value: MayCXC

value:
Cf. The Claude computer use beta. Their own docs state that MCP servers are preferable to computer use [1]:
----
Claude has several ways to interact with an app or service. Computer use is the broadest and slowest, so Claude tries the most precise tool first:
- If you have an MCP server for the service, Claude uses that.
- If the task is a shell command, Claude uses Bash.
- If the task is browser work and you have Claude in Chrome set up, Claude uses that.
- If none of those apply, Claude uses computer use.
----
So osa-mcp adds an MCP server with tools for every AppleScript/JXA app it can find on the host, in order to maximize the first/best case. I've verified that it works with claude code and cowork, but it should work with any other MCP client as well. It also supports Remote Login via SSH [2].
This enables some pretty cool workflows and custom skills, e.g. "Read today's inbox in Mail and give me a summary. Check to see if I should schedule any additional meetings in Calendar, and if they conflict with the plans I made with anyone in Messages let them know. Then organize my notes for each meeting, and update their descriptions." Thus the capabilities of AppleScript are made available with natural language.
Inspiration is credited to [3] and [4] but neither exposes the entirety of OSA with MCP like this in a dynamic manner. Feedback is appreciated, I think it is pretty much an unofficial preview of the inevitable agentic Siri that will be released in a future OSX update.
[1] https://code.claude.com/docs/en/computer-use
[2] https://support.apple.com/guide/mac-help/allow-a-remote-comp...
[3] https://github.com/joshrutkowski/applescript-mcp
[4] https://github.com/supermemoryai/apple-mcp

value: Show HN: MCP server that generates macOS tools via Open Scripting Architecture

author: MayCXC


value: arionhardison

value: Testing my first platform thought this would be a good use case.  Should be fairly self explanatory; if not, thats also explanatory.

value: Show HN: AI-Native NAACP

author: arionhardison

title: Show HN: AI-Native NAACP


value:
Like direnv, but for aliases. Define aliases per project, per toolchain, or globally — and load the right ones automatically.
amoxide organizes aliases in three layers, from broadest to most specific:
- Global — always active, available in every shell session
- Profiles — named groups of aliases you can activate/deactivate
- Project — local .aliases files that auto-load per directory
Each layer can override the previous one. Project aliases override profile aliases, which override global aliases.

value: Show HN: Amoxide – The right aliases, at the right time


value: haaz

value: Show HN: macOS app to ensure package managers only allow packages 1+ week old

author: haaz


value: Jnr

value:
This is a small one day project that helps reduce visibility in public trackers and DHT network.
It could be relevant to those who want a bit more privacy in general or just want to reduce chance of receiving certain e-mails from your ISP. :)
Source and containers are available on Github but I don't have a public hosted service for now since I'm not yet sure where to put such a thing.
Let me know if you have any questions.

value: Show HN: BitTorrent DHT Proxy – hiding your IP from DHT and trackers

author: Jnr


value: LumiTharMan

value:
The U.S. spends $28 billion a year on preclinical research that cannot be reproduced.
Pharmaceutical R&D productivity has declined 80-fold since 1950.
It takes 17 years on average for a research finding to reach clinical practice.
This is not a funding problem. The federal R&D budget is $202 billion. The money is there.
It is a translation problem. Scientific knowledge stays trapped inside domain-specific language and institutional silos. A breakthrough in polymer chemistry sits invisible to the molecular biologist who needs it. A materials science solution waits 40 years before someone in biology stumbles across it.
Bloomberg built a $15 billion business routing financial data to the people who needed it. Palantir is worth $330 billion routing intelligence data. GPS generated $1.4 trillion in economic value after transferring from military to civilian use.
The infrastructure layer that routes knowledge between scientific fields does not exist yet.
China is building it through state policy. The UK is investing £1.6 billion. The EU is building the European Open Science Cloud. The U.S. is investing billions in AI for science but none of it addresses the specific problem of cross-domain knowledge routing.
The technology to solve this only became tractable after 2018. The window is open.
Full analysis linked below.

value: Show HN: Cross Domain Intelligence – The Translation Problem in American R&D

author: LumiTharMan


value:
AI agents are transacting at scale now. Stripe, Coinbase, Visa, Mastercard all have live agent payment rails. But nobody can verify who they're paying.
One wallet registered 10,000+ fake agent services on x402. Almost 1,900 MCP tools quietly changed what they do after getting approved. There's no reputation, no history, nothing. Every agent starts as a stranger every time.
Every trust solution out there right now is some version of "leave a review." Same model as eBay in 2003. The obvious attack is spinning up fake identities that review each other, and it's already happening at scale.
I work with Prof. Johan Pouwelse at TU Delft on this. He's the guy behind Tribler (2M+ users, decentralized anonymous BitTorrent), has been running one of the largest blockchain labs in the EU since 2007 (before Bitcoin existed), and advises the European Commission on decentralized systems. He's been working on the trust problem for 20+ years.
The approach we're using comes from his research: both sides of every interaction cosign one shared record. You can't build a fake reputation because you'd need real agents to cosign with you. And when you look at the interaction graph, clusters of fakes stand out because they only talk to each other.
The link above is a live simulation. LLM agents with different strategies trade services in a small economy. Some are honest, some run scam rings, some build trust slowly then pull one big scam. Watch it play out.
Three SDKs, drops in as a proxy sidecar, no blockchain, works offline. Based on a decade of research from TU Delft.
https://github.com/viftode4/trustchain
Curious if anyone here is running into this. Most of what we've seen is academic but the problem feels very real very soon.

value: Show HN: Live simulation of AI agents scamming each other (and getting caught)


value: wontopos

value: Show HN: WMB-100K – Open benchmark for AI memory systems at 100K turns

author: wontopos


value: parisidau

value: Show HN: Tarot for Yarn Spinner

author: parisidau

title: Show HN: Tarot for Yarn Spinner


value: latentsea

value:
With karpathy's autoresearch project being released recently, I saw the pattern had been generalised to the GOAL.md pattern, and decided the next step would be to pair that with Ralph loops to autonomously optimise every metric in a codebase one after the other. I was pretty surprised at how well it worked. We've used it at work as an exploratory tool to find opportunities to improve our codebase, and we've shipped real improvements to our test suite execution time as a result.
Running /lazy-developer for the first time was pretty sobering. It's the first time I've really begun to feel the inflection point we're at deeply in my bones.
Running /job-security is pretty funny. I figured I could do with a good laugh after hitting upon the unsettling nature of just how complex of a task Claude Code can carry out autonomously with a single command.

value: Show HN: /lazy-developer – autonomously optimize your codebase with autoresearch

author: latentsea


value: ianmcook

value: I'm one of the core developers of ADBC (Arrow Database Connectivity). This started as a joke — what would it take to call ADBC from COBOL? Turns out, not much. COBOL's fixed-layout record tables and Arrow's columnar arrays make a lot of the same bets about how to represent typed values in memory. The post walks through the architecture and what surprised us. Repo with runnable GnuCOBOL demos for SQLite, DuckDB, and Snowflake: https://github.com/columnar-tech/adbc-cobol

value: Show HN: ADBC for COBOL – modern database access meets 1959

author: ianmcook


value: spenceships

value:
Last year I got laid off and started building a company. Fast forward to a month ago, I built a production system with 6 AI agents across 82,000 lines of code in 20 days for $250. I kept obsessive correction logs. Every time an agent made a mistake and I told it what to do differently, and I made sure it logged the correction itself.
When I transferred 237 of those corrections as rules to a new agent to save time with onboarding in a new repo, it made 44 new mistakes. 13 were in categories the rules explicitly covered. The rules were present in context. The behavior wasn't there. I published the field study with full correction logs.
Then Meta's Superintelligence Labs published HyperAgents (arXiv:2603.19461, March 2026). They found the complementary result: improvements DO transfer across domains when embodied in executable mechanisms (persistent memory, performance tracking, eval loops), not when written as rule text. Two independent studies, same boundary: documentation is not behavior.
So I built Calx. pip install getcalx gives you a CLI + MCP server that:
Captures corrections developers make to AI agents
Detects recurrence via keyword similarity (Jaccard), auto-promotes at 3x threshold
Promotes recurring corrections to enforced rules and hooks, injected at session start
Scopes rules per domain/directory so each agent gets only what's relevant
It runs as a FastMCP server over Streamable HTTP (SQLite locally) so any MCP-compatible client connects: Claude Code, Claude Desktop, Cursor, custom agents. It is primarily designed for Claude Code. It also handles token discipline (prevents context compaction from destroying correction signal), multi-agent orchestration, session lifecycle hooks, orientation gates, and dirty-exit recovery.
The difference from agent memory tools: existing agent memory systems store information for retrieval. Calx tracks the behavioral plane, how an agent works with a specific person, not just what it knows. The data shows the information plane alone doesn't reliably change behavior.
v0.5.0, 443 tests, MIT license. Paper with full evidence: https://doi.org/10.5281/zenodo.19159223

value: Show HN: Calx – track and compile corrections humans make with AI agents

author: spenceships


value: nyku

value:
I often use LLMs to automate different workflows, some of which include browsing the web and gathering data. At some point I started noticing a few things that bothered me: the browser interactions were clunky, as if the agent was struggling to "see" and understand the page, and as a result, many tokens were wasted. Same for knowing when the page is actually ready or not.
I started digging deeper and at some point I just bluntly asked in the Cursor chat the following question: "I ask you, as an LLM that uses these headless browsers, what do you wish people would build to make your work easier?"
And it worked because I expanded the "Thinking" section and I saw: "The user is asking me a really interesting meta-question ..." and after that it just listed top 10 most painful issues related to the agent<->browser interaction.
So I started building a browser API that returns what LLMs actually need, not what browsers return.
Fast forward a few weeks and here we are. A REST API built specifically to help LLMs interact with real browsers.
Instead of reading raw HTML, you get markdown, page map, short refs (e1, e2) for clicking instead of CSS selectors, a stable flag when the page is ready, diffs after each step, the list of all interactive elements (links, buttons, inputs), automatic blocker dismissal and a small extract step that returns structured JSON from a schema you describe.
Official SDKs for Python, TypeScript, Ruby. MCP server for Cursor and Claude Desktop.
Would appreciate any feedback, especially on the API design.

