Severance
23 by surprisetalk | 3 comments on Hacker News.
Friday, July 31, 2026
Thursday, July 30, 2026
New top story on Hacker News: Show HN: Supapool – a Supabase per coding agent in ~400 ms
Show HN: Supapool – a Supabase per coding agent in ~400 ms
6 by kevo1ution | 0 comments on Hacker News.
hi HN, I built supapool.io, an ephemeral full copy of supabase's services that you can spin up in ~400 ms (Auth, postgres, storage, realtime). so if you run multiple coding agents in parallel in different worktrees, they can now have their own copy of supabase without making changes that conflict with eachother. > why not use supabase docker locally? when I run 3-4 instances locally, my macbook gets hot and sometimes freezes. > why not use supabase branches? branches take minutes to setup, and are designed for persistence. this is expensive, and for a dev environment, it is too slow. > why not use mocks? mocks are bad for agents. i expect agents to test their migrations, SQL against real prod service behavior. agents hallucinate working mocks often. However, upside of mocks is that its faster and runs locally, but with supapool, the upside is less convincing. > how does it work/how is this economically viable? starting supabase in 400ms requires a few things: 1. a pool of ready supabase instances running warm, and colocated with region failover (us-east, us-west, europe-west, asia-southeast) 2. fast autoscaling when pool starts to shrink with microVM/firecracker 3. gutting strong persistence guarantees. dev agents don't need WAL, fsync, PITR, replication. anything for HA on a ephemeral supabase instance is bloat its in beta right now, and i'm using our gcp credits to bankroll this, so its free. the eventual pricing will be something like $/instance second and more cost effective than branching or self hosting/maintaining a supabase cluster. would love to get your feedback if you use supabase, and if you think there's something better that would fit your local coding agent setup. Thanks!
6 by kevo1ution | 0 comments on Hacker News.
hi HN, I built supapool.io, an ephemeral full copy of supabase's services that you can spin up in ~400 ms (Auth, postgres, storage, realtime). so if you run multiple coding agents in parallel in different worktrees, they can now have their own copy of supabase without making changes that conflict with eachother. > why not use supabase docker locally? when I run 3-4 instances locally, my macbook gets hot and sometimes freezes. > why not use supabase branches? branches take minutes to setup, and are designed for persistence. this is expensive, and for a dev environment, it is too slow. > why not use mocks? mocks are bad for agents. i expect agents to test their migrations, SQL against real prod service behavior. agents hallucinate working mocks often. However, upside of mocks is that its faster and runs locally, but with supapool, the upside is less convincing. > how does it work/how is this economically viable? starting supabase in 400ms requires a few things: 1. a pool of ready supabase instances running warm, and colocated with region failover (us-east, us-west, europe-west, asia-southeast) 2. fast autoscaling when pool starts to shrink with microVM/firecracker 3. gutting strong persistence guarantees. dev agents don't need WAL, fsync, PITR, replication. anything for HA on a ephemeral supabase instance is bloat its in beta right now, and i'm using our gcp credits to bankroll this, so its free. the eventual pricing will be something like $/instance second and more cost effective than branching or self hosting/maintaining a supabase cluster. would love to get your feedback if you use supabase, and if you think there's something better that would fit your local coding agent setup. Thanks!
Wednesday, July 29, 2026
New top story on Hacker News: Show HN: CheapFoodMap – A map of good meals under $10
Show HN: CheapFoodMap – A map of good meals under $10
16 by jaep1 | 15 comments on Hacker News.
I was recently laid off after 18 years, and gave myself 100 days to build soething useful in public. CheapFoodMap is a crowdsourced map of meal under $10, excluding franchises, local good eats only. It's inspried by 거지맵 (Begger's Map) a Korean crowdsourced map students use to find cheap eats. Ocverage is heaviest in Texas, since I live in Dallas, but have 1200 meals across 15 US cities. Seed data came from Google Review, 4.2 star or higher with at least 500 reviews, and verified price under $10 per menu item. Things I would love feedback on : whether the price-freshness model makes sense, and what would make you trust the price on a site like this. How to encourage people to update prices, since inflation is making food price very frequent. https://ift.tt/s7T9VdE Any and all suggestion will be super helpful. Thank you!
