The best Hacker News stories from Show from the past day
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Show HN: Rubrol – Sub-10ms PDF engine using Typst instead of Headless Chrome
Show HN: Three genlocked RP2350B make a console – 3k sprite pixels per line)
Show HN: I created an open source locally usable full fledged AI platform
hi to all the readers this post is for my recent opensource project called ENZO<p>now answering what is enzo so enzo is an opensource platform where i clubbed all the free available api for anyone use under one hood with more than 2000 models available to use for chatting coding researching and much more now answering the most common question of why you should put your time looking the project so it has few distinct feature meaning<p>it has a dedicated agents tab where you can describe your need and create a special agent just for one specific task with master ability in that domain<p>second it has the ability to connect your gmail drive and calendar and then you can ask it to perform some specific tasks like reading you the most important mail of the day or finding recruiter mails and creating personalized reply based on your data which it stores locally on your device<p>third the coding mode offers a dedicated preview window where you can see your code running and have a look of it feels and edit it in realtime as well as all the modes are packed with dedicated skills which delivers promising results<p>fourth the ui features some additional things such as music tab where you can listen to any music want and it has a custom personalized feature which runs in background and an llm understands your taste and recommends similar kind of music you like<p>fifth the most important why your trust it with your api key then to explain i would say enzo a dedicated vault which manages all your api and to secure it the vault as aes 256 bit encryption which prevents any person or any middle man to look at your api key and since the whole program runs locally on your device you have complete freedom to oversee all the backend work happening and it also features password lock which if you enable saves a backup key and then locks your whole platform work behind a pass screen though it is not foolproof as any third party or malware containing extension can still fetch login tokens from your browser so its security also depends upon how you access it concluding all of it.<p>i urge to anyone who reads this to have a look at the platform even if you hate it just curse it in the comment its fine or if you would like to drop any feedback i would highly encourage that and since its my first work open source platform i know it has a lot of errors and bugs so i apologize upfront for it and if you consider my work worthy please drop a star on the repo that'll make my day
Show HN: A competition for small neural networks that play strategy games
Show HN: Sigabrt.dev – cronjob monitor with an SSH TUI
Hello HN. I built this mostly to monitor the things I host myself. I know it's nothing too exciting.<p>Anyway, the TL;DR is: Create an endpoint, and if your script/cronjob fails to regularly ping it, you get notified (by email or ntfy). E.g.:<p><pre><code> 0 * * * * ./script.sh && curl -fsS https://sigabrt.dev/pulse/<id>/beat
</code></pre>
It also has an SSH TUI which is currently experimental and read-only, mostly because I'm not sure whether it is actually useful or just a gimmick :):<p><pre><code> ssh sigabrt.dev
</code></pre>
To use it, simply add your SSH public key in your account settings.<p>Yes, there are services like this already, and this is minimalistic by comparison. Feedback is welcome.
Show HN: Radius – A Meetup.com Alternative
Hello HN!<p>I'm relaunching Radius [1] - far too long after the original Show HN [2] (I'll blame the day job for that).<p>Radius started out from my frustration with not knowing what was going on around me. The initial plan was to "show all and every type of event" from big to small, but over time I narrowed the focus to communities, groups, events, and helping people connect through them.<p>The initial Show HN gave me 150+ comments (surprising, looking back, given how terrible the plain Bootstrap UI was...) with a huge amount of useful feedback and feature requests. I've spent the time since then working through that, along with additional requests from groups using Radius, and it finally feels ready for a proper relaunch.<p>I've also recently added "Activities" [3] - which are lightweight events, independent of groups, intended to help people find others to do things with - for example:<p>> John is going for a cycle and a coffee in New York at 10am<p>People can join and vote for a time that works for them.<p>I've got a bunch of ideas for where to go next with this, including things like open sourcing and federation, but those are for another day!<p>Any feedback would be greatly appreciated!<p>P.S. it's built with Ruby on Rails, for those interested. I highly suspect it would have taken me 2 more years to get to this point without Rails!<p>[1] <a href="https://radius.to/" rel="nofollow">https://radius.to/</a><p>[2] <a href="https://news.ycombinator.com/item?id=40717398">https://news.ycombinator.com/item?id=40717398</a><p>[3] <a href="https://radius.to/documentation/activities/post-an-activity" rel="nofollow">https://radius.to/documentation/activities/post-an-activity</a>
Show HN: I wrote a custom assembler for CHIP-8 in C++
I'm writing a custom assembler for CHIP-8 and just finished adding support for the DB/.byte directive so I can embed sprites in the ROM and finally get something drawn on the screen. It SHOULD support all OG CHIP-8 instructions, but still a ton of rough edges that I'm ironing out.<p>This is my first ever C++ project, just like the CHIP-8 emulator was my first ever C (and emudev) project. It's so satisfying to write assembly and have your program spit out a ROM that actually runs in the emulator you wrote.<p>All code is written by me (a lot of it livestreaming) as evident by the quality, no AI.
