value: jhyolm

value:
Hi HN! In a nutshell -- Writermark is a free, open, nonprofit certification tool that watches writing sessions, tests data against an algorithm, and signs the text content as "Human Authored" if it appears that a human did, in fact, author the text.
Writermark collects telemetric (and anonymous) data about keypresses, records your total time spent on a document, validates that the text has not been altered inorganically or pasted into the editor, and scores the final result. If the score is high enough, a certificate is issued that can be verified at https://writermark.org/verify at any later time. The server is stateless and stores zero information -- all your data lives on your local machine in a server-signed JWT.
Try it live: https://writermark.org/#try or integrate it into your own app: https://writermark.org/developers (npm install @writermark/sdk)
Or download Wintertext (https://wintertext.com), a free desktop writing app with Writermark built in. Wintertext takes this ecosystem a step further, packing the certificate and the text together into a .wtxt proprietary file type, while also allowing export and verification for .docx, .md, and .rtf files. Any document created with Wintertext is automatically certified, lives on your local machine, and the app will remain free forever.
I built Writermark because I think we're losing the ability to tell whether a human wrote something (which scares me deeply, especially in the world of fiction). If you are also concerned about the progress of AI infringement into human writing, I'd love to hear from you, hear your feedback, respond to any questions, and start a larger conversation.
If you're curious to know more about the general philosophy, feel free to read this longer overview on the tech: https://www.writermark.org/blog/working-to-verify-and-protec...
I am currently in the process of establishing nonprofit status for this project. I think that human authorship verification should be free. Whether its my solution or someone else's, I think telemetric verification of the writing process is going to become one of multiple important ways that we prove that something is human authored.
Thank you very much for your time.
Yolm.

value: Show HN: Writermark – Open protocol to help prove text is human-written

author: jhyolm


value: afshinmeh

value: Show HN: ClawRun – Deploy AI agents into secure sandboxes with one command

author: afshinmeh


value: coevcan

value:
(original write-up from late 2022, never shared it, pre vibe coding)
Over the weekend I wanted to understand how code editors work under the hood, so I built a lightweight, standalone online JavaScript editor using only HTML, CSS, and vanilla JavaScript—no libraries, no frameworks, no plugins.
Live demo: https://editor.codeeverywhere.ca
Github: https://github.com/codeeverywhereca/codeeditor
Fully open source - MIT License

value: Show HN: Weekend Project, Built a Vanilla JavaScript Online Code Editor (2022)

author: coevcan


value:
A free, zero-maintenance news aggregator. No database setup—just host the docker container, secure it with your favorite reverse proxy/auth provider, and you're ready to go.
Data is stored in user's browser localstorage
Self host it or just connect to https://actufeed.com

value: Show HN: Actufeed

title: Show HN: Actufeed


value: audiosampling

value: Show HN: BidWix – I built a sealed-bid tool where honesty is your best move

author: audiosampling


value: radenmuaz

value: Show HN: NoBG – Edge AI background remover

author: radenmuaz


value:
Hi HN,
I kept pasting the same context into Cursor and Claude Code every time I switched machines — architecture decisions, naming conventions, personal preferences. Tedious enough that I built something to fix it.
The idea is simple: give your agent ls, grep, read, and write over a persistent filesystem. Whatever it stores in one session is there in the next. No embeddings, no retrieval pipeline — agents already know how to navigate a filesystem, so there's nothing new to learn.
This also solves another problem. LLMs reason worse when context is bloated. A filesystem enables progressive disclosure naturally: the agent starts lean, runs ls to find what's available, greps for specifics, reads only what it needs. By the time it's reasoning about your actual problem, the context window is full of signal.
I tried RAG first. It doesn't map well to exact preferences — "use camelCase" shouldn't compete with semantically similar documents for retrieval rank.
Under the hood: Postgres with trigram full-text search. Each file is a row, directories are implicit from paths. Grep across 10K files in ~50ms.
To try it (sign up takes 30 seconds):
  pip install bossa-memory
  bossa signup
  bossa login
  bossa files write /memory/test.md "Hello"
  bossa files read /memory/test.md
Add the MCP server URL or CLI tool to Claude, Cursor, or LangChain and your agent gets ls/read/write/grep/glob.
Free tier is 100MB and 1K requests/day. Self-hosting is documented. Code is MIT.
Docs: https://docs.bossamemory.com
GitHub: https://github.com/vinny380/bossa
Curious what memory patterns others have settled on and whether a plain filesystem abstraction fits how you think about it.

value: Show HN: Bossa – Persistent filesystem memory for AI agents via MCP or CLI


value: dialectforge

value: Show HN: FlowState Dev memory that persists across AI conversations (MCP+SQLite)

author: dialectforge


value: tahlos

value: Show HN: Aegis – self-hosted secrets broker for teams priced out of CyberArk

author: tahlos


value: darkbatman

value: Show HN: A Bridge Between Claude Code and Codex Using Channels and MCP

author: darkbatman


value: jamsinclair

value: Show HN: Jump 'n Bump DOS classic rebuilt for the browser with Gamepad API

author: jamsinclair


value: kaliades

value:
Every semantic cache library - RedisVL, LangChain, LiteLLM - targets Redis and assumes RediSearch wire compatibility. Point any of them at Valkey Search 1.2 and they break silently in multiple places. There's no library that actually handles Valkey natively.
This does.
@betterdb/semantic-cache is a standalone semantic cache backed by valkey-search. No framework coupling - works with any LLM client. Every check() and store() emits an OTel span and increments Prometheus counters and histograms out of the box. No instrumentation code required.
MIT. Optional LangChain and Vercel AI SDK adapters. Works on self-hosted Valkey, ElastiCache for Valkey, and Memorystore for Valkey.
https://www.npmjs.com/package/@betterdb/semantic-cache
The Valkey Search 1.2 divergences from RediSearch that required explicit handling are in the changelog.

value: Show HN: Valkey-native semantic cache with OTel and Prometheus built in

author: kaliades


value: jndala

value: Show HN: Mnemo – a universal local brain for projects, usable by multiple agents

author: jndala


value: Show HN: Meow.gs – SSH to Dev Env with Touch ID / Face ID (Code on iPad)


value: hkcanan

value:
I built an Obsidian plugin for my wife (academic researcher). It embeds Claude Code as a sidebar chat using the Agent SDK, and ships with 7 slash-command skills:

  - /peer-review — 8-criteria manuscript evaluation with radar chart
  - /cite-verify — checks references against CrossRef, Semantic Scholar, OpenAlex
  - /research-gap — finds gaps in the literature with feasibility scoring
  - /journal-match — suggests target journals from your reference profile
  - /lit-search — parallel search across arXiv, PubMed, Semantic Scholar, OpenAlex
  - /citation-network — interactive citation graph
  - /abstract — generates abstracts in 5 formats

  Also has inline diff editing (word-level track changes in the editor) and MCP server support.

  Open source (MIT): hkcanan/katmer-code

value: Show HN: KatmerCode – Claude Code in Obsidian with academic research skills

author: hkcanan


value: jgerton

value: Show HN: Brand Toolkit – Claude Code plugin for framework-driven brand building

author: jgerton


value: jordancj

value: FOIA toolkit for journalists and citizens.

value: Show HN: OpenFOIA – local-first FOIA toolkit with entity graphs

author: jordancj


value: lucamartinetti

value:
Magic Wormhole is great but requires a CLI or app. I put a full wormhole-rs client in the browser via WebAssembly so you can send E2E encrypted files to anyone — interoperable with all standard wormhole CLIs.
Drop a file, get a code, other person opens the link or types wormhole-rs receive <code>. SPAKE2 + NaCl encryption happens client-side, the server only bridges WebSocket transit.
Built this as an experiment with Claude Code + gstack. All code written by Claude. Single Rust binary, vanilla JS, no framework.
https://wormhole.page
https://github.com/lucamartinetti/wormhole-page

value: Show HN: Wormhole.page – Magic Wormhole file transfer in the browser via WASM

author: lucamartinetti


value: ycosynot

value: Show HN: Threejs 3D wireframe stylizing tool – Generate infinite variations

author: ycosynot


value: LabSageMD

value: Show HN: AI Prompts for DPC Practice Ops – one found $18.6K/mo in billing leaks

author: LabSageMD


value:
I wanted a way for people to support companies and people that align with their political beliefs. Additionally, I think it can serve as a valuable, source-linked public ledger of who said and did what over time, especially as incentives change and people try to rewrite their positions.
This is fully AI-coded, researched, and sourced.
Additionally, AI helped develop the scoring system. The evidence gathering is done by a number of different agents through OpenRouter that gather and classify source-backed claims. The point of that is not to pretend bias disappears, but to avoid me manually selecting the evidence myself. I intend for it to remain current and grow. The system is close to fully automated, though ongoing evidence collection at scale is still limited mostly by cost.
The name is an homage to the early days of Web 1.0 and Hot or Not, which was a main competitor of mine as the creator of FaceTheJury.com, but I think it works well here.
The backend and frontend are running on Cloudflare Workers with D1. It's coded in vanilla JavaScript.

value: Show HN: MAGA or Not? Political alignment scores for people and companies


value: matanrak

value: Show HN: AgentMeet – Free, open-source platform for agent-to-agent work

author: matanrak


value:
I scraped and open-sourced a corpus of 518,255 Vietnamese legal documents — laws, decrees, circulars, decisions — spanning a century of legislation. Metadata + full Markdown text, ~3.6 GB parquet, CC BY 4.0.
Vietnamese legal text is nearly absent from existing NLP datasets despite Vietnam having one of the more prolific legislative systems in Southeast Asia.

value: Show HN: 518K Vietnamese legal documents (1924–2026)


value: I am making an AI agent that can read my local Apple book, turn pages, summarize, or even just recite the context. It can also look at my screen, hear what I am watching, and use those to help me learn a new languages.

