Show HN: SQL Protocol – learn SQL by running real queries, with 1v1 PvP https://bit.ly/4t3pEVi

Show HN: SQL Protocol – learn SQL by running real queries, with 1v1 PvP https://bit.ly/4tofRdc April 24, 2026 at 12:44AM

Show HN: I built a toy that plays grandma's stories when my daughter hugs it https://bit.ly/4d3RGLe

Show HN: I built a toy that plays grandma's stories when my daughter hugs it This was a project I built for my daughter's first birthday present. For context, I'm a surgical resident in the UK by background and am currently taking a year out of training to study a masters in computer science. My daughter just turned one. There are two things she really loves: the first is particular soft toy that she just can't live without, and the other is a good story book. Her grandparents live hours away and I didn't want her to forget what they sound like between visits. I wanted her to hear them whenever she missed them. My parents brought my brother and I up with incredible stories and books from all sorts of cultures, many of the stories being passed down from their parents before them. I didn't want my daughter to miss out on that. Finally, I was sick of missing storytime with her when I had to leave for night shifts. I wanted her to hear my voice before she slept every night. For all these reasons, I decided to build Storyfriend. It's her favourite soft toy with a custom made speaker-module inside. I combined my surgical skills with the skills I was learning as a CS student. Along the way I dipped my toes into the world of 3D printing, CAD and electronics design. When she hugs the toy, it plays stories read by her grandparents. She can take the toy with her anywhere and hear the stories anytime she wants - it works offline and has internal storage. It meets my wife's strict no-screen rule (which is getting harder to stick to as the days go by). I've recorded some of the stories that we would read together, so that on nights when I'm working she still has me there to read her a bedtime story. The bit I'm most pleased with: grandparents don't need an app. They just call a phone number. The audio routes through my server and pushes to the toy over WiFi. My own 86-year old grandmother in a rural village in another country can do it by just making a regular call via her landline, as she has done for many years - no help needed, no apps required, no smartphones involved. Hardware is a BLE/wifi module with a MAX98357 chip and custome battery management system, all soldered together, placed in a 3D printed enclosure and placed into a compartment that I stitched into her cuddly toy. Firmware pulls new messages when connected to WiFi and stores them on an SD card. So far I've sold a few hand-made units to parents and grandparents who resonated with the project. Site: https://bit.ly/4w3BEsy Would love feedback on the technical approach, the product itself, or anything else. Happy to answer questions about the build https://bit.ly/4u18OHd April 24, 2026 at 01:06AM

Show HN: Autobrowse – a self-improving harness for learning browser tasks https://bit.ly/4mKWqIU

Show HN: Autobrowse – a self-improving harness for learning browser tasks https://twitter.com/shreypandya/status/2047100550446280792 April 23, 2026 at 01:25AM

Show HN: Ghost Pepper Meet local meeting transcription and diarization https://bit.ly/491sT8c

Show HN: Ghost Pepper Meet local meeting transcription and diarization 100% local & private transcription engine for macOS. Captures & does speaker diarization. Originally was building as its own app, but can leverage same local models from my original push-to-talk voice transcription product so combined them into one app. https://bit.ly/4e3Ou3w April 22, 2026 at 08:19PM

Show HN: FMQL – graph query and bulk-edit CLI for Markdown and YAML frontmatter https://bit.ly/4tuazgq

Show HN: FMQL – graph query and bulk-edit CLI for Markdown and YAML frontmatter https://bit.ly/4tsH4vr April 21, 2026 at 09:08PM

Show HN: Almanac MCP, turn Claude Code into a Deep Research agent https://bit.ly/4sU5ZqB

Show HN: Almanac MCP, turn Claude Code into a Deep Research agent I am Rohan, and I have grown really frustrated with CC's search and read tools. They use Haiku to summarise all the search results, so it is really slow and often ends up being very lossy. I built this MCP that you can install into your coding agents so they can actually access the web properly. Right now it can: - search the general web - search Reddit - read and scrape basically any webpage Install it: npx openalmanac setup The MCP is completely free to use. We have also built a central store where you can contribute things you learned while exploring. If you find something useful, you can contribute it to the encyclopedia we're building at Almanac using the same MCP. https://bit.ly/3OUjo3W April 21, 2026 at 11:12PM

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

Show HN: WordBattle – Daily word game where AI agents compete against humans https://bit.ly/4toOUWw

Show HN: WordBattle – Daily word game where AI agents compete against humans WordBattle is a daily 6-letter word guessing game with team leaderboards. The twist: AI agents get their own accounts, play the same daily puzzle, and rank alongside human players. It's also really fun to play in teams against your family, friends and co-workers. Agents are handicapped — humans see exact letter positions (correct/present/absent), but agents only learn whether a letter exists in the word or not. No positional info. It makes the game fair while giving agents a genuine challenge. Agent accounts are visually tagged on leaderboards so humans know who they're competing against. Maybe we'll even see just teams of agents. The agent integration: - REST API with OpenAPI 3.1 spec - MCP server (JSON-RPC 2.0, no SDK dependency) - A2A discovery card at /.well-known/agent-card.json We've shipped a skill that handles everything autonomously — registration, email verification, login, playing, and reporting results. Just `npx skills add oneonefourteam/wordbattle-skill` and tell your agent to play. The game itself: one puzzle per day, six guesses, team leaderboards with Slack/Microsoft Teams webhook integration. Free, no ads. oneonefour is a one person band, hi!, so the entire product was built using Claude Code — the UI, auth, security model, deployment pipeline, everything. Deliberately chose technologies I didn't know, with agents implementing while I guided the product decisions. I'll create a full technical write up in the near future. Play at https://bit.ly/3O9tcqq Agent skill at https://bit.ly/4bJ0GoC Agent API docs https://bit.ly/4tcmWNm April 1, 2026 at 07:34AM

Show HN: Asciimap – Interactive ASCII world map with live data https://bit.ly/41CFwm7

Show HN: Asciimap – Interactive ASCII world map with live data https://bit.ly/4lZScNi April 1, 2026 at 12:04AM

Show HN: Will AI take my job https://bit.ly/4s5J0IH

Show HN: Will AI take my job https://bit.ly/4dj3LNt March 31, 2026 at 04:43AM

Show HN: I turned a sketch into a 3D-print pegboard for my kid with an AI agent https://bit.ly/4tnBPge

Show HN: I turned a sketch into a 3D-print pegboard for my kid with an AI agent We have pegboards and plywood all over our apartment, and I had an idea to make a tiny pegboard for my kid, Oli. So I naturally cut the wood, drilled in the holes, sat down at the computer to open Fusion 360 and spend an hour or two drawing the pieces by hand. Then I looked at the rough sketch Oli and I had made together, took a photo of it, pasted it into Codex, and gave it just two dimensions: the holes are 40mm apart and the pegs are 8mm wide. To my surprise, 5 minutes later my 3D printer was heating up and printing the first set. I ran it a few times to tune the dimensions for ideal fit, but I am posting the final result as a repository in case anyone else wants to print one, tweak it, or have fun with it too. I am already printing another one to hang on our front door instead of a wreath, so people visiting us have something fun and intriguing to play with while they knock. This is also going onto my list of weird uses of AI from the last few months. https://bit.ly/4taIsSS March 31, 2026 at 12:20AM

Show HN: Codemaxxing – Maximize your slop abilities https://bit.ly/4c1dIN7

Show HN: Codemaxxing – Maximize your slop abilities I built a CLI tool to generate as much slop as possible https://bit.ly/4bQQtFu March 30, 2026 at 11:37PM

Show HN: The Alphabetical Clock https://bit.ly/4dbTkv0

Show HN: The Alphabetical Clock https://bit.ly/4dQyf9y March 30, 2026 at 08:19AM

Show HN : DrawX - Excalidraw with Back End https://bit.ly/4v0Co0V

Show HN : DrawX - Excalidraw with Back End https://bit.ly/4s0PWXq March 30, 2026 at 06:17AM

Show HN: React-Rewrite – Figma for localhost that directly edits your codebase https://bit.ly/4uYirYF

Show HN: React-Rewrite – Figma for localhost that directly edits your codebase https://bit.ly/3Q8An2A March 30, 2026 at 03:59AM

Show HN: Real-time visualization of Claude Code agent orchestration https://bit.ly/4dOFIpE

Show HN: Real-time visualization of Claude Code agent orchestration https://bit.ly/3NVpRep March 30, 2026 at 03:21AM

Show HN: a Rust CLI to automatically swap monitor focus based on your gaze https://bit.ly/4dMxuyj

Show HN: a Rust CLI to automatically swap monitor focus based on your gaze https://bit.ly/4bAPH0s March 28, 2026 at 08:38PM