Show HN: CUA-S1 – A System One Model for Computer Use
Hello HN! We're Dillon and Francesco from Cua.<p>We were wondering how many computer use tasks actually need a full general purpose LLM (e.g. gpt-6-astra, claude-opus-5 etc.) to think through all their decisions and steps. Some tasks require thinking about a plan, exploring different paths, recovering from failure. Other tasks are a question of making local decisions, like this value should go in this box, or should I check this box, or this element should be ignored.<p>We wondered how far we could go with a small model trained to only make these kinds of decisions.<p>Our inspiration was Typesafe's Jev and its System One Model framing. This is a nod to the dichotomy between thinking quickly, automatically, and intuitively (system 1) vs. thinking slowly, analytically (system 2), as described by Daniel Kahneman.<p>The interesting question for us was: what happens if you give a model an interface of current context, and a set of possible choices, and you ask it to return a probability for each choice? This kind of model does not generate output token by token like most LLMs do, but rather scores the options you give it, which you can check, trust, and use to drive your app's behavior.<p>CUA-S1 is our answer for narrow, specialized decision models for computer use. Our first release is CUA-S1-FORMS. We built this from ideas and code in jevlike, and then trained a second model just to handle form interactions. It has 706k parameters, and the original checkpoint is 2.8 MB.<p>The first training iteration took less than 30 minutes on synthetic data. Given a set of structured elements and values extracted from a document, it predicts whether to use the given value, CHECK, CLICK, or SKIP for each element. It does not predict new values for text fields, and does not consider screenshots. Element decisions are scored together, and your code can order the actions, and Cua Driver will execute them one at a time.<p>A first evaluation of this specialist vs. hosted Jev on our form task:<p>- For the whole decision set: 99.7% correct vs 83.6%.<p>- For the subset of steps that require an action: 100% correct vs 96%.<p>- For the subset of steps that are just leaving already-filled fields alone: 100% correct vs 74%.<p>The specialist was trained specifically for this task and convention (just press skip for already filled boxes), while hosted Jev has not been fine-tuned for it, so this is an experiment in scoped specialization.<p>We measured 7-9 ms to score a form locally vs. 260-280 ms per call to hosted Jev including network latency, though those samples measure different things and are not end-to-end form completion times.<p>Our interest here is in the space between a brittle script and a general agent loop. The content and layout of form fields vary enough that scripts get unwieldy, but the set of available decisions can remain narrow and well scoped. We want to explore the possibility of a general agent encountering something novel, and passing well understood decisions over to specialists like this.<p>That is a direction we are looking into. The current release is for forms only. We're open sourced the synthetic data generation, training, evaluation, and Driver integration under libs/cua-s1 with an MIT license.<p>Comments welcome! Especially if you are building computer-use agents and have run into a recurring decision that is too variable to script but is too narrow to call another LLM for.