16 by jaep1 | 15 comments on Hacker News.
I was recently laid off after 18 years, and gave myself 100 days to build soething useful in public. CheapFoodMap is a crowdsourced map of meal under $10, excluding franchises, local good eats only. It's inspried by 거지맵 (Begger's Map) a Korean crowdsourced map students use to find cheap eats. Ocverage is heaviest in Texas, since I live in Dallas, but have 1200 meals across 15 US cities. Seed data came from Google Review, 4.2 star or higher with at least 500 reviews, and verified price under $10 per menu item. Things I would love feedback on : whether the price-freshness model makes sense, and what would make you trust the price on a site like this. How to encourage people to update prices, since inflation is making food price very frequent. https://ift.tt/s7T9VdE Any and all suggestion will be super helpful. Thank you!
Tuesday, July 28, 2026
Monday, July 27, 2026
Sunday, July 26, 2026
Saturday, July 25, 2026
New top story on Hacker News: Show HN: I made some transistor animations
Show HN: I made some transistor animations
30 by stunningllama | 2 comments on Hacker News.
Hi HN, I made some animations of the most important kinds of transistors using my semiconductor simulation, details of which are on the page. I tried to make the visuals as realistic as possible while also aiming for clarity. If you want to go beyond the charge carriers and look at, for example, the electric field, you can do so in the simulation software. The desktop software also has less common devices like IBGTs and SCRs that have similar animations. The last thread about my software was posted here about a year ago: https://ift.tt/izokYd3
30 by stunningllama | 2 comments on Hacker News.
Hi HN, I made some animations of the most important kinds of transistors using my semiconductor simulation, details of which are on the page. I tried to make the visuals as realistic as possible while also aiming for clarity. If you want to go beyond the charge carriers and look at, for example, the electric field, you can do so in the simulation software. The desktop software also has less common devices like IBGTs and SCRs that have similar animations. The last thread about my software was posted here about a year ago: https://ift.tt/izokYd3
Friday, July 24, 2026
New top story on Hacker News: Nvidia, Microsoft, Meta warn against overregulating open-weight models
Nvidia, Microsoft, Meta warn against overregulating open-weight models
55 by louiereederson | 79 comments on Hacker News.
Letter: https://ift.tt/5Jiz09D... [pdf] https://ift.tt/sRLG0CZ , https://ift.tt/ouUYjDg https://ift.tt/aL9hbDN... , https://ift.tt/IQfKvze
55 by louiereederson | 79 comments on Hacker News.
Letter: https://ift.tt/5Jiz09D... [pdf] https://ift.tt/sRLG0CZ , https://ift.tt/ouUYjDg https://ift.tt/aL9hbDN... , https://ift.tt/IQfKvze
Thursday, July 23, 2026
New top story on Hacker News: Show HN: Trifle – Open-source analytics that stores answers, not events
Show HN: Trifle – Open-source analytics that stores answers, not events
7 by iluzone | 0 comments on Hacker News.
