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There's no escaping it: an exploration of ANSI codes

Show HN: I Derived a Pancake After 25 years of making other people&#x27;s pancake recipes - always yearning for more tang, more fluff, and more predictability - I decided to derive the pancake recipe from the chemistry.<p>You mark checkboxes for what you have on hand (ricotta, sour cream, kefir, buttermilk, yogurt, cottage cheese, lemon, cream of tartar, etc.) and it computes the best recipe based on targets for acid, fat, salt, sugar, and CO2.<p>My particular favorite are the yeast-raised lemon ricotta kefir pancakes - the best I&#x27;ve ever had.<p>The math is done in a small pure-ESM library: ingredient composition to component masses and acid moles, a stoichiometry layer, and a bisection solver for the target deficits.<p>I&#x27;m not a chemist, so if something is off, tell me and I will fix it!

Unicode Fonts and Tools for X11

[Other] Meta enables ADB on deprecated Portal devices [video] <a href="https:&#x2F;&#x2F;developers.meta.com&#x2F;horizon&#x2F;blog&#x2F;build-apps-for-portal-with-ai&#x2F;" rel="nofollow">https:&#x2F;&#x2F;developers.meta.com&#x2F;horizon&#x2F;blog&#x2F;build-apps-for-port...</a>

Yon – a topos-oriented language with a content-addressed lattice heap Hello everyone. In the last two years I spent, as a dev, part of my free time stretching the limits of my knowledge. Not being a mathematician myself, I discovered that formalizing concepts in mathematical language could nonetheless be useful to improve symbolic reasoning about the concepts themselves. I made use of both books and AI, and I followed the development of the latter, mainly with a critical eye. I have several open projects, and from some observations and explorations on one of them I started asking myself what the current limits of reasoning, of logic, of mathematics itself are. So I explored categories, and topoi, above all starting from Mazzola&#x27;s theory of music. I asked myself whether this could influence type theory in programming, and I ran some experiments. Out of this came this programming language, Yon, inspired by Yoneda and by morphisms. From another project I drew observations on the Leech lattice; from yet another, some experiments with mmap and coordinate-based allocation in a structure that would be advantageous, again, in a topological sense. The language certainly has mistakes here and there and I wrote the documentation in a hurry; the work took 3 weeks in total. It compiles to LLVM for performance reasons, and for now I preferred to avoid a VM and a GC. It contains unusual data structures that turn out to be performant. It&#x27;s worth a look, and I hope it will win some converts, and that someone will want to help me with its development. I&#x27;d love for it to bring fresh stimuli to programming and maybe open a few new frontiers. A few concrete details, for those who want to look under the hood. The compiler is a real pipeline, not an interpreter: an OCaml frontend takes .yon source into a custom MLIR dialect I called &quot;topos&quot;, where the categorical constructs live as first-class operations; its lowering passes take everything down to LLVM IR and from there to a native executable. A single command, yonc, drives the whole chain, and you can stop at any intermediate stage to see what a categorical construct actually becomes on its way to silicon. The runtime is where the Leech lattice observations ended up. The heap is content-addressed over Λ₂₄: every value is mapped to a lattice point and canonicalized under the Conway group Co₀ (via libmmgroup), so the same content always lives at the same address. That buys three things I would now find hard to give up: equality is a single machine comparison no matter how big the value is (string equality benches flat at ~17 ns up to 32,768-character strings, because it compares handles, never bytes); deduplication is global and automatic, with no interning logic in user code; and giving up the GC stopped being a renunciation, since cells are immutable and content-addressed, so there is nothing to trace and nothing to move. Concurrency I kept deliberately simple-minded: no threads, no shared mutable state. A program splits into isolated &quot;Spaces&quot; (separate processes, isolation enforced by the MMU) that talk over shared-memory channels with explicit failure semantics. About what is verified and what is just hope: the ground truth is a regression suite of 112 examples plus a cross-Space scenario suite, with exit codes identical on Linux x86-64 and macOS Apple Silicon (Intel Macs: untested). The book on the site, 21 chapters plus appendices, had every snippet compiled and run before being written down. The benchmarks appendix declares its environment and method; I tried not to publish any number without one. The limits of 1.0 are written down as well, in a baseline document that lists every fixed pool (256 heaps per chain, 64 Spaces, 16 concurrent RPC sessions, and so on), with the rationale that a hard limit that fails loudly is a specification, while a soft limit that degrades silently is a bug. For the license I went with the GCC model: compiler and toolchain are AGPLv3, the runtime is AGPLv3 with an explicit linking exception, so the language itself stays free, and the programs you write in it are entirely yours, under any license you choose.<p>Site + book: <a href="https:&#x2F;&#x2F;yon-lang.org" rel="nofollow">https:&#x2F;&#x2F;yon-lang.org</a> Repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;yon-language&#x2F;yon" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;yon-language&#x2F;yon</a> (tag v1.0.0)<p>Happy to answer anything: the topos dialect, why a lattice rather than a hash, what the categorical constructs lower to, what broke along the way.

[CLI Tool] Open Code Review – An AI-powered code review CLI tool

Man-Computer Symbiosis J. C. R. Licklider (1960)

Introducing Boron Buckyballs: Theory that B80 cages can’t be made is disproved

[Other] Show HN: Formally verified polygon intersection – Opus 4.8 oneshots, prev failed To my knowledge, this is the first formally verified implementation of an intersection algorithm for polygons.<p>The experience of working with AI agents on this project changed a lot with recent model releases, as I describe in the readme. Opus 4.8 is able to provide algorithm implementation with formal proof in one shot, whereas previous models required me to provide proof strategies in multiple steps.<p>Trust in the correctness comes entirely from the Lean checker and human review of a small specification, not from the LLM.<p>Also check out the web demo built around the verified core linked in the readme: <a href="https:&#x2F;&#x2F;schildep.github.io&#x2F;verified-polygon-intersection&#x2F;" rel="nofollow">https:&#x2F;&#x2F;schildep.github.io&#x2F;verified-polygon-intersection&#x2F;</a>. It supports multipolygons including holes, self intersections, and overlapping edges.

