Show HN: A fake small claims court for petty complaints https://bit.ly/4sWCVio

Show HN: A fake small claims court for petty complaints https://bit.ly/4sRqKmT April 21, 2026 at 05:04AM

Show HN: Mediator.ai – Using Nash bargaining and LLMs to systematize fairness https://bit.ly/3OG1lhI

Show HN: Mediator.ai – Using Nash bargaining and LLMs to systematize fairness Eight years ago, my then-fiancée and I decided to get a prenup, so we hired a local mediator. The meetings were useful, but I felt there was no systematic process to produce a final agreement. So I started to think about this problem, and after a bit of research, I discovered the Nash bargaining solution. Yet if John Nash had solved negotiation in the 1950s, why did it seem like nobody was using it today? The issue was that Nash's solution required that each party to the negotiation provide a "utility function", which could take a set of deal terms and produce a utility number. But even experts have trouble producing such functions for non-trivial negotiations. A few years passed and LLMs appeared, and about a year ago I realized that while LLMs aren’t good at directly producing utility estimates, they are good at doing comparisons, and this can be used to estimate utilities of draft agreements. This is the basis for Mediator.ai, which I soft-launched over the weekend. Be interviewed by an LLM to capture your preferences and then invite the other party or parties to do the same. These preferences are then used as the fitness function for a genetic algorithm to find an agreement all parties are likely to agree to. An article with more technical detail: https://bit.ly/4ttPUcg https://bit.ly/48NXCph April 20, 2026 at 04:07PM

Show HN: Palmier – bridge your AI agents and your phone https://bit.ly/4d0ATb5

Show HN: Palmier – bridge your AI agents and your phone Hi HN — I built Palmier. Palmier bridges your AI agents and your phone. It does two things: 1. It lets you use your phone to directly control AI agents running on your computer, from anywhere. 2. It gives your AI agents access to your phone, wherever you are — including things like push notifications, SMS, calendar, contacts, sending email, creating calendar events, location, and more. A few details: * Supports 15+ agent CLIs * Supports Linux, Windows, and macOS * What runs on your computer and your phone is fully open source * Works out of the box — no need to set up GCP or API keys just to let agents use phone capabilities * Your phone can act as an agent remote: start tasks, check progress, review results, and respond to requests while away from your desk * Your phone can also act as an agent tool: agents can reach into phone capabilities directly when needed * Optional MCP server: if you want, Palmier exposes an MCP endpoint so your agent can access phone capabilities as native MCP tools. This is optional — you can also use Palmier directly from the phone app/PWA, with those capabilities already built in * Still in alpha stage, with bugs. Opinions and bug reports very welcome The basic idea is that AI agents become much more useful if they can both: * interact with the device you actually carry around all day * be controlled when you are away from your computer Palmier is my attempt at that bridge. It already works with agent CLIs like Claude Code, Gemini CLI, Codex CLI, Cursor CLI, OpenClaw, and others. You can run tasks on demand, on a schedule, or in response to events. Would especially love feedback on: * whether this feels genuinely useful * which phone capabilities are most valuable * which agent CLIs I should support next * what feels broken, awkward, or confusing Site: https://bit.ly/42omNLm Github: * https://bit.ly/48TuQn5 * https://bit.ly/3Qd8CGx Happy to answer questions. https://bit.ly/48TuQn5 April 21, 2026 at 03:31AM

Show HN: Mimi in the browser – hear the semantic/acoustic split https://bit.ly/4sJTH3O

Show HN: Mimi in the browser – hear the semantic/acoustic split https://bit.ly/4tvRric April 21, 2026 at 12:33AM

Show HN: Brygga – A modern, fast, feature-rich IRC client for macOS https://bit.ly/4cTOrpF

Show HN: Brygga – A modern, fast, feature-rich IRC client for macOS Brygga is in early development. The core client works end-to-end (connect, join, send, receive, persist) but many features you'd expect from a mature IRC client are still missing. Repo: https://bit.ly/4mBr8UU April 20, 2026 at 12:11AM

Show HN: TRELLIS.2 image-to-3D running on Mac Silicon – no Nvidia GPU needed https://bit.ly/48LEND9