Show HN: CUA-S1 – A System One Model for Computer Use
Hello HN! We're Dillon and Francesco from Cua.<p>We were wondering how many computer use tasks actually need a full general purpose LLM (e.g. gpt-6-astra, claude-opus-5 etc.) to think through all their decisions and steps. Some tasks require thinking about a plan, exploring different paths, recovering from failure. Other tasks are a question of making local decisions, like this value should go in this box, or should I check this box, or this element should be ignored.<p>We wondered how far we could go with a small model trained to only make these kinds of decisions.<p>Our inspiration was Typesafe's Jev and its System One Model framing. This is a nod to the dichotomy between thinking quickly, automatically, and intuitively (system 1) vs. thinking slowly, analytically (system 2), as described by Daniel Kahneman.<p>The interesting question for us was: what happens if you give a model an interface of current context, and a set of possible choices, and you ask it to return a probability for each choice? This kind of model does not generate output token by token like most LLMs do, but rather scores the options you give it, which you can check, trust, and use to drive your app's behavior.<p>CUA-S1 is our answer for narrow, specialized decision models for computer use. Our first release is CUA-S1-FORMS. We built this from ideas and code in jevlike, and then trained a second model just to handle form interactions. It has 706k parameters, and the original checkpoint is 2.8 MB.<p>The first training iteration took less than 30 minutes on synthetic data. Given a set of structured elements and values extracted from a document, it predicts whether to use the given value, CHECK, CLICK, or SKIP for each element. It does not predict new values for text fields, and does not consider screenshots. Element decisions are scored together, and your code can order the actions, and Cua Driver will execute them one at a time.<p>A first evaluation of this specialist vs. hosted Jev on our form task:<p>- For the whole decision set: 99.7% correct vs 83.6%.<p>- For the subset of steps that require an action: 100% correct vs 96%.<p>- For the subset of steps that are just leaving already-filled fields alone: 100% correct vs 74%.<p>The specialist was trained specifically for this task and convention (just press skip for already filled boxes), while hosted Jev has not been fine-tuned for it, so this is an experiment in scoped specialization.<p>We measured 7-9 ms to score a form locally vs. 260-280 ms per call to hosted Jev including network latency, though those samples measure different things and are not end-to-end form completion times.<p>Our interest here is in the space between a brittle script and a general agent loop. The content and layout of form fields vary enough that scripts get unwieldy, but the set of available decisions can remain narrow and well scoped. We want to explore the possibility of a general agent encountering something novel, and passing well understood decisions over to specialists like this.<p>That is a direction we are looking into. The current release is for forms only. We're open sourced the synthetic data generation, training, evaluation, and Driver integration under libs/cua-s1 with an MIT license.<p>Comments welcome! Especially if you are building computer-use agents and have run into a recurring decision that is too variable to script but is too narrow to call another LLM for.
Show HN: LiveWorld – Every 24/7 YouTube live camera on one globe
Show HN: Aclif – Agent CLI framework: one grammar, canonical names across SaaS
Simple discovery of the provider's default schemas is possible without any credentials (since they are built into the binary). If you want it to fetch customizations of your specific instance, give it credentials to your org.<p>The repo has more details. <a href="https://github.com/agent-cli-framework/aclif" rel="nofollow">https://github.com/agent-cli-framework/aclif</a>
Show HN: Navier-Stokes Visualized as 1kB i386 demos
I was making a web project about some of my 1996 era 386 graphics code when I saw the visualizations related to the recent solution to the smoothness problem of Navier-Stokes and thought "that would look cool as a 90s-era demo". So here you go, a palette cycler and a particle animation, both using the real equations and both as sub-1kB binaries, rendered online via js-dos/DOSBox as well as JavaScript ports with knobs to go far beyond 386 quality. All source and pre-compiled 386 binaries included and free in every way.<p>And since I know this is divisive on HN, I should be up front that this effort was AI-assisted, but hey, so was OpenAI's solution of the Millennium problem that inspired my effort.
Show HN: Ax-check.com – Can agents use your product?
I'm the co-founder of Gauge, and I built ax-check.com to quickly test how well coding agents can onboard to your product.<p>You'll get a scorecard, specific suggested fixes, and three full coding sessions that show how agents read your site and use your product.<p>I built this because similar checks were too noisy. Most suggested obscure technical changes that don't actually make a difference in agent experience (or AX, hence ax-check.com).<p>This check starts by using DeepSeek 4.1 Flash to try to find key information about your product, starting from the homepage. In actual agent traffic data, we've seen that the key pages are the homepage, llms.txt, pricing, and the docs site (by traffic volume, and by influence), so we focus on those and ignore the rest. We also find that content negotiation for Markdown is legitimately helpful for agents to complete tasks faster and find what they're looking for, so the scan tests that your key pages can serve Markdown.<p>The other key piece is that we run actual coding agents in sandboxes, and have them try to onboard to your product. You can see the full trace and watch it happen live (we kick it off fresh when you enter a new site). We surface interesting findings like hallucinated URLs, inaccurate docs instructions, or product confusion.<p>It also detects whether the agents could complete a fully working onboarding autonomously, without being blocked by a login wall. This is still controversial, but I think finding ways to let agents safely onboard autonomously is going to be table stakes within a year for developer tools in particular.<p>The whole site is agent-friendly itself! You can generally just talk to your coding agent about ax-check.com and it can do the rest. Would really appreciate any feedback to make this useful.