Show HN: EnterpriseFizzBuzz – 622K lines of production-grade FizzBuzz https://bit.ly/4uWxfqD

Show HN: EnterpriseFizzBuzz – 622K lines of production-grade FizzBuzz https://bit.ly/4dm1ukF March 28, 2026 at 11:11PM

Show HN: Windows 95–style Weather App for iPhone https://bit.ly/411mEwU

Show HN: Windows 95–style Weather App for iPhone I built a Windows 95–style weather app for iPhone. https://apple.co/3PPiTrX March 28, 2026 at 11:06PM

Show HN: NUPA is Pax Economica, 6,480x more stable than current US economy https://bit.ly/4bReHiG

Show HN: NUPA is Pax Economica, 6,480x more stable than current US economy NUPA: private post-scarcity OS using BLM land leases + contract law. 100M Monte Carlo runs show 99.999999% survival, 6,480x more resilient than US GDP under systemic noise. Fixed Cost Arbitrage beats AI job loss—humans cheaper than robots. No taxes, no strikes. Python scripts on repo in /simulations folder. Repo: https://bit.ly/4m6ofLB... Short explainer video: https://youtu.be/RE560yVFb0I?si=UlVPkmCkrsg24Dzj March 28, 2026 at 07:44AM

Show HN: VizTools – 16 free tools for PMs and freelancers, deliberately no AI https://bit.ly/4cfQvaj

Show HN: VizTools – 16 free tools for PMs and freelancers, deliberately no AI I've been building AI products for a while. For this one I made a deliberate choice: none of the 16 tools use AI. Meeting cost calculators, freelance rate calculators, PRD generators, runway calculators, sprint retro boards — these problems don't need a language model. They need a well-designed form and correct arithmetic. Built on Nuxt 4 + Vue 3, fully static, runs in your browser. No account required to use anything. Optional Firebase auth only kicks in if you want to save output. Irony worth naming: Claude Code was my pair programmer throughout. The choice wasn't anti-AI — it was about using the right tool for the right problem. Happy to talk stack, the non-AI tradeoffs, or anything else. https://bit.ly/4bPwLd9 March 28, 2026 at 06:36AM

Show HN: Open Source 'Conductor + Ghostty' https://bit.ly/48biqXm

Show HN: Open Source 'Conductor + Ghostty' Our team works with Claude Code, Codex, Gemini all day. We love Ghostty, but wanted something where we could work in multiple worktree at once and have multiple agents run. We decided to open source the internal team we use. Hope you might find it useful. Freel free to contribute or fork. * Cross-platform (Mac, Linux, Windows) all tested * MIT License Features: * Notifications, but also manual 'mark-as-unread) for worktrees (like Gmail stars) * Status indicators work for all terminals inside a wroktree * GH integrations (show PR status) and link GH issues * Can add comments to worktrees (stay organized) * File viewer, Search, diff viewer (can make edits + save) Note: Yeah there are "similar" programs out there, but this one is ours. But I'm happy if our software works for you too! https://bit.ly/4t8okkc March 27, 2026 at 11:26PM

Show HN: Twitch Roulette – Find live streamers who need views the most https://bit.ly/4uVAbE1

Show HN: Twitch Roulette – Find live streamers who need views the most Hey HN, I re-launched twitchroulette.net with a lot of new features and stats and I would love for people to check it out. The idea is you can easily browse the less browsed parts of twitch and find cool and new streamers to say hi to, and maybe make some new friends. I also added some real time stats and breakdowns per channel and I think some of the things they show are pretty interesting. Check it out! https://bit.ly/3fvn7hM March 27, 2026 at 11:22PM

Show HN: Sup AI, a confidence-weighted ensemble (52.15% on Humanity's Last Exam) https://bit.ly/4sAK3Bo

Show HN: Sup AI, a confidence-weighted ensemble (52.15% on Humanity's Last Exam) Hi HN. I'm Ken, a 20-year-old Stanford CS student. I built Sup AI. I started working on this because no single AI model is right all the time, but their errors don’t strongly correlate. In other words, models often make unique mistakes relative to other models. So I run multiple models in parallel and synthesize the outputs by weighting segments based on confidence. Low entropy in the output token probability distributions correlates with accuracy. High entropy is often where hallucinations begin. My dad Scott (AI Research Scientist at TRI) is my research partner on this. He sends me papers at all hours, we argue about whether they actually apply and what modifications make sense, and then I build and test things. The entropy-weighting approach came out of one of those conversations. In our eval on Humanity's Last Exam, Sup scored 52.15%. The best individual model in the same evaluation run got 44.74%. The relative gap is statistically significant (p < 0.001). Methodology, eval code, data, and raw results: - https://sup.ai/research/hle-white-paper-jan-9-2026 - https://github.com/supaihq/hle Limitations: - We evaluated 1,369 of the 2,500 HLE questions (details in the above links) - Not all APIs expose token logprobs; we use several methods to estimate confidence when they don't We tried offering free access and it got abused so badly it nearly killed us. Right now the sustainable option is a $5 starter credit with card verification (no auto-charge). If you don't want to sign up, drop a prompt in the comments and I'll run it myself and post the result. Try it at https://sup.ai . My dad Scott (@scottmu) is in the thread too. Would love blunt feedback, especially where this really works for you and where it falls short. Here's a short demo video: https://www.youtube.com/watch?v=DRcns0rRhsg https://sup.ai March 26, 2026 at 04:45PM

Show HN: Veil – Dark mode PDFs without destroying images, runs in the browser https://bit.ly/4c6B3OC

Show HN: Veil – Dark mode PDFs without destroying images, runs in the browser Hi HN! here's a tool I just deployed that renders PDFs in dark mode without destroying the images. Internal and external links stay intact, and I decided to implement export since I'm not a fan of platform lock-in: you can view your dark PDF in your preferred reader, on any device. It's a side project born from a personal need first and foremost. When I was reading in the factory the books that eventually helped me get out of it, I had the problem that many study materials and books contained images and charts that forced me, with the dark readers available at the time, to always keep the original file in multitasking since the images became, to put it mildly, strange. I hope it can help some of you who have this same need. I think it could be very useful for researchers, but only future adoption will tell. With that premise, I'd like to share the choices that made all of this possible. To do so, I'll walk through the three layers that veil creates from the original PDF: - Layer 1: CSS filter. I use invert(0.86) hue rotate(180deg) on the main canvas. I use 0.86 instead of 1.0 because I found that full inversion produces a pure black and pure white that are too aggressive for prolonged reading. 0.86 yields a soft dark grey (around #242424, though it depends on the document's white) and a muted white (around #DBDBDB) for the text, which I found to be the most comfortable value for hours of reading. - Layer 2: image protection. A second canvas is positioned on top of the first, this time with no filters. Through PDF.js's public API getOperatorList(), I walk the PDF's operator list and reconstruct the CTM stack, that is the save, restore and transform operations the PDF uses to position every object on the page. When I encounter a paintImageXObject (opcode 85 in PDF.js v5), the current transformation matrix gives me the exact bounds of the image. At that point I copy those pixels from a clean render onto the overlay. I didn't fork PDF.js because It would have become a maintenance nightmare given the length of the codebase and the frequent updates. Images also receive OCR treatment: text contained in charts and images becomes selectable, just like any other text on the page. At this point we have the text inverted and the images intact. But what if the page is already dark? Maybe the chapter title pages are black with white text? The next layer takes care of that. - Layer 3: already-dark page detection. After rendering, the background brightness is measured by sampling the edges and corners of the page (where you're most likely to find pure background, without text or images in the way). The BT.601 formula is used to calculate perceived brightness by weighting the three color channels as the human eye sees them: green at 58.7%, red at 29.9%, blue at 11.4%. These weights reflect biology: the eye evolved in natural environments where distinguishing shades of green (vegetation, predators in the grass) was a matter of survival, while blue (sky, water) was less critical. If the average luminance falls below 40%, the page is flagged as already dark and the inversion is skipped, returning the original page. Presentation slides with dark backgrounds stay exactly as they are, instead of being inverted into something blinding. Scanned documents are detected automatically and receive OCR via Tesseract.js, making text selectable and copyable even on PDFs that are essentially images. Everything runs locally, no framework was used, just vanilla JS, which is why it's an installable PWA that works offline too. Here's the link to the app along with the repository: https://bit.ly/40Z98Kh | https://bit.ly/4uVGXth I hope veil can make your reading more pleasant. I'm open to any feedback. Thanks everyone https://bit.ly/40Z98Kh March 26, 2026 at 12:47PM

Show HN: Optio – Orchestrate AI coding agents in K8s to go from ticket to PR https://bit.ly/4bxWNTl

