Are AI Labs Pelicanmaxxing?
6 by dcastm | 0 comments on Hacker News.
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