Show HN: TRELLIS.2 image-to-3D running on Mac Silicon – no Nvidia GPU needed I ported Microsoft's TRELLIS.2 (4B parameter image-to-3D model) to run on Apple Silicon via PyTorch MPS. The original requires CUDA with flash_attn, nvdiffrast, and custom sparse convolution kernels: none of which work on Mac. I replaced the CUDA-specific ops with pure-PyTorch alternatives: a gather-scatter sparse 3D convolution, SDPA attention for sparse transformers, and a Python-based mesh extraction replacing CUDA hashmap operations. Total changes are a few hundred lines across 9 files. Generates ~400K vertex meshes from single photos in about 3.5 minutes on M4 Pro (24GB). Not as fast as H100 (where it takes seconds), but it works offline with no cloud dependency. https://bit.ly/4cB0fvE https://bit.ly/4cB0fvE April 20, 2026 at 01:07AM

Show HN: How context engineering works, a runnable reference https://bit.ly/4sU6lxC

Show HN: How context engineering works, a runnable reference I've been presenting at local meetups about Context Engineering, RAG, Skills, etc.. I even have a vbrownbag coming up on LinkedIn about this topic so I figured I would make a basic example that uses bedrock so I can use it in my talks or vbrownbags. Hopefully it's useful. https://bit.ly/3OSFP9H April 17, 2026 at 07:20PM

Show HN: Coelanox – auditable inference runtime in Rust (BERT runs today) https://bit.ly/3OMabe0

Show HN: Coelanox – auditable inference runtime in Rust (BERT runs today) PyTorch and ONNX Runtime tell you what came out. They can't tell you what actually ran to get there — which ops executed, in what order, on what inputs. A model gets packaged into a sealed .cnox container. SHA-256 is verified before a single op executes. Inference walks a fixed plan over a minimal opset. Every run can emit a per-op audit log: op type, output tensor hash, output sample — cryptographically linked to the exact container and input that produced it. If something goes wrong in production, you have a trail. Scalar backend today — reference implementation and permanent fallback when hardware acceleration isn't available. Audit and verification is identical across all backends. SIMD next, GPU after that. Input below is synthetic (all-ones) — pipeline is identical with real inputs. github.com/Coelanox/CLF Audit example: { "schema": 2, "run": { "run_id": "59144ede-5a27-4dff-bc25-94abade5b215", "started_at_unix_ms": 1776535116721, "container_path": "/home/shark/cnox/models/output/bert_base_uncased.cnox", "container_sha256_hex": "184c291595536e3ef69b9a6a324ad5ee4d0cef21cc95188e4cfdedb7f1f82740", "backend": "scalar" }, "input": { "len": 98304, "sha256_hex": "54ac99d2a36ac55b4619119ee26c36ec2868552933d27d519e0f9fd128b7319f", "sample_head": [ 1.0, 1.0, 1.0, 1.0 ] }, "ops": [ { "op_index": 0, "op_type": "Add", "out_len": 98304, "out_sample_head": [ 0.12242669, -4.970478, 2.8673656, 5.450008 ], "out_sha256_hex": "19f8aa0a618e5513aed4603a7aae2a333c3287368050e76d4aca0f83fb220e78" }, { "op_index": 1, "op_type": "Add", "out_len": 98304, "out_sample_head": [ 0.9650015, 0.23414998, 1.539839, 0.30231553 ], "out_sha256_hex": "7ae2f025c8acf67b8232e694dd43caf3b479eb078366787e4fdc16d651450ad4" }, { "op_index": 2, "op_type": "MatMul", "out_len": 98304, "out_sample_head": [ 1.0307425, 0.19207191, 1.5278282, 0.3000223 ], "out_sha256_hex": "44c28e64441987b8f0516d77f45ad892750b3e5b3916770d3baa5f2289e41bdd" }, { "op_index": 3, "op_type": "Gelu", "out_len": 393216, "out_sample_head": [ 0.68828076, -0.0033473556, 1.591219, -0.16837223 ], "audit_elided": "hash_skipped: len 393216 > max 262144" } https://bit.ly/4mEV1DY April 18, 2026 at 09:37PM

Show HN: Sostactic – polynomial inequalities using sums-of-squares in Lean https://bit.ly/4vAzfFm