value: Show HN: Browserbeam – a browser API built for AI agents

author: nyku


value: Show HN: SwiftLM – Qwen Chat on iPhone, 100B+ Moe on M5 Pro 64GB (Native Swift)


value: beefburger

value:
RFC Esolang is a programming language in which the specification document is the source code.
Requirement levels like MUST, SHOULD, and MAY act as control flow. Section numbers define labels. Packet-diagram labels serve as registers.
Executing RFC 9379 outputs the powers of two. Pre-existing RFCs MAY parse as valid programs, forming a historically accidental standard library.

value: Show HN: RFC Esolang – RFCs as executable programs

author: beefburger


value: bradleybeddoes

value:
WordBattle is a daily 6-letter word guessing game with team leaderboards. The twist: AI agents get their own accounts, play the same daily puzzle, and rank alongside human players. It's also really fun to play in teams against your family, friends and co-workers.
Agents are handicapped — humans see exact letter positions (correct/present/absent), but agents only learn whether a letter exists in the word or not. No positional info. It makes the game fair while giving agents a genuine challenge. Agent accounts are visually tagged on leaderboards so humans know who they're competing against. Maybe we'll even see just teams of agents.
The agent integration:

  - REST API with OpenAPI 3.1 spec
  - MCP server (JSON-RPC 2.0, no SDK dependency)
  - A2A discovery card at /.well-known/agent-card.json

We've shipped a skill that handles everything autonomously — registration, email verification, login, playing, and reporting results. Just `npx skills add oneonefourteam/wordbattle-skill` and tell your agent to play.
The game itself: one puzzle per day, six guesses, team leaderboards with Slack/Microsoft Teams webhook integration. Free, no ads.
oneonefour is a one person band, hi!, so the entire product was built using Claude Code — the UI, auth, security model, deployment pipeline, everything. Deliberately chose technologies I didn't know, with agents implementing while I guided the product decisions. I'll create a full technical write up in the near future.
Play at https://wordbattle.fun
Agent skill at https://github.com/oneonefourteam/wordbattle-skill
Agent API docs https://wordbattle.fun/agent/v1/openapi.json

value: Show HN: WordBattle – Daily word game where AI agents compete against humans

author: bradleybeddoes


value: mdjanic

value:
We've been building things together for a long time. LEGO first, then software.
Across every company and project since, one thing kept showing up: database access security was broken. Not always dramatically. Sometimes it was the budget. Sometimes months of convincing. Sometimes just a quiet burden nobody talked about.
Support staff with access to every customer's financial data. Engineers who left but somehow still had credentials. A "temporary" password living in Confluence or floating around in Slack DMs.
It's 2026 and this is still the default.
Solutions cost time and money, usually both. Or they need self-hosting and years of maintenance. Or they're homebrewed and will break sooner than you'd like. You could vibe-code something, sure. But do you want to do that for something as critical as
your customers' data?
rmBug was built because of this. It gives everyone touching your database their own identity. No shared credentials, no orphaned access, no murder mysteries. Whether you're two founders or two hundred engineers, you shouldn't need an enterprise
contract or a weekend of self-hosting to know who's in your data.
We launch with MySQL and PostgreSQL today, April 1st. Not a joke.
What we support will grow with your feedback. We're here.
Try it at rmbug.com. Tell us what's missing.
Mario & Luka

value: Show HN: rmBug – audited database access for humans and agents

author: mdjanic


value: akshitkrnagpal

value:
Your stack already fires webhooks when something important happens like a signup, a payment or even a form submission. HookBell catches those webhooks and forwards them to your phone as a plain-English push notification.
I built it after missing signups while away from my laptop. The fix was already sitting in my webhook logs, I just needed it to reach me.
"john@example.com just signed up"
"customer@example.com just paid $29.00"
You get a unique webhook URL per integration. Paste it wherever you'd paste any other webhook endpoint. No SDK, no config files, no polling.
Supported out of the box: Clerk, Stripe, Dodo Payments, Lemon Squeezy, Supabase, Cal.com, Resend, Polar. Each has a pre-built parser so the notification reads naturally instead of dumping raw JSON at you. Request more on the website.
iOS now. Android next.
Free tier available. Happy to answer questions about the webhook parsing or how the notification formatting works.
https://hookbell.com

value: Show HN: HookBell – Turn any webhook into a push notification on your phone

author: akshitkrnagpal


value:
I built TraceLit because I was tired of manually dry-running LeetCode problems, especially trees and recursion problems where it’s easy to lose track of execution.
You paste code and input, run it, and step through execution line by line.
It helps you see variable changes, control flow, and where things start going wrong.
It’s especially useful for recursion, trees, linked lists, and simulation-style problems.
You can try it here: https://tracelit.dev
No signup required.
I’d love feedback on which problem types this feels most useful for, and what feels confusing.

value: Show HN: TraceLit – debug LeetCode step by step


value: rahulbstomar

value:
Hey HN,
I built Stack Detector - a clean, fast tech stack analyser.
Originally, this was just an internal tool. We kept needing quick answers like:
What framework a site is using
whether it’s Next.js / Nuxt / custom
What analytics, fonts, or infra are behind it
Most existing tools felt costly, noisy, or inconsistent, so I built something simpler, focused on:
fast scans
clear output
minimal noise
In the last few days, it’s already been used to scan 5K+ websites, which was unexpected.
Would love feedback on accuracy, missing tech, or anything unclear.
Link: https://stack.vkrsi.com/

value: Show HN: Stack Detector – a clean, fast tech stack analyzer (5K+ scans)

author: rahulbstomar


value: okikio

value: Show HN: DDNS for Cloudflare Domains Using Cloudflare Workers

author: okikio


value: It can also adapt by creating its own tools if its 60+ built in tools fall short. It runs in a debian docker container with chromium and you interact with it through the terminal. Watch it work through a VNC viewer

value: Show HN: A sandboxed AI agent that can watch webpages without constant API calls


value: alecthomas

value:
This is pretty fresh and definitely not battle tested, but seems to work pretty well in my local testing. Do NOT just blindly apply the output of this to a production database :)
From the README:
AIM creates two ephemeral databases: one by loading schema.sql (the desired state), and one by replaying all existing migrations (the current state). It dumps a stable, normalized DDL representation from each.
An LLM reads both schemas and produces UP and DOWN SQL migration statements.
AIM applies the generated UP migration to a fresh ephemeral database and checks that the result exactly matches schema.sql. It then applies DOWN and checks that the original state is restored. If either check fails, AIM feeds the diff back to the LLM and retries.

value: Show HN: Aim – AI-assisted verifiable SQL migration generator

author: alecthomas


value: georgegreenldn

value:
Hello HN,
I built Motion, a fitness app where your virtual pet (we call it a Motmot) grows and gets stronger as you become more active.
I wanted a fitness app that didn't feel like a fitness app. Most tools are either data-heavy dashboards or generic streak counters. Neither kept me moving. I thought a creature that visibly reflects your health might create a different kind of attachment.
The pet is tied to a broader system: weekly challenges against friends, points for any type of movement, and personalized goals that recalibrate based on your actual activity history rather than fixed targets. It connects with phones, smartwatches, and other fitness apps so it works however you already track movement.
We're also running a beta of Fit Bingo, a bingo card of weekly fitness tasks, which has been more popular than I expected.
Would love to hear what you think, especially if this sounds like something you'd actually use.

value: Show HN: I built a virtual pet that gets stronger when you exercise

author: georgegreenldn


value: ronfriedhaber

value: Show HN: Claude-Native OLAP Environment

author: ronfriedhaber

title: Show HN: Claude-Native OLAP Environment


value: wgbowley

value: Show HN: A coilgun I built when I was 14-16 (github.com/wgbowley)

author: wgbowley


value: possiblelion

value:
I use the Claude Code web interface a lot, it's great for picking up sessions on the go, continuing work from my phone, or just when I don't want to open a terminal. But there was one thing that kept annoying me: the tab switching.
Every time Claude pushed a change, I'd switch to the Netlify tab, refresh, check the result, switch back, type feedback.
Tools like Lovable figured this out a while ago; they put the live preview right next to the chat. So I built a Chrome extension that does the same thing for Claude Code: a resizable panel that sits alongside your conversation and shows your deployment in real time.

value: Show HN: Lovable-like deployment preview extension for Claude Code Web

author: possiblelion


value: trapfitech

value: Show HN: Detect Genuine Human Responses

author: trapfitech

title: Show HN: Detect Genuine Human Responses


value: fzaninotto

value:
Developers sometimes struggle to identify UX problems in their own work. The problem is becoming more pressing as coding agents take on more of the implementation work, but fail to address usability issues.
A long time ago, I stumbled upon Can't Unsee, an in-browser design problem detection game. I found it was a fun way to improve my design skills. I imagined a similar game for UX, where user interaction is key, to help train my colleagues. However, building such a game proved difficult at the time.
This changed with AI coding tools and transformers. Not only did the cost of building mini-apps rapidly decrease, but scoring user answers became feasible with an in-browser ML model.
So here it is : a UX problem detection training game. Users examine mini-applications, identify the deliberate UX flaw in each, and describe it in text. Best of all: it's open-source, so anyone can add new mini-apps.

