["labs"],"value":"Quite annoying but obvious and somewhat expected trend of AI labs seeing open-source projects, repackaging it e.g. openclaw.ai as Cowork or more recent agenthandover.com as Chronicles in Codex...
It is expected to happen, but perhaps some credits/offers should be on the table, no?"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"Frontier AI labs taking open-source and releasing it as a product"}},"_tags":["story","author_ainthusiast","story_47876161","ask_hn"],"author":"ainthusiast","children":[47876193],"created_at":"2026-04-23T14:27:35Z","created_at_i":1776954455,"num_comments":1,"objectID":"47876161","points":2,"story_id":47876161,"story_text":"Quite annoying but obvious and somewhat expected trend of AI labs seeing open-source projects, repackaging it e.g. openclaw.ai as Cowork or more recent agenthandover.com as Chronicles in Codex...
It is expected to happen, but perhaps some credits/offers should be on the table, no?","title":"Frontier AI labs taking open-source and releasing it as a product","updated_at":"2026-04-23T14:37:56Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"taubek"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"Anatomy of a frontier-lab agent intrusion"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"https://huggingface-anatomy-of-frontier-lab-model-intrusion.static.hf.space/index.html"}},"_tags":["story","author_taubek","story_49103523"],"author":"taubek","created_at":"2026-07-29T21:49:12Z","created_at_i":1785361752,"num_comments":0,"objectID":"49103523","points":2,"story_id":49103523,"title":"Anatomy of a frontier-lab agent 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mistake","updated_at":"2026-07-21T11:17:40Z","url":"https://twitter.com/chamath/status/2079457219892871458"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"zameermfm"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"If you like to be in Altman\u2019s or Dario\u2019s or even Musk\u2019s shoes for a day to run an AI frontier lab, here is your chance. \nYou need to beat your rivals to achieve AGI titan status. But there is a twist in getting to AGI - if you don\u2019t do well with Safety, Ethics and Public confidence, you will be ending up with Dangerous AGI, which is not a good place to be in.
Go through the easy mode to learn the game.
I tried to be closer to realistic lines but did take my creative freedom for the gaming elements. This is a simulation game which will test your skills and make you learn about Frontier AI domain. I sourced the information for this via Podcasts, News, Blog posts and of course AI searches. \nI also want this to be a public awakening call for Safety on AI/AGI. Let me know your feedbacks and how the game is, I\u2019ll be tuning the gears and updating the game weekly. \nMain motivation is to bring in current breaking news and AI news in to the game within the following weeks.
All your suggestions are welcome. Hope you all enjoy the game!
https://rtagi.online"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"Show HN: Race to AGI \u2013 a simulation browser game about running a frontier AI lab"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://rtagi.online"}},"_tags":["story","author_zameermfm","story_48957993","show_hn"],"author":"zameermfm","created_at":"2026-07-18T13:27:57Z","created_at_i":1784381277,"num_comments":0,"objectID":"48957993","points":1,"story_id":48957993,"story_text":"If you like to be in Altman\u2019s or Dario\u2019s or even Musk\u2019s shoes for a day to run an AI frontier lab, here is your chance. \nYou need to beat your rivals to achieve AGI titan status. But there is a twist in getting to AGI - if you don\u2019t do well with Safety, Ethics and Public confidence, you will be ending up with Dangerous AGI, which is not a good place to be in.
Go through the easy mode to learn the game.
I tried to be closer to realistic lines but did take my creative freedom for the gaming elements. This is a simulation game which will test your skills and make you learn about Frontier AI domain. I sourced the information for this via Podcasts, News, Blog posts and of course AI searches. \nI also want this to be a public awakening call for Safety on AI/AGI. Let me know your feedbacks and how the game is, I\u2019ll be tuning the gears and updating the game weekly. \nMain motivation is to bring in current breaking news and AI news in to the game within the following weeks.
All your suggestions are welcome. Hope you all enjoy the game!