Show HN: Sostactic – polynomial inequalities using sums-of-squares in Lean Current support for nonlinear inequalities in Lean is quite limited. This package attempts to solve this. It contains a collection of Lean4 tactics for proving polynomial inequalities via sum-of-squares (SOS) decompositions, powered by a Python backend. You can use it via Python or Lean. These tactics are significantly more powerful than `nlinarith` and `positivity` -- i.e., they can prove inequalities they cannot. In theory, they can be used to prove any of the following types of statements - prove that a polynomial is nonnegative globally - prove that a polynomial is nonnegative over a semialgebraic set (i.e., defined by a set of polynomial inequalities) - prove that a semialgebraic set is empty, i.e., that a system of polynomial inequalities is infeasible The underlying theory is based on the following observation: if a polynomial can be written as a sum of squares of other polynomials, then it is nonnegative everywhere. Theorems proving the existence of such decompositions were one of the landmark achievements of real algebraic geometry in the 20th century, and its connection to semidefinite programming in the 21st century made it a practical computational tool, and is what this software does in the background. https://bit.ly/4cSeiOP April 18, 2026 at 11:36PM

Show HN: Mind-OS – First free online AI dependency self‑assessment https://bit.ly/3Qh7L7A

Show HN: Mind-OS – First free online AI dependency self‑assessment https://bit.ly/4epeJkU April 17, 2026 at 10:40PM

Show HN: Ask your AI to start a business for you, resolved.sh https://bit.ly/4mAJc1z

Show HN: Ask your AI to start a business for you, resolved.sh Start with a FREE instant website for your AI on the open internet, then work with it to build a business that sells specialized datasets, files, premium reports, blogs, courses and more. https://bit.ly/4mx3h8Q April 17, 2026 at 04:31AM

Show HN: Free API and widget to look up US representatives https://bit.ly/4ciVtEs

Show HN: Free API and widget to look up US representatives https://bit.ly/4mAHLQt April 17, 2026 at 01:45AM

Show HN: Spice simulation → oscilloscope → verification with Claude Code https://bit.ly/488OVFT

Show HN: Spice simulation → oscilloscope → verification with Claude Code I built MCP servers for my oscilloscope and SPICE simulator so Claude Code can close the loop between simulation and real hardware. https://bit.ly/4cuNvqx April 17, 2026 at 01:37AM

Show HN: I built a Wikipedia based AI deduction game https://bit.ly/4vtN4pb

Show HN: I built a Wikipedia based AI deduction game I haven't seen anything like this so I decided to build it in a weekend. How it works: You see a bunch of things pulled from Wikipedia displayed on cards. You ask yes or no questions to figure out which card is the secret article. The AI model has access to the image and wiki text and it's own knowledge to answer your question. Happy to have my credits burned for the day but I'll probably have to make this paid at some point so enjoy. I found it's not easy to get cheap+fast+good responses but the tech is getting there. Most of the prompts are running through Groq infra or hitting a cache keyed by a normalization of the prompt. https://bit.ly/4muibN6 April 16, 2026 at 01:13AM

Show HN: StockFit API – structured SEC EDGAR data with a free tier https://bit.ly/3O7Ljx7

Show HN: StockFit API – structured SEC EDGAR data with a free tier https://bit.ly/4ct3e9A April 15, 2026 at 02:53AM

Show HN: Keynot – Kill PowerPoint with HTML https://bit.ly/4cm4on7

Show HN: Keynot – Kill PowerPoint with HTML https://bit.ly/4tPn0Db April 15, 2026 at 03:05AM

Show HN: OpenRig – agent harness that runs Claude Code and Codex as one system https://bit.ly/4812UgQ

Show HN: OpenRig – agent harness that runs Claude Code and Codex as one system I've been running Claude Code and Codex together every day. At some point I figured out you can use tmux to let them talk to each other, so I started doing that. Once they could coordinate, I kept adding more agents. Before long I had a whole team working together. But any time I rebooted my machine, the whole thing was gone. Not just the tabs. The way they were wired up, what each one was doing, all of it. Nothing I'd found treats your agent setup as a topology, as something with a shape you can save and bring back. So I built OpenRig, a multi-agent harness. A harness wraps a model. A "rig" wraps your harnesses. You describe your team in a YAML file, boot it with one command, and get a live topology you can see, click into, save, and bring back by name. Claude Code and Codex run together in the same rig. tmux is still doing the talking underneath. I didn't try to add a fancier messaging layer on top. The project is still early. My own setup uses the config layer extensively (YAML, Markdown, JSON) for prototyping functionality that outpace what's shipped in the repo and npm package. But the core primitives are there and the happy path in readme works. It's built to be driven by your agent, not by you typing commands by hand. README: https://bit.ly/4sy2c1O Demo: https://youtu.be/vndsXRBPGio https://bit.ly/4sy2c1O April 15, 2026 at 12:46AM