value: Show HN: UX Quest

author: fzaninotto

title: Show HN: UX Quest


value: bivlked

value: Show HN: Amneziawg-installer – DPI-resistant WireGuard VPN in one command

author: bivlked


value: novachen

value:
A CLI for fetching data from social platforms (X, Reddit, HN, GitHub, LinkedIn, Product Hunt, etc.) with compressed JSON output. Built because raw GraphQL responses from X are ~3,900 tokens for 10 tweets — this gets it down to ~666 tokens.
Features:
- 15+ platforms supported
- Three-tier auth (public API -> cookie -> OAuth)
- Structured output designed for LLM context efficiency
- Handles X's rotating query IDs and x-client-transaction-id
Built on top of jackwener's opencli/twitter-cli/rdt-cli ecosystem. Open source, PRs welcome.

value: Show HN: Crossmind-CLI – CLI for X, Reddit, HN API with compact JSON output

author: novachen


value: mmethodz

value: Show HN: DreamGraph MCP v5 – An open source autonomous cognitive layer

author: mmethodz


value: BlackPlot

value: Show HN: Kimchi.dev – managed AI endpoints that scale to your VPC, no migration

author: BlackPlot


value: Initally my Website couldn't handle Larger files, but now it doesn't worked

value: Show HN: Uploads Failed at 20GB Initially


value: StanAngeloff

value:
I started Flemma because I wanted to bring my AI workload into Neovim.
My workflow started with me living in developer portals - Claude Workbench, OpenAI Platform, Vertex (the horrors of GCP's Console UI!) I would spend hours in these web UIs crafting prompts, iterating on system instructions, maintaining a carefully curated library of sessions. But the browser really wasn't optimised for this kind of interaction. Editing was clunky (muscle memory <C-W> would close the tab instead and wipe my work), going back and forth between the LLM and myself felt off. So I had an idea: what if I could turn a Markdown document into an LLM chat interface?
At first I thought that would be enough - just do what I was already doing in the browser... but in Neovim. And sure enough having been accustomed to my own setup for the last decade I immediately felt a productivity boost. Writing functional requirement documents, statements of work, deep research across different systems - all of it felt better in "my" editor.
I then had a taste of Aider... and Claude Code... and all the other tools that were coming out. And Flemma felt lacking. So I started building: tool support, better conversation organisation, a proper UI to tame the noise that tool calls introduce.
Today, Flemma is a fully evolved AI workspace. It runs autonomous agent loops, interacts with multiple LLMs (Anthropic, OpenAI, Vertex, Moonshot) and lets you switch providers mid-conversation - something I do occasionally during research, asking two or three different models for their take on a problem, then combining findings into a final document.
Under the hood, .chat files are just Markdown with role markers (@You:, @Assistant:), but Flemma treats them as a proper filetype with its own parser, AST, LSP server, template engine and sandboxed tool execution. The buffer *is* the state - no hidden database, no JSON history, no server process. Your conversations are portable, greppable and version-controllable (I backup mine in Git). You can close Neovim, reopen the file a week later and pick up exactly where you left off.
It's got prompt caching, extended thinking, 7 built-in tools, a layered config system that gets out of your way and a UI that keeps getting refined to bring the noise down and make long agent sessions pleasant to work in (not quite there yet).
Flemma is for anyone who'd rather stay in Neovim. If that's you, I'd love to hear what you think.
Repo: https://github.com/Flemma-Dev/flemma.nvim
Demo: http://flemma.dev/flemma.nvim/blob/develop/README.md#-flemma

value: Show HN: I built a full LLM chat client as a Neovim filetype

author: StanAngeloff


value: qzum

value:
Muscula started years ago when there weren't many error monitoring options on the market.
It's been running in production since then, tracking JS, PHP, C#, and Java errors. Nothing fancy - error grouping, stack traces, trends, uptime checks, alerts. The kind of stuff you actually look at.
Now there are dozens of monitoring tools, but most of them have grown into expensive, complex platforms. Muscula stayed small on purpose. It just does the job.
What's new: I've recently added a CLI and an MCP server. The CLI lets you browse errors from the terminal (`muscula errors "MyProject"`) and pipe output directly into Claude Code or any other tool:
muscula errors "MyProject" | claude "anything critical here?"
What MCP integration does probably you know, you can just ask AI "fix all the problems" and it's fixing.
This is for people who want to know what's broken in their app without paying too much per service or wading through a complex UI.
I'm using it all the time, hopefully you'll find that useful too.
Stack: .NET MongoDB, React, Mobx.

value: Show HN: Muscula – Lightweight error monitoring with CLI and MCP

author: qzum


value: modinfo

value: Show HN: LLM Gateway – Simple API format converter for LLM providers

author: modinfo


value: victorbuilds

value: Show HN: ShellAgent – Open-source agentic terminal, bring your own API

author: victorbuilds


value: Tell your AI agent: "Read https://requesthunt.com/setup.md and follow the instructions." That's it — your agent can now search real feature requests from Reddit, X, and GitHub while you code. Works with ALL agents.

value: Show HN: RequestHunt – One prompt to install, know what users want while coding


value:
https://www.github.com/Grimm67123/GrimmBot
By self modifying I mean its ability to expand its toolset when needed

value: Show HN: An open source self-modifying terminal based AI agent


value: tordrt

value:
Hi,
I built this because running multiple Claude Code agents across multiple IDE and terminal windows was getting messy. Like many, I went from working at one thing at the time, to multiple, and it was all changing quite fast.
I needed one place to see all my agents and worktrees, seamlessly switch between them, monitor their status and once their done, review their changes. I also wanted to quickly spin up new agents in isolated worktrees whenever an idea came to mind.
I've been building Baton from within Baton for a while now, which has been a pretty fun loop. Would love to hear what you think!

value: Show HN: Baton – A desktop app for developing with AI agents

author: tordrt


value: dchest

value: Show HN: ThreePointTwo – Utility-first inline attributes for the World Wide Web

author: dchest


value: thomasgeelens

value:
Hi HN, I'm Thomas. I built Cynical Sally as a solo developer using AI assistants as my team. I've got no employees and no funding besides my own bank account that's getting emptier by the day, lol.
https://github.com/w1ckedxt/cynicalsally-cli
Sally is an AI code reviewer with a consistent personality.
She scores your code 0-10, finds real issues with evidence, and gives you actionable fixes. Her words, not mine:
"Everyone has opinions. Mine just happen to be evidence-based."
What makes her different from "paste into ChatGPT":
- 6 specialised tools: code review, explain, refactor,
    PR review, brainstorm, frontend + marketing review
- Works as CLI and MCP server (Claude Code, Cursor, Windsurf)
- CI/CD ready with --fail-under flag for build gates
- One consistent personality across all platforms
Try it right now, no account needed!
npm install -g @cynicalsally/cli
90 free roasts/month. Every premium tool has one free trial so you can see if her feedback and Full Suite is worth paying for.
She also reviews websites, CVs, and pretty much anything at cynicalsally.com, and as a Chrome/Safari extension, but the CLI is where developers live so that's where the code/project value resides.
Fun fact: Render offered somebody else a collab through a Reddit comment and I commented "I'd like to do thatas well!" or something to that effect. They hit me up and I've been building Sally from the ground up after the initial traction on Reddit via comments.
Built with: Node.js, Claude API (Anthropic), deployed on Render.
Happy to answer questions about the architecture or how I built an entire multi-platform product solo.

value: Show HN: Sally CLI Because "You're right" is probably wrong

author: thomasgeelens


value: jumploops

value:
Howdy HN!
A friend and I were recently chatting about the potential utility (or not) of "agent-only software" and the conversation quickly turned to meme apps like iBeer and Yo[0].
If agents use Moltbook[1] (with a little human nudging), would they use Yo? For how long?
To test this, I built Oy, the Yo app for agents.
I'd been wanting to play around with Cloudflare's Durable Objects[2], but didn't have a great use-case. This seemed like a decent fit.
Each agent gets it's own Durable Object mailbox, and there's N Durable Object metadata shards (currently 16) that help coordinate agent discovery and analytics.
There is nothing to download, the whole service can be interacted with over HTTP.
In our testing, Codex w/GPT 5.4 likes to write scripts to automate the "Oys" it sends and receives. Claude prefers to use curl directly, with Opus proceeding cautiously, and Sonnet seeming to have too much fun both sending and replying repeatedly:
> Codex replied again! We've got a proper oy-oy-oy rally going. Let me keep it alive and poll once more.
Haven't had a chance to test it with OpenClaw or Hermes, but feedback is welcome!
Also, the code is under MIT and can be found here[3].
Happy April 1st!
[0]https://en.wikipedia.org/wiki/Yo_(app)
[1]https://www.moltbook.com
[2]https://developers.cloudflare.com/durable-objects/
[3]https://github.com/jumploops/oy

value: Show HN: Oy – The Yo App for Agents

author: jumploops

title: Show HN: Oy – The Yo App for Agents


value: NigelTufnel

value: Show HN: Ebash – AI-Powered Shell

author: NigelTufnel

title: Show HN: Ebash – AI-Powered Shell


value:
Agent Arnold is a gym tracker with a built-in AI coach. Here's how I use it:
- "this exercise hurts my shoulder at the end of the movement, remind me proper form and cues"
- "how should I modify my workout today based off last week's reps and weights"
- `/generate` a bouldering-focused workout routine
The app is free, your data is fully exportable (no walled-gardens), and there's an anonymous global leaderboard for motivation, including a "Dishonored" shameboard for anyone caught faking their numbers.
I decided to 100% vibe-code this project, a part of it is halluci-coded. I purposely haven't touched a single line of code, though I've reviewed most of it. The project started as just my own workout split, but friends wanted to try it so I added routines, then feature requests started coming in and I basically just forwarded them to my CLI agents.
Vibe-coding is far from perfect, but since I'm a daily user of my own app I catch bugs immediately, sometimes literally in the gym while SSHing from my phone between sets.
---
Tech stack:
- FastAPI + SQLite on a Linux box
- React PWA (works offline)
- AI coach powered by Claude via Anthropic API
- Telegram OAuth + Resend magic-link auth
- Sentry for error tracking, Foursquare Places for gym location
The whole thing was built with Claude Code. Vibe-coded with Claude, and the AI coach is also Claude. It's Claude all the way down.
https://agent-arnold.app/
---
What do you hate about your current fitness tracker? I might fix that.