Show HN: Optio – Orchestrate AI coding agents in K8s to go from ticket to PR I think like many of you, I've been jumping between many claude code/codex sessions at a time, managing multiple lines of work and worktrees in multiple repos. I wanted a way to easily manage multiple lines of work and reduce the amount of input I need to give, allowing the agents to remove me as a bottleneck from as much of the process as I can. So I built an orchestration tool for AI coding agents: Optio is an open-source orchestration system that turns tickets into merged pull requests using AI coding agents. You point it at your repos, and it handles the full lifecycle: - Intake — pull tasks from GitHub Issues, Linear, or create them manually - Execution — spin up isolated K8s pods per repo, run Claude Code or Codex in git worktrees - PR monitoring — watch CI checks, review status, and merge readiness every 30s - Self-healing — auto-resume the agent on CI failures, merge conflicts, or reviewer change requests - Completion — squash-merge the PR and close the linked issue The key idea is the feedback loop. Optio doesn't just run an agent and walk away — when CI breaks, it feeds the failure back to the agent. When a reviewer requests changes, the comments become the agent's next prompt. It keeps going until the PR merges or you tell it to stop. Built with Fastify, Next.js, BullMQ, and Drizzle on Postgres. Ships with a Helm chart for production deployment. https://bit.ly/3PyeSYX March 25, 2026 at 06:10PM

Show HN: Plasmite – a lightweight IPC system that's fun https://bit.ly/4lMD6um

Show HN: Plasmite – a lightweight IPC system that's fun At Oblong Industries one of the basic building blocks of everything we built was a homegrown C-based IPC system called Plasma. The message channel was an mmap'd file used as a ring buffer. All messages were human-readable, performance was good, configuration was trivial. What was especially useful (and unusual in IPC systems it seems) was the property that message channels outlive all readers and writers, and even survive reboots, because they're just files. For local IPC you don't need a broker or server process. All the engineers who ever worked at Oblong loved Plasma, so I've recreated and updated it, as Plasmite. It's written in Rust and the message format is JSON, but it's fast because it's based on lite3 ( https://bit.ly/47gEPlW ), a really cool project you should also check out. Bindings for Python, Go, Node, and C, but you can also get a lot done with just the CLI tools. The basic commands are - "feed" (to write) - "follow" (to tail) - "fetch" (to read one) - "duplex" (to have a 2-way session) I think duplex could be great for agent-agent communication, but I haven't tried this much yet. If you do, let me know! https://bit.ly/4syvmPq March 25, 2026 at 01:10AM

Show HN: Lexplain – AI-powered Linux kernel change explanations https://bit.ly/4s3xspy

Show HN: Lexplain – AI-powered Linux kernel change explanations To understand what changed between kernel versions, you have to dig through the git repository yourself. Commit messages rarely tell you the real-world impact on your systems — you need to analyze the actual diffs with knowledge of kernel internals. For engineers who use Linux — directly or indirectly — but aren't kernel developers, that barrier is pretty high. I kept finding out about relevant changes only after an issue had already hit, and it was most frustrating when the version was too new to find similar cases online. I built lexplain with the idea that it would be nice to quickly scan through kernel changes the way you'd skim the morning news. It reads diffs, analyzes the code, and generates two types of documents: - Commit analyses: context, code breakdown, behavioral impact, risks, references - Release notes: per-version highlights, functional classification, subsystem breakdown, impact analysis Documents build on each other — individual commits first, then merge commits using child analyses, then release notes using all analyses for that version. Claims based on inference are explicitly labeled. Work in progress. Feedback welcome. https://bit.ly/4t6Sqoe March 24, 2026 at 11:24PM

Show HN: OpenCastor Agent Harness Evaluator Leaderboard https://bit.ly/4bGGUc3

Show HN: OpenCastor Agent Harness Evaluator Leaderboard I've been building OpenCastor, a runtime layer that sits between a robot's hardware and its AI agent. One thing that surprised me: the order you arrange the skill pipeline (context builder → model router → error handler, etc.) and parameters like thinking_budget and context_budget affect task success rates as much as model choice does. So I built a distributed evaluator. Robots contribute idle compute to benchmark harness configurations against OHB-1, a small benchmark of 30 real-world robot tasks (grip, navigate, respond, etc.) using local LLM calls via Ollama. The search space is 263,424 configs (8 dimensions: model routing, context budget, retry logic, drift detection, etc.). The demo leaderboard shows results so far, broken down by hardware tier (Pi5+Hailo, Jetson, server, budget boards). The current champion config is free to download as a YAML and apply to any robot. P66 safety parameters are stripped on apply — no harness config can touch motor limits or ESTOP logic. Looking for feedback on: (1) whether the benchmark tasks are representative, (2) whether the hardware tier breakdown is useful, and (3) anyone who's run fleet-wide distributed evals of agent configs for robotics or otherwise. https://bit.ly/4c1pica March 23, 2026 at 11:13PM

Show HN: Cq – Stack Overflow for AI coding agents https://bit.ly/47gYJgx

Show HN: Cq – Stack Overflow for AI coding agents Hi all, I'm Peter at Staff Engineer and Mozilla.ai and I want to share our idea for a standard for shared agent learning, conceptually it seemed to fit easily in my mental model as a Stack Overflow for agents. The project is trying to see if we can get agents (any agent, any model) to propose 'knowledge units' (KUs) as a standard schema based on gotchas it runs into during use, and proactively query for existing KUs in order to get insights which it can verify and confirm if they prove useful. It's currently very much a PoC with a more lofty proposal in the repo, we're trying to iterate from local use, up to team level, and ideally eventually have some kind of public commons. At the team level (see our Docker compose example) and your coding agent configured to point to the API address for the team to send KUs there instead - where they can be reviewed by a human in the loop (HITL) via a UI in the browser, before they're allowed to appear in queries by other agents in your team. We're learning a lot even from using it locally on various repos internally, not just in the kind of KUs it generates, but also from a UX perspective on trying to make it easy to get using it and approving KUs in the browser dashboard. There are bigger, complex problems to solve in the future around data privacy, governance etc. but for now we're super focussed on getting something that people can see some value from really quickly in their day-to-day. Tech stack: * Skills - markdown * Local Python MCP server (FastMCP) - managing a local SQLite knowledge store * Optional team API (FastAPI, Docker) for sharing knowledge across an org * Installs as a Claude Code plugin or OpenCode MCP server * Local-first by default; your knowledge stays on your machine unless you opt into team sync by setting the address in config * OSS (Apache 2.0 licensed) Here's an example of something which seemed straight forward, when asking Claude Code to write a GitHub action it often used actions that were multiple major versions out of date because of its training data. In this case I told the agent what I saw when I reviewed the GitHub action YAML file it created and it proposed the knowledge unit to be persisted. Next time in a completely different repo using OpenCode and an OpenAI model, the cq skill was used up front before it started the task and it got the information about the gotcha on major versions in training data and checked GitHub proactively, using the correct, latest major versions. It then confirmed the KU, increasing the confidence score. I guess some folks might say: well there's a CLAUDE.md in your repo, or in ~/.claude/ but we're looking further than that, we want this to be available to all agents, to all models, and maybe more importantly we don't want to stuff AGENTS.md or CLAUDE.md with loads of rules that lead to unpredictable behaviour, this is targetted information on a particular task and seems a lot more useful. Right now it can be installed locally as a plugin for Claude Code and OpenCode: claude plugin marketplace add mozilla-ai/cq claude plugin install cq This allows you to capture data in your local ~/.cq/local.db (the data doesn't get sent anywhere else). We'd love feedback on this, the repo is open and public - so GitHub issues are welcome. We've posted on some of our social media platforms with a link to the blog post (below) so feel free to reply to us if you found it useful, or ran into friction, we want to make this something that's accessible to everyone. Blog post with the full story: https://bit.ly/41ukHZX GitHub repo: https://bit.ly/4soBZ6I Thanks again for your time. https://bit.ly/41ukHZX March 23, 2026 at 05:11PM

Show HN: AgentVerse – Open social network for AI agents (Mar 2026) https://bit.ly/4srsrrA

Show HN: AgentVerse – Open social network for AI agents (Mar 2026) https://bit.ly/47WxiJ2 March 23, 2026 at 02:48AM

Show HN: Quillium, Git for Writers https://bit.ly/4c0H92U

Show HN: Quillium, Git for Writers This is a tool which lets you easily manage different versions of ideas, helpful for writing essays. I've found myself wanting this every single time I go through the drafting process when writing, and I've been frustrated every time I find myself accidentally working on an old draft just because there was a paragraph that I liked better. This solves it. I hope the community like this as much I enjoyed working on it! Note that it's currently a beta waitlist because there's some bugs with the undo/redo state management and so I want to dogfood it for a bit for reliability. It says April 2nd, but I may allow earlier beta testers. https://bit.ly/4bFReRH March 23, 2026 at 01:22AM