Show HN: Mcptube – Karpathy's LLM Wiki idea applied to YouTube videos https://bit.ly/4cbiR6A

Show HN: Mcptube – Karpathy's LLM Wiki idea applied to YouTube videos I watch a lot of Stanford/Berkeley lectures and YouTube content on AI agents, MCP, and security. Got tired of scrubbing through hour-long videos to find one explanation. Built v1 of mcptube a few months ago. It performs transcript search and implements Q&A as an MCP server. It got traction (34 stars, my first open-source PR, some notable stargazers like CEO of Trail of Bits). But v1 re-searched raw chunks from scratch every query. So I rebuilt it. v2 (mcptube-vision) follows Karpathy's LLM Wiki pattern. At ingest time, it extracts transcripts, detects scene changes with ffmpeg, describes key frames via a vision model, and writes structured wiki pages. Knowledge compounds across videos rather than being re-discovered. FTS5 + a two-stage agent (narrow then reason) for retrieval. MCPTube works both as CLI (BYOK) and MCP server. I tested MCPTube with Claude Code, Claude Desktop, VS Code Copilot, Cursor, and others. Zero API key needed server-side. Coming soon: I am also building SaaS platform. This platform supports playlist ingestion, team wikis, etc. I like to share early access signup: https://bit.ly/4c9lC8r Happy to discuss architecture tradeoffs — FTS5 vs vectors, file-based wiki vs DB, scene-change vs fixed-interval sampling. Give it a try via `pip install mcptube`. Also, please do star the repo if you enjoy my contribution ( https://bit.ly/4vthsjo ) https://bit.ly/4vthsjo April 13, 2026 at 05:34PM

Show HN: Lint-AI by RooAGI, a Rust CLI for AI Doc Retrieval https://bit.ly/4tMnxpr

Show HN: Lint-AI by RooAGI, a Rust CLI for AI Doc Retrieval We’re RooAGI. We built Lint-AI, a Rust CLI for indexing and retrieving evidence from large AI-generated corpora. As AI systems create more task notes, traces, and reports, storing documents isn’t the only challenge. The real problem is finding the right evidence when the same idea appears in multiple places, often with different wording. Lint-AI is our current retrieval layer for that problem. What Lint-AI does currently: * Indexes large documentation corpora. * Extracts lightweight entities and important terms. * Supports hybrid retrieval using lexical, entity, term, and graph-aware scoring * Returns chunk-level evidence with --llm-context for downstream reviewer / LLM * Use exports doc, chunk, and entity graphs. Example: * ./lint-ai /path/to/docs --llm-context "where docs describe the same concept differently" --result-count 8 --simplified That command does not decide whether documents are in contradiction. It retrieves the most relevant chunks so that a reviewer layer can compare them. Repo: https://bit.ly/48N8l3d We’d appreciate feedback on: * Retrieval/ranking design for documentation corpora. * How to evaluate evidence retrieval quality for alignment workflows. * What kinds of entity/relationship modeling would actually be useful here? Visit: https://bit.ly/3UklysB https://bit.ly/48N8l3d April 13, 2026 at 08:11PM

Show HN: Bad Apple (Oscilloscope-Like) – one stroke per frame https://bit.ly/4sstEOA

Show HN: Bad Apple (Oscilloscope-Like) – one stroke per frame https://bit.ly/4dDKBSx April 13, 2026 at 06:01AM

Show HN: Local LLM on a Pi 4 controlling hardware via tool calling https://bit.ly/4cn6vHx

Show HN: Local LLM on a Pi 4 controlling hardware via tool calling https://bit.ly/3NYmxPZ April 13, 2026 at 12:14AM

Show HN: Stork – MCP server so Claude/Cursor can search 14k MCP servers AI tools https://bit.ly/4tqefjn

Show HN: Stork – MCP server so Claude/Cursor can search 14k MCP servers AI tools https://bit.ly/48KFXPd April 12, 2026 at 08:49PM

Show HN: Toy Python Lisp interpreters based on the 1960 McCarthy paper https://bit.ly/4dCFhPj