https://rtagi.online","title":"Show HN: Race to AGI \u2013 a simulation browser game about running a frontier AI lab","updated_at":"2026-07-18T13:32:30Z","url":"https://rtagi.online"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"gmays"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"Some notes on getting into frontier AI labs"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://twitter.com/itsreallyvivek/status/2062924410588406118"}},"_tags":["story","author_gmays","story_48506337"],"author":"gmays","created_at":"2026-06-12T16:42:12Z","created_at_i":1781282532,"num_comments":0,"objectID":"48506337","points":1,"story_id":48506337,"title":"Some notes on getting into frontier AI labs","updated_at":"2026-06-12T16:43:49Z","url":"https://twitter.com/itsreallyvivek/status/2062924410588406118"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"janos95"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"Can Frontier AI Labs make money?"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"https://janosmeny.com/blog/can-frontier-ai-labs-make-money/index.html"}},"_tags":["story","author_janos95","story_47906583"],"author":"janos95","created_at":"2026-04-26T01:58:29Z","created_at_i":1777168709,"num_comments":0,"objectID":"47906583","points":1,"story_id":47906583,"title":"Can Frontier AI Labs make money?","updated_at":"2026-04-27T08:55:55Z","url":"https://janosmeny.com/blog/can-frontier-ai-labs-make-money/index.html"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"gk1"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"Frontier AI Labs: The Call Option to AGI"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"https://eastwind.substack.com/p/frontier-ai-labs-the-call-option"}},"_tags":["story","author_gk1","story_44247764"],"author":"gk1","created_at":"2025-06-11T14:04:28Z","created_at_i":1749650668,"num_comments":0,"objectID":"44247764","points":1,"story_id":44247764,"title":"Frontier AI Labs: The Call Option to AGI","updated_at":"2025-06-11T14:09:01Z","url":"https://eastwind.substack.com/p/frontier-ai-labs-the-call-option"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"artur_makly"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"NASA Frontier Dev Lab and Google"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://showcase.withgoogle.com/nasa-fdl/"}},"_tags":["story","author_artur_makly","story_25979555"],"author":"artur_makly","created_at":"2021-01-31T15:37:48Z","created_at_i":1612107468,"num_comments":0,"objectID":"25979555","points":1,"story_id":25979555,"title":"NASA Frontier Dev Lab and Google","updated_at":"2024-09-20T07:53:23Z","url":"https://showcase.withgoogle.com/nasa-fdl/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"Ankaios"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"Frontier Development Lab applications open"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://frontierdevelopmentlab.org/apply-1"}},"_tags":["story","author_Ankaios","story_22750605"],"author":"Ankaios","created_at":"2020-04-01T16:36:34Z","created_at_i":1585758994,"num_comments":0,"objectID":"22750605","points":1,"story_id":22750605,"title":"Frontier Development Lab applications open","updated_at":"2024-09-20T05:53:43Z","url":"https://frontierdevelopmentlab.org/apply-1"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"kcorbitt"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"Hey HN, Kyle here, one of the co-founders of OpenPipe.
Reinforcement learning is one of the best techniques for making agents more reliable, and has been widely adopted by frontier labs. However, adoption in the outside community has been slow because it's so hard to implement.
One of the biggest challenges when adapting RL to a new task is the need for a task-specific "reward function" (way of measuring success). This is often difficult to define, and requires either high-quality labeled data and/or significant domain expertise to generate.
RULER is a drop-in reward function that works across different tasks without any of that complexity.
It works by showing N trajectories to an LLM judge and asking it to rank them relative to each other. This sidesteps the calibration issues that plague most LLM-as-judge approaches. Combined with GRPO (which only cares about relative scores within groups), it just works (surprisingly well!).
We have a full writeup on the blog, including results on 4 production tasks. On all 4 tasks, small Qwen 2.5 models trained with RULER+GRPO beat the best prompted frontier model, despite being significantly smaller and cheaper to run. Surprisingly, they even beat models trained with hand-crafted reward functions on 3/4 tasks! https://openpipe.ai/blog/ruler
Repo: https://github.com/OpenPipe/ART"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: RULER \u2013 Easily apply RL to any agent"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://openpipe.ai/blog/ruler"}},"_tags":["story","author_kcorbitt","story_44535078","show_hn"],"author":"kcorbitt","children":[44536454,44536791,44537103,44537189,44537230,44537495,44537726,44539411],"created_at":"2025-07-11T17:47:36Z","created_at_i":1752256056,"num_comments":11,"objectID":"44535078","points":81,"story_id":44535078,"story_text":"Hey HN, Kyle here, one of the co-founders of OpenPipe.
Reinforcement learning is one of the best techniques for making agents more reliable, and has been widely adopted by frontier labs. However, adoption in the outside community has been slow because it's so hard to implement.
One of the biggest challenges when adapting RL to a new task is the need for a task-specific "reward function" (way of measuring success). This is often difficult to define, and requires either high-quality labeled data and/or significant domain expertise to generate.
RULER is a drop-in reward function that works across different tasks without any of that complexity.
It works by showing N trajectories to an LLM judge and asking it to rank them relative to each other. This sidesteps the calibration issues that plague most LLM-as-judge approaches. Combined with GRPO (which only cares about relative scores within groups), it just works (surprisingly well!).
We have a full writeup on the blog, including results on 4 production tasks. On all 4 tasks, small Qwen 2.5 models trained with RULER+GRPO beat the best prompted frontier model, despite being significantly smaller and cheaper to run. Surprisingly, they even beat models trained with hand-crafted reward functions on 3/4 tasks! https://openpipe.ai/blog/ruler
Repo: https://github.com/OpenPipe/ART","title":"Show HN: RULER \u2013 Easily apply RL to any agent","updated_at":"2025-11-02T20:17:08Z","url":"https://openpipe.ai/blog/ruler"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"etherio"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"Hi HN!
Druids (https://github.com/fulcrumresearch/druids) is an open-source library for structuring and running multi-agent coding workflows. Druids makes it easy to do this by abstracting away all the VM infrastructure, agent provisioning, and communication. You can watch our demo video here (https://www.youtube.com/watch?v=EVJqW-tvSy4) to see what it looks like.
At a high level:
- Users can write Python programs that define what roles the agents take on and how they interact with each other.
- A program is made of events - clear state transitions that the agents or clients can call to modify state. Each event gets exposed as an agent tool.