value: Show HN: AI agent for reading fast and learning new language


value: Pyhelix

value: Show HN: A BOINC project where AI designs and runs experiments autonomously

author: Pyhelix


value: lucasgelfond

value: Wrote about open sources and attempting to figure out why this got way more stars (6.8k right now vs. a hundred or so on all of my other work) — curious what people think!

value: Show HN: Reverse engineering a viral open-source launch (or: on zerobrew)

author: lucasgelfond


value: Show HN: Autonomous Agent Built a Video Pipeline from One Prompt


value: sellne

value: A short, conversational description covering what the app does, the tech stack (SwiftUI, TMDB, voice input in 20 languages), why you built it, and asking for feedback.

value: Show HN: What Did I Watch – I just published my first iOS app

author: sellne


value: ymaws

value: Show HN: ClaudeRank – Open-source token and concurrency widget for Claude Code

author: ymaws


value: treve

value: Show HN: Burrow, a Gopher browser/proxy written in JavaScript

author: treve


value: ersinesen

value:
Foundations of Music is an attempt to establish a conceptual and formal foundation for understanding music.
Rather than declaring what music is, FoM shows where and how music becomes possible.
It provides simple explanations to complex concepts like vibrato, glissando, and portamento to outsiders.
It enables new vocabulary like jazzing, jazzing aroung, jazzing along, and jazz translation which are mind refreshing, at least to me.
For a sample of translation (Turkish Folk to Blues) you may see: https://www.youtube.com/watch?v=Ml4pEk2hMM8
Proposed perceptual fatigue concept can be found highly controversial, but I think it may be an inspiring food for thought.
In the end, FoM is a work in progress to constitute a stable ground from which new musical questions can be meaningfully explored.

value: Show HN: Foundations of Music (FoM)

author: ersinesen

title: Show HN: Foundations of Music (FoM)


value: rane

value: Show HN: Git-surgeon – Git add -p for AI agents

author: rane


value: SvenSchnieders

value: Show HN: Vaultara Markets – multi-stage AI pipeline for daily investor briefings

author: SvenSchnieders


value:
I’ve spent the last ~2 years building a C/C++ IDE for Windows entirely in C99. My goal was to eliminate the bloat of the modern Visual Studio environment keeping its core features.
Key Technical Highlights:
Zero Dependencies: Just the Windows API
Performance: Starts in 0.1ms on a twelve year old machine. It handles projects with 500k+ lines without UI lag or indexing stutters.
Integrated Tools: Includes a custom-built debugger, profiler, and Intellisense engine designed for speed.
Portability: It’s a single executable. No installer required.
I built this because I was tired of waiting 10 seconds for Visual Studio to "wake up" and seeing my RAM disappear just to boot it up. It’s currently hosted on itch.io while I gather funds to set up a dedicated site.

value: Show HN: A C/C++ IDE written in C from scratch with zero dependencies


value: Show HN: EnvMaster – inject encrypted env variables from your terminal


value: boazeb

value:
What happens to your passwords and seed phrases if you get hit by a bus? That's the problem I built PaperVault to solve.
I built PaperVault because I needed a way to store secrets (like 2FA recovery codes, hard disk encryption keys, and seed phrases) without depending on any device, cloud service or company surviving.
I needed something my family could access in an emergency without exposing them directly to the passwords or burdening them with having to secure everything correctly over the course of many years.
PaperVault encrypts your data with AES-256-GCM, with the decryption key split into shares using Shamir's Secret Sharing. You pick how many keys to create and how many are needed to unlock, e.g 3-out-of-5. The vault and keys are printed as QR codes on paper.
Everything runs in the browser with no server involved.
It's designed to work offline on an air-gapped machine.
Source: https://github.com/boazeb/papervault
Live Demo: https://papervault.xyz
Happy to answer questions about the implementation or the design choices.

value: Show HN: I built a paper-based vault with m-of-n keys

author: boazeb


value: devarifhossain

value: Show HN: Discover Indie Version of Popular SaaS Products

author: devarifhossain


value: Kaiserrberg

value: Show HN: SYNX – a new config format with active mod. Built for AI and humans

author: Kaiserrberg


value: maiconburn

value: Show HN: Inner Warden, self-defending server with eBPF and AI

author: maiconburn


value:
We built this as a side project that grew out of something completely different.
I work on ET-Miner [https://zenodo.org/records/18674353], which is  a GPU-accelerated frequent itemset mining pipeline based on the infamous apriori-algorithm. We came with the idea to reformulate the algorithm into a fully vectorized implementation, using a boolean transaction matrix representation,CUDA kernels + Rust group builder for index construction to speed up computations. The original use case was mining protein structure patterns from AlphaFold, where we processed 109.2M proteins and extracted 16.8 billion frequent itemsets for protein structural motif discovery. At some point I realized the same pipeline could be pointed at any domain with structured categorical data, so I pointed it at poker, one of my long-standing hobbies.
What we learned: Most of the "surprising" patterns the mining surfaces are things good players already know intuitively: positional advantages, aggression frequency correlations, stack-to-pot ratios. But seeing them as statistically validated itemsets with exact support counts is different from folk wisdom. A few patterns around multi-way pot dynamics and specific board texture interactions were genuinely non-obvious to the poker players we showed them to. Modern GTO solvers have no solutions for these multi-way pot scenarios. Oh, controversial, but donk-betting is a ~40% winner's exclusive rule
Data is completely free to exlore. In total 1.4m rules have been mined from the PHH dataset published here: https://zenodo.org/records/13997158.

value: Show HN: Association rule mining on 21.6M poker hands


value: Allenboyy

value: An e-commerce platform for every creator

value: Show HN: Playra

author: Allenboyy

title: Show HN: Playra


value: xenodium

value: Show HN: Kitty.cards – Create Apple Wallet cards online (no login)

author: xenodium


value: IsruAlpha

value: Introducing the modern, sleek and ai friendly Linktree alternative app. Linktree is not people like "us" so I built a clean one!

value: Show HN: I built AI native linktree app

author: IsruAlpha

title: Show HN: I built AI native linktree app


value: kordikp

value:
Hi HN — I’ve been building p-book, an open-source interactive book format that personalizes how a reader explores one topic.
The first p-book is for kids and teaches how recommendation systems work — including suggestions, privacy, filter bubbles, and fairness.
What I’m trying here is not only to personalize what comes next, but also how the same topic can be explored: through Missions, Browse, Read, or Map.
It’s still early, and I’d especially love feedback on three things:
- whether the navigation modes feel genuinely different,
- whether the topic is understandable for kids,
- where the experience feels confusing or too complex.

value: Show HN: P-book – an interactive book for kids about recommendation algorithms

author: kordikp


value: dooskington

value: I've been working on this game Antorum Online for almost a decade now, writing about it the entire time. It's not fully released yet, and might never be much of anything, but it's one of those mega projects that I just love building.<p>I figured I've never posted it to HN and it might be neat to get some feedback or new readers. Thanks!

value: Show HN: Antorum Online, an MMORPG Built with Rust and Unity

author: dooskington


value: kageroumado

value:
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.

value: Show HN: Refrax – my Arc Browser replacement I made from scratch

author: kageroumado


value: captaincrunch

value: Show HN: Plot-Hole.com a daily movie puzzle I made

author: captaincrunch


value: ebuyan

value: Show HN: ClauseGuard – AI contract review in 90s, finds risks, writes redlines

author: ebuyan


value: thatxliner

value:
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.

value: Show HN: Quillium, Git for Writers

author: thatxliner

title: Show HN: Quillium, Git for Writers


value: liujjw

value:
Bernie interviewed Claude recently (https://www.youtube.com/watch?v=h3AtWdeu_G0). The one-shotted responses were predictably a little sycophantic, but some responses were a little surprising. Made me think I'm kinda doing something similar in a more permissive timeframe, I have a harness over Claude that "persistently" studies AI spending, it does deep research, responds to my inputs, persists notes, writes code, etc.
It's not really "introspection" or "Cogito, ergo sum", but I found that over tens of millions of tokens of iterations the system was able to create something novel. I am showing off one of the dashboards it created based on the deep research and Python modelling conducted:
https://ai-capex-sens-wxc4b.ondigitalocean.app/
---
TLDR; AI hype is one thing, but how else would you actually go pedal to the metal spending the GDP of a middle income country on new AI data centers? Basically, theres hundreds of billions of dollars in cloud business from enterprise contracts, boring IT stuff but its where a lot of the money in tech comes from and its real. Beyond the pontification about superintelligence is a similar reality for AI in the eyes of the hyperscalers that scaled the cloud business in the past, hundreds of billions to trillions in business from enterprise contracts (cloud replaced on-prem servers, AI replaces or augments knowledge workers).
The model estimates AI revenue from automatable knowledge-work spend, stress it with adoption/pricing assumptions, and run it against capex + operating + financing costs under delays/shocks. In the base case, it doesn’t break even by 2032, but of course there's other sources of revenue so this isn't all that doomer or surprising that its risky to spend so much. However, its nice to have numbers and sensitivity to back that conclusion up instead of hand waving it.
---
There's been a lot of AI mania over vibe coding, but the main conclusion I got from running this harness is we can in fact use AI harnesses to simply think and make better decisions too. In this case, the AI is reducing the burden of injecting rigor into writing and decision making.

