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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: 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: 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.

Show HN: Cactus Needle 3: 8-29MB automation models can match DeepSeek V4 Flash

Hey HN, Henry from Cactus here.<p>We submitted Needle 2 here a few weeks ago, and the feedback in the discussion thread was incredibly valuable, thanks! Thanks to all that feedback, we’ve been able to move quickly to release Needle 3 and I'd love to hear what you think again.<p>The key features:<p>1) Automation (tool calls & structured JSON output): Needle still doesn't chat by design, its quite challenging to pack general capacity into such small models, so we focus on tool calls and structured JSON. If no tool you declared fits the request, you get an empty list back (note for when playing with the demo).<p>2) Intelligence Laddering: Every layer (2 to 20) is a deployable subnetwork, so one set of weights, 25 to 121 million parameters at 2-bit, shipping as 8-29MB binaries. On a Raspberry Pi 5 it decodes at up to 4k tokens/sec and prefills at up to 10k.<p>3) Monarch Hadamard MLP: replaces the dense FFN with three learnable Walsh-Hadamard-initialized Kronecker (Monarch) factor pairs interleaved with per-channel diagonal scales, fixed permutations, a SiLU nonlinearity, and a rank-8 input-conditioned gate, so each token gets a fully mixed nonlinear transform of its d_model channels at O(d√d) parameters and compute instead of the O(d²) a dense 4x-expansion MLP would cost.<p>4) Performance: On Mobile Actions (phone commands, scored on the exact call) the 20-layer model gets 86.0 through the shipped 2-bit binary; LFM2.5 1.2B is at 82.4, Qwen3.5 0.8B at 76.0, Apple's on-device model at 57.6, all at f16. More results on the link, we do not win everywhere ofc.<p>5) Multilingual: Needle 3 now supports English, French, Spanish, German, Dutch, Italian, Polish, with more languages coming.<p>6) Finetuning: You can achieve DeepSeek v4 Flash grade performance on a narrow task with just 4L, stress on "narrow task", we found that production users often prefer tuning before production.<p>7) Triggers: Grounding is a common challenge for tool call, at least for Needle 2, so we added support case-insensitive regular expressions matched against each request to gate false negatives.<p>8) Confidence: Every response also carries a calibrated confidence score, the minimum of a judgement on the finished call and its decode probability. Act above your threshold, show the call and ask below it, or escalate to a bigger model.<p>9) Supported Platforms: macOS, Linux on x86-64, ARM64, ARMv7, RISC-V and MIPS32, Windows x64 and ARM, Android, iOS, watchOS, tvOS, the browser as WebAssembly, and a WASI component.<p>Thanks for reading and as always, thoughts appreciated!

Show HN: AttaLambda: a language where types and data are made of untyped lambdas

I made a programming language!<p>I call it AttaLambda.<p>The idea is this: a usable Lisp-shaped language where all the meaningful computation is done in untyped lambda calculus. Logic, arithmetic, data structures, control flow, even the types — all untyped lambdas. A small, explicit Racket layer sits at the boundary to handle the outside world, plus some macros for syntactic sugar.<p>This is its story:<p>A couple years ago, I wanted to play with untyped lambda calculus and go beyond where tutorials usually stop. They show booleans, numbers, arithmetic, maybe the Y-combinator — and then stop. I wanted them to keep going.<p>So I started a project called All The Lambdas. Using Racket set to lazy, I used only one Racket construct for actual computation — lambda — and built integers, rationals, lists, binary digit-list number encodings, search algorithms, and more.<p>Then I found Functional Programming Through Lambda Calculus by Greg Michaelson. In it, Michaelson sketches the bones of a language built in untyped lambda calculus, including a type system where typed objects are themselves pair functions containing a type tag and value.<p>I found that intriguing and implemented and extended the idea, still entirely with untyped lambdas. I don't have a background in programming language theory, so I was figuring it out as I went.<p>Then I stopped tinkering with it for a while.<p>Recently I came back and thought: why not turn this into a real usable language with the help of coding agents? I reused most of All The Lambdas as the foundation.<p>Thus AttaLambda was born.<p>Some additional details:<p>* Rat, its number type, uses binary digit-list encodings instead of Church numerals, so numbers scale with their number of binary digits rather than their value<p>* errors are lambda-encoded values, not Racket exceptions, and propagate through the language like ordinary data<p>* the Racket host only performs irreducibly external operations; even things like HTTP parsing, routing, and response construction stay in the pure lambda world<p>* recursion uses lambda-calculus recursion: no loops or true self-reference, just the Y-combinator underneath<p>* automated purity checks catch accidental cheating, like native computation leaking into the pure parts<p>* syntax like multi-argument lambdas, let, cond, and list is just macro sugar that reduces to unary lambdas and application<p>A couple code examples: (short of print, every single thing here reduces to unary untyped lambdas)<p>Factorial:<p><pre><code> #lang attalambda (rec factorial n = (cond ((eq n 0) 1) (else (mult n (factorial (sub n 1)))))) (print (factorial 10)) </code></pre> Which prints:<p><pre><code> 3628800 </code></pre> Or an exact harmonic sum:<p><pre><code> #lang attalambda (print (reduce add 0 (map (lambda (n) (unwrap-ok (div 1 n))) (range 1 8)))) </code></pre> Which prints exactly:<p><pre><code> 363/140 </code></pre> As far as I know, no programming language combines all these features: Michaelson-style type tags built from untyped lambdas, exact rationals backed by binary digit lists, errors as lambda values, and real-world programs where almost all computation stays inside the lambda core. None of those pieces are individually new, but I don't know of another language combining them this way.<p>Download: <a href="https://github.com/kserrec/attalambda/releases/tag/v0.7.0" rel="nofollow">https://github.com/kserrec/attalambda/releases/tag/v0.7.0</a><p>Code: <a href="https://github.com/kserrec/attalambda" rel="nofollow">https://github.com/kserrec/attalambda</a><p>Original All The Lambdas: <a href="https://github.com/kserrec/all_the_lambdas" rel="nofollow">https://github.com/kserrec/all_the_lambdas</a>