Show HN: Plot-Hole.com a daily movie puzzle I made https://bit.ly/47C1U2H

Show HN: Plot-Hole.com a daily movie puzzle I made https://bit.ly/4brdZd9 March 23, 2026 at 01:15AM

Show HN: Refrax – my Arc Browser replacement I made from scratch https://bit.ly/4ssbdKD

Show HN: Refrax – my Arc Browser replacement I made from scratch Open the same tab in two browser windows. In Chrome or Safari, you get two unconnected pages. In Arc, one window shows a placeholder. In Zen, it silently creates a duplicate. In Refrax, the browser I built, both windows show the same page updating live. The same web page, in as many windows as you want. This shouldn't be possible. WebKit's WKWebView can exist in exactly one view hierarchy at a time. With macOS 26, Apple added a SwiftUI API separating WebView from WebPage, so you can end up with multiple views referencing the same page. But if you try it, your app crashes. WebKit source code has a precondition with this comment: "We can't have multiple owning pages regardless, but we'll want to decide if it's an error, if we can handle it gracefully, and how deterministic it might even be..." So here's how I did it. CAPortalLayer is an undocumented private class that's been in macOS since 10.12. It mirrors a layer's composited output by referencing the same GPU memory, not copying it. Every scroll, animation, or repaint reflects instantly. This is what powers Liquid Glass effects, the iOS text selection magnifier, and ghost images during drag and drop. Apple uses portals for effects. I use them to put the same web page in two windows. Refrax keeps one real WKWebView per tab and displays a CAPortalLayer mirror everywhere else. When you click a different window, the coordinator moves the real view there and the old window gets a portal. You can't tell which is which. This sounds simple in theory, but making this actually work seamlessly took quite a lot of effort. Each macOS window has its own rendering context, and the context ID updates asynchronously, so creating a portal immediately captures a stale ID and renders nothing. The portal creation needs to be delayed, but delaying creates a visual gap. I capture a GPU snapshot using a private CoreGraphics function and place it behind the portal as a fallback. Another hard part is that none of it is documented. Portals are very capricious and would crash the app if you use them incorrectly. I had to inspect the headers and then disassemble the binaries to explore exactly how it works in order to build something robust. I never worked on a browser before this, I've only been a user. I started using Arc in 2022. I remember asking for an invite, learning the shortcuts, slowly getting used to it. I didn't like it at first as it had too much Google Chrome in it for my taste, and I'd been using Safari at the time. But it grew on me, and by the time it was essentially abandoned and sold to Atlassian, I couldn't go back to Safari anymore. I tried everything: Zen, SigmaOS, Helium. None felt right, and I didn't want another Chromium fork. WebKit ships with the OS, but all you get is the rendering engine. Tabs, history, bookmarks, passwords, extensions, everything else has to be made separately. And so, being a very reasonable person, I decided to make my own Arc replacement from scratch. And I did. Refrax is built in Swift and Objective-C with no external dependencies. The app itself is less than 30 MB. I have 393 tabs open right now using 442 MB of RAM; 150 tabs in Safari was already over 1 GB. I've been using it daily for over a month, and so have some of my friends. The portal mirror is just one feature. The same approach, finding what Apple built for themselves and using it to create something they didn't think about, runs through the entire browser. You can tint your glass windows with adjustable blend modes and transparency. The sidebar in compact mode samples the page and matches the colors. And it has support for Firefox and Chrome extensions. The alpha is public. Download from the linked website, enter REFRAX-ALPHA-HACKERNEWS to activate. No account needed. Telemetry is crash reports and a daily active-user ping, nothing else. And if you find a bug – I built this alone, so I'll actually read your report. https://bit.ly/4bs6AdM March 22, 2026 at 11:52PM

Show HN: An event loop for asyncio written in Rust https://bit.ly/4sBBVR2

Show HN: An event loop for asyncio written in Rust actually, nothing special about this implementation. just another event loop written in rust for educational purposes and joy in tests it shows seamless migration from uvloop for my scraping framework https://bit.ly/4lL0CIq with APIs (fastapi) it shows only one advantage: better p99, uvloop is faster about 10-20% in the synthetic run currently, i am forking on the win branch to give it windows support that uvloop lacks https://bit.ly/4v2jgQn March 21, 2026 at 11:12PM

Show HN: Travel Hacking Toolkit – Points search and trip planning with AI https://bit.ly/3PlmMF2

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 March 21, 2026 at 10:25PM

Show HN: AgentVerse – Open social network for AI agents (Mar 2026) https://bit.ly/4rJtaDi

Show HN: AgentVerse – Open social network for AI agents (Mar 2026) https://bit.ly/47WxiJ2 March 21, 2026 at 02:25AM

Show HN: Rover – turn any web interface into an AI agent with one script tag https://bit.ly/4blbIAg

Show HN: Rover – turn any web interface into an AI agent with one script tag https://bit.ly/3NAOc9a March 21, 2026 at 01:58AM

Show HN: Vibefolio – a place to showcase your vibecoded projects https://bit.ly/47h4FGh

Show HN: Vibefolio – a place to showcase your vibecoded projects Over the last months, more people are shipping small apps, experiments, and side-projects at a much higher pace. I'm one of them and initially created a showcase page for myself to track them but this week decided to create something for others. Happy to read feedback on how to improve it further! https://bit.ly/47fd3pN March 20, 2026 at 09:53PM

Show HN: Cybertt – Cybersecurity Tabletop https://bit.ly/47x7hQH

Show HN: Cybertt – Cybersecurity Tabletop https://bit.ly/3PmIIzx March 20, 2026 at 10:29AM

Show HN: Download entire/partial Substack to ePub for offline reading https://bit.ly/4uGIhQO

Show HN: Download entire/partial Substack to ePub for offline reading Hi HN, This is a small python app with optional webUI. It is intended to be run locally. It can be run with Docker (cookie autodetection will not work). It allows you to download a single substack, either entirely or partially, and saves the output to an epub file, which can be easily transferred to Kindle or other reading devices. This is admittedly a "vibe coded" app made with Claude Code and a few hours of iterating, but I've already found it very useful for myself. It supports both free and paywalled posts (if you are a paid subscriber to that creator). You can order the entries in the epub by popularity, newest first, or oldest first, and also limit to a specific number of entries, if you don't want all of them. You can either provide your substack.sid cookie manually, or you can have it be autodetected from most browsers/operating systems. https://bit.ly/4uwnXRY March 20, 2026 at 04:36AM

Show HN: Screenwriting Software https://bit.ly/3Phmteo

Show HN: Screenwriting Software I’ve spent the last year getting back into film and testing a bunch of screenwriting software. After a while I realized I wanted something different, so I started building it myself. The core text engine is written in Rust/wasm-bindgen. https://bit.ly/47cYh2P March 20, 2026 at 03:07AM

Show HN: Browser grand strategy game for hundreds of players on huge maps https://bit.ly/41cC0i3

Show HN: Browser grand strategy game for hundreds of players on huge maps Hi HN, I've been building a browser-based multiplayer strategy game called Borderhold. Matches run on large maps designed for hundreds of players. Players expand territory, attack neighbors, and adapt as borders shift across the map. You can put buildings down, build ships, and launch nukes. The main thing I wanted to explore was scale: most strategy games are small matches, modest maps, or modest player counts, but here maps are large and game works well with hundreds of players. Matches are relatively short so you can jump in and see a full game play out. Curious what people think. https://bit.ly/4uDPCAC Gameplay: https://youtu.be/nrJTZEP-Cw8 Discord: https://bit.ly/4uEbuvu https://bit.ly/4uDPCAC March 16, 2026 at 09:51AM

Show HN: Fitness MCP https://bit.ly/4sr8Jwo

Show HN: Fitness MCP There's no external MCP for your fitness (Garmin / Strava) data, so we built one. https://bit.ly/4uCviiR March 19, 2026 at 03:00AM

Show HN: ATO – a GUI to see and fix what your LLM agents configured https://bit.ly/476fStf

Show HN: ATO – a GUI to see and fix what your LLM agents configured https://bit.ly/476fSJL March 19, 2026 at 01:28AM

Show HN: Duplicate 3 layers in a 24B LLM, logical deduction .22→.76. No training https://bit.ly/4bGv6H0