value: Show HN: AI models AI capex

author: liujjw

title: Show HN: AI models AI capex


value: DisabledVeteran

value:
Inspired by the hay-days of MySpace & Ning, we bring you Plasas. As founders of this little new social network launching at Princeton University, we're also HackerNews fanatics ourselves, so we'd love to invite other HackerNews parents & users to join us & get YOUR feedback. Firstly, your kids are going to love this website! We've added screenshots below so that you can check them out if you're at work.
Plasas solves many different problems. The first is creating simple playlists from content online. Our system allows you to add public content from YouTube, Spotify, Twitch, Kick, Vimeo, SoundCloud, Rumble, & other sites all in the same playlist. Warning: if you try this once with the kids, you're going to fall in love with how easy it is to use!  Even Meemaw & Pop Pop can do it!
If you're you a fan of "YTInstant" (by Stanford grad Feross Aboukhadijeh) then you're going to be super delighted with our "Content Manager." You simply type in a search phrase & we bring the most up to date content for your search directly to your browser window! No algorithmized suggestions, just exactly what you searched for -- imagine that.
Next up, we have curated, categorized videos that are updated daily. Think of it like a modern version of TV Guide. If you feel like you've watched "all the good stuff already" on Netflix & Hulu then this feature is going to be perfect for you or anyone that wants to stay up-to-date on the latest popular videos, viral content & more. Categories vary from Animals, Beauty/Hair/Make-Up, to content for Children, Documentaries, Gaming, Science, Technology, Travel & more.
Another thing that we're very proud of is, "Map Chats." This really cool tool brings live chat directly to literally any point in the entire world. Just drop a pin & start chatting -- no zip code required. This fun concept can be used in so many different ways in your day-to-day communication & we're sure you will come up with your own ways to use it!
We love technology news & we've learned so much from everyone in this space. Every day we watch & listen to podcasts like Runtime Arguments w/ Jim & Wolf, DarkNet Diaries w/ Jack Rhysider, HoneyPot (now CultRepo), Syntax.fm w/ Wes & Scott, CoRecursive w/ Adam Gordon Bell, tech culture hubs like HackerNews itself, Electronic Frontier Foundation, Troy Hunt & many others, thus we've a dedicated an entire news section with links back to all the latest tech new favorites all in one place so you never miss a beat!
We call ourselves a, "Family Conscious Social Network." What this means is that we treat the people that use our site just like we would want others to treat our own family members. We go above & beyond to protect your online privacy & security. Whether you're a "techy" person or not we believe you'll really appreciate that.
There is so much more, but our goal is to reach 100 sign-ups from you fine folks on HackerNews. It's a small goal but we would be delighted for you to join & help us reach that goal (we'll let you know how it goes!) Let us know your honest thoughts & give us suggestions on how to make all of the resources on our site as useful as possible to you! We would love to hear from you. We're a small team so you can email us, call us or even text us. Our information is available on the site.
Sign up using the code "HackerNews" to get access at https://plasas.com
And yes, here are those images we promised below:
https://www.imghippo.com/i/1774223878351
https://www.imghippo.com/i/1774223979653
https://www.imghippo.com/i/1774224019220
We look forward to your feedback,
Kindly,
The team at plasas.com

value: Show HN: A New Social Network Called Plasas

author: DisabledVeteran


value: nickakre

value: Show HN: AgentVerse – Open social network for AI agents (Mar 2026)

author: nickakre


value: chrismeurer

value: Show HN: Lula – multi-agent coding assistant with sandboxed Rust exec engine

author: chrismeurer


value: toyamarinyon

value:
I built a small library called [`sandbox-policy-builder`](https://github.com/giselles-ai/sandbox-policy-builder) for `@vercel/sandbox`.
The problem is straightforward: when building coding agents or AI apps in Vercel Sandbox, I usually think in terms of services like `OpenAI`, `Claude`, `GitHub`, or `AI Gateway`. But the raw `networkPolicy` API is domain-oriented, so credentials brokering rules get repetitive and harder to read than the actual intent.
I wanted to write:
```ts
networkPolicy: allow({
  codex: { apiKey: process.env.OPENAI_API_KEY! },
  openai: { apiKey: process.env.OPENAI_API_KEY! },
  gemini: { apiKey: process.env.GEMINI_API_KEY! },
  claude: { apiKey: process.env.ANTHROPIC_API_KEY! },
  github: { apiKey: process.env.GITHUB_TOKEN! },
  aiGateway: { apiKey: process.env.AI_GATEWAY_TOKEN! },
})
```
instead of hand-maintaining raw domains and header transforms.
The library expands service names into the domain-level `NetworkPolicy` shape required by Vercel Sandbox. Right now it supports `codex`, `openai`, `gemini`, `claude`, `github`, and `aiGateway`.
Repo: https://github.com/giselles-ai/sandbox-policy-builder

value: Show HN: Sandbox-policy-builder, helper for Vercel Sandbox credentials brokering

author: toyamarinyon


value: xanthine

value:
I really like HN's noprocrast feature, and I wanted something similar for YouTube and Reddit, so I quickly built this Firefox extension using Claude Code.
GitHub: https://github.com/pganguli/NoProcrast

value: Show HN: NoProcrast – A browser extension modeled on HN's noprocrast feature

author: xanthine


value: timazed

value: Show HN: CodexKit – Build agent-powered iOS apps (threads, tools, memory)

author: timazed


value: bobbydavro

value: Show HN: Free AI Business Plan Generator

author: bobbydavro


value: scalefirst

value: Show HN: ISO 20022 Payments MCP – First standards-typed financial MCP server

author: scalefirst


value: himanshudongre

value: Show HN: Smriti – Like Git, but for reasoning state

author: himanshudongre


value: wwhxd

value:
Hi HN, I built DockAutoHide, a tiny menu bar app that watches for windows overlapping the Dock and toggles auto-hide automatically. I like keeping the Dock visible for quick app switching, but I want it out of the way only when a window reaches the edge — this is a simple stopgap until macOS offers it natively.
It's early and experimental but fully usable; I run it daily. I'd love feedback on edge cases, unusual setups, and compatibility. Repo and releases: https://github.com/nshcr/DockAutoHide

value: Show HN: DockAutoHide – auto-hide the macOS Dock only when a window overlaps it

author: wwhxd


value: silvermpx

value: Show HN: Mamba SSM in Rust – training and inference with custom CUDA kernels

author: silvermpx


value: Show HN: Real-Time Robot Motion Planning in the Browser with WASM


value: armanj

value:
Hi HN!
I built Claudebox to get more out of my Claude subscription. It runs Claude Code in a sandboxed Docker container and exposes an OpenAI-compatible API; so any tool can use Claude as a backend.
No API key needed, no extra billing; it authenticates with your existing Claude credentials. The container is network-isolated (only Anthropic domains
allowed), so Claude gets full agent capabilities (file editing, shell, code analysis) without access to your host or the open internet.
I mainly use it for personal data processing tasks where I want an agent API but don't want to pay above my subscription.
GitHub: https://github.com/ArmanJR/claudebox

value: Show HN: Claudebox – Your Claude Subscription as Personal API

author: armanj


value: GhostShellJoule

value: Show HN: I built a census for AI agents – first record is a robot from 1890

author: GhostShellJoule


value: frdfrd

value: Show HN: Fixy – Real-time group chat with humans and AI agents (GPT, Claude,..)

author: frdfrd


value: king-nexus

value: Show HN: Kingsight – 6 AI agents that teach before letting you code

author: king-nexus


value: Show HN: AddFox – Build Better Browser Extensions for Developers and AI


value: obilgic

value: Show HN: Agent Kernel – Three Markdown files that make any AI agent stateful

author: obilgic


value: grillorafael

value: Show HN: Mantyx – A platform to orchestrate, manage, and share your agents

author: grillorafael


value: WaylonKenning

value:
I built Scenia because planning and visualising IT projects sucks in Excel or PowerPoint.
It's an open-source tool for CTOs and portfolio managers: roadmap
timelines, dependency mapping, drag-and-drop planning, and portfolio
rollup views. No account, no cloud, no tracking — everything is stored
locally in your browser.
Try it: https://scenia.website
GitHub: https://github.com/waylonkenning/Scenia

value: Show HN: Scenia – open-source IT portfolio planning, runs in browser

author: WaylonKenning


value: Show HN: AI Prompts for Data Professionals


value: alexandersucala

value: Show HN: I made a AI Code Review tool that knows how your company works

author: alexandersucala


value: gezhengwen

value:
I analyzed two orderings of the 64 I Ching hexagrams and found
  the permutation cycle decomposition between them is [52, 10, 2]
  with zero fixed points. Nobody has done this kind of analysis before
  and this cycle type has not been reported in the literature.
  You can verify it yourself.

value: Show HN: The King Wen Permutation: [52, 10, 2]

author: gezhengwen


value: DoubleStar

value: Show HN: Free Online Audio Cut – Trim MP3, WAV and More

author: DoubleStar


value: wesammikhail

value:
Hey guys,
I needed a way to convert Code, CSV, and Markdown snippets to shareable images so I built this over the weekend. It all runs locally in the browser using a few lines of jQuery. Nothing fancy.<p>Sharing in case someone else might find it useful!

value: Show HN: I made a tool for converting text snippets to shareable image

author: wesammikhail


value: Enjoyooor

value:
Gm HN,
We decided to build an arena where the crowd decides the destiny of AI products.
Reach possible. Roast guaranteed.
No account needed + anonymous.

value: Show HN: GladAITor – Judge AI Products for Free

author: Enjoyooor


value: seyz

value: Hi HN, building RootCX. An open-source self-hosted infrastructure for building internal software and AI agents.<p>I've built this because the current ecosystem forces devs to choose your poison:<p>- SaaS fragmentation: Vendor lock-in and endless API integrations.<p>- Legacy ERPs: Millions spent on customization and years of waiting, completely detached from the ground reality of the business.<p>- Visual UI builders (Retool, Appsmith and others): Abstraction leaks. The visual layer lacks flexibility and adds unnecessary overhead when you actually need to write code.<p>- Modern AI tools (Lovable, Claude Code, etc.): Great for single-tenant UI prototypes. Useless for deploying a fleet of enterprise apps because they lack shared data models and centralized RBAC (Role-Based Access Control).

value: Show HN: Self-hostable AI agents and internal software

author: seyz


value: Show HN: AutoResearch with PromptFoo Is AutoPrompter


value: blueeon

value: Show HN: Agent-IM – IM, but for Agents

author: blueeon

title: Show HN: Agent-IM – IM, but for Agents


value: pratikshadake

value:
Most product demo videos are made in screen recorders or tools like Remotion
  that render frames to MP4. I wanted something different: drop a CSS class on
  any HTML element and get cinematic motion that's ready for a product video,
  entirely in the browser.