Show HN: I built a new version of my fun spatial 3D online meeting app

Hi HN!<p>flat.social is a fun spatial online meeting app for remote teams and communities. It's largely built like a web multiplayer game, and the 3D virtual spaces are highly customisable with elements and activities (I recently added surfing!).<p>Live demo to try it out: <a href="https://app.flat.social/f/demo-flat" rel="nofollow">https://app.flat.social/f/demo-flat</a><p>Here's a quick demo video: <a href="https://www.youtube.com/watch?v=GeinBNqL23s" rel="nofollow">https://www.youtube.com/watch?v=GeinBNqL23s</a><p>It's a solo, bootstrapped project built with Three.js, LiveKit and Rapier for physics. I built the first version during the pandemic and posted it here back then: <a href="https://news.ycombinator.com/item?id=31833415">https://news.ycombinator.com/item?id=31833415</a><p>I had a couple of months off this year, so I locked myself in an (almost) jungle house in Brazil and rebuilt it into what I've always wanted it to be.<p>Would love to hear what you think!

Show HN: I built a new version of my fun spatial 3D online meeting app

Hi HN!<p>flat.social is a fun spatial online meeting app for remote teams and communities. It's largely built like a web multiplayer game, and the 3D virtual spaces are highly customisable with elements and activities (I recently added surfing!).<p>Live demo to try it out: <a href="https://app.flat.social/f/demo-flat" rel="nofollow">https://app.flat.social/f/demo-flat</a><p>Here's a quick demo video: <a href="https://www.youtube.com/watch?v=GeinBNqL23s" rel="nofollow">https://www.youtube.com/watch?v=GeinBNqL23s</a><p>It's a solo, bootstrapped project built with Three.js, LiveKit and Rapier for physics. I built the first version during the pandemic and posted it here back then: <a href="https://news.ycombinator.com/item?id=31833415">https://news.ycombinator.com/item?id=31833415</a><p>I had a couple of months off this year, so I locked myself in an (almost) jungle house in Brazil and rebuilt it into what I've always wanted it to be.<p>Would love to hear what you think!

Show HN: Share your AI Setup, Learn from others

I kept seeing engineers share what they were building with AI; however, I was always more curious about how they worked. Which agents did they use? What skills and tools had stuck or been thrown out the window? How did they manage longer-running tasks? So I built this with the hope we could have a dedicated space to share and be open about our setups.

Show HN: Share your AI Setup, Learn from others

I kept seeing engineers share what they were building with AI; however, I was always more curious about how they worked. Which agents did they use? What skills and tools had stuck or been thrown out the window? How did they manage longer-running tasks? So I built this with the hope we could have a dedicated space to share and be open about our setups.