Show HN: Ax-check.com – Can agents use your product?
I'm the co-founder of Gauge, and I built ax-check.com to quickly test how well coding agents can onboard to your product.<p>You'll get a scorecard, specific suggested fixes, and three full coding sessions that show how agents read your site and use your product.<p>I built this because similar checks were too noisy. Most suggested obscure technical changes that don't actually make a difference in agent experience (or AX, hence ax-check.com).<p>This check starts by using DeepSeek 4.1 Flash to try to find key information about your product, starting from the homepage. In actual agent traffic data, we've seen that the key pages are the homepage, llms.txt, pricing, and the docs site (by traffic volume, and by influence), so we focus on those and ignore the rest. We also find that content negotiation for Markdown is legitimately helpful for agents to complete tasks faster and find what they're looking for, so the scan tests that your key pages can serve Markdown.<p>The other key piece is that we run actual coding agents in sandboxes, and have them try to onboard to your product. You can see the full trace and watch it happen live (we kick it off fresh when you enter a new site). We surface interesting findings like hallucinated URLs, inaccurate docs instructions, or product confusion.<p>It also detects whether the agents could complete a fully working onboarding autonomously, without being blocked by a login wall. This is still controversial, but I think finding ways to let agents safely onboard autonomously is going to be table stakes within a year for developer tools in particular.<p>The whole site is agent-friendly itself! You can generally just talk to your coding agent about ax-check.com and it can do the rest. Would really appreciate any feedback to make this useful.
Show HN: Scry, programmable internet search w/ congestion pricing
Meet Scry, a 500 TB NVMe internet index in ClickHouse that you can run ~arbitrary readonly SQL and some of Datalog over, and I handle the problem of resource-contention with congestion-based micro-auction pricing. When there's capacity, the service is free for non-commercial use.<p>---<p>Hello. It's 2026, we're training simulated fruit fly brains to play Beat Saber, do we still have to be stuck with internet (re)search as fn: natural language -> black box we can't do anything about -> ranked_list/summary?<p>There is a long history of people trying to do very fancy things that end up being done in relational databases and a little SQL. There is a gravity to them, a bitter lesson, just like scaling of generalized ml training methods. I mean many, many information products can be built off essentially giant real-time OLAP databases and frontier LLMs writing brilliant SQL+Datalog+vector+Jev etc. queries.<p>Google Search, Tavily, Exa essentially have the problem of <i>mapping</i> your agents' context you are willing to provide, to a tiny subset of their index. You pay a fixed cost to an extremely hard problem that has a distribution of hardness, which means YOU eat the downsides when they are running out of budgeted compute to help you out.<p>Their algorithms are opaque to the caller, there's really not much user control, and there's not a serious opportunity to communally improve search recipes, like the lexical+Jev recipes you trust to select bleeding edge AI builders.<p>Furthermore, search companies aren't even pursuing text-to-SQL anymore (several have talked to me)... they made up their minds during the traumatic 2024 text-to-sql days. They were just too early.<p>I hope you enjoy. I'm intent on scaling this paradigm on differentiated hardware over much more data, so any compelling use cases or queries I could show off, would be much appreciated!