Trifle is an open-source time-series analytics library that aggregates nested counters instead of storing raw events. All in the database you already have. After rebuilding it twice over 10 years, it now tracks ~1B events a day at my day job. It started in 2015 as my own Rails APM. I plugged into ActiveSupport::Notifications, got a few small users, and one bigger one whose scraping app broke everything. That sparked the core idea: aggregate counters into pre-defined time buckets, so a single write increments multiple buckets at once. The APM eventually faded away without much traction. Later in 2021 I needed analytics at my day job. Instead of going for something out there I revised the idea of Trifle as a more generic analytics library, borrowing some data warehouse ideas. First used Redis, then Postgres, eventually MongoDB. Hence why Trifle::Stats comes with multiple drivers that keep the DSL unified while storage layer changes with your needs. In our case (huge write volume, some reads) PG read faster but slowed on large writes. The nested values are the whole trick here. Single: Trifle::Stats.track( key: 'requests::aws::s3_uploads', values: { count: 1, status: { request.response_code => 1 }, size: payload.bytes, duration: { sum: request.duration, count: 1 } } ) builds up counts for requests, success rate, result status codes, duration for multiple time buckets at once. Single bucket from 2am then looks like: { count: 14, status: { 200: 12, 500: 2 }, size: 5628341, duration: { sum: 43, count: 14 } } If request.duration is in seconds, then sum stored under duration would be in seconds as well. Success rate is never stored, but it is calculated by dividing 200s over total number of requests. Same with average duration: sum over count. You ask for a metrics key, granularity and timeframe and you get back aggregated values at each point. Ready for charts or to answer "Average response time over last 30 days". There's a Series wrapper for aggregating and formatting values for charts in a simple call. And as building dashboards is not as much fun for other devs as I thought, I built Trifle App - a visual layer with dashboards, scheduled digests and alerts. It's written in Elixir, so I ported the library to Elixir too. And later to Go for a CLI. All three are compatible, write in one and read in another. Today we track activity from over 100M background jobs a day which turns into about 1B events. It runs surprisingly cheap when you're willing to trade some safety away (turn off journaling and write concerns in Mongo). 3-node Hetzner MongoDB cluster where the primary does 20% utilization costs us around $1k/month. It has its limitations. Payloads can't hold tens of thousands of keys. Documents becomes too large to update efficiently. Some planning ahead is needed. And then there are no dimensions. Sometimes you can nest them (country - there are only so many countries), sometimes it's better to have dedicated metrics key per dimension (customer - growing forever). That multiplies tracked events, hence 1B events from 100M jobs. The libraries are MIT. The App is source-available under ELv2 - free to self-host and paid cloud if you want it managed. I build this on the side with no investor money to burn on a free service. Happy to answer anything about architecture, storage models, my failures or why I didn't give up on this yet.
7 by iluzone | 0 comments on Hacker News.
Trifle is an open-source time-series analytics library that aggregates nested counters instead of storing raw events. All in the database you already have. After rebuilding it twice over 10 years, it now tracks ~1B events a day at my day job. It started in 2015 as my own Rails APM. I plugged into ActiveSupport::Notifications, got a few small users, and one bigger one whose scraping app broke everything. That sparked the core idea: aggregate counters into pre-defined time buckets, so a single write increments multiple buckets at once. The APM eventually faded away without much traction. Later in 2021 I needed analytics at my day job. Instead of going for something out there I revised the idea of Trifle as a more generic analytics library, borrowing some data warehouse ideas. First used Redis, then Postgres, eventually MongoDB. Hence why Trifle::Stats comes with multiple drivers that keep the DSL unified while storage layer changes with your needs. In our case (huge write volume, some reads) PG read faster but slowed on large writes. The nested values are the whole trick here. Single: Trifle::Stats.track( key: 'requests::aws::s3_uploads', values: { count: 1, status: { request.response_code => 1 }, size: payload.bytes, duration: { sum: request.duration, count: 1 } } ) builds up counts for requests, success rate, result status codes, duration for multiple time buckets at once. Single bucket from 2am then looks like: { count: 14, status: { 200: 12, 500: 2 }, size: 5628341, duration: { sum: 43, count: 14 } } If request.duration is in seconds, then sum stored under duration would be in seconds as well. Success rate is never stored, but it is calculated by dividing 200s over total number of requests. Same with average duration: sum over count. You ask for a metrics key, granularity and timeframe and you get back aggregated values at each point. Ready for charts or to answer "Average response time over last 30 days". There's a Series wrapper for aggregating and formatting values for charts in a simple call. And as building dashboards is not as much fun for other devs as I thought, I built Trifle App - a visual layer with dashboards, scheduled digests and alerts. It's written in Elixir, so I ported the library to Elixir too. And later to Go for a CLI. All three are compatible, write in one and read in another. Today we track activity from over 100M background jobs a day which turns into about 1B events. It runs surprisingly cheap when you're willing to trade some safety away (turn off journaling and write concerns in Mongo). 3-node Hetzner MongoDB cluster where the primary does 20% utilization costs us around $1k/month. It has its limitations. Payloads can't hold tens of thousands of keys. Documents becomes too large to update efficiently. Some planning ahead is needed. And then there are no dimensions. Sometimes you can nest them (country - there are only so many countries), sometimes it's better to have dedicated metrics key per dimension (customer - growing forever). That multiplies tracked events, hence 1B events from 100M jobs. The libraries are MIT. The App is source-available under ELv2 - free to self-host and paid cloud if you want it managed. I build this on the side with no investor money to burn on a free service. Happy to answer anything about architecture, storage models, my failures or why I didn't give up on this yet.