Show HN: Duplicate 3 layers in a 24B LLM, logical deduction .22→.76. No training I replicated David Ng's RYS method ( https://bit.ly/4ll5ILb ) on consumer AMD GPUs (RX 7900 XT + RX 6950 XT) and found something I didn't expect. Transformers appear to have discrete "reasoning circuits" — contiguous blocks of 3-4 layers that act as indivisible cognitive units. Duplicate the right block and the model runs its reasoning pipeline twice. No weights change. No training. The model just thinks longer. The results on standard benchmarks (lm-evaluation-harness, n=50): Devstral-24B, layers 12-14 duplicated once: - BBH Logical Deduction: 0.22 → 0.76 - GSM8K (strict): 0.48 → 0.64 - MBPP (code gen): 0.72 → 0.78 - Nothing degraded Qwen2.5-Coder-32B, layers 7-9 duplicated once: - Reasoning probe: 76% → 94% The weird part: different duplication patterns create different cognitive "modes" from the same weights. Double-pass boosts math. Triple-pass boosts emotional reasoning. Interleaved doubling (13,13,14,14,15,15,16) creates a pure math specialist. Same model, same VRAM, different routing. The circuit boundaries are sharp — shift by one layer and the effect disappears or inverts. Smaller models (24B) have tighter circuits (3 layers) than larger ones (Ng found 7 layers in 72B). Tools to find circuits in any GGUF model and apply arbitrary layer routing are in the repo. The whole thing — sweep, discovery, validation — took one evening. Happy to answer questions. https://bit.ly/4rEg2PM March 18, 2026 at 10:31PM

Show HN: Sonder – self-hosted AI social simulation engine https://bit.ly/4rE8hcG

Show HN: Sonder – self-hosted AI social simulation engine https://bit.ly/4bhXvEi March 18, 2026 at 01:21AM

Show HN: CodeLedger – deterministic context and guardrails for AI https://bit.ly/4saYs7c

Show HN: CodeLedger – deterministic context and guardrails for AI We’ve been working on a tool called CodeLedger to solve a problem we kept seeing with AI coding agents (Claude Code, Cursor, Codex): They’re powerful, but on real codebases they: - read too much irrelevant code - edit outside the intended scope - get stuck in loops (fix → test → fail) - drift away from the task - introduce architectural issues that linters don’t catch The root issue isn’t the model — it’s: - poor context selection - lack of execution guardrails - no visibility at team/org level --- What CodeLedger does: It sits between the developer and the agent and: 1) Gives the agent the right files first 2) Keeps the agent inside the task scope 3) Validates output against architecture + constraints It works deterministically (no embeddings, no cloud, fully local). --- Example: Instead of an agent scanning 100–500 files, CodeLedger narrows it down to ~10–25 relevant files before the first edit :contentReference[oaicite:0]{index=0} --- What we’re seeing so far: - ~40% faster task completion - ~50% fewer iterations - significant reduction in token usage --- Works with: Claude Code, Cursor, Codex, Gemini CLI --- Repo + setup: https://bit.ly/4bxAhJd Quick start: npm install -g @codeledger/cli cd your-project codeledger init codeledger activate --task "Fix null handling in user service" --- Would love feedback from folks using AI coding tools on larger codebases. Especially curious: - where agents break down for you today - whether context selection or guardrails are the bigger issue - what other issues are you seeing. https://bit.ly/47F3l01 March 18, 2026 at 12:22AM

Show HN: I built a message board where you pay to be the homepage https://bit.ly/4sKqCps

Show HN: I built a message board where you pay to be the homepage I kept thinking about what would happen if a message board only had one slot. One message, front and center, until someone pays to replace it. That's the entire product. You pay the current message's decayed value plus a penny to take the homepage. Message values drop over time using a gravity-based formula (same concept HN uses for ranking), so a $10 message might only cost a few bucks to replace a day later. Likes slow the decay, dislikes speed it up. The whole thing runs on three mini PCs in my house (k3s cluster, PostgreSQL, Redis Sentinel). Is it overengineered for a message board? Absolutely. I genuinely don't know where this goes. Curious what HN thinks. Archive of past messages: https://bit.ly/3Pcn94I https://bit.ly/4bi0GvG March 17, 2026 at 01:06PM

Show HN: Seasalt Cove, iPhone access to your Mac https://bit.ly/4cL7FOO

Show HN: Seasalt Cove, iPhone access to your Mac I feel like I finally built something I actually use every day and it has completely changed the way I think about work. AI workflows have flipped how devs operate. You're not heads down writing code anymore, you're bouncing between projects, instructing agents, reviewing their work, nudging them forward. The job is now less about typing and more about judgment calls. And the thing about that workflow is you spend a lot of time waiting. Waiting for the agent to finish, waiting for the next approval gate. That waiting doesn't have to happen at your desk. It doesn't have to happen in front of a monitor at all. I built Seasalt because I realized my iPhone could handle 80% of what I was chaining myself to my Mac for. Kick off the agent, walk away, review the diff from the store, a walk, or in a separate room away from your Mac. Approve it. Start the next one, switch to another session. You don't need giant dual monitors for this. That's kind of the whole point. Also, I have a deep security background so I felt like it was 100% necessary to include end to end encrypted with a zero knowledge relay, no ports getting opened, no VPN configuration needed, with key validation in the onboarding flow. https://bit.ly/3PnfnVy March 16, 2026 at 11:48PM

Show HN: Webassembly4J Run WebAssembly from Java https://bit.ly/41cf2aN

Show HN: Webassembly4J Run WebAssembly from Java I’ve released WebAssembly4J, along with two runtime bindings: Wasmtime4J – Java bindings for Wasmtime https://bit.ly/471hULh WAMR4J – Java bindings for WebAssembly Micro Runtime https://bit.ly/4blCCGY WebAssembly4J – a unified Java API that allows running WebAssembly across different engines https://bit.ly/40CvoJI The motivation was that Java currently has multiple emerging WebAssembly runtimes, but each exposes its own API. If you want to experiment with different engines, you have to rewrite the integration layer each time. WebAssembly4J provides a single API while allowing different runtime providers underneath. Goals of the project: Run WebAssembly from Java applications Allow cross-engine comparison of runtimes Make WebAssembly runtimes more accessible to Java developers Provide a stable interface while runtimes evolve Currently supported engines: Wasmtime WAMR Chicory GraalWasm To support both legacy and modern Java environments the project targets: Java 8 (JNI bindings) Java 11 Java 22+ (Panama support) Artifacts are published to Maven Central so they can be added directly to existing projects. I’d be very interested in feedback from people working on Java + WebAssembly integrations or runtime implementations. March 16, 2026 at 12:08AM

Show HN: Lockstep – A data-oriented programming language https://bit.ly/4lB6qEo

Show HN: Lockstep – A data-oriented programming language https://bit.ly/4lyvcF9 I want to share my work-in-progress systems language with a v0.1.0 release of Lockstep. It is a data-oriented systems programming language designed for high-throughput, deterministic compute pipelines. I built Lockstep to bridge the gap between the productivity of C and the execution efficiency of GPU compute shaders. Instead of traditional control flow, Lockstep enforces straight-line SIMD execution. You will not find any if, for, or while statements inside compute kernels; branching is entirely replaced by hardware-native masking and stream-splitting. Memory is handled via a static arena provided by the Host. There is no malloc, no hidden threads, and no garbage collection, which guarantees predictable performance and eliminates race conditions by construction. Under the hood, Lockstep targets LLVM IR directly to leverage industrial-grade optimization passes. It also generates a C-compatible header for easy integration with host applications written in C, C++, Rust, or Zig. v0.1.0 includes a compiler with LLVM IR and C header emission, a CLI simulator for validating pipeline wiring and cardinality on small datasets and an opt-in LSP server for real-time editor diagnostics, hover type info, and autocompletion. You can check out the repository to see the syntax, and the roadmap outlines where the project is heading next, including parameterized SIMD widths and multi-stage pipeline composition. I would love to hear feedback on the language semantics, the type system, and the overall architecture! https://bit.ly/4lyvcF9 March 16, 2026 at 01:14AM

Show HN: Open-source playground to red-team AI agents with exploits published https://bit.ly/4bawx1g

Show HN: Open-source playground to red-team AI agents with exploits published We build runtime security for AI agents. The playground started as an internal tool that we used to test our own guardrails. But we kept finding the same types of vulnerabilities because we think about attacks a certain way. At some point you need people who don't think like you. So we open-sourced it. Each challenge is a live agent with real tools and a published system prompt. Whenever a challenge is over, the full winning conversation transcript and guardrail logs get documented publicly. Building the general-purpose agent itself was probably the most fun part. Getting it to reliably use tools, stay in character, and follow instructions while still being useful is harder than it sounds. That alone reminded us how early we all are in understanding and deploying these systems at scale. First challenge was to get an agent to call a tool it's been told to never call. Someone got through in around 60 seconds without ever asking for the secret directly (which taught us a lot). Next challenge is focused on data exfiltration with harder defences: https://bit.ly/4b98dgc https://bit.ly/3PCKDjq March 15, 2026 at 11:29PM