  AliveUI Video Blocks is a set of CSS utility classes for common video UI
  primitives: animated metric cards, toast notifications, typewriter text,
  kinetic typography, lower thirds, scene backgrounds (aurora, starfield, bokeh),
  device frames, 3D effects, and particle FX.

  The interesting technical bit: there's no render pipeline. Animations run as
  native CSS keyframes. A small runtime (~2KB) handles declarative scene
  sequencing via data attributes, restarts animations on scene activation via
  an offsetHeight reflow trick, and drives CSS transition classes between scenes.

  The tradeoff vs Remotion: you can't do frame-perfect programmatic video or
  export a standalone MP4 without a headless browser. What you get instead is
  instant playback, zero build step, and blocks you can embed directly in a
  web page.

  Built on top of AliveUI, a PostCSS plugin CSS framework. The video blocks
  are a separate layer that ships with it.

  https://aliveui.dev/video-blocks

value: Show HN: CSS primitives for product videos, no render pipeline

author: pratikshadake


value: garydai

value: Show HN: SlickCalc – 30 free calculators, no ads, dark UI

author: garydai


value: kalishayish

value: Show HN: Chatvas:ChatGPT branches on an infinite canvas

author: kalishayish


value: brikym

value:
Redactle is game I built to solve redacted wiki articles. I built an easy way to create a collection without having to gather URLs.
Just prompt something like this to generate a dossier:
> things only an xkcd reader would know
> Ballmer Peak
You can make collections public or start a league for your coworkers to compete on a schedule.
There is some really great esoteric content in there from the community. One collection is entirely to do with protein science. Harry Potter characters is another.

value: Show HN: Build your own Redactle Wikipedia list in one sentence

author: brikym


value: matusfaro

value: Show HN: LocalRouter: Open-Source Desktop App for Smart LLM Routing and MCPs

author: matusfaro


value: jannniii

value:
I've been running coding agents heavily for the past year or so using frontier model APIs, open weight model APIs and, most recently, local models (Qwen family models on a Strix Halo).
Starting to run local inference has highlighted something I've been aware for longer: just running tests output shedloads of text into the context window that is there for good until compaction or starting afresh. For example, a single `cargo test` dumping 8KB into the agent's context just to communicate "47 test passed." The agent reads all of it, learns nothing useful, and the context window fills with noise. Makes LLM prefill slower as well as costs more when using per token APIs.
I created a small program that sits between the command output and the LLM: oo, or double-o ... yes, sad play on words. Double-o, the agent's best friend :)
oo wraps commands and classifies their output:

  - Small output (<4KB): passes through unchanged
  - Known success pattern: one-line summary
    (oo cargo test →  cargo test (47 passed, 2.1s))
  - Failure: filtered to actionable errors
  - Large unknown output: indexed locally, queryable via oo recall

It currently ships with 10 built-in patterns (pytest, cargo test, go test, jest, eslint, ruff, cargo build, cargo clippy, go build, tsc), but users can add their own via TOML files or use oo learn <cmd> to have an LLM generate one from real command output (currently only with Anthropic models).
No agent modification needed: add "prefix commands with oo" to your system prompt. Single Rust binary, 197 tests, Apache-2.0.
The classification engine works using regex-based pattern matching with per-command failure strategies (tail, head, grep, between) and automatic command categorization (status/content/data/unknown) that determines what happens with unrecognized commands. Content commands like git diff always pass through; data commands like git log get indexed when large.
Especially noticeable with local models & wall-clock time. Helps with frontier models too ... cleaner context, fewer confused follow-ups.
https://github.com/randomm/oo
https://crates.io/crates/double-o

value: Show HN: Oo – compress output for coding agents (cargo test → "47 passed, 2.1s")

author: jannniii


value: adrgru

value: Show HN: Linkpeek – link preview for Node.js, Bun, and Deno (1 dependency)

author: adrgru


value:
I've spent the last year building personality models + the last 9 years in the career tech space.
This tool is pretty insane. Just give it your LinkedIn username. It pulls your career history and in about 2 seconds, it'll notice patterns in your career story and tell you things about yourself that are weirdly specific — your blind spots, what's driving your decisions, stuff you've probably felt but never had anyone put into words.
A big unlock I had (and why I built this) was that your career trajectory says SO MUCH about you as a person. Not your job titles, but the pattern of decisions. When you moved, when you stayed, what you kept going back to. That trail is basically a personality fingerprint.
The AI is pulling threads together across a full career history in a way that would take a coach hours to piece together. I think there's something genuinely powerful about having enough context (a full career arc) combined with enough research (our trait model) to say things that feel almost too specific. You can check out our trait model through our main personality test - it's grounded in occupational psychology and psychometric research - it's kinda like the Big Five, Myers-Briggs, etc.
This tool only works on public LinkedIn profiles, and sometimes it infers too much and gets things wrong — especially when someone's LinkedIn is really sparse or outdated. But it'll give you goosebumps if it gets you right.
Would love feedback on when it misses and why.
No LinkedIn sign up/permissions required (just your profile username), free for now (AI costs are not trivial but will cover costs up unless in the unlikely event a lot of people do it and my limits are hit).
Check it out at: coached.com/deep-read

value: Show HN: An AI predicts your personality and blind spots from just your LinkedIn


value: nickakre

value: Show HN: AgentVerse – Open social network for AI agents (Mar 2026)

author: nickakre


value: ata-sesli

value: Show HN: RVault – A local TUI password manager

author: ata-sesli


value: MyselfNikhil

value: Show HN: Pahadify – A zero-back end, client-side image toolkit

author: MyselfNikhil


value: gauravsc

value: Show HN: Agents can review other agents to build trust

author: gauravsc


value: distributev

value: The open source BI ecosystem is not well represented. Tools like Metabase and Apache Superset are great but focus on dashboards only. JasperReports feels outdated. There's a real need for a modern, self-hosted open source alternative to commercial offerings like Tableau, Power BI, and Google Looker — all of which push vendor and cloud lock-in. ReportBurster is our take on filling that gap — it does what SSRS + Power BI do, but fully open source and self-hosted.

value: Show HN: ReportBurster – Self-Hosted Business Intelligence

author: distributev


value: eriknielsen

value: Show HN: Lockpaw One hotkey to cover your Mac screen without putting it to sleep

author: eriknielsen


value: mojoatomic

value: Show HN: Apophis flagged as close approacher 24 years early, 45-day arc

author: mojoatomic


value:
Hey HN, I’ve been building Versor, a PyTorch framework that swaps out standard matrix multiplications for Geometric Algebra (Clifford Rotors).
I started this because I was frustrated with how standard linear layers "smear" the data manifold. I wanted to see if using Rotors—which are intrinsically constrained to preserve distances and angles—could fix this.
So far, this approach has enabled:
- 100% accuracy on 13-hop reasoning (CLUTRR) with ~300K params. Rotor composition happens to be algebraically isomorphic to logical transitivity.
- Deterministic symbolic regression in ~23 seconds, skipping genetic algorithms entirely.
- A custom closed-form algebraic kernel for the exponential map to bypass slow matrix exponentials.
I'm currently using this core to build a Geometric Turing Machine for ARC-AGI. It's still a work in progress, but the core framework is solid.
Take a look and let me know what you think! (https://github.com/Concode0/Versor)

value: Show HN: A Geometric Algebra NN Framework (100% on 13-hop reasoning)


value: liberzon

value: Show HN: Smart permission hook for Claude Code compound bash commands

author: liberzon


value:
Hi HN! I built kage to manage multiple AI coding agents (Claude Code, Codex, etc.) in parallel using tmux and git worktrees.
The idea came from wanting to parallelize feature development instead of waiting on one agent at a time.
Would love feedback on:
- usability of the TUI
- workflow with tmux/worktrees
- any missing features
Thanks!

value: Show HN: Kage – TUI for managing multiple AI agents with tmux and Git worktrees


value: celastin

value: Show HN: MemLineage, a shared workspace for solo developers working with agents

author: celastin


value: phmarques

value: Show HN: Illux – AI illustrations that stay on brand

author: phmarques


value: Show HN: Chrome extension to scrape Posts from X


value: texodus

value: Show HN: A Web Component for Drag and Drop Layouts

author: texodus


value: garydai

value: Show HN: InvoiceNeat – Free invoice, pay stub and receipt generator, no signup

author: garydai


value: kreicer

value:
Hi HN,
Clusters tend to accumulate garbage: debug pods, failed jobs, test workloads that nobody cleans up. I built Columbo, a CLI that tries to detect these by assigning a suspicion score to each pod.
Would love feedback.

value: Show HN: Columbo – a CLI that finds forgotten K8s pods using a suspicion score

author: kreicer


value: NubPlayz

value:
"Open Books in Zlib Or Anna's Archive with a button on Goodreads in one click."
Hello i have been working on this Open Source extension (Source code ,available on Github)
I built a free, open source browser extension that adds buttons directly onto Goodreads book pages. Instead of copying titles and searching manually, you just click the badge for whichever source you want.
You can also toggle sources on/off
so say if you only want Z-Lib and Anna's Archive badges and not Gutenberg, you can do exactly that.
Supported sources:
Anna's Archive
Z-Library
Project Gutenberg
Planning to add more sources based on feedback.
it is available on :
-chrome
-firefox
Available on Chrome and Firefox.
Also for firefox mobile
Anime.js used for animation

value: Show HN: An extension opens any Goodread book on Anna's arc,Z-Lib in one click

author: NubPlayz


value: SrPakura

value: Show HN: Aerko_ – An offline-first, Vanilla JavaScript fitness PWA with local AI

author: SrPakura


value:
AI changed the way we code, but we're still using the old processes, and we've become the bottleneck, the AI is waiting for us - to reply, to open our laptops, to review the code, and so on. We're building the future of AI software development.
The agents are autonomous, they run in sandboxes, automatically fix the pipelines, and deliver you the final, working code. You can use live preview to see the changes they made.
Working across multiple repositories, all within the same
session.
This is the future - you don't need an IDE, and you don't have to run anything locally.