Wednesday, July 22, 2026
Tuesday, July 21, 2026
Monday, July 20, 2026
Sunday, July 19, 2026
New top story on Hacker News: Show HN: I replaced a $120k bowling center system with $1,600 in ESP32s
Show HN: I replaced a $120k bowling center system with $1,600 in ESP32s
123 by section33 | 10 comments on Hacker News.
I might be the only SRE on Earth with his own bowling center. It's a more in-depth gig than you'd think. My family and I bought an abandoned 8-lane bowling center in the rural mid-west. In our small town there weren't many recreation options for families. You've heard of a food desert? This is an R&R desert. It had been abandoned for a good reason. The roof leaks, the electrical system was constantly surging, and my 70-year-old bowling equipment (still) doesn't work perfectly. The system that keeps your score is particularly interesting to me. It's the thing you watch during your game, but it fades into the background beyond that. Turns out these things are really cool, but absurdly expensive. Ours was installed in 2008 and cost six figures. It's calculating ball speed and trajectory, camera-based pin detection (object detection and trig, on ICs!), runs the fouling, the animations, the pinsetting machine and ball return. Very cool stuff for its age. From the business perspective, my facility only cost me $105k. To forklift-replace the score keeping system runs anywhere between $80-$120k, depending on features, vendor, and unit age. No upgrades or service contracts, mind you, and every feature and customization is a new line item. That's for a 1:1 replacement on a system installed in 2008. Incredible, given how fast the tech world moves. Replacement parts cost a shocking $4000 per pair of lanes. But wait, the bowling machines themselves are 70 years old, so what's this "advanced" system actually doing back there? Actuating a single relay to trigger that big old machine. Everything else is strictly mechanical. Meanwhile I've got a six-figure invoice in my hand. I'm upset. Given the state of open hardware, computer vision, real-time event streaming, and open source running megascale products worldwide, there had to be a way to do this myself. So far I've built an equivalent prototype for about $200 per lane-pair, $400 if you're fancy. ESP32 and ESPNow with an RS485 fallback, reporting to a raspberry pi lane computer that's really just redis and a state machine bolted to an ESP32 gateway for the mesh. Since it's all ESP32, I've got a fistful of spare controllers in a drawer, pre-flashed or waiting to be. All common off-the-shelf hardware: microcontrollers wired to relays, optocouplers, and IR-break-beam sensors, each running slightly different firmware. Writing the firmware and protocol is the actual hard part. It's an ESPNow star-topology mesh: each node emits events from its sensors and accepts commands for its controls, reporting to a gateway node connected to the raspi over UART. From there it's event streaming: RX packets get translated and tossed into redis, commands relay back out to the mesh as needed. RS485 sits underneath as a wired fallback for noisy RF environments. Once the data's in redis, it's familiar middleware/React/websocket/pub-sub stuff. Any React dev can build their own UI and bowling animations. Since it all runs on commodity hardware, I can do legit anything I want as the proprietor, and I own all my data. Repairs take five minutes; I can swap the rig on a lane pair in under 10. I'd bet a house like mine could go from zero to running in an hour or two. We're calling it OpenLaneLink, and I plan to open source the hardware, firmware, and software stack when it's ready. Bowling is fun, and I want to help keep it affordable for alleys like mine. I hate vendor lock-in. I'm not a fan of closed systems, calling support for every hiccup, or paying to "white label" my own equipment. Want to go Tron-themed for a night? Good luck finding a neon neumorphic theme in something bought at the turn of the century. All that bugged me. Sure, bowling equipment is niche, but the open hardware and software landscape is amazing. Thanks for reading! Let me know if anyone's interested in more posts about this bowling nonsense.