- Druids provisions full VMs so that the agents can run continuously and communicate effectively.
We made Druids because we were making lots of internal coding tools using agents and found it annoying to have to rearrange the wiring every time.
As we were building Druids, we realized a lot of our internal tools were easier to express as an event-driven architecture \u2013 separating deterministic control flow from agent behavior \u2013 and this design also made it possible to have many agents work reliably.
We had issues with scaling the number of concurrent agents within a run, so we decided to have each program run in an isolated sandbox program runtime, kind of the same way you run a Modal function. Each agent then calls the runtime with an agent token, which checks who can talk to who or send files across VMs, and then applies the tool call.
Our early users have found the library useful for:
- running many agents to do performance optimization
- building custom automated software pipelines for eg code review, pentesting, large-scale migrations, etc...
We've heard that the frontier labs have the infrastructure to quickly spin up 100 agents and have them coordinate with each other smoothly in various ways. We're hoping that Druids can be a starting point to make that infrastructure more accessible."},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Druids \u2013 Build your own software factory"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/fulcrumresearch/druids"}},"_tags":["story","author_etherio","story_47695666","show_hn"],"author":"etherio","children":[47703375,47712429,47712658,47713053,47713503,47713963,47714094,47719022,47723551,47736186,47736943,47781482],"created_at":"2026-04-08T20:12:38Z","created_at_i":1775679158,"num_comments":15,"objectID":"47695666","points":64,"story_id":47695666,"story_text":"Hi HN!
Druids (https://github.com/fulcrumresearch/druids) is an open-source library for structuring and running multi-agent coding workflows. Druids makes it easy to do this by abstracting away all the VM infrastructure, agent provisioning, and communication. You can watch our demo video here (https://www.youtube.com/watch?v=EVJqW-tvSy4) to see what it looks like.
At a high level:
- Users can write Python programs that define what roles the agents take on and how they interact with each other.
- A program is made of events - clear state transitions that the agents or clients can call to modify state. Each event gets exposed as an agent tool.
- Druids provisions full VMs so that the agents can run continuously and communicate effectively.
We made Druids because we were making lots of internal coding tools using agents and found it annoying to have to rearrange the wiring every time.
As we were building Druids, we realized a lot of our internal tools were easier to express as an event-driven architecture \u2013 separating deterministic control flow from agent behavior \u2013 and this design also made it possible to have many agents work reliably.
We had issues with scaling the number of concurrent agents within a run, so we decided to have each program run in an isolated sandbox program runtime, kind of the same way you run a Modal function. Each agent then calls the runtime with an agent token, which checks who can talk to who or send files across VMs, and then applies the tool call.
Our early users have found the library useful for:
- running many agents to do performance optimization
- building custom automated software pipelines for eg code review, pentesting, large-scale migrations, etc...
We've heard that the frontier labs have the infrastructure to quickly spin up 100 agents and have them coordinate with each other smoothly in various ways. We're hoping that Druids can be a starting point to make that infrastructure more accessible.","title":"Show HN: Druids \u2013 Build your own software factory","updated_at":"2026-05-01T15:30:56Z","url":"https://github.com/fulcrumresearch/druids"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"atleastoptimal"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"A lot of people seem to take it as a given that the AI bubble will "pop", leading to a mass devaluation of AI companies from their current peaks.
What I'm confused about though is what makes current AI evaluations a bubble.
Bubbles usually exists when future speculation outpaces productivity: eventually some realization leads the market to no longer believe in that future speculation, causing devaluation which triggers a mass sell-off.
However, AI companies currently have very high revenues and are growing extremely fast. Their valuation is backed by actual commerce. I can't imagine that there is any room for a bubble, as it is very clear where the market is at, and why demand for AI is so high.
Now, certain specific companies I can imagine losing a lot of valuation, but only contingent on the fact that they serve a middle-man role in the market that improvements in the underlying AI models will solve, which would likely only mean more revenue for the frontier labs, and thus less reason for a bubble."},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: What Makes AI a Bubble?"}},"_tags":["story","author_atleastoptimal","story_47928683","ask_hn"],"author":"atleastoptimal","children":[47928712,47928879,47928900,47929603,47929660,47930286,47930415,47930464,47930703,47930820,47931141,47931221,47931246,47931392,47931428,47931468,47932356,47932979,47933738,47936773,47936980,47941472,47959311,48031107],"created_at":"2026-04-27T23:32:00Z","created_at_i":1777332720,"num_comments":42,"objectID":"47928683","points":19,"story_id":47928683,"story_text":"A lot of people seem to take it as a given that the AI bubble will "pop", leading to a mass devaluation of AI companies from their current peaks.
What I'm confused about though is what makes current AI evaluations a bubble.
Bubbles usually exists when future speculation outpaces productivity: eventually some realization leads the market to no longer believe in that future speculation, causing devaluation which triggers a mass sell-off.
However, AI companies currently have very high revenues and are growing extremely fast. Their valuation is backed by actual commerce. I can't imagine that there is any room for a bubble, as it is very clear where the market is at, and why demand for AI is so high.