Show HN: Ordewell – turn one goal into an ordered plan of coding-agent tasks

Show HN: Pizza Bot – An inbox for AI agents that work in the background

Hi HN - long-time lurker (since 2012!), first time poster.<p>Pizza Bot is a self-hosted desktop app for Mac, Windows, and Linux that runs AI agents in the background and exposes them through an email-like UI. Finished work shows up in Unread, and anything waiting on your approval shows up in Action. It's Apache 2.0-licensed, there's no signup and no telemetry, and you bring your own model provider: Anthropic, Amazon Bedrock, Google Gemini, OpenAI, OpenRouter, or a local model through Ollama. There are builds on the releases page, or you can run it from source.<p>Pizza Bot started as an internal passion project I worked on with a small team at Amazon.<p>The whole thing came out of my frustration at having to manually log CRM activities through a browser form. I built a simple REST API called "JoeBot" that connected to my authenticated browser session over CDP and filled out the form for me using Playwright. Then I hacked up a quick Obsidian plugin so I could trigger it from my local notes (no AI and no MCP servers involved).<p>This caught on quickly. My fellow AWS Solutions Architect Igor Fil joined up with me, and we rebranded the project as "Pizza Bot," named after Amazon's two-pizza teams. We started seeing what other automations we could build. We found a GraphQL API we could query and hacked up some "recipes" to pull data out of the CRM to help with meeting prep. That worked great, and it was right around the time MCP servers seemed to be taking off, so we decided to expose Pizza Bot as an MCP server instead, so it would be available to AI tools through natural language.<p>This was a decent solution for technical users, but the Account Managers who live inside our CRM system wanted something too. We decided to rebuild Pizza Bot as an Electron desktop app modeled after an email inbox, so it would be familiar to non-technical users and would run on both Mac and Windows. We also bundled internal MCP servers as OCI images and hosted them in Amazon ECR as an "addon marketplace" so users could install them with one click without having to set up Amazon developer tooling.<p>The project took off organically and expanded outside of AWS into the wider Amazon organization globally. More than 2,000 people ended up using it for meeting prep, email drafting, Slack summaries, CRM logging, prioritizing their day, and web research.<p>Once apps like Claude Cowork and Amazon's own Quick Desktop came out, we realized the real growth opportunity was outside of Amazon. Rather than try to rip out the Amazon-specific integrations, we rebuilt Pizza Bot once more as an open source project. We leaned on coding agents heavily, which is the only reason a team our size could pull off a full rewrite. I'm pleased to say it's finally public, and we're hoping to bring in community members and see where it goes. We'd like to do for knowledge workers what Claude Code and Codex have done for programmers.<p>A couple of things to know up front. Most of what made Pizza Bot useful on day one inside Amazon came from that internal catalog of skills and MCP servers for Amazon's own systems, and none of it could come out with the app. So it ships thinner than the version those 2,000 people used, and building that catalog back up for tools other people actually use is where we need the most help. It's also a community project and not an AWS service, so there's no support or SLA behind it. The Windows and Linux builds aren't signed yet either.<p>On the technical side, Pizza Bot is a server and a client. The desktop app bundles both, or you can point a client at a remote backend; personally, I self-host the server on my home network and reach it from my phone over Tailscale. The server owns the thread lifecycle and checkpoints state with DeepAgents and LangGraph, and clients rehydrate from it as needed, so you can disconnect mid-run and pick the thread back up from another client. Approval pauses outlive the session that created them and collect in an Action filter, so you can answer an hour later from a different device. The agent you talk to has a sandboxed QuickJS interpreter that can reach your filesystem only if you grant it a folder, but its main job is to delegate. Each subagent is a 1:1 mapping of a Skill, and an Activity bar shows that subagent and the tool calls it's making as it works. Memory is opt-in and stored as plain markdown files on your machine. Every tool call is explicit, including looking up a memory - we err on the side of transparency to reduce surprises. Tools come from MCP servers, and skills are ordinary SKILL.md files with a per-tool approval policy, so existing skills that don't require a code interpreter should still work.<p>What I'd most like to hear about is where the app itself gets in your way, the kind of problem you can't fix by writing a skill or an MCP server. I'm around today to answer questions!