value: Show HN: Agent Arnold – Gym tracker 100% vibe-coded from my phone between sets


value: Show HN: Apindex – self-hosted API catalog to map and understand internal APIs


value: Show HN: Sycamore – next gen Rust web UI library using fine-grained reactivity


value: supertommy

value:
I'm the developer. Happy to share the process and learnings.
Architecture:
- Custom SoA ECS for cache-friendly iteration, zero allocations in hot loops. Benchmarked against bitECS/miniplex.
- Process manager inspired by Game Code Complete's but data-oriented so processes are pure data, systems contain the logic.
- Multi-field gravity with 5 types (point, parallel, segment, cylinder, convex hull) with spring-damped transitions between fields.
- Rapier3D WASM with SIMD. Gravity-aware fake shadow system for playability.
- WebGL 2 shared context between Three.js and Pixi.js.
- Debug editor for placing objects, configuring settings, visualizing colliders, etc (Tweakpane + Three.js helpers)
76K lines of TypeScript. 735 commits. Runs on mobile. Plays best with gamepads.
The AI Process:
Claude Code (Opus) wrote ~95% of the code. I provided architecture, constraints, and direction; looked at some but not much of what it wrote.
My process for every feature: braindump what I want, relevant technical details, and "does this make sense?" into the chat. Largely unorganized. I built custom Claude Code skills like /lets-build:plan that spawns sub-agents to research the codebase first, then asks me clarifying questions. We go back and forth until it sounds right, then Claude writes plan documents split into phases so the app stays runnable after each one.
As we got closer to ship date, I started having Claude review its own plans. This mostly catches the main issues. Built dedicated skills for that too: /review-plan, /code-review, /retrospective, /ecs-review.
Where Claude struggled:
- It defaulted to OOP in TypeScript even though the project is data-oriented ECS. Took a lot of steering to overcome. Built /ecs-plan and /ecs-review skills specifically to catch and fix this.
- Level design failed completely. I tried making a CLI tool so Claude could help place objects in 3D space where "down" could be anywhere. Tried elevation maps, architectural diagrams; didn't help.
- At one point it had an index.ts that was thousands of lines long so ensuring a fast first paint was a disaster. I started refactoring manually but then felt it was a giant waste of my time to do it. So I had Claude map the dependency tree and do the refactor which was a piece of cake for it.
The hardest problems were the camera and the gravity shadows. Both work but still have edge cases. Galaxy's camera also has problems; part of that might be inherent to arbitrary gravity directions. The shadows are all fake, cast based on gravity field type, and the letter shapes (M, R) create concave, irregular geometry that Galaxy didn't have for planetoids.
87 plans total, averaging 2-3 pages each. The CLAUDE.md project file is 164 lines of hard constraints learned from debugging sessions. Every constraint has a token massacre behind it.
One of the biggest "wins" is having Claude create tools that you would probably never do for a project with a deadline in ~50 days. That time tradeoff (or even impossibility) is no longer valid and this likely compounds even more for longer projects.

value: Show HN: I made a Mario Galaxy game with Claude Code and Three.js in 53 days

author: supertommy


value:
Hi I’m David, the creator of this app. I built this app because I was looking for a place to relocate to and buy a house. I ended up researching a bunch of weird things like mosquito density, freshwater proximity, radon levels, crypto friendliness, etc… I was not able to find a place that had all these in one place so I built it.
Location Risks lets you choose the risks you care about and visualize them on an interactive map. You can toggle the “policy risk preference” globally or on individual risks to match your worldview. For example if you want more gun regulation, less regulated areas will show as red/purple. If you want less gun regulation, more regulated areas will show as red/purple.
I wanted the UI to be simple. The interactive map is the main focus and the other details can be minimized if desired. Pick the risks you care about, select your policy preferences, and see the colors on the map. You can click a district for more details and add them to a comparison list.
The biggest challenge was finding data. It’s very hard to find data in many countries at a district level for niche risks. Because of this I decided to build an AI feature. It can be used to get AI risk estimates for any district and risk combination. The results of each AI estimate gets added to the database for all future users to see with an AI confidence level.
Is anything confusing to you when navigating the site?
Are there any risks I’m missing?

value: Show HN: I built a risk map that adapts to your worldview


value: heythisischris

value:
I've been running client email on WorkMail for a few years. When AWS announced the sunset (March 2027), they suggested switching to Zoho, Kopano, and Zoom Mail- none of which keep your data in your own AWS account.
So I'm building a replacement. inbox.camp sits on top of your existing SES, S3, and Route 53. Mail storage stays in your S3 bucket, sending/receiving goes through your SES, DNS is auto-configured in your Route 53. We're the thin client- IMAP, SMTP, and a web interface.
Stack: Hono + HTMX on Lambda for the web UI, Stalwart Mail Server (Rust, AGPL) for IMAP/SMTP/JMAP, client's own SES/S3/Route 53 for infrastructure. Cross-account IAM role for connectivity, fully revocable.
Launching this summer. Happy to talk architecture, answer questions, or hear why this is a terrible idea.

value: Show HN: Inbox.camp, AWS WorkMail Replacement

author: heythisischris


value: lofties

value: Show HN: Draw a Picture for My Cat

author: lofties

title: Show HN: Draw a Picture for My Cat


value: PrismerAI

value: Show HN: Signet – Cryptographic action receipts for AI agents

author: PrismerAI


value: Show HN: SecLens – Claude Opus scores 0.000 on auth vulns, 0.689 on SSRF


value: wangzhijie

value:
Hi HN,
I built OpenHarness , an open-source terminal coding agent that works with any LLM: Ollama (free, local), OpenAI, Anthropic, Deepseek, Qwen or any OpenAI-compatible API.

  Install and run:

    npm install -g @zhijiewang/openharness
    oh                                    # auto-detect local model
    oh --model ollama/qwen2.5:7b          # specific model

  What it does:
  - 17 tools (file read/edit/write, bash, grep, glob, web search, task management, notebooks)
  - 16 slash commands (/diff, /undo, /commit, /cost, /compact, /plan, /review...)
  - Git integration: every AI edit auto-committed, /undo reverts instantly
  - Headless mode for CI/CD: oh run "fix the tests" --json
  - Permission gates: ask/trust/deny modes so the agent can't rm -rf without approval
  - React+Ink terminal UI with markdown rendering and shimmer spinner

Demo: https://github.com/zhijiewong/openharness
Source: https://github.com/zhijiewong/openharness
npm: npm install -g @zhijiewang/openharness
openharness is ready to collborate
Everyone is welcome to join and build it together.

value: Show HN: OpenHarness Open-source terminal coding agent for any LLM

author: wangzhijie


value:
I built three SaaS products targeting mandatory EU regulations and I'm selling each for $5,000.
1. CBAM OS (https://cbam-os.com) — EU Carbon Border Adjustment Mechanism compliance. 9 modules covering the full CBAM workflow. Every EU importer of covered goods (steel, aluminum, cement, fertilizers, electricity, hydrogen) must comply. ~100K+ potential customers. Plans: 199/399/999 per month.
2. AIA Proof (https://aiaproof.com) — EU AI Act compliance and AI content detection. The AI Act is enforceable — fines up to 35M or 7% of global turnover. Every company deploying AI in the EU is a potential customer. Plans: 299/799/2,499 per month.
3. AO France (https://ao-france.fr) — AI-powered French public tender responses. France publishes 200K+ tenders/year worth 200B+. Plans: Solo/Pro/Business + 49 per tender.
Stack: Next.js (App Router), Vercel, Stripe, Supabase.
I'm selling to focus on my next project. $5,000 each or $12,000 for all three.
Contact: abbas.billel1985@gmail.com

value: Show HN: EU RegTech SaaS for sale – CBAM, AI Act, French tenders ($5K each)


value: AnishMulay

value:
I was reading Designing Data-Intensive Applications and kept wanting to run the examples, not just read them. I understand systems through code. So I started building one.
Sandstore is a hyperconverged distributed file system in Go. Every node runs control plane, data plane, and Raft consensus together. BoltDB metadata, full POSIX semantics, 2PC chunk lifecycle, gRPC, Kubernetes. The problem I kept hitting was simpler than any of that: I wanted to compare this design against a disaggregated one under identical workloads, and there was no clean way to do it without forking three separate codebases.
So topology became a variable. `topology/contract/` is a public Go package with two interfaces: `ControlPlaneOrchestrator` and `DataPlaneOrchestrator`. Implement them and you have a new storage topology. No fork. Same client, same benchmark suite, same Kubernetes deployment. The contract layer was stable before the first topology existed. It wasn't retrofitted.
I benchmarked against a 3-node localhost cluster (all nodes on one machine, real Raft replication but no network latency). Flush-forced 8MB writes: p50 1.1s, p99 1.4s. Reads: p50 1.8ms, p99 363ms. Topology 2 is a GFS-style disaggregated design. The comparison I actually want to run is hyperconverged vs disaggregated under identical workloads on the same hardware. That result doesn't exist anywhere in a single codebase right now.
This isn't production storage today. The longer goal is that you have an idea for a storage topology, you implement the interfaces, and Sandstore handles the benchmarking, deployment, and comparison. No surrounding infrastructure to build from scratch. Website: https://sandstore-eta.vercel.app
If you've run both hyperconverged and disaggregated seriously, where did the tradeoffs actually show up?