Now, certain specific companies I can imagine losing a lot of valuation, but only contingent on the fact that they serve a middle-man role in the market that improvements in the underlying AI models will solve, which would likely only mean more revenue for the frontier labs, and thus less reason for a bubble.","title":"Ask HN: What Makes AI a Bubble?","updated_at":"2026-07-31T07:34:00Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"ramoz"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"We're releasing early efforts on coding agent governance with Cupcake [1] - an open-source policy enforcement layer with native integrations. You write rules in policy-as-code (OPA/Rego), and Cupcake integrates them into the agent runtime via Hooks.
See it in action (Desktop only): https://cupcake-policy-studio.vercel.app/example-policies/se...
Help us build: https://github.com/eqtylab/cupcake
We are EQTY Lab, our mission is verifiable AI (identity, provenance, and governance). With the rise of capable agents like Claude Code, it became immediately clear that those deploying these agents need the ability to conduct their own alignment and safety controls. We can\u2019t rely solely on the frontier labs.
This is why we created the feature request for Hooks in Claude Code [2], and pivoted away from filesystem and OS-level monitoring once those hooks were implemented. Hooks provide the critical points we need:
* Evaluation: Checking agent intent and actions.
* Prevention: Stopping unsafe or unwanted actions.
* Modification: Adjusting the agent's output before execution.
Policy-as-Code with OPA/Rego - While many agent security papers suggest similar policy architectures using invented DSLs, Cupcake is fundamentally built on Open Policy Agent (OPA) and its policy language, Rego [3].
We chose Rego because it is:
* Industry-Robust: Widely adopted across enterprise DevSecOps and cloud-native environments.
* Purpose-Built: Offers unique, mature advantages for defining, managing, and enforcing policy as code.
* Enterprise-Oriented: This makes Cupcake compatible with existing enterprise governance frameworks.
Cupcake is released under the Apache-2.0 license. We will formalize a path to v1.0.0 in Q1 of 2026. This is an early preview version. The goal with Cupcake is not suppression, but to ensure an agent is able to drive fast without crashing. To collaborate, or join forces: ramos at eqtylab dot io.
[1] https://github.com/eqtylab/cupcake
[2] https://github.com/anthropics/claude-code/issues/712
[3] https://www.openpolicyagent.org/"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Cupcake \u2013 Better performance and security for coding agents (via OPA)"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/eqtylab/cupcake"}},"_tags":["story","author_ramoz","story_46218813","show_hn"],"author":"ramoz","children":[46237661],"created_at":"2025-12-10T15:31:48Z","created_at_i":1765380708,"num_comments":1,"objectID":"46218813","points":12,"story_id":46218813,"story_text":"We're releasing early efforts on coding agent governance with Cupcake [1] - an open-source policy enforcement layer with native integrations. You write rules in policy-as-code (OPA/Rego), and Cupcake integrates them into the agent runtime via Hooks.
See it in action (Desktop only): https://cupcake-policy-studio.vercel.app/example-policies/se...
Help us build: https://github.com/eqtylab/cupcake
We are EQTY Lab, our mission is verifiable AI (identity, provenance, and governance). With the rise of capable agents like Claude Code, it became immediately clear that those deploying these agents need the ability to conduct their own alignment and safety controls. We can\u2019t rely solely on the frontier labs.
This is why we created the feature request for Hooks in Claude Code [2], and pivoted away from filesystem and OS-level monitoring once those hooks were implemented. Hooks provide the critical points we need:
* Evaluation: Checking agent intent and actions.
* Prevention: Stopping unsafe or unwanted actions.
* Modification: Adjusting the agent's output before execution.
Policy-as-Code with OPA/Rego - While many agent security papers suggest similar policy architectures using invented DSLs, Cupcake is fundamentally built on Open Policy Agent (OPA) and its policy language, Rego [3].
We chose Rego because it is:
* Industry-Robust: Widely adopted across enterprise DevSecOps and cloud-native environments.
* Purpose-Built: Offers unique, mature advantages for defining, managing, and enforcing policy as code.
* Enterprise-Oriented: This makes Cupcake compatible with existing enterprise governance frameworks.
Cupcake is released under the Apache-2.0 license. We will formalize a path to v1.0.0 in Q1 of 2026. This is an early preview version. The goal with Cupcake is not suppression, but to ensure an agent is able to drive fast without crashing. To collaborate, or join forces: ramos at eqtylab dot io.
[1] https://github.com/eqtylab/cupcake
[2] https://github.com/anthropics/claude-code/issues/712
[3] https://www.openpolicyagent.org/","title":"Show HN: Cupcake \u2013 Better performance and security for coding agents (via OPA)","updated_at":"2026-03-05T23:08:22Z","url":"https://github.com/eqtylab/cupcake"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"songrenchu"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"Hey HN! We are building HarnessRouter, a canonical API for running Codex, Claude Code, Hermes, and other managed agent harnesses as your product backend.
Before building HarnessRouter, I used to build our own agent harness for our products. I tried LangGraph, agent SDKs from different vendors, pydantic, LLM tool use / function call, and so on. It's a very heavy lifting engineering effort, and I am disappointed about the agent deliveries compared to what Codex, CC can deliver. That changed my mindset. The frontier labs and famous open source communities are already putting so much engineering effort to build the world's best harnesses, why not leverage them directly instead of building our own, just like how we call LLM chat completion endpoints instead of training our own models?