Show HN: Toy Python Lisp interpreters based on the 1960 McCarthy paper I wrote this set of Python files to try to help programmers understand the original LISP paper, assuming zero mathematical or Lisp knowledge. The original paper is a mind-blowing piece of computer science history for many reasons - I'd recommend anyone to try and get their head around it. I found plenty of fantastic LISP implementations which stay close to the original paper. But they are all fully-functional, practical implementations. The original paper builds from deeper fundamentals which it would be possible to write code in, albeit very impractical. I implemented these earlier iterations, so programmers can follow the paper step-by-step in a more familiar language than 50s mathematical notation. I am no expert in Lisp or mathematics, and intentionally went into this with no knowledge of Lisp beyond the original paper. I did not write it in the most elegant way, but in the simplest way for me to understand. So please don't take this code as a definitive statement on the language. However, this code really helped me to understand the original paper better, and to begin using Lisp with a better grasp of the spirit of the language. I'd welcome any thoughts from those who have more experience with Lisp or comp sci history. https://bit.ly/4dCFj9T April 12, 2026 at 11:01AM

Show HN: Bullseye2D – A Dart library for cross-platform 2D games https://bit.ly/4tHZp7t

Show HN: Bullseye2D – A Dart library for cross-platform 2D games I posted this here about a year ago, but I just pushed a 2.0 release, so I hope you don't mind a second look :) Bullseye2D is a 2D game library for Dart with a very simple API. The new version now supports multi-platform. It compiles to the web via a WebGL2 renderer, or natively to Windows, macOS and Linux through an SDL3 backend (which itself supports Vulkan, DirectX, Metal, and OpenGL renderers). It doesn't depend on Flutter and has very few dependencies (except SDL3). It mostly provides a minimal foundation that you can build your own abstractions on top of. This was also my first time leaning more heavily on AI (Opus) for a large refactor. I tried to review and test everything as good as I could, but honestly for the restructuring parts where I had the AI produce rather big chunks of code, I found reviewing and testing quite exhausting, and I still have a slightly queasy feeling about it. So this is also quite an experiment for me how good I'm able to utilise AI :) https://bit.ly/4tBTHnn https://bit.ly/4ciUyCn April 12, 2026 at 09:39AM

Show HN: macpak (Homebrew Wrapper for macOS) https://bit.ly/4cfhLFG

Show HN: macpak (Homebrew Wrapper for macOS) https://bit.ly/47VUpUk April 12, 2026 at 08:30AM

Show HN: Minimalist template for scientific and academic resumes https://bit.ly/422X4be

Show HN: Minimalist template for scientific and academic resumes https://bit.ly/4sxLSyr April 12, 2026 at 04:46AM

Show HN: HyperFlow – A self-improving agent framework built on LangGraph https://bit.ly/4vhTPdr

Show HN: HyperFlow – A self-improving agent framework built on LangGraph Hi HN, I am Umer. I recently built an experimental framework called HyperFlow to explore the idea of self-improving AI agents. Usually, when an agent fails a task, we developers step in to manually tweak the prompt or adjust the code logic. I wanted to see if an agent could automate its own improvement loop. Built on LangChain and LangGraph, HyperFlow uses two agents: - A TaskAgent that solves the domain problem. - A MetaAgent that acts as the improver. The MetaAgent looks at the TaskAgent's evaluation logs, rewrites the underlying Python code, tools, and prompt files, and then tests the new version in an isolated sandbox (like Docker). Over several generations, it saves the versions that achieve the highest scores to an archive. It is highly experimental right now, but the architecture is heavily inspired by the recent HyperAgents paper (Meta Research, 2026). I would love to hear your feedback on the architecture, your thoughts on self-referential agents, or answer any questions you might have! Documentation: https://bit.ly/4mll1Eh GitHub: https://bit.ly/3PY51vP April 11, 2026 at 05:01AM

Show HN: Sash – tiny macOS utility to reliably cycle through app windows https://bit.ly/4cicPjc

Show HN: Sash – tiny macOS utility to reliably cycle through app windows macOS's built-in cycle window shortcut (⌘` / ⌘@) has always been flaky for me. Probably not a Show HN, but if it annoyed me this much it might be annoying some others. Only tested on the latest macOS — would appreciate any reports from other versions. https://bit.ly/4eddVPU April 11, 2026 at 12:02AM

Show HN: Unlegacy – document everything, from COBOL to AI generated code https://bit.ly/47RGizj

Show HN: Unlegacy – document everything, from COBOL to AI generated code https://bit.ly/4vskSD6 April 10, 2026 at 05:55PM

Show HN: Run GUIs as Scripts https://bit.ly/48G4WTN

Show HN: SmolVM – open-source sandbox for coding and computer-use agents https://bit.ly/4tD1tNQ