Show HN: Pizza Bot – An inbox for AI agents that work in the background

Hi HN - long-time lurker (since 2012!), first time poster.<p>Pizza Bot is a self-hosted desktop app for Mac, Windows, and Linux that runs AI agents in the background and exposes them through an email-like UI. Finished work shows up in Unread, and anything waiting on your approval shows up in Action. It's Apache 2.0-licensed, there's no signup and no telemetry, and you bring your own model provider: Anthropic, Amazon Bedrock, Google Gemini, OpenAI, OpenRouter, or a local model through Ollama. There are builds on the releases page, or you can run it from source.<p>Pizza Bot started as an internal passion project I worked on with a small team at Amazon.<p>The whole thing came out of my frustration at having to manually log CRM activities through a browser form. I built a simple REST API called "JoeBot" that connected to my authenticated browser session over CDP and filled out the form for me using Playwright. Then I hacked up a quick Obsidian plugin so I could trigger it from my local notes (no AI and no MCP servers involved).<p>This caught on quickly. My fellow AWS Solutions Architect Igor Fil joined up with me, and we rebranded the project as "Pizza Bot," named after Amazon's two-pizza teams. We started seeing what other automations we could build. We found a GraphQL API we could query and hacked up some "recipes" to pull data out of the CRM to help with meeting prep. That worked great, and it was right around the time MCP servers seemed to be taking off, so we decided to expose Pizza Bot as an MCP server instead, so it would be available to AI tools through natural language.<p>This was a decent solution for technical users, but the Account Managers who live inside our CRM system wanted something too. We decided to rebuild Pizza Bot as an Electron desktop app modeled after an email inbox, so it would be familiar to non-technical users and would run on both Mac and Windows. We also bundled internal MCP servers as OCI images and hosted them in Amazon ECR as an "addon marketplace" so users could install them with one click without having to set up Amazon developer tooling.<p>The project took off organically and expanded outside of AWS into the wider Amazon organization globally. More than 2,000 people ended up using it for meeting prep, email drafting, Slack summaries, CRM logging, prioritizing their day, and web research.<p>Once apps like Claude Cowork and Amazon's own Quick Desktop came out, we realized the real growth opportunity was outside of Amazon. Rather than try to rip out the Amazon-specific integrations, we rebuilt Pizza Bot once more as an open source project. We leaned on coding agents heavily, which is the only reason a team our size could pull off a full rewrite. I'm pleased to say it's finally public, and we're hoping to bring in community members and see where it goes. We'd like to do for knowledge workers what Claude Code and Codex have done for programmers.<p>A couple of things to know up front. Most of what made Pizza Bot useful on day one inside Amazon came from that internal catalog of skills and MCP servers for Amazon's own systems, and none of it could come out with the app. So it ships thinner than the version those 2,000 people used, and building that catalog back up for tools other people actually use is where we need the most help. It's also a community project and not an AWS service, so there's no support or SLA behind it. The Windows and Linux builds aren't signed yet either.<p>On the technical side, Pizza Bot is a server and a client. The desktop app bundles both, or you can point a client at a remote backend; personally, I self-host the server on my home network and reach it from my phone over Tailscale. The server owns the thread lifecycle and checkpoints state with DeepAgents and LangGraph, and clients rehydrate from it as needed, so you can disconnect mid-run and pick the thread back up from another client. Approval pauses outlive the session that created them and collect in an Action filter, so you can answer an hour later from a different device. The agent you talk to has a sandboxed QuickJS interpreter that can reach your filesystem only if you grant it a folder, but its main job is to delegate. Each subagent is a 1:1 mapping of a Skill, and an Activity bar shows that subagent and the tool calls it's making as it works. Memory is opt-in and stored as plain markdown files on your machine. Every tool call is explicit, including looking up a memory - we err on the side of transparency to reduce surprises. Tools come from MCP servers, and skills are ordinary SKILL.md files with a per-tool approval policy, so existing skills that don't require a code interpreter should still work.<p>What I'd most like to hear about is where the app itself gets in your way, the kind of problem you can't fix by writing a skill or an MCP server. I'm around today to answer questions!

Show HN: How Stale Is Your AI? Release age and training cutoff for 20 models

Show HN: How Stale Is Your AI? Release age and training cutoff for 20 models

Show HN: I made a flight simulator, except you're just a passenger

Buckle your seatbelt, secure your tray table, and open your window shade. Now you can simulate flying anywhere in the world as a commercial passenger, from takeoff to touchdown. Terrain, weather, and realtime sun position included. Don't worry, the latest release now features legs, so you can get up and go to the bathroom on longer flights. Enjoy!

Show HN: I made a flight simulator, except you're just a passenger

Buckle your seatbelt, secure your tray table, and open your window shade. Now you can simulate flying anywhere in the world as a commercial passenger, from takeoff to touchdown. Terrain, weather, and realtime sun position included. Don't worry, the latest release now features legs, so you can get up and go to the bathroom on longer flights. Enjoy!

Show HN: Check if your IP has appeared in a residential proxy network