We provide a docker image to run HarnessRouter locally.
----------
Quickstart:
docker pull harnessrouter/harnessrouter\n\n docker run -d --name harnessrouter -p 127.0.0.1:3000:3000 -v harnessrouter:/data harnessrouter/harnessrouter\n\n docker logs -f harnessrouter\n\n Wait for the "ready on :3000" show up, then open the browser at http://localhost:3000.\n Default username/password is harnessrouter/harnessrouter\n\n Then in Integrations page, add your model provider credentials or API keys.\n In Harnesses tab, as of today we provide routing to Codex, Claude Code, and Hermes as base harnesses.\n You can customize any of them and configure harness instruction, MCP tools, and skills.\n\n Then go to Tasks and let them do jobs.\nEvery harness has its own request/response format and incompatible with each other. We propose Unified Harness Procotol [1] to standardize how an application talks to an agent harness. It covers harness selection and configuration, task execution, event streaming, sessions start cancel and resume, artifact management and delivery, and failure handling. It's similar idea like LiteLLM, but for harnesses rather than models.
HarnessRouter implements UHP. We provide an AGENTS.md [2] and your coding agent can follow it to integrate your application with the harnesses available.
We also provide starter kits [3] to demonstrate some types of agentic products that can be built on HarnessRouter. It currently includes PPT agent, Spreadsheet agent, BI Dashboard agent, and Video generation agent.
Can't wait to hear what you think!
[1] https://unifiedharnessprotocol.org
[2] https://harnessrouter.ai/agents.md
[3] https://github.com/harnessrouter/starter-kit"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: HarnessRouter: Unified interface for agent harnesses"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/harnessrouter/harnessrouter"}},"_tags":["story","author_songrenchu","story_49335595","show_hn"],"author":"songrenchu","children":[49336407,49336605,49336675,49342449,49344414,49346800,49366027],"created_at":"2026-08-17T18:33:38Z","created_at_i":1786991618,"num_comments":14,"objectID":"49335595","points":10,"story_id":49335595,"story_text":"Hey HN! We are building HarnessRouter, a canonical API for running Codex, Claude Code, Hermes, and other managed agent harnesses as your product backend.
Before building HarnessRouter, I used to build our own agent harness for our products. I tried LangGraph, agent SDKs from different vendors, pydantic, LLM tool use / function call, and so on. It's a very heavy lifting engineering effort, and I am disappointed about the agent deliveries compared to what Codex, CC can deliver. That changed my mindset. The frontier labs and famous open source communities are already putting so much engineering effort to build the world's best harnesses, why not leverage them directly instead of building our own, just like how we call LLM chat completion endpoints instead of training our own models?
We provide a docker image to run HarnessRouter locally.
----------
Quickstart:
docker pull harnessrouter/harnessrouter\n\n docker run -d --name harnessrouter -p 127.0.0.1:3000:3000 -v harnessrouter:/data harnessrouter/harnessrouter\n\n docker logs -f harnessrouter\n\n Wait for the "ready on :3000" show up, then open the browser at http://localhost:3000.\n Default username/password is harnessrouter/harnessrouter\n\n Then in Integrations page, add your model provider credentials or API keys.\n In Harnesses tab, as of today we provide routing to Codex, Claude Code, and Hermes as base harnesses.\n You can customize any of them and configure harness instruction, MCP tools, and skills.\n\n Then go to Tasks and let them do jobs.\nEvery harness has its own request/response format and incompatible with each other. We propose Unified Harness Procotol [1] to standardize how an application talks to an agent harness. It covers harness selection and configuration, task execution, event streaming, sessions start cancel and resume, artifact management and delivery, and failure handling. It's similar idea like LiteLLM, but for harnesses rather than models.
HarnessRouter implements UHP. We provide an AGENTS.md [2] and your coding agent can follow it to integrate your application with the harnesses available.
We also provide starter kits [3] to demonstrate some types of agentic products that can be built on HarnessRouter. It currently includes PPT agent, Spreadsheet agent, BI Dashboard agent, and Video generation agent.
Can't wait to hear what you think!
[1] https://unifiedharnessprotocol.org
[2] https://harnessrouter.ai/agents.md
[3] https://github.com/harnessrouter/starter-kit","title":"Show HN: HarnessRouter: Unified interface for agent harnesses","updated_at":"2026-08-22T08:59:34Z","url":"https://github.com/harnessrouter/harnessrouter"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"AbstractH24"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"Feels to me like local models are an under-covered aspect of this whole AI boom.
If everything improves over time, at some point a good chunk of tasks won\u2019t need to be done in data centers or be subject to the whims of a few frontier AI labs.
How close are we to that? Or is my thinking flawed?"},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: How close are we to local LLMs being useful? What's the impact?"}},"_tags":["story","author_AbstractH24","story_48630423","ask_hn"],"author":"AbstractH24","children":[48630434,48630601,48630602,48630706,48633319,48638399,48662653,48667875],"created_at":"2026-06-22T14:13:47Z","created_at_i":1782137627,"num_comments":7,"objectID":"48630423","points":7,"story_id":48630423,"story_text":"Feels to me like local models are an under-covered aspect of this whole AI boom.