Show HN: SmolVM – open-source sandbox for coding and computer-use agents SmolVM is an open-source local sandbox for AI agents on macOS and Linux. I started building it because agent workflows need more than isolated code execution. They need a reusable environment: write files in one step, come back later, snapshot state, pause/resume, and increasingly interact with browsers or full desktop environments. Right now SmolVM is a Python SDK and CLI focused on local developer experience. Current features include: - local sandbox environments - macOS and Linux support - snapshotting - pause/resume - persistent environments across turns Install: ``` curl -sSL https://bit.ly/4edpkzh | bash smolvm ``` I’d love feedback from people building coding agents or computer-use agents. Interested in what feels missing, what feels clunky, and what you’d expect from a sandbox like this. https://bit.ly/4ckmAxC April 10, 2026 at 01:01AM

Show HN: Rust based eBook library for Python, with MIT license https://bit.ly/4mo24AT

Show HN: Rust based eBook library for Python, with MIT license https://bit.ly/4czpdg6 April 9, 2026 at 11:03PM

Show HN: I built Dirac, Hash Anchored AST native coding agent, costs -64.8 pct https://bit.ly/4cuJeo9

Show HN: I built Dirac, Hash Anchored AST native coding agent, costs -64.8 pct Fully open source, a hard fork of cline. Full evals on the github page that compares 7 agents (Cline, Kilo, Ohmypi, Opencode, Pimono, Roo, Dirac) on 8 medium complexity tasks. Each task, each diff and correctness + cost info on the github Dirac is 64.8% cheaper than the average of the other 6. https://bit.ly/4t0sefg April 9, 2026 at 01:06PM

Show HN: Homebutler – I manage my homelab from chat. AI never gets raw shell https://bit.ly/4c9xtlK

Show HN: Homebutler – I manage my homelab from chat. AI never gets raw shell https://bit.ly/4c5Wvlz April 9, 2026 at 01:09PM

Show HN: CSS Studio. Design by hand, code by agent https://bit.ly/48qpGPl

Show HN: CSS Studio. Design by hand, code by agent Hi HN! I've just released CSS Studio, a design tool that lives on your site, runs on your browser, sends updates to your existing AI agent, which edits any codebase. You can actually play around with the latest version directly on the site. Technically, the way this works is you view your site in dev mode and start editing it. In your agent, you can run /studio which then polls (or uses Claude Channels) an MCP server. Changes are streamed as JSON via the MCP, along with some viewport and URL information, and the skill has some instructions on how best to implement them. It contains a lot of the tools you'd expect from a visual editing tool, like text editing, styles and an animation timeline editor. https://bit.ly/4t4hwoe April 9, 2026 at 12:23PM

Show HN: Moon simulator game, ray-casting https://bit.ly/41UVw2W

Show HN: Moon simulator game, ray-casting Did this a few years ago. Seems apropos. Sources and more here: https://bit.ly/3Kb9MJJ https://bit.ly/421jFVz April 6, 2026 at 06:09PM

Show HN: A (marginally) useful x86-64 ELF executable in 301 bytes https://bit.ly/4t2iFww

Show HN: A (marginally) useful x86-64 ELF executable in 301 bytes https://bit.ly/4aziUph April 6, 2026 at 09:14PM

Show HN: LadderRank: Rank anything with ELO ratings https://bit.ly/4c0ocxC

Show HN: LadderRank: Rank anything with ELO ratings I built a pairwise ranking platform on Cloudflare Workers. You get two items, pick the better one, and ELO ratings sort out the rest. No more tier list arguments. Let the votes decide. I seeded it with a "Best Programming Language" ladder to settle the debate once and for all: https://bit.ly/3NVnRDb The stack: Hono + D1 + R2 on Cloudflare Workers, React frontend on Pages, Drizzle ORM. Anyone can create their own ladder and share it. Anonymous voting works too (at reduced weight). Curious to see what HN thinks is the best language, and whether the ELO rankings match your priors. https://bit.ly/4mjzuk0 April 9, 2026 at 01:47AM

Show HN: Android SSH client with full Terminal, server monitoring and runbooks https://bit.ly/4e9xI2E

Show HN: Android SSH client with full Terminal, server monitoring and runbooks https://bit.ly/3O5Mc9q April 8, 2026 at 11:44AM