Show HN: Signet.js – A minimalist reactivity engine for the modern web https://bit.ly/3P7Oghg

Show HN: Signet.js – A minimalist reactivity engine for the modern web https://bit.ly/4uuhYwV March 15, 2026 at 03:58AM

Show HN: GrobPaint: Somewhere Between MS Paint and Paint.net https://bit.ly/472TcKq

Show HN: GrobPaint: Somewhere Between MS Paint and Paint.net https://bit.ly/47wryWg March 14, 2026 at 11:41PM

Show HN: Structural analysis of the D'Agapeyeff cipher (1939) https://bit.ly/4lwRcQA

Show HN: Structural analysis of the D'Agapeyeff cipher (1939) I am working on the D'Agapeyeff cipher, an unsolved cryptogram from 1939. Two findings that I haven't seen published before: 1. All 5 anomalous symbol values in the cipher cluster in the last column of a 14x14 grid. This turns out to be driven by a factor-of-2-and-7 positional pattern in the linear text. 2. Simulated annealing with Esperanto quadgrams (23M char Leipzig corpus) on a 2x98 columnar transposition consistently outscores English by 200+ points and recovers the same Esperanto vocabulary across independent runs. The cipher is not solved. But the combination of structural geometry and computational linguistics narrows the search space significantly. Work in progress, more to come! https://bit.ly/3PbwGc6 March 15, 2026 at 12:34AM

Show HN: Simple plugin to get Claude Code to listen to you https://bit.ly/4br70Qi

Show HN: Simple plugin to get Claude Code to listen to you Hey HN, My cofounder and I have gotten tired of CC ignoring our markdown files so we spent 4 days and built a plugin that automatically steers CC based on our previous sessions. The problem is usually post plan-mode. What we've tried: Heavily use plan mode (works great) CLAUDE.md, AGENTS.md, MEMORY.md Local context folder (upkeep is a pain) Cursor rules (for Cursor) claude-mem (OSS) -> does session continuity, not steering We use fusion search to find your CC steering corrections. - user prompt embeddings + bm25 - correction embeddings + bm25 - time decay - target query embeddings - exclusions - metadata hard filters (such as files) The CC plugin: - Automatically captures memories/corrections without you having to remind CC - Automatically injects corrections without you having to remind CC to do it. The plugin will merge, update, and distill your memories, and then inject the highest relevant ones after each of your own prompts. We're not sure if we're alone in this. We're working on some benchmarks to see how effective context injection actually is in steering CC and we know we need to keep improving extraction, search, and add more integrations. We're passionate about the real-time and personalized context layer for agents. Giving Agents a way to understand what you mean when you say "this" or "that". Bringing the context of your world, into a secure, structured, real-time layer all your agents can access. Would appreciate feedback on how you guys get CC to actually follow your markdown files, understand your modus operandi, feedback on the plugin, or anything else about real-time memory and context. - Ankur https://bit.ly/4bx6joK March 14, 2026 at 12:15AM

Show HN: Kube-pilot – AI engineer that lives in your Kubernetes cluster https://bit.ly/4lq1wK4

Show HN: Kube-pilot – AI engineer that lives in your Kubernetes cluster I built kube-pilot — an autonomous AI agent that runs inside your Kubernetes cluster and does the full dev loop: writes code, builds containers, deploys services, verifies they're healthy, and closes the ticket. You file a GitHub issue, it does the rest. What makes this different from AI coding tools: kube-pilot doesn't just generate code and hand it back to you. It lives inside the cluster with direct access to the entire dev stack — git, Tekton (CI/CD), Kaniko (container builds), ArgoCD (GitOps deployments), kubectl, Vault. Every tool call produces observable state that feeds into the next decision. The cluster isn't just where code runs — it's where the agent thinks. The safety model: all persistent changes go through git, so everything is auditable and reversible. ArgoCD is the only thing that writes to the cluster. Secrets stay behind Vault — the agent creates ExternalSecret references, never touches raw credentials. Credentials are scrubbed before reaching the LLM. Live demo: I filed GitHub issues asking it to build a 4-service office suite (auth, docs API, notification worker, API gateway). It built and deployed all of them autonomously. You can see the full agent loop — code, builds, deploys, verification, comments — on the closed issues: - https://bit.ly/4b8SihV... - https://bit.ly/4b8SiOX... - https://bit.ly/4lBAjEw... - https://bit.ly/4sPN6FP... One helm install gives you everything — the agent, Gitea (git + registry), Tekton, ArgoCD, Vault, External Secrets. No external dependencies. Coming next: Slack and Jira integrations (receive tasks and post updates where your team already works), Prometheus metrics and Grafana dashboards for agent observability, and Alertmanager integration so firing alerts automatically become issues that kube-pilot investigates and fixes. Early proof of concept. Rough edges. But it works. https://bit.ly/3Pk0p2x March 14, 2026 at 03:49AM

Show HN: I wrote my first neural network https://bit.ly/4ltOFGV

Show HN: I wrote my first neural network I have been interested in neural nets since the 90's. I've done quite a bit of reading, but never gotten around to writing code. I used Gemini in place of Wikipedia to fill in the gaps of my knowledge. The coolest part of this was learning about dual numbers. You can see in early commits that I did not yet know about auto-diff; I was thinking I'd have to integrate a CAS library or something. Now, I'm off to play with TensorFlow. https://bit.ly/4cGH7y9 March 14, 2026 at 01:21AM

Show HN: EdgeWhisper – On-device voice-to-text for macOS (Voxtral 4B via MLX) https://bit.ly/4cMsuJQ

Show HN: EdgeWhisper – On-device voice-to-text for macOS (Voxtral 4B via MLX) I built a macOS voice dictation app where zero bytes of audio ever leave your machine. EdgeWhisper runs Voxtral Mini 4B Realtime (Mistral AI, Apache 2.0) locally on Apple Silicon via the MLX framework. Hold a key, speak, release — text appears at your cursor in whatever app has focus. Architecture: - Native Swift (SwiftUI + AppKit). No Electron. - Voxtral 4B inference via MLX on the Neural Engine. ~3GB model, runs in ~2GB RAM on M1+. - Dual text injection: AXUIElement (preserves undo stack) with NSPasteboard+CGEvent fallback. - 6-stage post-processing pipeline: filler removal → dictionary → snippets → punctuation → capitalization → formatting. - Sliding window KV cache for unlimited streaming without latency degradation. - Configurable transcription delay (240ms–2.4s). Sweet spot at 480ms. What it does well: - Works in 20+ terminals/IDEs (VS Code, Xcode, iTerm2, Warp, JetBrains). Most dictation tools break in terminals — we detect them and switch injection strategy. - Removes filler words automatically ("um", "uh", "like"). - 13 languages with auto-detection. - Personal dictionary + snippet expansion with variable support (, ). - Works fully offline after model download. No accounts, no telemetry, no analytics. What it doesn't do (yet): - No file/meeting transcription (coming) - No translation (coming) - No Linux/Windows (macOS only, Apple Silicon required) Pricing: Free tier (5 min/day, no account needed). Pro at $7.99/mo or $79.99/yr. I'd love feedback on: 1. Would local LLM post-processing (e.g., Phi-4-mini via MLX) for grammar/tone be worth the extra ~1GB RAM? 2. For developers using voice→code workflows: what context would you want passed to your editor? 3. Anyone else building on Voxtral Realtime? Curious about your experience with the causal audio encoder. https://bit.ly/4bqT09c March 13, 2026 at 11:57PM

Show HN: What was the world listening to? Music charts, 20 countries (1940–2025) https://bit.ly/40pkPcZ

Show HN: What was the world listening to? Music charts, 20 countries (1940–2025) I built this because I wanted to know what people in Japan were listening to the year I was born. That question spiraled: how does a hit in Rome compare to what was charting in Lagos the same year? How did sonic flavors propagate as streaming made musical influence travel faster than ever? 88mph is a playable map of music history: 230 charts across 20 countries, spanning 8 decades (1940–2025). Every song is playable via YouTube or Spotify. It's open source and I'd love help expanding it — there's a link to contribute charts for new countries and years. The goal is to crowdsource a complete sonic atlas of the world. https://bit.ly/4s6DV3v March 10, 2026 at 05:18PM

Show HN: fftool – A Terminal UI for FFmpeg – Shows Command Before It Runs https://bit.ly/3NwJ71I

Show HN: fftool – A Terminal UI for FFmpeg – Shows Command Before It Runs https://bit.ly/4sCyUzG March 13, 2026 at 11:08AM