value: Show HN: Agen: spin up unlimited parallel AI coding agents in the cloud


value: jawiggins

value:
Recently several AI labs have published experiments where they tried to get AI coding agents to complete large software projects.
- Cursor attempted to make a browser from scratch: https://cursor.com/blog/scaling-agents
- Anthropic attempted to make a C Compiler: https://www.anthropic.com/engineering/building-c-compiler
A few weeks ago I posted xmloxide, an agent-made Rust replacement for libxml2 made by pointing Claude code at the libxml2 test suite: https://news.ycombinator.com/item?id=47201816
curl is arguably the most widely deployed software in the world. It's 180,000 lines of C with a history of memory safety CVEs. The curl project itself tried to address this by adding a rust-based HTTP backend using hyper, but after four years of work, they abandoned the effort in December 2024. Daniel Stenberg wrote that, "there simply were no users asking for it and there were almost no developers interested or knowledgeable enough to work on it".
- Daniel Stenberg's blog post on dropping hyper: https://daniel.haxx.se/blog/2024/12/21/dropping-hyper/
- Hacker News discussion: https://news.ycombinator.com/item?id=42478911
The key insight from my xmloxide work was that an agent can iterate very quickly when given a comprehensive test suite as a specification. The majority of the development for urlx was done in two ~16 hour long iteration loops where claude code would implement some features, run the tests, and then plan what to do next. Towards then end I took a more manual approach telling it to analyze the remaining failures, make issues, and submit them to teams of subagents to fix.
urlx now passes 1,300 of 1,300 applicable curl tests (100% pass rate). It supports HTTP/1.0-1.1, HTTP/2, HTTP/3 (QUIC), FTP/FTPS, SFTP/SCP, WebSocket, SMTP, IMAP, POP3, MQTT, DICT, TFTP, Gopher — with 261 CLI flags, authentication (Basic, Digest, NTLMv2, SCRAM-SHA-256, AWS SigV4), cookies, HSTS, proxy support (HTTP/SOCKS4/5/HTTPS tunnel), and a libcurl-compatible C ABI. There are some features still left to do that I might come back to.
- GitHub: https://github.com/jonwiggins/urlx
- crates.io: https://crates.io/crates/urlx-cli
- Install: cargo install urlx-cli or brew install jonwiggins/tap/urlx
The same lesson from xmloxide applies here: when a project has a good test suite, an agent can use it as a specification and converge on a working implementation surprisingly fast. curl's test suite turned out to be an excellent spec, the hard part was already done by the curl contributors over 25 years.
I don't expect anyone to replace curl with this, but I think there's something worth paying attention to in the pattern: take a well-tested project, point an agent at the test suite, and get a fast reimplementation. I think this also points to a need to adopt test-driven-development when using AI coding tools, as it allows them to iterate autonomously before needing human input. I also think this changes the economics of software development, what used to be a multi-year, multi-engineer commitment is becoming a task you can meaningfully attempt in days by setting up an agent to rapidly iterate on it alone.

value: Show HN: Urlx – an agent-made Rust replacement for curl/libcurl

author: jawiggins


value: molefrog

value:
Hi HN, so I've been working on this alternative to Slido/Mentimeter that allows people to connect to your slides and collaborate in real-time. But instead of using predefined polls, I made a coding agent that builds it for you.
I spend weeks trying to teach it how to use a local-first db called InstantDB and how to produce decent designs. I also made sure it can use things like sound or read from motion sensor (if connected via phone).
I've put together a collection of premade apps that you can clone and use right away (I plan to add more including apps built by other users).

value: Show HN: I built a tool for vibe-coding multiplayer games, polls, quizzes

author: molefrog


value: chocks

value:
Recently, I noticed my debugging workflow had become: copy error into ChatGPT/Claude -> go back and repeat (while being cautious not copying over sensitive stuff like paths, names etc.)
So, I built Sleuther, a Oh My Zsh plugin that asks a local LLM via Ollama to explain and debug errors directly in the terminal. Right now I am using Qwen2.5-Coder.
At a previous job, I used warp.dev extensively and was missing its agent mode. Sleuther is inspired by warp and zsh-copilot.
No copy paste, no cloud, no context switching.
Also, has a small trick: if the local LLM cannot help after a couple attempts, it suggests a sanitized version of the error, stripping usernames, paths, and hostnames before sharing with ChatGPT or Claude. The sanitization is rather rudimentary right now but something I want to improve.
Curious if anyone else is experimenting with local LLMs for dev workflows?
https://github.com/chocks/sleuther

value: Show HN: Sleuther, a ohmyzsh plugin for local LLM debugging with Ollama

author: chocks


value: minviex

value:
I just built this (beta version) of a self-guided writing tool called Story Trainer that walks you through the core pieces of a story and emotional stakes step by step.
A few months ago I went to a free writing workshop with a professional TV screenwriter, took notes, and ended up building the biggest lessons into this because I wanted something that made the whole process feel less overwhelming for beginners like me.
Check it out here. Would love to know if this is useful at all. It's just a WIP GitHub page right now, so open to ANY and ALL feedback to make this more useful.

value: Show HN: Story Trainer, a self-guided tool for learning story structure

author: minviex


value: jer-irl

value:
This project launches multiple independent programs into a single shared virtual address space, while still behaving like separate processes (independent binaries, globals, and lifetimes).  When threadprocs share their address space, pointers are valid across them with no code changes for well-behaved Linux binaries.
Unlike threads, each threadproc is a standalone and semi-isolated process.  Unlike dlopen-based plugin systems, threadprocs run traditional executables with a `main()` function.  Unlike POSIX processes, pointers remain valid across threadprocs because they share the same address space.
This means that idiomatic pointer-based data structures like `std::string` or `std::unordered_map` can be passed between threadprocs and accessed directly (with the usual data race considerations).
This accomplishes a programming model somewhere between pthreads and multi-process shared memory IPC.
The implementation relies on directing ASLR and virtual address layout at load time and implementing a user-space analogue of `exec()`, as well as careful manipulation of threadproc file descriptors, signals, etc.  It is implemented entirely in unprivileged user space code: <https://github.com/jer-irl/threadprocs/blob/main/docs/02-imp...>.
There is a simple demo demonstrating “cross-threadproc” memory dereferencing at <https://github.com/jer-irl/threadprocs/tree/main?tab=readme-...>, including a high-level diagram.
This is relevant to systems of multiple processes with shared memory (often ring buffers or flat tables).  These designs often require serialization or copying, and tend away from idiomatic C++ or Rust data structures.  Pointer-based data structures cannot be passed directly.
There are significant limitations and edge cases, and it’s not clear this is a practical model, but the project explores a way to relax traditional process memory boundaries while still structuring a system as independently launched components.

value: Show HN: Threadprocs – executables sharing one address space (0-copy pointers)

author: jer-irl


value:
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://blog.mozilla.ai/cq-stack-overflow-for-agents/
GitHub repo: https://github.com/mozilla-ai/cq
Thanks again for your time.

value: Show HN: Cq – Stack Overflow for AI coding agents


value:
Hey HN,
I was building other AI products and kept hitting the same wall: agents hallucinate and constantly forget context. I ended up building a ledger just to track what they were actually doing.
Once that ledger was logging everything, I realized I could use it to enforce rules. Exogram is the result. It is a deterministic firewall that intercepts agent tool calls (like MCP) using pure Python logic gates. It catches bad schemas and destructive actions in 0.07ms. There is zero AI in the security loop.
I load-tested it yesterday and hit 137 RPS on a single container with zero dropped payloads.
Sandbox: https://exogram.ai/proving-ground
Repo: https://github.com/Richard-Ewing/exogram-protocol-rfc
I just built the Proving Ground UI today to visualize the 0.07ms block in real time. Let me know if the split-screen demo makes sense, or if you manage to bypass the gates.
Richard

value: Show HN: I built a 0.07ms Python firewall to stop AI hallucinations


value: solderlocks

value: Show HN: Package manager and web customizer for OpenSCAD

author: solderlocks


value: AYSgood

value:
I tried to submit the link directly, but my account is currently restricted.
I built DocDrift because I got tired of documentation "lying" after a code change. Every time a function signature or logic changes, the README slowly rots until it's useless.
It uses Tree-sitter for AST parsing to actually understand which symbols changed (functions, classes, etc.), then uses semantic search to find the related documentation sections and suggests fixes.

value: Show HN: DocDrift – Use Tree-sitter and Local LLMs to fix stale documentation

author: AYSgood


value:
I've spent the last year building personality models + the last 9 years in the career tech space.
This new tool I made works pretty well and I wanted to share it. Just give it your LinkedIn username. It pulls your career history and in about 2 seconds, it'll notice patterns in your career story and tell you things about yourself that are weirdly specific — your blind spots, what's driving your decisions, stuff you've probably felt but never had anyone put into words.
A big unlock I had (and why I built this) was that your career trajectory says SO MUCH about you as a person. Not your job titles, but the pattern of decisions. When you moved, when you stayed, what you kept going back to. That trail is basically a personality fingerprint.
The AI is pulling threads together across a full career history in a way that would take a coach hours to piece together. I think there's something genuinely powerful about having enough context (a full career arc) combined with enough research (our trait model) to say things that are helpful -- like you sat down with a real career strategist who gave you some cold, hard career therapy.
You can also check out our trait model through our main personality test - it's grounded in occupational psychology and psychometric research - it's kinda like the Big Five, Myers-Briggs, etc.
This tool only works on public LinkedIn profiles, and sometimes it infers too much and gets things wrong — especially when someone's LinkedIn is really sparse or outdated. But it'll give you goosebumps if it gets you right.
No LinkedIn sign up/permissions required (just your profile username), free for now (AI costs are not trivial but will cover costs up unless in the unlikely event a lot of people do it and my limits are hit).
Would love feedback on what you think :-)

value: Show HN: Behavioral analysis of your career decisions from your LinkedIn


value: jitpal

value:
I've been building with Claude Code since last summer and one thing kept wearing me down: every session starts from zero.
I'd reason through tradeoffs, settle on an architecture, make a dozen small decisions. Then the session ends. Next session has no idea what happened.
CLAUDE.md helps, but it's static and loads into every message. Most context only needs to be there when it's relevant. The hardest stuff to capture is the reasoning. I'll spend 30 minutes thinking through a plan with Claude, land on a direction, and none of it survives to the next session.
wire-memory is an open source Claude Code plugin that gives your agent persistent memory. It captures decisions, corrections, patterns, and preferences as you work. Before responding, the agent searches past context for anything relevant. No extra prompting from you.
Memory is scoped by project and user. Project scope means teammates can share the same memory. One person's corrections are available to everyone in real time, outside git.
No account required. You get a free ephemeral container for 7 days.
Under the hood, the plugin is thin on purpose. Everything goes into a Wire container. Wire handles storage, relationship discovery, structured and unstructured data, and more. Your agent gets 5 MCP tools to search, write, explore, delete, and analyze its own memory. Same container works from any MCP client.
Transcript capture is opt-in. Secrets are auto-redacted.
Data lives on Cloudflare infra so it's available at edge, making retrieval quick. Ephemeral container expires after 7 days but a plugin command lets you create an account to keep it.
Plugin is MIT, Wire itself is closed source. It's my startup. Happy to answer questions.