If everything improves over time, at some point a good chunk of tasks won\u2019t need to be done in data centers or be subject to the whims of a few frontier AI labs.
How close are we to that? Or is my thinking flawed?","title":"Ask HN: How close are we to local LLMs being useful? What's the impact?","updated_at":"2026-07-05T09:58:14Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"kok14"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"Many people think that we won't reach AGI or even ASI if LLM's don't have something called "continual learning". Basically, continual learning is the ability for an AI to learn on the job, update its neural weights in real-time, and get smarter without forgetting everything else (catastrophic forgetting). This is what we do everyday, without much effort.
What's interesting now, is if you look at what the top labs are doing, they\u2019ve stopped trying to solve the underlying math of real-time weight updates. Instead, they\u2019re simply brute-forcing it. It is exactly why, in the past ~ 3 months or so, there has been a step-function increase in how good the models have gotten.
Long story short, the gist of it is, if you combine:
very long context windows
reliable summarization
structured external documentation,
you can approximate a lot of what people mean by continual learning.
How it works is, the model does a task and absorbs a massive amount of situational detail. Then, before it \u201chands off\u201d to the next instance of itself, it writes two things: short \u201cmemories\u201d (always carried forward in the prompt/context) and long-form documentation (stored externally, retrieved only when needed). The next run starts with these notes, so it doesn't need to start from scratch.
Through this clever reinforcement learning (RL) loop, they train this behaviour directly, without any exotic new theory.
They treat memory-writing as an RL objective: after a run, have the model write memories/docs, then spin up new instances on the same, similar, and dissimilar tasks while feeding those memories back in. How this is done, is by scoring performance across the sequence, and applying an explicit penalty for memory length so you don\u2019t get infinite \u201cnotes\u201d that eventually blow the context window.
Over many iterations, you reward models that (a) write high-signal memories, (b) retrieve the right docs at the right time, and (c) edit/compress stale notes instead of mindlessly accumulating them.
This is pretty crazy. Because when you combine the current release cadence of frontier labs where each new model is trained and shipped after major post-training / scaling improvements, even if your deployed instance never updates its weights in real-time, it can still \u201cget smarter\u201d when the next version ships AND it can inherit all the accumulated memories/docs from its predecessor.
This is a new force multiplier, another scaling paradigm, and likely what the top labs are doing right now (source: TBA).
Ignoring any black swan level event (unknown, unknowns), you get a plausible 2026 trajectory:
We\u2019re going to see more and more improvements, in an accelerated timeline. The top labs ARE, in effect, using continual learning (a really good approximation of it), and they are directly training this approximation, so it rapidly gets better and better.
Don't believe me? Look at what both OpenAi(https://openai.com/index/introducing-openai-frontier/) and Anthropic(https://resources.anthropic.com/2026-agentic-coding-trends-report) have mentioned as their core things they are focusing on. It's exactly why governments & corporations are bullish on this; there is no wall...."},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["labs"],"value":"We don't need continual learning for AGI. What top labs are currently doing"}},"_tags":["story","author_kok14","story_47259384","ask_hn"],"author":"kok14","children":[47260591,47261212,47262125,47263241,47274364,47420981],"created_at":"2026-03-05T09:06:52Z","created_at_i":1772701612,"num_comments":7,"objectID":"47259384","points":7,"story_id":47259384,"story_text":"Many people think that we won't reach AGI or even ASI if LLM's don't have something called "continual learning". Basically, continual learning is the ability for an AI to learn on the job, update its neural weights in real-time, and get smarter without forgetting everything else (catastrophic forgetting). This is what we do everyday, without much effort.
What's interesting now, is if you look at what the top labs are doing, they\u2019ve stopped trying to solve the underlying math of real-time weight updates. Instead, they\u2019re simply brute-forcing it. It is exactly why, in the past ~ 3 months or so, there has been a step-function increase in how good the models have gotten.
Long story short, the gist of it is, if you combine:
very long context windows
reliable summarization
structured external documentation,
you can approximate a lot of what people mean by continual learning.
How it works is, the model does a task and absorbs a massive amount of situational detail. Then, before it \u201chands off\u201d to the next instance of itself, it writes two things: short \u201cmemories\u201d (always carried forward in the prompt/context) and long-form documentation (stored externally, retrieved only when needed). The next run starts with these notes, so it doesn't need to start from scratch.
Through this clever reinforcement learning (RL) loop, they train this behaviour directly, without any exotic new theory.
They treat memory-writing as an RL objective: after a run, have the model write memories/docs, then spin up new instances on the same, similar, and dissimilar tasks while feeding those memories back in. How this is done, is by scoring performance across the sequence, and applying an explicit penalty for memory length so you don\u2019t get infinite \u201cnotes\u201d that eventually blow the context window.
Over many iterations, you reward models that (a) write high-signal memories, (b) retrieve the right docs at the right time, and (c) edit/compress stale notes instead of mindlessly accumulating them.