123 by section33 | 10 comments on Hacker News.
I might be the only SRE on Earth with his own bowling center. It's a more in-depth gig than you'd think. My family and I bought an abandoned 8-lane bowling center in the rural mid-west. In our small town there weren't many recreation options for families. You've heard of a food desert? This is an R&R desert. It had been abandoned for a good reason. The roof leaks, the electrical system was constantly surging, and my 70-year-old bowling equipment (still) doesn't work perfectly. The system that keeps your score is particularly interesting to me. It's the thing you watch during your game, but it fades into the background beyond that. Turns out these things are really cool, but absurdly expensive. Ours was installed in 2008 and cost six figures. It's calculating ball speed and trajectory, camera-based pin detection (object detection and trig, on ICs!), runs the fouling, the animations, the pinsetting machine and ball return. Very cool stuff for its age. From the business perspective, my facility only cost me $105k. To forklift-replace the score keeping system runs anywhere between $80-$120k, depending on features, vendor, and unit age. No upgrades or service contracts, mind you, and every feature and customization is a new line item. That's for a 1:1 replacement on a system installed in 2008. Incredible, given how fast the tech world moves. Replacement parts cost a shocking $4000 per pair of lanes. But wait, the bowling machines themselves are 70 years old, so what's this "advanced" system actually doing back there? Actuating a single relay to trigger that big old machine. Everything else is strictly mechanical. Meanwhile I've got a six-figure invoice in my hand. I'm upset. Given the state of open hardware, computer vision, real-time event streaming, and open source running megascale products worldwide, there had to be a way to do this myself. So far I've built an equivalent prototype for about $200 per lane-pair, $400 if you're fancy. ESP32 and ESPNow with an RS485 fallback, reporting to a raspberry pi lane computer that's really just redis and a state machine bolted to an ESP32 gateway for the mesh. Since it's all ESP32, I've got a fistful of spare controllers in a drawer, pre-flashed or waiting to be. All common off-the-shelf hardware: microcontrollers wired to relays, optocouplers, and IR-break-beam sensors, each running slightly different firmware. Writing the firmware and protocol is the actual hard part. It's an ESPNow star-topology mesh: each node emits events from its sensors and accepts commands for its controls, reporting to a gateway node connected to the raspi over UART. From there it's event streaming: RX packets get translated and tossed into redis, commands relay back out to the mesh as needed. RS485 sits underneath as a wired fallback for noisy RF environments. Once the data's in redis, it's familiar middleware/React/websocket/pub-sub stuff. Any React dev can build their own UI and bowling animations. Since it all runs on commodity hardware, I can do legit anything I want as the proprietor, and I own all my data. Repairs take five minutes; I can swap the rig on a lane pair in under 10. I'd bet a house like mine could go from zero to running in an hour or two. We're calling it OpenLaneLink, and I plan to open source the hardware, firmware, and software stack when it's ready. Bowling is fun, and I want to help keep it affordable for alleys like mine. I hate vendor lock-in. I'm not a fan of closed systems, calling support for every hiccup, or paying to "white label" my own equipment. Want to go Tron-themed for a night? Good luck finding a neon neumorphic theme in something bought at the turn of the century. All that bugged me. Sure, bowling equipment is niche, but the open hardware and software landscape is amazing. Thanks for reading! Let me know if anyone's interested in more posts about this bowling nonsense.
Saturday, July 18, 2026
Friday, July 17, 2026
Thursday, July 16, 2026
Wednesday, July 15, 2026
Tuesday, July 14, 2026
Monday, July 13, 2026
Sunday, July 12, 2026
Saturday, July 11, 2026
New top story on Hacker News: The Oral History of the Dinosaur Input Device (DID)
The Oral History of the Dinosaur Input Device (DID)
3 by fabiensanglard | 0 comments on Hacker News.