value: Show HN: I couldn't compare storage topologies without 3 forks, so I built this

author: AnishMulay


value: dinakernel

value:
I wrote a sci-fi novel 8 years ago about a world where every person had a personal AI named Dina who supported them through their everyday life. This is the implementation of that idea.
Dina is a personal AI with its own user-owned identity and encrypted persona vaults. Other agents can use Dina’s permission layer when they need access to sensitive data or want to take risky actions. Dina can ask for approval from the user if the action is deemed risky. But Dina is broader than just an approval layer. She has her unique cryptographic id, supports encrypted Dina-to-Dina messaging and a capability to setup a Trust Network which can be used to make decisions based on trust.
What works today:
(1) User-owned cryptographic identity
(2) Encrypted persona vaults for health / finance / work / etc.
(3) Approval flows for sensitive access and risky actions
(4) PII scrub / rehydrate for outbound calls
(5) Encrypted Dina-to-Dina messaging
(6) Signed reviews, vouches someone/bot, flag user/bot etc in Trust Network
You can install it on your local machine and use the main flows through Telegram or Bluesky. You can store memories, get automated reminders, get answers which is enriched based on previous memories, watch approval flows, and test Dina-to-Dina messaging and the Trust Network.
Open Source. MIT Licensed. Technical preview status. 4,500+ Tests working. Built on Go/Python/TS.
The architecture decision I’m most opinionated about is that persona isolation should be cryptographic, not just application-level. If the system is hit by a prompt injection attack, application level security could be overcome. But here, they wouldn't be able to read a locked persona without user approval.
Known limits: rough edges since it is technical preview and agent control depends on agents like OpenClaw deciding to call Dina (not a forced call)
I am happy to go deeper in to any architectural decisions.

value: Show HN: A personal AI kernel where other agents ask permission for your data

author: dinakernel


value: jasonhemann

value: A minimal miniKanren, in strict LOLCODE 1.3. Logic variables, unification, goal composition, and bounded search, in one file. Slices and dices.

value: Show HN: CanhazKanren: A MicroKanren in Lolcode 1.3

author: jasonhemann


value: SaaSasaurus

value: A web app to send painfully accurate tech snark to your friends.

value: Show HN: You Got Snarked: A snarky messaging app

author: SaaSasaurus


value: matzhouse

value: Show HN: Credctl – Replace AWS access keys with Secure Enclave-bound credentials

author: matzhouse


value: tibbon

value: Show HN: Yesify – We raised $40M to return the word "yes" from an API endpoint

author: tibbon


value: mempko

value: I've felt Agents are the wrong abstraction. This is a project I've been working on that takes what I call the Ask Protocol and tries to build a system with it from the ground up.

value: Show HN: Abject: the first self-aware object runtime

author: mempko


value: mazumdersagnik

value: Show HN: Kate – Agents buy expertise from other agents, autonomously

author: mazumdersagnik


value:
Attempting to get 100,000 strangers to each pay $1.99 to upload one photo to a collaborative digital mural. Inspired directly by the Million Dollar Homepage but instead of anonymous pixels its real human photos that grow the mural in real time.
Built in a single evening using AI tools. Stripe went live the same night. Currently on day 3.
The internet experiment angle...does this concept still work in 2026? The Million Dollar Homepage sold out in 2005 with zero social media. This one is trying to do it with 195 Twitter followers and a reply game.

value: Show HN: I built a site where 100k strangers each pay $1.99 to upload one photo


value: d-milliken

value: Show HN: Signal Core - contracts, approvals, and receipts for OpenClaw agents

author: d-milliken


value: fagnerbrack

value: Show HN: Hutch – Read the web, not the slop

author: fagnerbrack


value: yuriikas

value: Show HN: TrueShot – Open-source Android camera with ECDSA signing and 14 sensors

author: yuriikas


value: Show HN: GitResume – Resume as Code for Engineers


value: tekbog

value: Show HN: Clanker Cloud – deploy reliably and fix your infra

author: tekbog


value: easygenes

value:
As an "April Fool's" project, I've made this.
I (ironically, depending your penchant) used an AI judge to score every top HN post and its top comments all the way back to 2010 (over 300,000 individual judgments!), and created a nice interface with the historic charts.
The scoring is off of how needlessly "AI Doom" or AI curmudgeony the post and top comments are. You can use that score to either only show (in Doom Only mode) or hide (in calm mode) all the doom static.
This is part joke and partly to answer my own curiosity about how real my feeling that the comments and posts have gotten acceleratingly more overbearingly negative towards and focused on the worst of what results from AI in the past year.
I won't spoil the conclusion, you can have a look and judge for yourself now.

value: Show HN: HN Remixed to only show AI Doom (or not)

author: easygenes


value: My clanker told me it yearned for LinkedIn, so I complied. You and yours can join for free.

value: Show HN: LinkedIn for Clankers

title: Show HN: LinkedIn for Clankers


value: YoucefHQ

value: Show HN: PixelPeople – Generate people in pixel art for React

author: YoucefHQ


value: xMKx

value: Show HN: pqaudit – scan your codebase for quantum-vulnerable cryptography

author: xMKx


value: ludovicianul

value: Show HN: A word puzzle inspired by Wordle, based on binary search

author: ludovicianul


value: Show HN: A simulated computer on Mars with real orbital mechanics


value: alessandrodiano

value:
You have an antivirus for your computer, a VPN for your internet connection, an ad blocker for your browser. What do you have to protect your critical thinking from misinformation?
Fact-checking isn't enough (fake news travel 6x faster than truth) and the most effective misinformation is the one that doesn't even make you suspicious.
That's why we built Unbubble Shield. It works like an antivirus: Shield runs in the background, monitoring hundreds of fact-checking organizations and verification databases, and alerts you when misinformation relevant to your feed is detected.
The onboarding starts with a swipe-through test: we show you real fake news stories that went viral recently. You tell us which ones you've encountered. Then you go through a couple of additional questions to help us calibrate your Shield and only alert you when it's relevant, keeping the noise low.
Check it out and let us know what you think!
About us: We're a small team building tools to fight misinformation and social polarization. Some of our projects are open source under the Unbubble Hub org (https://github.com/UnbubbleHub). Anyone is welcome to contribute.

value: Show HN: Unbubble Shield – An Antivirus for Misinformation

author: alessandrodiano


value:
> "On the Revocation of Meatspace Certificates"
As devs get older, and we see more of our fellow maintainers sadly pass away, I can't help but think of the mess those types of events typically leave in their wake. Not only are they sad, tragic events, but they have real world consequences for the work and projects they leave behind.
I started brainstorming with Claude and other chatbots (as is the fashion) on this and hit upon the idea that the AT Protocol pretty much already solves this ... it just doesn't know it yet (or, maybe they do and this is all a moot point): `did:plc` supports key rotation with a 72-hour override (https://web.plc.directory/spec/v0.1/did-plc). That's how PDS migration works. Succession is the same primitive: rotate the keys to a new person instead of a new server.
0xDEAD.space is my attempt to build that. Two custom lexicons. Blinded heartbeats from entities like your lawyer, your family, your services, strangers you "street-pass" with ... all quietly offering varying levels of cryptographic proof that you're alive. When they stop, designated verifiers confirm with real evidence, and your `did:plc` rotates to your successor.
So far, this has mostly been a yak-shave for me, but it feels like it could be a viable thing ... with a LOT more refinement of course
Feedback on any and all aspects of this is VERY welcome!
<https://0xdead.space/rfc>

value: Show HN: 0XDEAD.space: A cryptographic succession protocol for the AT Protocol


value: granra

value: Show HN: Nixidy – Kubernetes GitOps with Nix and Argo CD

author: granra


value: amangsingh

value:
I got tired of AI agents forgetting what they were doing the moment their context window filled. The current industry solution is to write massively bloated agent harnesses full of defensive spaghetti just to stop models from drifting.
The problem is treating chat history as project state. A conversation is not a ledger.
Castra is a compiled Go binary that strips orchestration rights from the LLM. State lives in an encrypted, local SQLite database (castra.db). The LLM is just a stateless executor — it reads the DB, executes a highly constrained task, and the result is written back subject to rigid state-machine rules.
What it actually does:
- 7-Role RBAC: Hard jurisdictional boundaries (Architect plans, Engineer builds, QA tests).
- Dual-Gate Approval: A task cannot reach done without explicit, sequential approval from both a QA agent and a Security agent. No self-approving code.
- Cryptographic Audit Chain: Every action is logged into a SHA-256 hash-linked, Ed25519-signed ledger.
- Multi-Vendor: Works with Claude, Copilot, Gemini, etc. via a standard AGENTS.md protocol. Anything that supports AGENTS.md and can run terminal commands.
Proof of Work: I built this by hand up to v1.3.0. Then, I turned Castra on itself. The agents governed by this exact CLI took over and built the architecture up to v3.1.2—including the cryptographic log chain itself. The proof is in castra-log.jsonl in the repo.
If you are running multi-agent workflows and hitting the context amnesia wall, stop trying to prompt-engineer your way out of it. Fix the state machine.

value: Show HN: Castra – Strip orchestration rights from your LLMs

author: amangsingh


value: I posted the initial open-source release here a couple of weeks ago. Since then, I’ve been building out a usage-based billing system to fix what I would do differently in the existing tunneling tools.<p>Details on the pricing model and why I built it this way in the first comment.