This is pretty crazy. Because when you combine the current release cadence of frontier labs where each new model is trained and shipped after major post-training / scaling improvements, even if your deployed instance never updates its weights in real-time, it can still \u201cget smarter\u201d when the next version ships AND it can inherit all the accumulated memories/docs from its predecessor.
This is a new force multiplier, another scaling paradigm, and likely what the top labs are doing right now (source: TBA).
Ignoring any black swan level event (unknown, unknowns), you get a plausible 2026 trajectory:
We\u2019re going to see more and more improvements, in an accelerated timeline. The top labs ARE, in effect, using continual learning (a really good approximation of it), and they are directly training this approximation, so it rapidly gets better and better.
Don't believe me? Look at what both OpenAi(https://openai.com/index/introducing-openai-frontier/) and Anthropic(https://resources.anthropic.com/2026-agentic-coding-trends-report) have mentioned as their core things they are focusing on. It's exactly why governments & corporations are bullish on this; there is no wall....","title":"We don't need continual learning for AGI. What top labs are currently doing","updated_at":"2026-03-18T02:35:57Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"spprashant"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"I suspect at some point LLM in its current form will be deemed good enough for general research and coding tasks. I don't get why we need to continue with a de-facto cloud-based approach. Cloud in my opinion solves operational complexity, which is worth paying a premium for. But it seems it isn't quite all that complex to get an open source model running locally as long as you have the hardware. Over time I suspect the models get better and cheaper.
Is there a future where we can expect people to just buy "AI" from BestBuy, like a TV set? It ll probably come with some model preloaded - cheaper if open-source, premium pricing for frontier lab models. The hardware is basically a bunch of GPUs enough for local inference.
Take it home and plug it into your home network and you can open a chat instance by going to the IP on any local device. You can give it access to internet if you want. Maybe it can also receive OTA updates.
Curious how others think about this - does local-first AI feel like a possibility? What are the economic and social challenges with this?"},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: Is consumer AI boxes a viable idea?"}},"_tags":["story","author_spprashant","story_47546796","ask_hn"],"author":"spprashant","children":[47547065,47547978],"created_at":"2026-03-27T18:57:09Z","created_at_i":1774637829,"num_comments":1,"objectID":"47546796","points":5,"story_id":47546796,"story_text":"I suspect at some point LLM in its current form will be deemed good enough for general research and coding tasks. I don't get why we need to continue with a de-facto cloud-based approach. Cloud in my opinion solves operational complexity, which is worth paying a premium for. But it seems it isn't quite all that complex to get an open source model running locally as long as you have the hardware. Over time I suspect the models get better and cheaper.
Is there a future where we can expect people to just buy "AI" from BestBuy, like a TV set? It ll probably come with some model preloaded - cheaper if open-source, premium pricing for frontier lab models. The hardware is basically a bunch of GPUs enough for local inference.
Take it home and plug it into your home network and you can open a chat instance by going to the IP on any local device. You can give it access to internet if you want. Maybe it can also receive OTA updates.
Curious how others think about this - does local-first AI feel like a possibility? What are the economic and social challenges with this?","title":"Ask HN: Is consumer AI boxes a viable idea?","updated_at":"2026-03-28T05:45:10Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"guru3s"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"A striking number of people who had already built successful companies or held unusually high-status roles are choosing to work at Anthropic.
> Recently, Tom Blomfield, co-founder of Monzo and GoCardless and former YC partner
> Mike Krieger, co-founder of Instagram
> Andrej Karpathy, founding OpenAI member
> Peter Bailis, CTO of Workday
I am mostly curious because they could presumably have raised money for another startup, invested, or retired. Instead, they chose to become employees with deliberately ordinary titles.
If this is not a biased view, what really explains it? Confidence in Anthropic\u2019s leadership, It's research direction, equity ahead of a possible IPO, or somthing else?
I\u2019d especially like to hear from people who have considered joining a frontier lab after founding a company."},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: Why are so many accomplished founders joining Anthropic?"}},"_tags":["story","author_guru3s","story_48902505","ask_hn"],"author":"guru3s","children":[48902513],"created_at":"2026-07-14T05:04:59Z","created_at_i":1784005499,"num_comments":3,"objectID":"48902505","points":4,"story_id":48902505,"story_text":"A striking number of people who had already built successful companies or held unusually high-status roles are choosing to work at Anthropic.
> Recently, Tom Blomfield, co-founder of Monzo and GoCardless and former YC partner
> Mike Krieger, co-founder of Instagram
> Andrej Karpathy, founding OpenAI member
> Peter Bailis, CTO of Workday
I am mostly curious because they could presumably have raised money for another startup, invested, or retired. Instead, they chose to become employees with deliberately ordinary titles.
If this is not a biased view, what really explains it? Confidence in Anthropic\u2019s leadership, It's research direction, equity ahead of a possible IPO, or somthing else?
I\u2019d especially like to hear from people who have considered joining a frontier lab after founding a company.","title":"Ask HN: Why are so many accomplished founders joining Anthropic?","updated_at":"2026-07-14T18:31:02Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"pierreb-aiva"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"I've built a browser agent harness that outperforms Browser Code on their own benchmark (BU Bench v1), on success rate, speed and cost.
