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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: 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
Show HN: Check if your IP has appeared in a residential proxy network
Show HN: Macros with a Behringer FCB1010 MIDI Pedalboard in macOS
Show HN: Macros with a Behringer FCB1010 MIDI Pedalboard in macOS
Show HN: Sunk Cost – How long until a local LLM rig pays for itself?
I kept hearing "just buy a Mac and run models locally, it pays for itself" and wanted to check. Sunk Cost takes a machine, a model and how many tokens you use a day, and works out how long the hardware takes to pay back against renting the same model by the token.<p>Obviously there are other reasons to buy your own hardware aside from just saving money on llms but this is just looking at it from a raw cost saving perspective.<p>If you have any ideas on how I can make this more helpful lmk!
Show HN: Redis City – Explore how Redis works in an interactive 3D model
Show HN: Redis City – Explore how Redis works in an interactive 3D model
Show HN: Redis City – Explore how Redis works in an interactive 3D model
Show HN: Hacking a $20 4G wireless hotspot into a texting device
Show HN: Hacking a $20 4G wireless hotspot into a texting device