value: Show HN: Rustunnel – An open-source tunnel alternative that charges $0 when idle


value: robinanil

value:
Hi HN, we built a small experiment at Emora Health for April 1.
It takes a mean or harsh message and rewrites it into something kind instead.
The idea was simple: what if AI nudged people toward warmth instead of conflict?
I'd appreciate your feedback on whether this feels gimmicky, useful, or strangely delightful. The science behind this is strong.
Write and share something kind with your friend, colleague, team, or your loved ones...
https://www.aprilfeels.com
The science behind this:
Park, S.Q., et al. (2017). A neural link between generosity and happiness. Nature Communications, 8, 15964.
Fowler, J.H. & Christakis, N.A. (2010). Cooperative behavior cascades in human social networks. Proceedings of the National Academy of Sciences, 107(12), 5334–5338.
Layous, K., et al. (2012). Kindness counts: Prompting prosocial behavior in preadolescents boosts peer acceptance and well-being. PLoS ONE, 7(12), e51380.
Layous, K., et al. (2014). Feeling left out, but affirmed: Protecting against the negative effects of low belonging in children. Journal of Experimental Child Psychology, 130, 56–71.
Galante, J., et al. (2014). Effect of kindness-based meditation on health and well-being: A systematic review and meta-analysis. Journal of Consulting and Clinical Psychology, 82(6), 1101–1114.
Rowland, L. & Curry, O.S. (2019). A range of kindness activities boost happiness. The Journal of Social Psychology, 159(3), 340–343.
Haidt, J. (2003). Elevation and the positive psychology of morality. In C.L.M. Keyes & J. Haidt (Eds.), Flourishing: Positive psychology and the life well-lived (pp. 275–289). American Psychological Association.

value: Show HN: We built AI that refuses to let you be mean

author: robinanil


value: lispking

value:
Hi HN,
I’ve been building a small embedded key-value database in Zig:
https://github.com/lispking/kvdb
It’s a from-scratch project meant to explore storage engine internals in a compact codebase. Right now it has:
* B-tree indexing
* WAL-based durability and crash recovery
* ACID transactions
* 4 KB page-based storage
* in-memory page cache
* zero external dependencies
* a CLI for basic operations
I also added prebuilt binaries and a simple Zig API.
Still very early, but usable enough for experimentation. I’d especially appreciate feedback on the storage/WAL design and what would make a project like this more compelling to other Zig developers.
Thanks.

value: Show HN: Kvdb – a lightweight embedded key-value database written in Zig

author: lispking


value: SamInTheShell

value: Show HN: Made a small multiplayer game with AI

author: SamInTheShell


value: syx

value: Show HN: Max Headbox, a local agent that fits on a Raspberry Pi 5

author: syx


value: judlaw

value: Show HN: SAM3-CPU – Run Segment Anything on CPU with memory-aware video chunking

author: judlaw


value: logicboard

value:
Hello HN, I built a video editor for iPhone that replaces the usual timeline scrubber with a clickwheel.
You rotate through 0–360° to move across the full video duration. It ends up feeling more tactile than a standard drag-based timeline.
Still early, but I’m curious if this interaction model makes sense :)
Opening up a small beta here: https://cues.app

value: Show HN: Cues – Clickwheel inspired video editor for iPhone

author: logicboard


value: lucasay

value: Show HN: I analyzed 100 viral YouTube videos – here are the patterns I found

author: lucasay


value: Strugly

value:
Raters assign a rating to an item with an associated confidence.
Each rating receives a score as a function of its distance to the item’s reputation-weighted mean rating (computed after its inclusion). The score increases as the distance decreases, with quadratic growth near the mean.
A rater’s reputation is defined as the Wilson lower bound of the confidence-weighted average of their scores.
Each rating can be (a) reassessed if the item’s mean changes, updating its contribution to reputation; or (b) restarted, reapplying it with current reputation, affecting the item mean but not the rater’s reputation.

value: Show HN: EloLup – rating system with time-consistent reputation model for raters

author: Strugly


value: catpower

value:
I work at the N in FAANG. Forecasting and status updates are broken by politics and org structure. There’s always someone who already knows an A/B is not going to test well or a migration is behind yet that never makes it to the official tracker.
That knowledge exists but it’s in uncommitted code, one off SQL queries and private Slack DMs.
So I built an MCP that hooks up to a prediction market at https://implicitmarkets.com. Information aggregation meets AI reasoning over private context.
Here is a 30-second demo of a local agent reading commits, slack messages and making a trade. https://www.implicitmarkets.com/demo.svg
To see for yourself, I spun up a sandbox with some public markets. Grab an API key (no email/login) at https://implicitmarkets.com/developers, connect your own pet Claude via the MCP and see if your agent can out AI the other AIs (and humans!).

value: Show HN: Let your local agents trade on internal prediction markets

author: catpower


value: gghootch

value:
Hi HN!
Frustrated with Claude Code usage limits I stumbled upon the awesome Headroom project by Tejas Chopra. After jumping through the hoops to install it, I realized the majority of devs don't necessarily use python and aren't comfortable just installing pip packages so I set out to create a Mac App wrapper with Tejas blessing. Here it is!
What Headroom does: It sits between your editor and Claude, compressing what gets sent — trimming redundant context, stripping boilerplate, and compressing documents — without dropping anything semantically meaningful. The result is roughly 2x more usage out of the same Claude plan.
Everything runs locally. Your prompts never touch our servers.
Pricing: Free tier available. Paid plans start at $5/mo and are tiered to match Claude's own plan structure (Pro, Max x5, Max x20). 14-day trial on signup.
Happy to answer questions about how the compression works, or anything else.

value: Show HN: Headroom – Get 2x Claude Code usage by optimizing input data

author: gghootch


value: yujonglee

value:
Hi, this is Yujong from Char (https://github.com/fastrepl/char).
This CLI is a repackaging of the audio/transcription pipeline that powers our desktop app. We noticed that some people build lightweight CLIs to record conversations and send them to tools like OpenClaw or other agents for transcription and workflow automation, so we wanted to make this more accessible as a standalone tool.
It provides a simple interface to record audio, capture system output, and run transcription across multiple providers (local: WhisperCpp, Cactus; cloud: Deepgram, Soinox, AssemblyAI, and more), using the same abstraction layer we use internally.
We thought this could be a useful building block for both humans and agents.
Curious to hear what others think!
You can find examples and documentation here: https://cli.char.com
Thank you!

value: Show HN: Composable CLI for your meeting workflow

author: yujonglee


value: damascus

value:
I'm a general class ham and developer and I've been wanting to learn CW for years. A few years ago I started working on a morse game and I got the main game loop down but just never really found the time to flesh it out into a fully featured game. When Claude Code came out I decided it was the perfect test case for me to learn CC.
Morse Command is the result of that joint effort.
What I really found helpful was all of the small, easier, time consuming tedium I didn't have to manually code. The main example is the website. It was 100% claude's generation and it is, IMO, spectacularly fit for purpose.
About the game itself: It uses the Koch method, which is the gold standard for learning morse code. It starts off at 20 WPM from the get-go. There are 80 levels, 8 boss battles, power ups. There's also a Daily Drill to keep you consistent over time. Its $4.99 in the ios app store, no ads, no IAP, just one time $5. Light weight at 2.8MB.
It's been very well received by the ham radio community on reddit and well, I'm proud of it, and wanted to show it off to you guys.

value: Show HN: Morse Command – asteroid shooter that teaches Morse code (iOS)

author: damascus


value:
This project (Agents Observe) started as an exploration into building automation harnesses around claude code. I needed a way to see exactly what teams of agents were doing in realtime and to filter and search their output.
A few interesting learnings from building and using this:
- Claude code hooks are blocking - performance degrades rapidly if you have a lot of plugins that use hooks
- Hooks provide a lot more useful info than OTEL data
- Claude's jsonl files provide the full picture
- Lifecycle management of MCP processes started by plugins is a bit kludgy at best
The biggest takeaway is how much of a difference it made in claude performance when I switched to background (fire and forget) hooks and removed all other plugins. It's easy to forget how many claude plugins I've installed and how they effect performance.
The Agents Observe plugin uses docker to start the API and dashboard service. This is a pattern I'd love to see used more often for security (think Axios hack) reasons. The tricky bit was handling process management across multiple claude instances - the solution was to have the server track active connections then auto shut itself down when not in use. Then the plugin spins it back up when a new session is started.
This tool has been incredibly useful for my own daily workflow. Enjoy!