Browser Agent: 88% success rate, $5.37, 32,694 seconds\nBrowser Code: 78% success rate, $8.34, 47,970 seconds
For browser agents to become ubiquitous, they need to be faster, cheaper, and more reliable. The harness was built with token efficiency in mind (and uses 91% fewer tokens than Browser Code) to reduce the compute and VRAM budget necessary to fine tune a small, specialised model.
Because browser agent inference is intermittent, a small specialised model could serve each step with minimal GPU time. This makes per-task pricing viable, offering users cheaper, more predictable costs and giving the operator a counter-positioning advantage over frontier labs pricing their models by token usage.
More about the comparison between Browser Agent and Browser Code here: https://www.pierrebarreau.com/blog/improving-the-state-of-th...
If anyone is interested in fine tuning that model, would love to talk!"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Browser Agent \u2013 cost efficient browser automation"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/visnia-ai/browser-agent"}},"_tags":["story","author_pierreb-aiva","story_49257523","show_hn"],"author":"pierreb-aiva","created_at":"2026-08-11T12:53:46Z","created_at_i":1786452826,"num_comments":0,"objectID":"49257523","points":4,"story_id":49257523,"story_text":"I've built a browser agent harness that outperforms Browser Code on their own benchmark (BU Bench v1), on success rate, speed and cost.
Browser Agent: 88% success rate, $5.37, 32,694 seconds\nBrowser Code: 78% success rate, $8.34, 47,970 seconds
For browser agents to become ubiquitous, they need to be faster, cheaper, and more reliable. The harness was built with token efficiency in mind (and uses 91% fewer tokens than Browser Code) to reduce the compute and VRAM budget necessary to fine tune a small, specialised model.
Because browser agent inference is intermittent, a small specialised model could serve each step with minimal GPU time. This makes per-task pricing viable, offering users cheaper, more predictable costs and giving the operator a counter-positioning advantage over frontier labs pricing their models by token usage.
More about the comparison between Browser Agent and Browser Code here: https://www.pierrebarreau.com/blog/improving-the-state-of-th...
If anyone is interested in fine tuning that model, would love to talk!","title":"Show HN: Browser Agent \u2013 cost efficient browser automation","updated_at":"2026-08-11T13:13:41Z","url":"https://github.com/visnia-ai/browser-agent"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"SteveVeilStream"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["frontier","labs"],"value":"Introducing SlothSpeak: An open-source, bring-your-own-API-keys, mobile app for voice chat with LLMs that prioritizes response quality over latency.
APK file available on GitHub in the releases. Currently only for Android. Is anyone interested in porting to iPhone?
My preferred way to interact with LLMs is talking and listening while I'm walking, biking, driving, etc. The problem with the apps from the frontier labs is that their voice mode prioritizes real-time interactions and so they use watered down models.
Even today with a paid subscription, ChatGPT Voice will tell you there are two r's in strawberry. The answers are also relatively brief and so there is a need to chain together a series of simpler requests when you want to do a deeper dive.
SlothSpeak goes to the opposite end of the spectrum. It might leave you hanging for a few minutes but when it gets back to you, it will be an answer from a state of the art model. You can even do deep research queries to get very long and comprehensive responses."},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: SlothSpeak: open-source BYO-API-K mobile chat with the best AI models"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/JonesSteven/SlothSpeak"}},"_tags":["story","author_SteveVeilStream","story_47136158","show_hn"],"author":"SteveVeilStream","children":[47138410],"created_at":"2026-02-24T12:14:20Z","created_at_i":1771935260,"num_comments":2,"objectID":"47136158","points":3,"story_id":47136158,"story_text":"Introducing SlothSpeak: An open-source, bring-your-own-API-keys, mobile app for voice chat with LLMs that prioritizes response quality over latency.
APK file available on GitHub in the releases. Currently only for Android. Is anyone interested in porting to iPhone?
My preferred way to interact with LLMs is talking and listening while I'm walking, biking, driving, etc. The problem with the apps from the frontier labs is that their voice mode prioritizes real-time interactions and so they use watered down models.
Even today with a paid subscription, ChatGPT Voice will tell you there are two r's in strawberry. The answers are also relatively brief and so there is a need to chain together a series of simpler requests when you want to do a deeper dive.
SlothSpeak goes to the opposite end of the spectrum. It might leave you hanging for a few minutes but when it gets back to you, it will be an answer from a state of the art model. You can even do deep research queries to get very long and comprehensive responses.","title":"Show HN: SlothSpeak: open-source BYO-API-K mobile chat with the best AI models","updated_at":"2026-03-05T23:36:18Z","url":"https://github.com/JonesSteven/SlothSpeak"}],"hitsPerPage":50,"nbHits":227,"nbPages":5,"page":0,"params":"query=Frontier+Labs&tags=story&hitsPerPage=50&advancedSyntax=true&analyticsTags=backend","processingTimeMS":13,"processingTimingsMS":{"_request":{"roundTrip":15},"afterFetch":{"format":{"highlighting":2,"total":2}},"fetch":{"query":7,"scanning":4,"total":12},"total":13},"query":"Frontier Labs","serverTimeMS":16}