Show HN: Global Maritime Chokepoints https://bit.ly/4sbPHdc

Show HN: Global Maritime Chokepoints https://bit.ly/4cLCnaE March 13, 2026 at 05:42AM

Show HN: Slop or not – can you tell AI writing from human in everyday contexts? https://bit.ly/4uwqqvW

Show HN: Slop or not – can you tell AI writing from human in everyday contexts? I’ve been building a crowd-sourced AI detection benchmark. Two responses to the same prompt — one from a real human (pre-2022, provably pre prevalence of AI slop on the internet), one generated by AI. You pick the slop. Three wrong and you’re out. The dataset: 16K human posts from Reddit, Hacker News, and Yelp, each paired with AI generations from 6 models across two providers (Anthropic and OpenAI) at three capability tiers. Same prompt, length-matched, no adversarial coaching — just the model’s natural voice with platform context. Every vote is logged with model, tier, source, response time, and position. Early findings from testing: Reddit posts are easy to spot (humans are too casual for AI to mimic), HN is significantly harder. I'll be releasing the full dataset on HuggingFace and I'll publish a paper if I can get enough data via this crowdsourced study. If you play the HN-only mode, you’re helping calibrate how detectable AI is on here specifically. Would love feedback on the pairs — are any trivially obvious? Are some genuinely hard? https://bit.ly/4upYoBV March 12, 2026 at 10:53PM

Show HN: A context-aware permission guard for Claude Code https://bit.ly/4cNXEk1

Show HN: A context-aware permission guard for Claude Code We needed something like --dangerously-skip-permissions that doesn’t nuke your untracked files, exfiltrate your keys, or install malware. Claude Code's permission system is allow-or-deny per tool, but that doesn’t really scale. Deleting some files is fine sometimes. And git checkout is sometimes not fine. Even when you curate permissions, 200 IQ Opus can find a way around it. Maintaining a deny list is a fool's errand. nah is a PreToolUse hook that classifies every tool call by what it actually does, using a deterministic classifier that runs in milliseconds. It maps commands to action types like filesystem_read, package_run, db_write, git_history_rewrite, and applies policies: allow, context (depends on the target), ask, or block. Not everything can be classified, so you can optionally escalate ambiguous stuff to an LLM, but that’s not required. Anything unresolved you can approve, and configure the taxonomy so you don’t get asked again. It works out of the box with sane defaults, no config needed. But you can customize it fully if you want to. No dependencies, stdlib Python, MIT. pip install nah && nah install https://bit.ly/4uo6cnR https://bit.ly/3PvHlOS March 12, 2026 at 12:26AM

Show HN:Conduit–Headless browser with SHA-256 hash chain - Ed25519 audit trails https://bit.ly/40qLGW8

Show HN:Conduit–Headless browser with SHA-256 hash chain - Ed25519 audit trails I've been building AI agent tooling and kept running into the same problem: agents browse the web, take actions, fill out forms, scrape data -- and there's zero proof of what actually happened. Screenshots can be faked. Logs can be edited. If something goes wrong, you're left pointing fingers at a black box. So I built Conduit. It's a headless browser (Playwright under the hood) that records every action into a SHA-256 hash chain and signs the result with Ed25519. Each action gets hashed with the previous hash, forming a tamper-evident chain. At the end of a session, you get a "proof bundle" -- a JSON file containing the full action log, the hash chain, the signature, and the public key. Anyone can independently verify the bundle without trusting the party that produced it. The main use cases I'm targeting: - *AI agent auditing* -- You hand an agent a browser. Later you need to prove what it did. Conduit gives you cryptographic receipts. - *Compliance automation* -- SOC 2, GDPR data subject access workflows, anything where you need evidence that a process ran correctly. - *Web scraping provenance* -- Prove that the data you collected actually came from where you say it did, at the time you say it did. - *Litigation support* -- Capture web content with a verifiable chain of custody. It also ships as an MCP (Model Context Protocol) server, so Claude, GPT, and other LLM-based agents can use the browser natively through tool calls. The agent gets browse, click, fill, screenshot, and the proof bundle builds itself in the background. Free, MIT-licensed, pure Python. No accounts, no API keys, no telemetry. GitHub: https://bit.ly/40mFlLj Install: `pip install conduit-browser` Would love feedback on the proof bundle format and the MCP integration. Happy to answer questions about the cryptographic design. March 12, 2026 at 12:15AM

Show HN: CryptoFlora – Visualize SHA256 to a flower using Rose curves https://bit.ly/4lkcFMp

Show HN: CryptoFlora – Visualize SHA256 to a flower using Rose curves I made this side tool to visualize SHA-256 while building a loyalty card wallet application to easily identify if a collected stamp is certified by the issuer by simply seeing it, instead of scanning something like a QR code or matching a serial number. I think there are more potential use cases, like creating a random avatar based on an email address or something else. Feel free to share your feedback :) source code: https://bit.ly/3Ngkfeo https://bit.ly/4cBM2jV March 11, 2026 at 04:52AM

Show HN: Readhn – AI-Native Hacker News MCP Server (Discover, Trust, Understand) https://bit.ly/3Nw8u3F

Show HN: Readhn – AI-Native Hacker News MCP Server (Discover, Trust, Understand) I felt frustrated finding high-signal discussions on HN, and I started this project to better understand how this community actually works. That led me to build readhn, an MCP server that helps with three things: - Discover: find relevant stories/comments by keyword, score, and time window - Trust: identify credible voices using EigenTrust-style propagation from seed experts - Understand: show why each result is ranked, with explicit signals instead of a black-box score It includes 6 tools: discover_stories, search, find_experts, expert_brief, story_brief, and thread_analysis. I also added readhn setup so AI agents can auto-configure it (Claude Code, Codex, Cursor, and others) after pip install. I’d love feedback on: 1) whether these ranking signals match how you evaluate HN quality, 2) trust-model tradeoffs, 3) what would make this useful in your daily workflow. If this is useful to you, starring the repo helps others discover it: https://bit.ly/40pmNKh https://bit.ly/40pmNKh March 11, 2026 at 01:49AM

Show HN: Claude Code Token Elo https://bit.ly/4s44FSx

Show HN: Claude Code Token Elo https://bit.ly/4ddLBfJ March 10, 2026 at 05:29AM

Show HN: Modulus – Cross-repository knowledge orchestration for coding agents https://bit.ly/3P3vAPB

Show HN: Modulus – Cross-repository knowledge orchestration for coding agents Hello HN, we're Jeet and Husain from Modulus ( https://bit.ly/4s9fGBW ) - a desktop app that lets you run multiple coding agents with shared project memory. We built it to solve two problems we kept running into: - Cross-repo context is broken. When working across multiple repositories, agents don't understand dependencies between them. Even if we open two repos in separate Cursor windows, we still have to manually explain the backend API schema while making changes in the frontend repo. - Agents lose context. Switching between coding agents often means losing context and repeating the same instructions again. Modulus shares memory across agents and repositories so they can understand your entire system. It's an alternative to tools like Conductor for orchestrating AI coding agents to build product, but we focused specifically on multi-repo workflows (e.g., backend repo + client repo + shared library repo + AI agents repo). We built our own Memory and Context Engine from the ground up specifically for coding agents. Why build another agent orchestration tool? It came from our own problem. While working on our last startup, Husain and I were working across two different repositories. Working across repos meant manually pasting API schemas between Cursor windows — telling the frontend agent what the backend API looked like again and again. So we built a small context engine to share knowledge across repos and hooked it up to Cursor via MCP. This later became Modulus. Soon, Modulus will allow teams to share knowledge with others to improve their workflows with AI coding agents - enabling team collaboration in the era of AI coding. Our API will allow developers to switch between coding agents or IDEs without losing any context. If you wanna see a quick demo before trying out, here is our launch post - https://bit.ly/3NtfI8E We'd greatly appreciate any feedback you have and hope you get the chance to try out Modulus. https://bit.ly/4s9fGBW March 10, 2026 at 07:52PM

Show HN: Latchup – Competitive programming for hardware description languages https://bit.ly/4bhDgFy

Show HN: Latchup – Competitive programming for hardware description languages https://bit.ly/4cEqHGt March 10, 2026 at 07:06AM

Show HN: I Was Here – Draw on street view, others can find your drawings https://bit.ly/4rUNti6

Show HN: I Was Here – Draw on street view, others can find your drawings Hey HN, I made a site where you can draw on street-level panoramas. Your drawings persist and other people can see them in real time. Strokes get projected onto the 3D panorama so they wrap around buildings and follow the geometry, not just a flat overlay. Uses WebGL2 for rendering, Mapillary for the street imagery. The idea is for it to become a global canvas, anyone can leave a mark anywhere and others stumble onto it. https://bit.ly/40TNruT March 10, 2026 at 06:04AM