Show HN: Scry, programmable internet search w/ congestion pricing
Meet Scry, a 500 TB NVMe internet index in ClickHouse that you can run ~arbitrary readonly SQL and some of Datalog over, and I handle the problem of resource-contention with congestion-based micro-auction pricing. When there's capacity, the service is free for non-commercial use.<p>---<p>Hello. It's 2026, we're training simulated fruit fly brains to play Beat Saber, do we still have to be stuck with internet (re)search as fn: natural language -> black box we can't do anything about -> ranked_list/summary?<p>There is a long history of people trying to do very fancy things that end up being done in relational databases and a little SQL. There is a gravity to them, a bitter lesson, just like scaling of generalized ml training methods. I mean many, many information products can be built off essentially giant real-time OLAP databases and frontier LLMs writing brilliant SQL+Datalog+vector+Jev etc. queries.<p>Google Search, Tavily, Exa essentially have the problem of <i>mapping</i> your agents' context you are willing to provide, to a tiny subset of their index. You pay a fixed cost to an extremely hard problem that has a distribution of hardness, which means YOU eat the downsides when they are running out of budgeted compute to help you out.<p>Their algorithms are opaque to the caller, there's really not much user control, and there's not a serious opportunity to communally improve search recipes, like the lexical+Jev recipes you trust to select bleeding edge AI builders.<p>Furthermore, search companies aren't even pursuing text-to-SQL anymore (several have talked to me)... they made up their minds during the traumatic 2024 text-to-sql days. They were just too early.<p>I hope you enjoy. I'm intent on scaling this paradigm on differentiated hardware over much more data, so any compelling use cases or queries I could show off, would be much appreciated!
Show HN: Scry, programmable internet search w/ congestion pricing
Meet Scry, a 500 TB NVMe internet index in ClickHouse that you can run ~arbitrary readonly SQL and some of Datalog over, and I handle the problem of resource-contention with congestion-based micro-auction pricing. When there's capacity, the service is free for non-commercial use.<p>---<p>Hello. It's 2026, we're training simulated fruit fly brains to play Beat Saber, do we still have to be stuck with internet (re)search as fn: natural language -> black box we can't do anything about -> ranked_list/summary?<p>There is a long history of people trying to do very fancy things that end up being done in relational databases and a little SQL. There is a gravity to them, a bitter lesson, just like scaling of generalized ml training methods. I mean many, many information products can be built off essentially giant real-time OLAP databases and frontier LLMs writing brilliant SQL+Datalog+vector+Jev etc. queries.<p>Google Search, Tavily, Exa essentially have the problem of <i>mapping</i> your agents' context you are willing to provide, to a tiny subset of their index. You pay a fixed cost to an extremely hard problem that has a distribution of hardness, which means YOU eat the downsides when they are running out of budgeted compute to help you out.<p>Their algorithms are opaque to the caller, there's really not much user control, and there's not a serious opportunity to communally improve search recipes, like the lexical+Jev recipes you trust to select bleeding edge AI builders.<p>Furthermore, search companies aren't even pursuing text-to-SQL anymore (several have talked to me)... they made up their minds during the traumatic 2024 text-to-sql days. They were just too early.<p>I hope you enjoy. I'm intent on scaling this paradigm on differentiated hardware over much more data, so any compelling use cases or queries I could show off, would be much appreciated!
Show HN: Snapdrop: Instantly share files between devices. No setup, no signup
Show HN: Snapdrop: Instantly share files between devices. No setup, no signup
Show HN: Microsoft Office running with Wine on Linux with no virtualization
Microsoft Office famously hasn't worked well with Wine since Office 2007. There are lots of solutions out there to work around this, but all of them involve fairly heavy virtualization.<p>Multiple people have told me that their biggest hangup for switching to Linux from Windows is the lack of the desktop version of Microsoft Office, and they don't want to run a VM to get around it.<p>I recently fell into a bunch of free time with a Claude Max subscription, so I got Fable to hack at this until it worked! And with this, we have Microsoft Office running on Linux, with no Windows in sight!<p>I can't promise that this will work on everyone's computer, <i>but</i> I did put this all in Nix Flakes, so ideally it should be relatively reproducible on any Linux system with the Nix package manager and Flakes enabled. Also, it does <i>not</i> work very well with X, but Wayland appears to work fine.<p>This is not warez. You need a valid license for Office 365 for this to work. You also need to register <i>on Microsoft's website in a browser</i>; the flow for registering in the app proved to be tricky and not worth pursuing.<p>This is more proof of concept than anything else; I have no doubt that there are bugs and improvements to be made. I have only tested this with NixOS on Sway and KDE, and as stated it only really works with Wayland and the absolute latest GE-Proton, but I believe it would work fine with Gnome or any other Wayland desktop.<p>Full disclosure, Claude Code with Fable did the vast <i>vast</i> majority of the work on this. I ended up primarily just reading log messages and complaining back to it.