[IDE/Editor] Show HN: Mercek – A Desktop IDE for AWS ECS Hey HN I&#x27;ve been using ECS for a while now and found it annoying having to log into the console everytime<p>I use Lens for Kubernetes but couldnt find an equivalent for ECS so i built one!<p>The project is open source as well <a href="https:&#x2F;&#x2F;github.com&#x2F;utibeabasi6&#x2F;mercek" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;utibeabasi6&#x2F;mercek</a>

[Other] Show HN: FFmpeg WebCLI – Full FFmpeg in Browser, Offline PWA, No Uploads(WASM) Built a browser-based FFmpeg editor that runs entirely client-side via WebAssembly. Your files never leave your device -- all processing happens in a Web Worker. Works offline as an installable PWA after first load.

[Other] Anthropic's open-source framework for AI-powered vulnerability discovery

[Other] Show HN: Digger Solo – Local AI File Explorer After a lot of work I present Digger Solo 0.5.0 - the AI file explorer that respects your privacy (everything runs locally).<p>Demo video: <a href="https:&#x2F;&#x2F;vimeo.com&#x2F;1198414414" rel="nofollow">https:&#x2F;&#x2F;vimeo.com&#x2F;1198414414</a><p>New features: - LLM Chat with RAG (bring your own OpenAI compatible API key - ideally host a local model) - fresh redesign with light theme available in settings - multi-tabbed GUI: open multiple semantic maps at once - smart music player: auto-plays similar songs<p>Digger Solo offers semantic search and maps that allow you to browse your files intuively - uncovering hidden connections and near duplicates easily.<p>Happy to answer questions.

Games Between Programs: The Ruliology of Competition

OCaml Onboarding: Introduction to the Dune build system

[Other] Show HN: Hitoku Draft – Context aware local assistant Hi guys.<p>I have been working on Hitoku Draft, an open-source, voice-first AI assistant that runs entirely locally. I posted about it already, and now it has also transcription with voice editing. Looking for feedback, as I found that outside tech circles other people still do not use this tech much.<p>It&#x27;s context-aware, in the sense that it reads your screen, documents, and active app to understand what you&#x27;re working on. You can ask about PDFs, reply to emails, create calendar events, use web search, editing text, all by voice.<p>You can download a compiled version for free with the code HITOKUHN2026 <a href="https:&#x2F;&#x2F;hitoku.me&#x2F;draft&#x2F;" rel="nofollow">https:&#x2F;&#x2F;hitoku.me&#x2F;draft&#x2F;</a> (base price is 5 dollars)<p>It supports Gemma 4 and Qwen 3.5 for text generation, plus multiple STT backends (Parakeet, Qwen3-ASR).<p>Examples: - Gemma4 in action, <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=OgfI-3YjEVU" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=OgfI-3YjEVU</a> - query a pdf document, <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=ggaDhut7FnU" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=ggaDhut7FnU</a> - reply to email, <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=QFnHXMBp1gA" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=QFnHXMBp1gA</a> - and the usual voice dictation (with optional polishing)<p>I currently use it a lot with Claude Code and Logseq. Now with some friends we are also building a new cross-platform version. The goal is on the long run to have AI interactive local models serving people and professionals.

[Other] NVIDIA Cosmos is an open platform of world models, datasets, and tools that enables developers to build Physical AI for robots, autonomous vehicles, smart infrastructure, and more.

Speculative KV coding: losslessly compressing KV cache by up to ~4×

[Other] Show HN: I embedded 685M public texts in 32 minutes (on 8x A100, Rust, TensorRT) Quick note on how it works and how I&#x27;ve done my batch embedding engine IgniteMS.<p>The whole thing runs as one process using Rust, reading input, tokenizing, packing batches, keeping the queue full. TensorRT handles inference. Python is only as a wrapper.<p>I built it this way because when you use more than couple of GPUs, the GPUs stop being the problem. CPU cannot feed them fast enough. One A100 can go through batches faster than Python can tokenize and feed, so the GPU just sits there idle waiting for work. Most of my time went into optimizing this. At 8 GPUs that was basically the entire challenge.<p>On cost. I ran the big 2B messages job on a spot p4d instance (8x A100 40GB). After filtering and dedupping I got 685M raw texts. With my new engine the whole production run finishes in about half an hour. Previously I used on-demand for these jobs, now switched to spots. If AWS reclaims the box, I just rerun it. It&#x27;s roughly $7 for half-an-hour run. And at least right now spots are easier to get than on-demand.<p>Open warning: it&#x27;s batch only and NVIDIA only. You can use it both as a docker image and native. I used some optimizations for my production run. With default settings you can expect to see ~250K msg&#x2F;sec if you run the benchmark script on your p4d box. <a href="https:&#x2F;&#x2F;github.com&#x2F;Artain-AI&#x2F;ignite-ms&#x2F;blob&#x2F;main&#x2F;BENCHMARKING.md" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;Artain-AI&#x2F;ignite-ms&#x2F;blob&#x2F;main&#x2F;BENCHMARKIN...</a><p>v1.1.0 added TensorRT 11 and 60 models, 23 tested on 1x and 4x A100.<p>Happy to share details.

[Other] KVarN: Native vLLM backend for KV-cache quantization by Huawei