3 by fabiensanglard | 0 comments on Hacker News.
Friday, July 10, 2026
Thursday, July 9, 2026
New top story on Hacker News: Show HN: Abralo – Free, easy way to run several Claude Code agents in one window
Show HN: Abralo – Free, easy way to run several Claude Code agents in one window
15 by cwbuilds | 4 comments on Hacker News.
Hi guys, I've been using Claude Code for almost everything lately. Have given one an email account so it can research business leads, draft emails, fact-check them and clear them with me before sending (works really well by the way). I also tend to have a few Claude Code agents running at any one time for coding. I used to create a split terminal to manage them from there, but found working in the terminal all day pretty depressing and, more importantly, found it hard to follow Claude Code's process and see which agents needed my immediate attention. I tried Anthropic's VS Code Claude Code extension and it had a great UI (more info on Claude Code's process and easier to read), but it crashed my PC when I ran more than 3 and I couldn't watch multiple agents in parallel (had to constantly switch between them). So I built a lightweight Tauri desktop app which lets you run multiple Claude Code agents in one window alongside each other. It's easier to read the output and see which agents need your attention than a terminal. Have been using this all day everyday instead of an IDE and have obsessed over every detail to make sure it's easy-to-use, but also lightweight and fast (so you can manage multiple agents without your PC crashing). There are some nice features like better usage alerts for when you're going to hit your 5-hour and weekly limits (with sparklines to show when usage peaked, and which agents are the most token-intensive). It's free to use (you just need to log in with your existing Claude Code account) for up to 4 agents simultaneously. This app doesn't store your Claude Code account details and doesn't store any of your interactions with Claude Code. They remain between you and Anthropic. It's compatible with Windows, MacOS and 64-bit Linux. Would really appreciate any feedback, so if you have any thoughts, issues or suggestions please let me know. Thanks, Chris
15 by cwbuilds | 4 comments on Hacker News.
Hi guys, I've been using Claude Code for almost everything lately. Have given one an email account so it can research business leads, draft emails, fact-check them and clear them with me before sending (works really well by the way). I also tend to have a few Claude Code agents running at any one time for coding. I used to create a split terminal to manage them from there, but found working in the terminal all day pretty depressing and, more importantly, found it hard to follow Claude Code's process and see which agents needed my immediate attention. I tried Anthropic's VS Code Claude Code extension and it had a great UI (more info on Claude Code's process and easier to read), but it crashed my PC when I ran more than 3 and I couldn't watch multiple agents in parallel (had to constantly switch between them). So I built a lightweight Tauri desktop app which lets you run multiple Claude Code agents in one window alongside each other. It's easier to read the output and see which agents need your attention than a terminal. Have been using this all day everyday instead of an IDE and have obsessed over every detail to make sure it's easy-to-use, but also lightweight and fast (so you can manage multiple agents without your PC crashing). There are some nice features like better usage alerts for when you're going to hit your 5-hour and weekly limits (with sparklines to show when usage peaked, and which agents are the most token-intensive). It's free to use (you just need to log in with your existing Claude Code account) for up to 4 agents simultaneously. This app doesn't store your Claude Code account details and doesn't store any of your interactions with Claude Code. They remain between you and Anthropic. It's compatible with Windows, MacOS and 64-bit Linux. Would really appreciate any feedback, so if you have any thoughts, issues or suggestions please let me know. Thanks, Chris
Wednesday, July 8, 2026
Tuesday, July 7, 2026
New top story on Hacker News: Show HN: Halo – open-source, tamper-evident runtime evidence for AI agents
Show HN: Halo – open-source, tamper-evident runtime evidence for AI agents
4 by brian_kuan | 0 comments on Hacker News.