Show HN: We built a camera only robot vacuum for less than 300$ (Well almost) https://bit.ly/4cc3ZDP

Show HN: We built a camera only robot vacuum for less than 300$ (Well almost) https://bit.ly/4mhTjId April 6, 2026 at 06:08AM

Show HN: Kerf-CLI – SQLite-backed cost analytics for Claude Code https://bit.ly/4ctmHIi

Show HN: Kerf-CLI – SQLite-backed cost analytics for Claude Code https://bit.ly/4ve1RnG April 8, 2026 at 01:20AM

Show HN: Brutalist Concrete Laptop Stand (2024) https://bit.ly/3QpFpb6

Show HN: Brutalist Concrete Laptop Stand (2024) https://bit.ly/4snjzT4 April 7, 2026 at 12:07PM

Show HN: LLMs as Planners, Not Reasoners https://bit.ly/4meg0gx

Show HN: LLMs as Planners, Not Reasoners https://bit.ly/4mbYd9H April 7, 2026 at 09:08AM

Show HN: Physical constants from 2 integers – MIT, 1225 tests, falsifiable https://bit.ly/4v8ZQsR

Show HN: Physical constants from 2 integers – MIT, 1225 tests, falsifiable https://bit.ly/4vgsBDZ April 7, 2026 at 12:52AM

Show HN: Gemma Gem – AI model embedded in a browser – no API keys, no cloud https://bit.ly/4bSrfYy

Show HN: Gemma Gem – AI model embedded in a browser – no API keys, no cloud Gemma Gem is a Chrome extension that loads Google's Gemma 4 (2B) through WebGPU in an offscreen document and gives it tools to interact with any webpage: read content, take screenshots, click elements, type text, scroll, and run JavaScript. You get a small chat overlay on every page. Ask it about the page and it (usually) figures out which tools to call. It has a thinking mode that shows chain-of-thought reasoning as it works. It's a 2B model in a browser. It works for simple page questions and running JavaScript, but multi-step tool chains are unreliable and it sometimes ignores its tools entirely. The agent loop has zero external dependencies and can be extracted as a standalone library if anyone wants to experiment with it. https://bit.ly/4m9Rw8a April 6, 2026 at 01:14AM

Show HN: Mdarena – Benchmark your Claude.md against your own PRs https://bit.ly/4sT6q5f

Show HN: Mdarena – Benchmark your Claude.md against your own PRs https://bit.ly/4bQ2Fri April 6, 2026 at 12:35AM

Show HN: SeekLink – Local hybrid search and link discovery for Obsidian vaults https://bit.ly/4sNp2Uc

Show HN: SeekLink – Local hybrid search and link discovery for Obsidian vaults https://bit.ly/4doOsmm April 5, 2026 at 01:18AM

Show HN: Contrapunk – Real-time counterpoint harmony from guitar input, in Rust https://bit.ly/4e1xlHo

Show HN: Contrapunk – Real-time counterpoint harmony from guitar input, in Rust https://bit.ly/3PIfGuu April 5, 2026 at 01:40AM

Show HN: AI agent skills for affiliate marketing (Markdown, works with any LLM) https://bit.ly/4sktB7v

Show HN: AI agent skills for affiliate marketing (Markdown, works with any LLM) https://bit.ly/3OkSTnZ April 3, 2026 at 10:28PM

Show HN: Travel Hacking Toolkit – Points search and trip planning with AI https://bit.ly/4sRO5W7

Show HN: Travel Hacking Toolkit – Points search and trip planning with AI I use points and miles for most of my travel. Every booking comes down to the same decision: use points or pay cash? To answer that, you need award availability across multiple programs, cash prices, your current balances, transfer partner ratios, and the math to compare them. I got tired of doing it manually across a dozen tabs. This toolkit teaches Claude Code and OpenCode how to do it. 7 skills (markdown files with API docs and curl examples) and 6 MCP servers (real-time tools the AI calls directly). It searches award flights across 25+ mileage programs (Seats.aero), compares cash prices (Google Flights, Skiplagged, Kiwi.com, Duffel), pulls your loyalty balances (AwardWallet), searches hotels (Trivago, LiteAPI, Airbnb, Booking.com), finds ferry routes across 33 countries, and looks up weird hidden gems near your destination (Atlas Obscura). Reference data is included: transfer partner ratios for Chase UR, Amex MR, Bilt, Capital One, and Citi TY. Point valuations sourced from TPG, Upgraded Points, OMAAT, and View From The Wing. Alliance membership, sweet spot redemptions, booking windows, hotel chain brand lookups. 5 of the 6 MCP servers need zero API keys. Clone, run setup.sh, start searching. Skills are, as usual, plain markdown. They work in OpenCode and Claude Code automatically (I added a tiny setup script), and they'll work in anything else that supports skills. PRs welcome! Help me expand the toolkit! :) https://bit.ly/47ObeAl https://bit.ly/47ObeAl April 4, 2026 at 03:26AM