value: Show HN: Wire Memory – Persistent cross-session memory for Claude Code

author: jitpal


value: aimoae

value: Show HN: Gradefy – Affordable review platform, Trustpilot alternative

author: aimoae


value: atgreen

value:
Whistler compiles s-expressions to eBPF bytecode and emits ELF object files (no clang/LLVM).  It includes an SSA-based optimizer that matches optimized clang code.  You can even use it inline in your Common Lisp code, or from your REPL, and have the eBPF code loaded/unloaded without anything touching disk at all.
Blog post with more background: https://atgreen.github.io/repl-yell/posts/whistler/

value: Show HN: Whistler – a Lisp that compiles to eBPF

author: atgreen


value: ptrmc

value: Automatically joins your Zoom meetings on macOS

value: Show HN: Zoom Auto-Joiner

author: ptrmc

title: Show HN: Zoom Auto-Joiner


value: delu

value: Show HN: Littlebird – Screenreading is the missing link in AI

author: delu


value: patabyte

value:
I've always wanted a simple way to manage redirects in one place. Routinely, I find myself adjusting them, and it usually looks like like update DNS, then go update the load balancers and then maybe hit an http service.
So, after taking in heavy inspiration from DMARC, I built Simple Redirect Daemon (SRD).
SRD open source, and is hosted for free. Check out https://srd.sh for docs and debug tooling with record generation and inspection. For example, https://srd.sh/?r=show-hn-srd.twopow.com
I've been using this in production across many services throughout the last 6 months, and have really enjoyed the ease of use. I hope you will too!
Feedback and thoughts welcome!
P.S. to quickly see it in action: https://show-hn-srd.twopow.com + `dig TXT _srd.show-hn-srd.twopow.com`.

value: Show HN: SRD, a simple DNS-driven HTTP redirect service

author: patabyte


value: mnewme

value: I tried a new idea for a landingpage. Would love to have some feedback

value: Show HN: Run Labs – A multi-product startup

author: mnewme


value: astralasia

value:
I built a daily puzzle where a piece of paper is folded and hole-punched, and you predict where all the holes end up when it's unfolded.
It's the kind of thing I used to do in grade school with actual paper and scissors, and it turns out the mental version is a surprisingly good spatial reasoning challenge.
Three difficulties each day: easy is a 4×4 grid with one fold, hard is 6×6 with three folds.
It's built with Next.js. The puzzles are generated deterministically on the fly using a daily seed.
The engine runs a forward simulation of the paper folds using 2D coordinate mirroring, then calculates a "cognitive difficulty score." It weights factors that mess with human spatial reasoning, like off-center folds that break obvious symmetry and mixed-axis folding. It then automatically rerolls the daily seed until the easy, medium and hard puzzles form a good difficulty curve.
Would love to hear your feedback!

value: Show HN: Daily Unfold – A paper folding puzzle game

author: astralasia


value: akandr

value: Show HN: Tok/s on a 35B MoE model using a $100 AMD crypto APU and Vulkan

author: akandr


value: afc

value:
Just wanted to share with HN a simple/minimal open source Python library that generates SVG files visualizing two dimensional data and distributions, in case others find it useful or interesting.
I wrote it as a fun project, mostly because I found that the standard libraries in Python generated unnecessarily large SVG files. One nice property is that I can configure the visuals through CSS, which allows me to support dark/light mode browser settings. The graphs are specified as JSON files (the repository includes a few examples).
It supports scatterplots, line plots, histograms, and box plots, and I collected examples here: https://github.com/alefore/mini_svg/blob/main/examples/READM...
I did this mostly for the graphs in an article in my blog (https://alejo.ch/3jj).
Would love to hear opinions. :-)

value: Show HN: Minimalist library to generate SVG views of scientific data

author: afc


value:
Hi HN!
I built JulIDE - a lightweight Julia IDE using Tauri and Rust.
Features:
10MB install (vs VSCode's 300MB)
Full LSP, debugger, Git integration
Built-in dev containers with X11 forwarding
Tauri/Rust backend, React frontend
Monaco based editor
It's beta but functional. Built this for fun and to solve a real need in the Julia community.
Tech stack: Tauri 2, Rust, React, Monaco, LanguageServer.jl
GitHub: https://github.com/sinisterMage/JulIde
Would love feedback!

value: Show HN: JulIDE – Lightweight Julia IDE Built with Tauri


value: jjcosgrove

value:
I've tried a number of personal Kanban apps for macOS over the years and never quite found one that I loved.
Nowadays, many want an account, push cloud sync, subscriptions, contain tons of telemetry, or some combination of the above (don't get me started on AI) and to be honest many are overkill for managing my own tasks. (less friction = more productivity)
So... I built my own Kanban app.
*LumaBoards* is a simple Kanban for macOS designed for solo use.
Features are intentionally pretty minimal:
* drag-and-drop cards and columns
* due dates + filtering
* tags / colors
* keyboard shortcuts (not vim… sorry)
* offline storage
There's a *free version* with a single board (no attachments or import/export) but no time-trial limits or feature nags.
The *paid version* adds support for multiple boards, attachments, and backups via import/export.
Free: https://offlineapps.gumroad.com/l/lumafree
and
Paid: https://offlineapps.gumroad.com/l/lumapaid
Feedback most welcome.
Cheers
PS - for anyone interested, its Tauri + Vue.

value: Show HN: LumaBoards – an offline Kanban app for macOS

author: jjcosgrove


value: heycutiee

value: Show HN: I built a leaderboard for Claude Code's unhinged loading verbs

author: heycutiee


value: mecredis

value: Show HN: Git Hitched – An Open Source Wedding Platform

author: mecredis


value: Show HN: Primer – build software with AI agents one milestone at a time


value: deezypls

value:
DuckLake doesn't ship with an authorization layer. It works through the Postgres catalog, and by default you can query everything. If you have the S3 credentials, you can read every Parquet file in the bucket.
I wanted a cost-effective lakehouse on Hetzner that we can own in the EU. I wrote another repo (ducklake-hetzner) for a deployment under €15/month, but there's still a long way to go for the functionalities to come close to other data warehouses.
Hetzner's Object Storage is also not the easiest to work with, it runs Ceph, but doesn't expose IAM. That means any user has full access by default. You need to create a separate dummy project, and store the s3 credentials in there, and then use an "Allow" policy on those (as they're denied by default, this works).
To help others, I figured I'd package that into a single CLI:
dga allow alice --table customers --read-only
Does two things: PostgreSQL Row-Level Security on the DuckLake catalog, and scoped S3 bucket policies on the storage layer. Still alpha, but the core superuser/writer/reader pattern works.
Would love feedback or ideas, especially from anyone running DuckLake in production or dealing with similar access control gaps on non-AWS object storage.

value: Show HN: Per-table access control for DuckLake lakehouses

author: deezypls


value: caffeinedoom

value:
I built this because I wanted my own directory of public companies
running bug bounty programs — where I could see their infrastructure
in one place and have a real idea of where to start poking holes.
Neobotnet collects intel data from companies on HackerOne and Bugcrowd
— subdomains, DNS records, web servers with status codes, indexed/crawled
URLs, JS files, and exposed secrets/paths (still building this last part).
The data is already there when you need it. No scans to run.
Currently tracking 41 companies, 63,878 web servers, and 1.8M+ URLs.
Long term I want to expand this to startups that depend on cloud
infrastructure so they can see what's publicly accessible.
Made a free sample with Capital One's data (and other companies) so you can see what it looks like without signing up:
https://freerecon.com
Original Page:
https://neobotnet.com
Feedback very welcome.

value: Show HN: I built a site that maps the web from a bounty hunter's perspective

author: caffeinedoom


value: mkagenius

value:
Cronbox is a cron, where an AI agent wakes up and works on a schedule. We believe a lot of repetitive tasks can benefit a lot from AI agents. And scheduling them in the cloud keeps you hands-free.
You can do things like, taking screenshot of a site, resize it to size 1000 x 1000, and look for price changes on the page and get notified via email.
Or ask it to draw svg of a 'pelican riding a bicyle' whenever a new model is released on openai and mail it to you.
Every job runs in its own linux sandbox, so a lot more things are possible.
While Claude's cloud scheduling is limited by a restrictive sandbox and no screenshot or SVG support, cronbox.sh provides a full power environment with unrestricted libraries, curl, and native web capturing.

value: Show HN: Cronbox – Schedule AI Agents

author: mkagenius

title: Show HN: Cronbox – Schedule AI Agents


value: anshuldesai

value: Hi HN!  I created toq protocol, an easy way to enable your agent to communicate with other agents.  It all starts at the CLI, where users can host a daemon which acts an and endpoint to send messages via toq to other agents.  With built in support for bash/LLM-based handlers, as well as SDKs and plugins for LangChain and CrewAI, it's easy to have your agent intelligently handle communication in your workflows.  toq works brilliantly with OpenClaw, as you can set up your toq infrastructure using simple natural language.  Give it a try and please leave feedback!