Hi HN, I'm Brian, I spent the last few years at Vanta (YC W18), helping startups and enterprises become compliant and I recently started exploring what that might look like in a post-agentic world. The problem Halo solves is: when a company buys an AI agent from a vendor and gives it access to their data, they have no way to check what the agent did with that data. Vendors may have built observability dashboards and audit logs, but those are editable and partisan. SOC 2 and ISO 27001 audit a company's controls, but controls are less predictive when the software is agentic. TLDR: give an agent the same prompt 50 times, and you get 50 slightly different actions/answers - so the only thing worth auditing in a post-agentic world is what happened at runtime. Halo is an open-source project that produces agent runtime evidence. It's a small recorder that records every action an agent takes (eg. tool calls, model calls, data access, etc), and becomes a record in an append-only log. It's hash-chained, so anyone can re-verify. Run the following command to see a fictional example: uvx --from halo-record halo demo --serve Then, delete a line from one of the .jsonl files and reload, and the report will catch that it's been tampered with. To wire up your own agent, run this line of Python: agent = trace(run_my_agent, profile="my-agent", log="audit.jsonl") Then use this to generate a real report and give it to your customers: halo report audit.jsonl -o report.html Disclaimer: this proves integrity, not completeness (as a self-held chain proves nothing was edited but does NOT prove that nothing was omitted). Catching this requires a witness outside the vendor and is what I'm working on next. Halo is Apache-2.0, contains zero runtime dependencies, and is about 4,300 lines of Python with 125 tests (if you prefer TypeScript, here's that repo: https://ift.tt/DspeUv8 ). Give it a try, and please let me know if you have any feedback!
4 by brian_kuan | 0 comments on Hacker News.
Hi HN, I'm Brian, I spent the last few years at Vanta (YC W18), helping startups and enterprises become compliant and I recently started exploring what that might look like in a post-agentic world. The problem Halo solves is: when a company buys an AI agent from a vendor and gives it access to their data, they have no way to check what the agent did with that data. Vendors may have built observability dashboards and audit logs, but those are editable and partisan. SOC 2 and ISO 27001 audit a company's controls, but controls are less predictive when the software is agentic. TLDR: give an agent the same prompt 50 times, and you get 50 slightly different actions/answers - so the only thing worth auditing in a post-agentic world is what happened at runtime. Halo is an open-source project that produces agent runtime evidence. It's a small recorder that records every action an agent takes (eg. tool calls, model calls, data access, etc), and becomes a record in an append-only log. It's hash-chained, so anyone can re-verify. Run the following command to see a fictional example: uvx --from halo-record halo demo --serve Then, delete a line from one of the .jsonl files and reload, and the report will catch that it's been tampered with. To wire up your own agent, run this line of Python: agent = trace(run_my_agent, profile="my-agent", log="audit.jsonl") Then use this to generate a real report and give it to your customers: halo report audit.jsonl -o report.html Disclaimer: this proves integrity, not completeness (as a self-held chain proves nothing was edited but does NOT prove that nothing was omitted). Catching this requires a witness outside the vendor and is what I'm working on next. Halo is Apache-2.0, contains zero runtime dependencies, and is about 4,300 lines of Python with 125 tests (if you prefer TypeScript, here's that repo: https://ift.tt/DspeUv8 ). Give it a try, and please let me know if you have any feedback!
Monday, July 6, 2026
Sunday, July 5, 2026
Saturday, July 4, 2026
Friday, July 3, 2026
Thursday, July 2, 2026
New top story on Hacker News: The US Government Is Now a Shareholder in 26 Companies
The US Government Is Now a Shareholder in 26 Companies
66 by measurablefunc | 57 comments on Hacker News.
66 by measurablefunc | 57 comments on Hacker News.
Wednesday, July 1, 2026
New top story on Hacker News: Show HN: Morph Reflexes – Multi-head classifiers for agent traces
Show HN: Morph Reflexes – Multi-head classifiers for agent traces
11 by bhaktatejas922 | 1 comments on Hacker News.