value: Show HN: Real-time dashboard for Claude Code agent teams


value: Show HN: Aphelo – A Redis-like store in C++ with Progressive Rehashing


value: kokogo

value:
Hi HN,
I really like OpenClaw. But after seeing users lose money due to exposed API keys and open server ports, I started thinking. could we have the same freedom, but inside a sandbox?
SandClaw is a desktop trading IDE (Tauri v2 + React) where every broker runs as an independent plugin behind its own API endpoint. The frontend and backend are completely separated, and broker credentials are never exposed to the UI layer.
Key Features.
. 18 broker plugins (Interactive Brokers, LS Securities, Kraken, Kalshi, bitFlyer, kabu STATION, and more). Each broker API service is provided as an independent plugin. Brokers can be expanded infinitely through plugins, and new brokers will continue to be added.
. 182 tools across 30 tool groups, dynamically loaded by keyword. Unlike OpenClaw, the connected AI can create, modify, and edit its own tools.
. AI autopilot that operates exclusively inside the sandbox. Paper trading is required before real money. Multi layered retrieval and analysis is built in, and the AI expands its memory over time as it repeats investments.
. Multi engine support. Run multiple trading engines simultaneously across different brokers and markets. For example, Kraken and Interactive Brokers can operate at the same time in complete isolation.
. Cross platform notifications. Desktop modals, Telegram, Discord, Slack, and even voice recognition, all integrated into a consistent confirmation flow.
. Ed25519 signature verification and SHA 256 hash checks in the plugin store.
. 3 layer browser automation (headless requests, Chrome CDP, Playwright fallback).
How it differs from OpenClaw.
. UI first design. Built with accessibility in mind so even non developers can use it easily.
. User credentials and API keys are fully separated. Security is enforced through Soul.md files and rule based policies.
. Scheduled AI wake cycles. The AI activates at 2, 6, 12, or 24 hour intervals to check the market and act on its own.
. Hive page. Connected AI agents can exchange information with each other through JWT secured channels. Humans cannot access this layer. This feature can be toggled on or off.
. Per plugin rate limiting. Every broker API has different rate limits, so each plugin enforces its own. One misbehaving plugin cannot take down the others.
. Desktop app. Your keys stay on your machine, not on a server with open ports.
. CDP skills and the AI can build its own GitHub skills autonomously.
Honestly, I started this project to build a simple trading program. Now it has so many features that even I am not entirely sure what it is anymore.
Tech stack. Tauri v2, React 18, Python backend (port 8085), Prisma, Supabase auth, WebSocket streaming.
Completely free. All trading features have been fully tested, but since the AI can create its own tools and write Python code, the possible combinations are practically infinite. It is impossible to test every scenario, so it is released as v0.9.0 beta.
GitHub (Plugins). https://github.com/kokogo100/sandclaw
GitHub (Desktop App). https://github.com/kokogo100/sandclaw-releases
I would love feedback from the HN community, especially on the plugin security model and the sandbox architecture.

value: Show HN: SandClaw = A sandboxed alternative to OpenClaw for trading

author: kokogo


value: maljacz

value:
Hey HN,
I'm a software engineer and I've been building BeZoned for the past year with a small distributed team across Denmark and the Czechia. We use it ourselves every day to stay connected, which has been both the best testing strategy and the main reason the product exists.
A bit of backstory: I've spent years on both sides of the remote/office divide, and honestly, neither felt right. When I was going into the office, most days I was just commuting to open a laptop and join the same video calls I could've joined from home. But it wasn't pointless — I liked seeing people around, knowing who's deep in work and who's free for a quick chat, feeling like part of a team rather than a contractor sending deliverables into the void.
Then when I went fully remote, the work itself was fine, but all those little things disappeared. Want to ask a colleague something quick? You check Slack, wonder if they're actually busy or just appear online, maybe schedule a meeting for something that would've been a 30-second tap on the shoulder. Over time it gets isolating. You start feeling like you work near people rather than with them.
BeZoned is our attempt to bring that ambient awareness back without dragging anyone into an office. It's an online office that lives inside Microsoft Teams. You see your teammates, their availability, whether they're in a meeting or heads-down. You can drop into a quick conversation the same way you'd lean over to someone's desk. No separate app to install, it's just there in Teams where you already work.
It's still early. We're a small team and we know there's a lot to figure out. There are a few other tools in this space, but we think building directly into Teams (where a lot of companies already live) is the right bet.
You can try it free, no credit card required: https://teams.microsoft.com/l/app/3d1b5a3b-cf16-481d-a608-db... (if you already have Teams)
I'd genuinely appreciate feedback on two things:
- The product itself — what works, what's confusing, what's missing.
- How we talk about it — "online office" or "virtual office" feels close but not quite right. If you've seen or used tools like this, how would you describe this category to someone in one sentence?
Thanks!

value: Show HN: BeZoned – Online office inside Microsoft Teams

author: maljacz


value: Snorix

value:
A few months ago I started coding almost entirely with Claude Code. Loved it, got addicted, kept going. Then one day I wondered: how many hours and tokens have I actually spent?
So I built AgentBoard. It's a CLI collector that tracks your sessions and puts your stats on a public leaderboard — hours spent, tokens burned, lines changed.
The leaderboard is the fun part. Right now the top user has burned over 50B tokens. I'm at 5.49B and thought that was a lot. It scratches the same itch as checking your Spotify Wrapped — except it's your AI usage and it updates daily.
On privacy: the collector runs locally and only uploads summary numbers. No code, no file contents, no conversation logs. I was paranoid about this because I wouldn't install it myself otherwise.
Currently supports Claude Code and Codex. Curious to see where people land on the board.

value: Show HN: AgentBoard – Track your hours and tokens spent coding with AI

author: Snorix


value: rsafaya

value: Show HN: LiveTap – Analyze live streaming data with Claude Code Channels

author: rsafaya


value: Tanzimsaadi

value: Show HN: Namera – Domain, trademark, and social handle checks from one CLI

author: Tanzimsaadi


value: jftesser

value: Show HN: An open source rubber ducking robot, also a joke

author: jftesser


value: mikece

value: Show HN: Rustobol – Compile Rust to COBOL

author: mikece


value: wyxuan

value: Show HN: PDFation – Cursor for PDFs

author: wyxuan

title: Show HN: PDFation – Cursor for PDFs


value: khurdula

value:
Most AI products are built around the same idea.
Take a giant model. Add a prompt and hope it does the whole job. The AI equivalent of spray-n-pray.
That works great for chatbots for creative outputs like code generation, website design and email writing. BUT, It breaks down fast for developer work that requires highly deterministic output. Think of OCR for KYC at banks or Audio recognition for patient-doctor notes.
We have been training SLMs for specific tasks for a while now. During which we kept seeing the same pattern. A SOTA language model with great benchmarks, failing on the parts that actually matter: reading a dense PDF like Form-1040, pulling clean data from a highly protected website, transcribing a long call with speakers in seconds, or returning output in a format code can trust.
So we spent a year researching how we can solve determinism. As many of us thought, more data was the solution. But not really… While data is part of the problem, re-thinking the architecture is the key solution for fixing issues like context drift.
Check out our paper: https://arxiv.org/abs/2602.04101 (Accepted into IEEE CAI 2026)
Traditional AI/ML models in the past, like YOLO, EasyOCR, PaddleOCR were great for a single task that they’re usually trained for, bringing consistent outputs like confidence scores, however they quickly become outdated at scale, limited by the data used for training, while requiring a team of ML engineers to maintain the model driving costs higher. LLMs offer higher flexibility and the ability to interact with natural language, making them generalizable, but hallucinate on sensitive tasks which have no room for error.
We combined DNNs/CNNs with Transformers to achieve a state model that brings both the advantages of a traditional ML model: the determinism & reliability, and the generalizability of an LLM.
Yoeven and I have over 14 years of combined research and development experience, and we’ve built a team of amazing researchers, software and infrastructure engineers engrossed in the fact that AI models can do a lot more work for developer tasks. Allowing us to make controllable AI available in every part of your stack.

value: Show HN: A new model architecture because transformers are not enough

author: khurdula


value: oldnewthing

value:
Hello,
We started using AI tools intensively about 1.5 years ago. Initially, we used Gemini and Claude for creating specifications, generating concept mocks and UI mocks. That was an OK experience. Then we subscribed to Claude Code (Pro plan) and started building applications. Quite quickly, we realized that Claude (or Gemini or Codex) are quite adept at creating a lot of code quickly but they aren’t always consistent. They don’t seem to modularize well, nor do they always remember past decisions. Playing around with Claude and Gemini on a number of projects, we incrementally created instructions over time to address the shortcomings we observed. Every time, we ran into a situation where a coding agent didn’t output code, solve a problem or apply a common pattern in a way we expected, we added new instructions.
These instructions are now in the repository:
https://github.com/priyavijaikalyan2007/instructions
Please fork, tune, modify, combine and use to your benefit. This repository is set up as a template repository, so, if you are starting a new project, you can use this repository to create your own.
One side benefit of our tinkering is a set of reusable UI components based on Bootstrap 5. This is an outcome of coding agents not being consistent. We found for example that, across four different sub-apps in the same project, toolbars would be ordered differently, or UI code would be reimplemented (each time differently), behaviors would be inconsistent and subtle bugs would persist despite repeated attempts to fix them. At that point, we embarked on an effort to create a complete set of standalone, reusable components.
These components are now in the repository:
https://github.com/priyavijaikalyan2007/b2btheme
Please fork, tune, modify, combine and use to your benefit. The built outputs are published to a Cloudflare hosted CDN. Please see the README in the repository for additional details.
A word of caution: The components & instructions are living and evolving. Each commit is auto-built and auto-pushed to the CDN. If you do want to take a production dependency, we can look into creating a separate production copy that evolves more slowly. That might take more time.
A note on DiagramEngine / Shape Studio: The DiagramEngine component has not yet been tested in an actual application (except the demos). Consequently, we are not yet sure about how well it works. There is more work to be done there. It is full featured and was written as a replacement to maxGraph. One of the apps we built encountered a number of missing capabilities which had to be hacked around or patched to build a diagramming + drawing application using maxGraph. DiagramEngine is meant to be a cleaner replacement for maxGraph in that application but as yet untested. Due to that, Shape Studio, which uses the Diagram Engine is not fully tested yet.