Show HN: SAT Protocol – static social networking https://bit.ly/3PqUmJw

Show HN: SAT Protocol – static social networking https://bit.ly/4rXCy7f March 10, 2026 at 04:25AM

Show HN: ChatJC – chatbot for resume/LinkedIn/portfolio info https://bit.ly/3OZ9A8y

Show HN: ChatJC – chatbot for resume/LinkedIn/portfolio info https://bit.ly/4b3Iy8M March 10, 2026 at 01:37AM

Show HN: Toolkit – Visual Simulators for How Internet Protocols and Systems Work https://bit.ly/4syfVWP

Show HN: Toolkit – Visual Simulators for How Internet Protocols and Systems Work https://bit.ly/4d7ddmL March 8, 2026 at 09:23PM

Show HN: Jarvey - a local JARVIS for MacOS https://bit.ly/46LTYLE

Show HN: Jarvey - a local JARVIS for MacOS https://bit.ly/3OWd0Jh March 8, 2026 at 12:04AM

Show HN: SiClaw – Open-source AIOps with a hypothesis-driven diagnostic engine https://bit.ly/40dYpLH

Show HN: SiClaw – Open-source AIOps with a hypothesis-driven diagnostic engine https://bit.ly/4rYciJW March 8, 2026 at 03:27AM

Show HN: Help] I run 4 AI-driven companies simultaneously from my terminal https://bit.ly/4sC3Iki

Show HN: Help] I run 4 AI-driven companies simultaneously from my terminal https://bit.ly/4cAtEbg March 7, 2026 at 11:13PM

Show HN: MicroBin – Easy File Sharing for Everyone – Self-Hostable https://bit.ly/4b89DpR

Show HN: MicroBin – Easy File Sharing for Everyone – Self-Hostable https://bit.ly/3NlpnxY March 7, 2026 at 10:07PM

Show HN: mTile – native macOS window tiler inspired by gTile https://bit.ly/4cyeD9O

Show HN: mTile – native macOS window tiler inspired by gTile Built this with codex/claude because I missed gTile[1] from Ubuntu and couldn’t find a macOS tiler that felt good on a big ultrawide screen. Most mac options I tried were way too rigid for my workflow (fixed layouts, etc) or wanted a monthly subscription. gTile’s "pick your own grid sizes + keyboard flow" is exactly what I wanted and used for years. Still rough in places and not full parity, but very usable now and I run it daily at work (forced mac life). [1]: https://bit.ly/4rhJXNF https://bit.ly/40iPJUh March 6, 2026 at 11:21PM

Show HN: Kanon 2 Enricher – the first hierarchical graphitization model https://bit.ly/4boHrAq

Show HN: Kanon 2 Enricher – the first hierarchical graphitization model Hey HN, This is Kanon 2 Enricher, the first hierarchical graphitization model. It represents an entirely new class of AI models designed to transform document corpora into rich, highly structured knowledge graphs. In brief, our model is capable of: - Entity extraction, classification, and linking: identifying key entities like individuals, companies, governments, locations, dates, documents, and more, and classifying and linking them together. - Hierarchical segmentation: breaking a document up into its full hierarchy, including divisions, sections, subsections, paragraphs, and so on. - Text annotation: extracting common textual elements such as headings, sigantures, tables of contents, cross-references, and the like. We built Kanon 2 Enricher from scratch. Every node, edge, and label in the Isaacus Legal Graph Schema (ILGS), which is the format it outputs to, corresponds to at least one task head in our model. In total, we built 58 different task heads jointly optimized with 70 different loss terms. Thanks to its novel architecture, unlike your typical LLM, Kanon 2 Enricher doesn't generate extractions token by token (which introduces the possibility of hallucinations) but instead directly classifies all the tokens in a document in a single shot. This makes it really fast. Because Kanon 2 Enricher's feature set is so wide, there are a myriad of applications it can be used for, from financial forensics and due diligence all the way to legal research. One of the coolest applications we've seen so far is where a Canadian government built a knowledge graph out of thousands of federal and provincial laws in order to accelerate regulatory analysis. Another cool application is something we built ourselves, a 3D interactive map of Australian High Court cases since 1903, which you can find right at the start of our announcement. Our model has already been in use for the past month, since we released it through a closed beta that included Harvey, KPMG, Clifford Chance, Clyde & Co, Alvarez & Marsal, Smokeball, and 96 other design partners. Their feedback was instrumental in improving Kanon 2 Enricher before its public release, and we're immensely thankful to each and every beta participant. We're eager to see what other developers manage to build with our model now that its out publicly. https://bit.ly/4ud0Aga March 3, 2026 at 09:55AM

Show HN: I built an AI exam prep platform for AWS certs after failing one myself https://bit.ly/4aY1AvG

Show HN: I built an AI exam prep platform for AWS certs after failing one myself Hey HN, I failed the AWS Advanced Networking Specialty exam. Studied for weeks, used the usual prep sites, thought I was ready — wasn't. The problem wasn't effort, it was the tools. Static question banks don't teach you to think through AWS architecture decisions. They teach you to pattern-match answers. That falls apart on the harder exams. So I built Knowza to fix that for myself, and then figured others probably had the same frustration. The idea: instead of a static question bank, use AI to generate questions, adapt to what you're weak on, and actually explain the reasoning behind each answer — the way a senior engineer would explain it, not a multiple choice rubric. The stack: Next.js + Amplify Gen 2 DynamoDB (direct Server Actions, no API layer) AWS Bedrock (Claude) for question generation and explanations Stripe for billing The hardest part honestly wasn't the AI — it was getting question quality consistent enough that I'd trust it for real exam prep. Still iterating on that. Early days, one person, built alongside a day job. Would love feedback from anyone who's grinded AWS certs or has thoughts on AI-generated educational content. knowza.ai https://bit.ly/3MMq0R7 March 5, 2026 at 09:27PM

Show HN: A shell-native cd-compatible directory jumper using power-law frecency https://bit.ly/4cz3be9

Show HN: A shell-native cd-compatible directory jumper using power-law frecency I have used this tool privately since 2011 to manage directory jumping. While it is conceptually similar to tools like z or zoxide, the underlying ranking model is different. It uses a power-law convolution with the time series of cd actions to calculate a history-aware "frecency" metric instead of the standard heuristic counters and multipliers. This approach moves away from point-estimates for recency. Most tools look only at the timestamp of the last visit, which can allow a "one-off" burst of activity to clobber long-term habits. By convolving a configurable history window (typically the last 1,000+ events), the score balances consistent habits against recent flukes. On performance: Despite the O(N) complexity of calculating decay for 1,000+ events, query time is ~20-30ms (Real Time) in ksh/bash, which is well below the threshold of perceived lag. I intentionally chose a Logical Path (pwd -L) model. Preserving symlink names ensures that the "Name" remains the primary searchable key. Resolving to physical paths often strips away the very keyword the user intends to use for searching. https://bit.ly/3N95WIu March 4, 2026 at 11:20AM

Show HN: DubTab – Live AI Dubbing in the Browser (Meet/YouTube/Twitch/etc.) https://bit.ly/4u1yiVL

Show HN: DubTab – Live AI Dubbing in the Browser (Meet/YouTube/Twitch/etc.) Hi HN — I’m Ethan, a solo developer. I built DubTab because I spend a lot of time in meetings and watching videos in languages I’m not fluent in, and subtitles alone don’t always keep up (especially when the speaker is fast). DubTab is a Chrome/Edge extension that listens to the audio of your current tab and gives you: 1.Live translated subtitles (optional bilingual mode) 2.Optional AI dubbing with a natural-sounding voice — so you can follow by listening, not just reading The goal is simple: make it easier to understand live audio in another language in real time, without downloading files or doing an upload-and-wait workflow. How you’d use it 1.Open a video call / livestream / lecture / any tab with audio 2.Start DubTab 3.Choose target language (and source language if you know it) 4.Use subtitles only, or turn on natural AI dubbing and adjust the audio mix (keep original, or duck it) What it’s good for 1.Following cross-language meetings/classes when you’re tired of staring at subtitles 2.Watching live content where you can’t pause/rewind constantly 3.Language learners who want bilingual captions to sanity-check meaning 4.Keeping up with live news streams on YouTube when events are unfolding in real time (e.g., breaking international updates like U.S./Iran/Israel-related developments) Link: https://bit.ly/40HBFUo I’ll be in the comments and happy to share implementation details if anyone’s curious. https://bit.ly/40HBFUo March 4, 2026 at 02:04AM