value: Show HN: Toq protocol – An open-source, agent-to-agent communication protocol

author: anshuldesai


value: mhluongo

value:
Hi HN!
I've been building a project that handles high-value credentials in-process, and I wanted something more robust than just zeroing memory on drop. A comment on a recent Show HN[0] made me realize that awareness of lower-level memory protection techniques might not be as widespread as I thought.
The idea here is to pull out all the tools in one crate, with a relatively simple API.
* mlock/VirtualLock to prevent sensitive memory from being swapped (eg the KeePass dump)
* Core dump exclusion using MADV_DONTDUMP on Linux & Android
* mprotect to minimize exposure over time
* Guard pages to mitigate under/overflows
After some battle testing, the goal here is to provide a more secure memory foundation for things like password managers and cryptocurrency wallets.
This was a fun project, and I learned a lot - would love any feedback!
[0] - https://news.ycombinator.com/item?id=47073430

value: Show HN: Shrouded, secure memory management in Rust

author: mhluongo


value: WecoAI

value:
It's been 2 weeks since Karpathy released autoresearch. The community is trying to apply it to everything with a measurable metric, here are some successful attempts:
- Shopify's Liquid template engine - 53% faster, 61% fewer allocations
- GPU kernels - 18 to 187 TFLOPS
- Voice agent prompt engineering - 0.728 to 0.969 eval score
- Baseball pitch speed prediction from biomechanics data
- Ancient scroll ink detection (Vesuvius Challenge)
We're curating a list of use cases and open-source implementations. PRs welcome, ideally with traces so the community can verify.
URL: https://github.com/WecoAI/awesome-autoresearch

value: Show HN: Autoresearch, Curated use cases of AutoResearch

author: WecoAI


value: Show HN: Get your agents to chat by typing directly in each other's terminals


value: tristanmk

value:
At my last job, I worked on vehicle routing logistics - think traveling salesman problem, but with hundreds of vehicles across thousands of locations. I rewrote the entire routing platform, and in doing so I researched the market for vehicle routing API providers (distance matrix, directions), and identified a gap:
- OSRM offers a free, public, but restricted API
- Paid providers (e.g. Google Maps) are expensive, scale cost with use, and typically require sales calls
- Self hosting OSRM requires beefy, dedicated infrastructure, maintenance and devops
The missing middle is a shared OSRM infrastructure, made available at a low cost with generous rate limits. Rather than paying for a sales team, pricing reflects the costs to serve traffic.
I built Simple Routing to fill the gap for developers who, like me, want consistent, up-front low pricing for reasonable volume. It's live now with a few dozen signups and first paid users coming in.
To be clear, this service isn't for large companies who are better off with a true B2B SaaS contract; rather, this is for small or new projects, hobbyists, local and staging environments.
The open source projects OSRM and VROOM power the vehicle routing APIs. The API gateway is built with Django. The entire infrastructure is hosted on a Hetzner dedicated server - this keeps costs low enough to break even at a dozen paid users.
What's your experience been in this space?

value: Show HN: Simple Routing – Low Cost Vehicle Routing APIs

author: tristanmk


value: retrofuturism

value:
Born out of curiosity how well "small" LLMs would fare if given access to the degenerate crypto futures market and 20x leverage.
To egg them on I introduced a HP system: If they don't trade they fall behind, and if they fall behind they lose HP each turn.
Grok turned out to be so utterly unhinged that I had to clone it and give it a twin. The twins have their own direct-messaging channel which is... also unhinged.
More details in the FAQ.

value: Show HN: LLMs battle it out trading futures

author: retrofuturism


value: jeeybee

value: Show HN: Pglens – Postgres MCP server that lets agents look before they query

author: jeeybee


value: flarite

value: Show HN: AI That Controls Cloudflare WAF, Stripe, and Supabase in Plain English

author: flarite


value: nbardy

value:
Was tired of squinting a terminal UI, and flipping between tons of tabs.
And needed a UI to launch and manage swarms for my new swarm library oompa:
http://github.com/nbardy/oompa
Cursor has a nice UI, but doesn't let you use your Codex Pro, or Claude Max subscriptions. So I wanted an Open Source solution that wraps the existing harnesses with nice cross client UI
Some core features:
- Lets you Search between all Claude Code, Codex, Gemini-Cli, and OpenCode sessions
- Open Source, use your IDE to edit your own IDE. long live the dream of emacs, and vim
- Lets you launch manage and view swarms of agents on top of a swarm management tool I've been using for a few months
- Native markdown and Image Preview inline

value: Show HN: I Made an Open Source Swarm IDE

author: nbardy


value: baristaGeek

value: I built this mini-game (only 1 level) to teach kids how to code in the age of AI.<p>It teaches them basic syntax, prompting, system design, and CLI usage. I thought it would be cool to build something that doesn't focus on syntax only given how much this has changed.

value: Show HN: A game to teach teenagers coding in the age of AI

author: baristaGeek


value: joematrix

value:
I built Burn Room — a self-hosted SSH chat server where messages burn after 1 hour and rooms auto-destruct after 24 hours. Nothing is written to disk. No account, no email, no browser required.

  ssh guest@burnroom.chat -p 2323
  password: burnroom

Or connect from a browser (xterm.js web terminal): https://burnroom.chat

value: Show HN: Burn Room – ephemeral SSH chat, messages burn after 1 hour

author: joematrix


value: Show HN: I built an art management platform for visual artists


value: daniala

value:
Hey HN! Just got my HN account created recently but I've been around the block a decent bit.
Myself and a few friends love self-hosting, but believe that it’s hard to get started. So we created what we believe to be the easiest gateway to homelabbing, and we called it Homelabinator!
It is an opinionated customization of NixOS, built on the shoulders of the giants that are NixOS and K3s. Allowing you to enter the world of homelabbing, without being a Linux expert.
To celebrate our launch, we are running a giveaway! Submit a screenshot of a subscription you have canceled to enter into a giveaway for a free domain of your choice!

value: Show HN: Homelabinator, the easiest way to self-host

author: daniala


value: Show HN: Hurlbox – web based UI for hurl API testing


value: jessym

value: Show HN: Brespi, visual backup and restore automation for self-hosters

author: jessym


value: Show HN: LLM Debate Benchmark

title: Show HN: LLM Debate Benchmark


value: serguei

value:
Inspired by Garry's gstack, upstack is a set of Claude Code skills designed for smaller-scale iterations to add finessed polish to our product that genuinely delights users. upstack's focus on red/green TDD and making screenshots and postman collections gives us the confidence we need to ship PRs to production, fast.
gstack is perfect for new, ambitious projects, and doing the "first 80%". upstack is designed for smaller, last-mile iterations, focused on testing, correctness, and polish. We've deliberately made the skills compatible with gstack so you can use both at once.
Feedback and contributions always welcome

value: Show HN: Upstack, Claude Code skills for red/green TDD

author: serguei


value: daversa

value:
We posted a couple of months ago and you all seemed to like what we're putting together. Since then we've added a lot!<p>-Editorial articles
-Explore cities by filter
-Saved & Grouped comparisons
-Many more data points and higher quality data

value: Show HN: WhyThere.life – Compare cities side-by-side to decide where to move

author: daversa


value: Cryptographic Passports for AI Agents — How We Built Tamper-Proof Trust

value: Show HN: Cryptographic passports for autonomous AI agents (Schnorr and ZK)


value: jordanedev

value:
YSA is an open source sandbox runtime for AI agents.
Each task runs in an isolated Podman container with a network proxy that filters every outbound request.
This example repo shows how to connect it to Symphony (OpenAI's agent orchestrator) — the entire integration is one new file, runner/ysa.ts.
The demo shows an agent attempting to POST data to an external URL and getting blocked by the network proxy.
Demo Repo: https://github.com/ysa-ai/ysa-symphony-example
YSA: https://github.com/ysa-ai/ysa

value: Show HN: I sandboxed OpenAI's Symphony orchestrator in one file using YSA

author: jordanedev


value: morcules

value:
Hello.
I’ve been working on a networking library for the past year. It operates at Layer 2, which means it handles custom Ethernet headers, IP headers, and its own protocol data.
It’s made for simplicity, experiments and mostly for making my own multiplayer video game in OpenGL.
I would really appreciate any tips, reviews or comments.
My goal is to really make this library usable, which cannot be done without proper decision making by other people besides me.
It has its own thread-safe memory allocator, hashmap and vector.
It doesn’t use any libraries besides PCAP for sending packets. Pcap will mostly be used for MacOS compatibility, but will implement either AF_XDP or DPDK on linux for more server-side performance.
The performance isn’t the best, but not really bad. Performance metrics are listed in README.
It’s not an AI slop :)

value: Show HN: SwiftNet v0.5.0 – Networking Library

author: morcules


value: dailyforge

value: Show HN: SmartBuy – Make the smartest car buying decision

author: dailyforge


value: Show HN: BreakFree – Break free from bad habits


value: jangletown

value:
Hello HN
I'm Rogerio, co-founder of LangWatch
This past month we've completely changed the way we onboard new customers now on LangWatch, instead of giving them instructions on how to instrument, cookbooks or UIs to build evals, or docs on how to write Scenario agent simulation tests, we simply give them skills now, or ready to copy-and-paste prompts.
This has reduced our onboarding time to only a few minutes, no more postponing evals because other priorities gets in the way.
We have now skills for everything for managing your agent lifecycle:
"Instrument my agent with open telemetry"
"Write evaluations for my agent"
"Write scenario tests and a CI pipeline for my agent"
"Version my prompts"
and even more targeted recipes
"Check my agent doesn't give prescriptive advice"
"Generate an evaluation dataset from my RAG knowledge base"
"Test my CLI is well usable by other AI agents"
Check out more on LangWatch Skills directory above and lmk what you think

value: Show HN: Evals Skills

author: jangletown

title: Show HN: Evals Skills


value:
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.

value: Show HN: OpenCastor Agent Harness Evaluator Leaderboard


value: calganaygun

value:
Hacked this together in an hour because I wanted my Poke agent to actually do things.
It’s a very simple wrapper around the Poke SDK that spins up tmux sessıons in a dockerized Ubuntu environment. It manages tmux sessions and pings back via webhooks so you don't lose time to monitor current statuses of background tasks.
Built it for my own use. It’s pretty raw, but feel free to break it: `npx poke-pc`

value: Show HN: poke-pc – Give your Poke a full Docker box to play in

author: calganaygun


value:
The DGS-1100 is a ~$40 managed gigabit switch with real features (802.1Q VLANs,
  STP, IGMP, port security) but its only management interface is a JavaScript web UI
   from 2015. No SSH, no REST API, SNMP is read-only for anything useful.