The most common failures for production agents are behavioral: looping, reasoning leakage, user frustration, and more. Using a frontier model like GPT or Sonnet to judge every turn is too expensive and slow to run at scale. To solve this, we built Reflexes: semantic signals from agent traces, served fast and cheap over API. Built on custom kernels and a custom inference engine forked from vLLM. Under the hood, it is a small LLM architected around multi-head inference. Small models need to be trained for specific tasks, but running 50 separate small models on the same input for 50 tasks makes no sense. How it works: We use a modern LLM with hybrid attention and remove the decode step. We built an inference engine that lets prefill compute be 99% reused from reflex to reflex, similar in spirit to older 2019-era BERT/HYDRA and older multiple-head techniques. we built the inference engine to reuse the KV/cache across inputs and compute across all reflexes. One shared backbone reads the trace once, then many heads classify different signals. Our inference engine reuses the same KV/cache and compute across all reflexes, giving us sub-30ms inference with less than 0.1% overhead for each additional reflex. We took the same high-level idea and did the hard work to make it work with a modern architecture and attention. On it, we can run inference in under 30ms and serve the full request in under 90ms. If you run 4 reflexes or 100, the extra overhead is less than 2ms. Why does optimizing this matter? If you’re even a medium-sized startup, you’re dealing with tens of thousands of agent runs and millions of turns. If you want to track things like user frustration rates over time, frontier LLM-as-judge does not scale. I built a similar stack at Tesla. When ML engineers needed to sample data across petabytes for signals like `is_camera_obfuscated=true`, along with 200 other things, you need to 1) spin them up quickly 2) run at scale efficiently What it is not: A dashboard. 99% of dashboards go unused. 100% API first and made for devs who want to use this to trigger their own stuff. vibetrain a custom reflex in our dashboard, and/or then let it self improve in production: https://ift.tt/g0OYzTq Docs: https://ift.tt/mxT1osf I’d love feedback from people running agents in prod: what sorts of things do you wish you could track over time across 100% of turns but cant right now? TLDR: semantic signals from agent traces, super fast, cheap via API
11 by bhaktatejas922 | 1 comments on Hacker News.
The most common failures for production agents are behavioral: looping, reasoning leakage, user frustration, and more. Using a frontier model like GPT or Sonnet to judge every turn is too expensive and slow to run at scale. To solve this, we built Reflexes: semantic signals from agent traces, served fast and cheap over API. Built on custom kernels and a custom inference engine forked from vLLM. Under the hood, it is a small LLM architected around multi-head inference. Small models need to be trained for specific tasks, but running 50 separate small models on the same input for 50 tasks makes no sense. How it works: We use a modern LLM with hybrid attention and remove the decode step. We built an inference engine that lets prefill compute be 99% reused from reflex to reflex, similar in spirit to older 2019-era BERT/HYDRA and older multiple-head techniques. we built the inference engine to reuse the KV/cache across inputs and compute across all reflexes. One shared backbone reads the trace once, then many heads classify different signals. Our inference engine reuses the same KV/cache and compute across all reflexes, giving us sub-30ms inference with less than 0.1% overhead for each additional reflex. We took the same high-level idea and did the hard work to make it work with a modern architecture and attention. On it, we can run inference in under 30ms and serve the full request in under 90ms. If you run 4 reflexes or 100, the extra overhead is less than 2ms. Why does optimizing this matter? If you’re even a medium-sized startup, you’re dealing with tens of thousands of agent runs and millions of turns. If you want to track things like user frustration rates over time, frontier LLM-as-judge does not scale. I built a similar stack at Tesla. When ML engineers needed to sample data across petabytes for signals like `is_camera_obfuscated=true`, along with 200 other things, you need to 1) spin them up quickly 2) run at scale efficiently What it is not: A dashboard. 99% of dashboards go unused. 100% API first and made for devs who want to use this to trigger their own stuff. vibetrain a custom reflex in our dashboard, and/or then let it self improve in production: https://ift.tt/g0OYzTq Docs: https://ift.tt/mxT1osf I’d love feedback from people running agents in prod: what sorts of things do you wish you could track over time across 100% of turns but cant right now? TLDR: semantic signals from agent traces, super fast, cheap via API