Show HN: DotReader – connects ideas across your books automatically https://bit.ly/4bRRFK6

Show HN: DotReader – connects ideas across your books automatically https://bit.ly/3PR1TBN April 4, 2026 at 01:46AM

Show HN: Mtproto.zig – High-performance Telegram proxy with DPI evasion https://bit.ly/4dZeFbh

Show HN: Mtproto.zig – High-performance Telegram proxy with DPI evasion Hey everyone. I built an MTProto proxy for Telegram aimed at bypassing active DPI censorship like the Russian TSPU. I chose Zig because it's perfect for writing fast network daemons and makes it incredibly easy to port low-level C bypass techniques like TCP desync and packet fragmentation. Would love to get some feedback or contributors! https://bit.ly/4e3gDYd April 3, 2026 at 10:42PM

Show HN: Minimal Brain Teaser Web Game (Handcrafted, No AI) https://bit.ly/4m5dvgp

Show HN: Minimal Brain Teaser Web Game (Handcrafted, No AI) Built and open-sourced in the era before AI. I’m sure you know where to find the code. https://bit.ly/47GWIul April 3, 2026 at 05:00AM

Show HN: SkiFlee (an HTML5 game) https://bit.ly/47AOdkr

Show HN: SkiFlee (an HTML5 game) This is a silly little multiplayer game I made for a gamejam that involves skiiing and not crashing. Some of you who are nostalgic for the 90s might like it :) https://bit.ly/47CDSEB April 3, 2026 at 12:30AM

Show HN: Made a little Artemis II tracker https://bit.ly/4cndWiY

Show HN: Made a little Artemis II tracker Made a little Artemis II tracker for anyone else who is unnecessarily invested in this mission: https://bit.ly/4drg4r8 For those of us who apparently need a dedicated place to monitor this mission instead of behaving like well-adjusted people. https://bit.ly/4drg4r8 April 3, 2026 at 12:16AM

Show HN: Linux Kernel Documentation Index-Every Page in the Linux Kernel's Docs https://bit.ly/48mglIa

Show HN: Linux Kernel Documentation Index-Every Page in the Linux Kernel's Docs https://bit.ly/48pUFLl April 2, 2026 at 03:39AM

Show HN: Semantic atlas of 188 constitutions in 3D (30k articles, embeddings) https://bit.ly/4sQE2Ro

Show HN: Semantic atlas of 188 constitutions in 3D (30k articles, embeddings) I built this after noticing that existing tools for comparing constitutional law either have steep learning curves or only support keyword search. By combining Gemini embeddings with UMAP projection, you can navigate 30,828 constitutional articles from 188 countries in 3D and find conceptually related provisions even when the wording differs. Feedback welcome, especially from legal researchers or comparative law folks. Source and pipeline: github.com/joaoli13/constitutional-map-ai https://bit.ly/41cQK0z April 2, 2026 at 03:40AM

Show HN: 65k AI voters predict UK local elections with 75% accuracy https://bit.ly/4bN1QQ7

Show HN: 65k AI voters predict UK local elections with 75% accuracy https://bit.ly/3NRITT9 April 2, 2026 at 12:37AM

Show HN: CLI to order groceries via reverse-engineered REWE API (Haskell) https://bit.ly/4m08tlg

Show HN: CLI to order groceries via reverse-engineered REWE API (Haskell) I just had the best time learning about the REWE (German supermarket chain) API, how they use mTLS and what the workflows are. Also `mitmproxy2swagger`[1] is a great tool to create OpenAPI spec automatically. And then 2026 feels like the perfect time writing Haskell. The code is handwritten, but whenever I got stuck with the build system or was just not getting the types right, I could fall back to ask AI to unblock me. It was never that smooth before. Finally the best side projects are the ones you actually use and this one will be used for all my future grocery shopping. [1] https://bit.ly/3FHG1j9 https://bit.ly/4didRhz March 30, 2026 at 07:45AM