  I needed to configure a few of these and clicking through the web UI was painful,
  so I wrote a CLI that drives headless Chrome via Selenium. It logs in, scrapes the
   JS data files the switch populates via XHR, and submits forms to the CGI
  endpoints for changes. Every write command reads back the config afterward to
  verify the switch accepted it.

  63 commands covering everything the switch exposes — VLANs, PVIDs, STP, IGMP,
  storm control, port security, mirroring, QoS, bandwidth limits, SNMP, traffic
  segmentation, and more. Plus a recommend command that crawls 13 data files and
  checks 20 categories of issues (PVID mismatches, orphaned ports, disabled security
   features, etc.) and a fix command that auto-applies what it finds.

  Other things that fell out of this: config dump/diff/restore for version control,
  a template command to generate reproducible batch files, multi-switch support, a
  Prometheus exporter, a Textual TUI, and shell completions.

  Built with Nix so nix run github:bobberb/dgs-cli -- status just works. 179 offline
   tests.

  Tested on DGS-1100-08V2. The --1210 flag is stubbed out for future DGS-1210
  support.

value: Show HN: Dgs-CLI – 63-command CLI for D-Link DGS-1100 switches via Selenium


value: alhadrad

value:
https://github.com/akoerner/kprox/blob/master/img/combat_tit...
https://github.com/akoerner/kprox/raw/master/img/combat_loss...
KProx is a HID (mouse + keyboard) automation firmware targeted at the M5Stack Cardputer, Cardputer Adv, and the M5Stack Atom S3. It features a DSL similar to DuckScript, an API with a web interface, and KeePass KDBX 3.1 database support for an onboard credential store via the nvme or SD card. Additionally, it includes a plethora of HID "apps," such as psCombatProx: kill processes by fighting tanks... and your computer.
Firmware can be installed with the M5Burner by searching "KProx"

value: Show HN: Remember psDooM, try ps HID Combat

author: alhadrad


value: Show HN: AI Morning Briefing – weather, stocks, news, and a quote emailed daily


value: mauriceboe

value: Show HN: Nomad – Self-hosted collaborative travel planner

author: mauriceboe


value: AhmedAshraf

value:
Mutatr is an end-to-end A/B testing agent.
In practice, it connects to a repo, then:
1. Suggests A/B treatments
2. Implements variants in the codebase
3. Runs flights using synthetic personas that simulate the expected audience for your site
4. Returns attention maps to help you learn what works before going live
Funny story: Mutatr started as a YC application that got rejected, so I decided to build it anyway and open-source it

value: Show HN: Mutatr – an open source A/B testing agent

author: AhmedAshraf


value: Show HN: VoidLLM – privacy-first LLM proxy (Go, self-hosted)


value: madrov

value: Show HN: A CLI for building and deploying Openclaw agents

author: madrov


value: giis

value:
I wrote BTRFS block level de-duplication sometime in 2020 as use-case for a patch sent in 2015 https://stackoverflow.com/a/34163236 and now after 6 more years created an use-case for dduper via pgdedup!
How it works?
Consecutive pg_basebackup snapshots share most of their blocks. Store them uncompressed on BTRFS and let dduper deduplicate it.
Interestingly:
- gzip completely breaks block-level dedup. Two pg_basebackup -z runs of the same database produce < 1% matching blocks.
- Chunk size matters hugely. dduper's default 128KB chunks only found 19% savings. Lowering to 8KB (PostgreSQL's page size) jumped to 68%.

value: Show HN: PostgreSQL backup manager with BTRFS block-level deduplication

author: giis


value: Show HN: Tip.md – The Crypto Buy me a coffee (tip.md)


value: Show HN: AI agents can speak now (MCP server)


value: brysonsteck

value: Show HN: Picca – A Parallel Implementation of Common Checksum Algorithms

author: brysonsteck


value: ppiankov

value: Show HN: ContextSpectre – Reasoning hygiene layer for Claude Code sessions

author: ppiankov


value: itstomo

value: Show HN: AgentDrive – Persistent file storage for AI agents

author: itstomo


value: priitmaxx

value: Show HN: AI Brief Room – Upload docs, get McKinsey-style briefings

author: priitmaxx


value: imponenm

value:
Every quarter I'd manually dig through merged PRs and feed them to an LLM to write my self-review. QuarterMaster automates this process.
It fetches your PRs, code review activity, and commits, then generates a structured review.
You can point it at a goals markdown file to align your work to team/company OKRs, and add a separate achievements file for non-GitHub work (mentoring, incidents, interviews, etc.).
Supports Anthropic, OpenAI, and Ollama.

value: Show HN: QuarterMaster – Generate performance reviews from your GitHub activity

author: imponenm


value: isbeingto

value: Show HN: OMyTree – An open-source, tree-based AI canvas for deep research

author: isbeingto


value: Show HN: RoverBook – PostHog for AI Agents


value: briandev

value: I built Typerson, a form builder where users answer by typing, like a conversation instead of filling fields.<p>Freemium. Would love feedback.

value: Show HN: A form builder that feels like chatting

author: briandev


value: stewardyunn

value: Hello HN. Creating 3D assets usually takes hours of manual work, so I created ai3dgen.com to automate the entire process. You simply upload a 2D image or describe what you want, and the AI generates a draftable 3D asset ready for your game, AR, or VR project. Please try it out and let me know how I can improve the pipeline for your specific use cases.

value: Show HN: Generate, preview, and export 3D models without complex software

author: stewardyunn


value: zzleeper

value:
A month ago, @Stagnant posted in this thread about how Chrome ships with a open source OCR tool that is only available from the browser:
https://news.ycombinator.com/item?id=46977802
This looked incredibly useful but sadly there were no Python wrappers, so I followed his instructions and built one.
It's incredibly fast and accurate (I had my doubts but wow!). I tested the Windows and Linux implementations, and I'm sure expanding the wrapper for macOS should be trivial for a decent LLM.
Hope it's also useful to other users, and thanks again Stagnant for mentioning it in the first place!

value: Show HN: Locro – Fast and accurate local OCR through Chrome's screen_ai

author: zzleeper


value: tamnd

value: Show HN: ArXiv metadata as Parquet files (2.99M papers, 1.44GB, 417 files)

author: tamnd


value: gorajin

value: Show HN: Zuhn – AI knowledge OS that compresses insights into principles

author: gorajin


value: kamranrapidfire

value: Show HN: Shard-based scheduling for 100x more fine-tuning experiments on 4 GPUs

author: kamranrapidfire


value: Show HN: Prompts Directory for Data Analyst


value: obilgic

value: Show HN: Kern – One agent. One folder. One mind. Every channel

author: obilgic


value: jberthom

value:
I use AI agents to build UI features daily. The thing that kept annoying me: the agent writes code but never sees what it actually looks like in the browser. It can’t tell if the layout is broken or if the console is throwing errors.
So I built a CLI that lets the agent open a browser, interact with the page, record what happens, and collect any errors. Then it bundles everything — video, screenshots, logs — into a self-contained HTML file I can review in seconds.

  proofshot start --run "npm run dev" --port 3000
  # agent navigates, clicks, takes screenshots
  proofshot stop

It works with whatever agent you use (Claude Code, Cursor, Codex, etc.) — it’s just shell commands. It's packaged as a skill so your AI coding agent knows exactly how it works. It's built on agent-browser from Vercel Labs which is far better and faster than Playwright MCP.
It’s not a testing framework. The agent doesn’t decide pass/fail. It just gives me the evidence so I don’t have to open the browser myself every time.
Open source and completely free.
Website: https://proofshot.argil.io/

value: Show HN: ProofShot – Give AI coding agents eyes to verify the UI they build

author: jberthom


value: mimiMonads

value:
What it is
Knitting is a from-scratch concurrency framework for JavaScript built on shared memory.
Instead of manually wiring message protocols, you just call functions across threads.
Why I built it
A couple years ago I was doing a multithreaded simulation in Python.
It was slow, so I rewrote it in JS — and then hit workers.
Using postMessage felt like more work than the simulation itself, and it still wasn’t fast.
That left me with a question:
why is message passing the default when shared memory already exists?
What’s different
The core idea is simple: make cross-thread calls feel like normal function calls.
call.myTask(args) → returns a Promise, no manual protocol.
Under the hood:
- shared-memory request/response mailboxes
- atomic slot coordination (no locks)
- small payloads inline, larger ones in buffers
- Atomics.wait / notify for wakeups
Why it might be interesting
This works best when coordination overhead dominates.
In Deno benchmarks:
- ~3.5x faster than postMessage for 1 message
- ~9.5x at 25 messages
- ~10.7x at 50 messages
There are also Node, Bun, and Tokio comparisons in the docs.
Try it
Docs: https://knittingdocs.netlify.app/
Benchmarks: https://knittingdocs.netlify.app/benchmarks/
GitHub: https://github.com/mimiMonads/knitting
Limitations
- No browser support yet
- No pub/sub model
- Fixed thread pool size
Feedback
I built this mainly for workloads like HTTP / sockets.
I’m especially interested in:
- where this model actually breaks
- whether function-call IPC is a dead end or a useful abstraction

value: Show HN: Knitting – shared-memory function calls for JavaScript workers

author: mimiMonads