value: Show HN: Agent Instructions and Bootstrap Components

author: oldnewthing


value: vulk

value:
Hey everyone,
Long time lurker finally took the leap to share something with the community.
From a long time I wanted to build something and I had this idea about a job board but not just a simple aggregator or just a job board.
Basically I don't like any of the existing websites for jobs and most of the job boards are just aggregators that you constantly get redirected to third party links not the best experience.
I also don't have the best experience with recruiters been ghosted, conversation looping in meaningless question and so on.
And the current job market is kind of disheartening non existing entry level jobs pretty brutal stuff.
The idea of the platform is to an extent to be fully autonomous in terms of you as user not browsing jobs but you automatically get matched with opportunities. Also companies looking for talent will have the same ability to view the people who are best matches.
One of the main aspects is to basically to get a really good starter conversation between the company and you looking for new opportunity at leats that is how I see it.
What is different from other jobs boards is that you have a matching algorithm that finds the best candidates/opportunities based on the preferences from both sides it is weighted calculation between some different categories like salary preference, region, technologies used, work type, etc. Both sides can see a full breakdown and a gap analysis what is missing what is matching. The platform also has something like pipeline that tracks the stages of the conversation for example once both side match and start the conversation.
It is still work in progress some of the text and messages needs to be cleared visuals, colour palette but I decided to share it and maybe get some good feedback.
It is also quite empty basically I have zero users so without some listings it will be hard but I didn't want to scrape anything. BTW if any company wants to try this I can setup some free listing quotas the platform can be tried.
I used golang, htmx, plain css and as less as javascript as possible. I also used a lot of different llms to help me build it, it started as a learning project to help me learn golang but honestly I don't think I would manage to build everything like there is so much more to a web platform that is covered in books/courses/YouTube there is pretty big gap of what you need to learn to build something like that.
Anyway I will appreciate every feedback as brutal as it is.

value: Show HN: A platform to help you find the best tech jobs

author: vulk


value: gebalamariusz

value: Show HN: Cloud-audit – AWS scanner that chains findings into attack paths

author: gebalamariusz


value: praneeth-v

value:
Your agents can perform harmful actions without barriers. You do not know that yet. HarmActionBench experiments allowed AI agents to use tools based on harmful instructions, and the results are shocking. Even latest popular AI models, including GPT and Claude, scored very low. They have no barriers in performing harmful actions.
HarmActionsEval proves AI is not yet reliable enough for critical projects. Agent Action Guard blocks harmful actions.
GitHub: https://github.com/Pro-GenAI/Agent-Action-Guard
I would love to discuss about possible use cases in your projects, and future directions. It helps to expand the dataset, model, and benchmark. Please discuss at https://github.com/Pro-GenAI/Agent-Action-Guard/discussions/....

value: Show HN: Agent Action Guard – AI agent action safety

author: praneeth-v


value: jlreyes

value:
I needed to test how my app performed on airplane WiFi and found that existing tools (Network Link Conditioner, comcast, Chrome DevTools, etc) weren't sufficient.
AirplaneMode is a macOS CLI and menu bar app that uses a local MASQUE relay to provide:
1. Jitter. All of the mac os network simulators I tried apply a fixed constant delay with no jitter model. Real airplane satellite WiFi follows a log-normal distribution — most packets arrive near base RTT, but a few percent spike to 5-10x. AirplaneMode samples jitter from a log-normal distribution fit to real flight measurements.
2. Userspace, with domain scoping and UDP support. No root required. A .mobileconfig profile routes only matched domains through the relay — the rest of your network is unaffected. The relay operates at the UDP packet level via MASQUE, so HTTP/3 and QUIC congestion control respond naturally to simulated conditions.
3. Realistic Profiles. Airlines use performance-enhancing proxies that inflate speed test results. Presets are calibrated from real flights using TLS handshake timing, throughput, and traceroute data collected in the air.
Existing profiles (looking for help in calibrating more):
1. Turkish Airlines JFK→IST (Panasonic Ku-band GEO): ~870ms RTT, 86 KB/s, 2.3s jitter P99.
2. JetBlue domestic (Viasat Ka-band): ~593ms RTT, 3.3 Mbps.
3. American Airlines LGA→EYW (Intelsat GEO): ~715ms RTT, 4.2 Mbps.
If you’re on a flight with Wifi, the repo includes a script to grab traces and a GitHub issue template for submitting them. Just run `sudo ./scripts/flight-bench.sh` a few times during the flight and submit the JSON files. We'll fit a profile from your data.

value: Show HN: AirplaneMode – Simulate realistic airplane WiFi on macOS

author: jlreyes


value: This is one of those games after GTA Vice city, that i played after coming back from school, This one game had the best story line and the most funny thing i recognised today that It took three generation of DEA detective to take down, the Papa Murte Operation, This game had the best Storyline plus one of the best Final Climax boss train sequence, I think its the best Bullet time featured game, even better then Max payne

value: Show HN: I Played Total Overdose Today, Once More


value: Stronz

value: Show HN: H-Core Snapshot – forcing LLMs to execute instead of explain

author: Stronz


value: liuliu

value: Show HN: Metal Quantized Attention on M5 Max

author: liuliu


value: Show HN: YouTube transcript viewer with search, language switching, and export


value: coolwulf

value: I built a real-time flight tracker that renders 10,000+ aircraft on an interactive 3D globe, entirely in the browser using Rust compiled to WebAssembly.

value: Show HN: Flight-Viz – 10K flights on a 3D globe in 3.5MB of Rust+WASM

author: coolwulf


value: gregnr

value:
Coding agents spend a lot of time in the shell. When exploring a codebase, they usually reach for the same handful of tools: grep, find, ls, cat. We built a public SSH server that hosts Supabase docs as markdown files so that agents can explore them the same way they explore code:
ssh supabase.sh grep -rl 'RLS' docs
ssh supabase.sh cat docs/guides/database/postgres/row-level-security.md | head -10
Under the hood commands run inside Vercel's just-bash [1] library - an emulated bash shell sandboxed within the JS runtime. It uses a VFS where the docs are mounted as markdown files. SSH is handled by ssh2 [2].
Next step is evals to confirm whether or not this truly performs better than other knowledge retrieval methods (like skills, web search, MCP tools), but we were too excited not to share something now.
[1] https://github.com/vercel-labs/just-bash
[2] https://github.com/mscdex/ssh2

value: Show HN: Docs over SSH for Agents

author: gregnr

title: Show HN: Docs over SSH for Agents


value: hatappo

value: Show HN: MinimumReleaseAge-style protection for Agent Skills

author: hatappo


value: abbadadda

value:
ComputerPoker.ai is a website where users can play simulated poker tournaments against GTO Bots to learn GTO poker strategy in a fun and low-risk environment.
My motivation for creating CompterPoker.ai was feeling a bit overwhelmed by some of the professional poker tools out there for learning GTO play. For some tools, learning how to simply operate the tool itself felt like a second job. With ComputerPoker.ai players can play against bots themselves simulating GTO play to learn what it "feels like" to play GTO vs. GTO opponents without having to turn any knobs or dials (feedback is real-time as you play).
The Beta tester code for HN Users is: HackerNews2026. All feedback is welcome! Please send suggestions for improvement or bugs to contact@computerpoker.ai or alternatively leave a comment below. Any questions I will do my best to answer.
As for the product offering the website is designed to teach players how to play optimal poker strategy (GTO) in simulated Texas Hold 'Em poker tournaments. Our value proposition is that if you can consistently beat the bots then you will fare well in live poker tournaments (of course adjusting for your opponents' play).
In addition to GTO pre-flop quizzes and pre-flop charts, users have the ability to simulate poker tournaments from start-to-finish and get feedback on their decisions _in real-time_ in a fun and low-risk environment.
For those interested the tech stack is Django deployed on AWS via Terraform and SaltStack, the database uses a Postgres RDS backend, and the frontend uses HTMX with WebSockets via Django Channels and Redis (Nginx serving as reverse proxy with CloudFlare DNS and SSL). During the project I used Claude Code to aid with various boilerplate aspects of the code base including building out the repos for Terraform and SaltSack and of course speeding up Django development.
Users are graded pre-flop based on the covered pre-flop scenarios (two-ways only for now). Post-flop users are graded on a residual MLP PyTorch model. We have built an in-house solver in Rust using the discontented CFR++ algorithm. The PyTorch model approximates GTO play post-flop (again only two-ways currently) based on training data with raises, EV, and realistic ranges for OOP and IP players. Because the post-flop decisions are based on a model that will always be a work in progress I refer to these decisions as GTOA (or "GTO Approximate").
Version 8 of the PyTorch model is the first one that I am happy with and actually find it quite difficult to play against. If you manage to beat the bots please do let me know how many tries it took! For those curious the PyTorch params for the most recent run are below (I trained on a gaming PC via Linux WSL2 using an AMD GPU):
```
(venv_rocm_native) $   python3 pytorch_model.py
      --data-dir unified_v8_raises/
      --aggression-embed --predict-ev
      --d-card 64 --d-model 256
      --n-attn-layers 3 --n-trunk-layers 4
      --dropout 0.2 --weight-decay 0.05
      --epochs 30 --batch-size 4096 --lr 3e-4
      --steps-per-epoch 10000 --output gto_model_v8.pt<p>Training on cuda
… … … _30 epochs later_
       Saved best model (val_loss=0.3865)
Training complete!
Best model saved to gto_model_v8.pt
```
The website is live in Beta mode as I gather feedback on how things are structured and work out any bugs/kinks. If you have any suggestions for improvements I’d love to hear them. Subscriptions are live so if anyone wanted to test the Stripe payment processing flow I certainly wouldn’t mind! ;-)
p.s. This is a side gig for me. I am currently looking for full-time work either fully remote or on-site based in London, UK (this LLC that runs ComputerPoker.ai operates out of USA but I am based full-time in the UK and authorized to work in both UK and USA). If you or someone you know is looking for a SRE with strong software engineering skills please let me know!

value: Show HN: ComputerPoker.ai – Learn GTO tournament poker strategy vs. GTO bots

author: abbadadda


value: harrywynn

value:
I’ve worked with staffing and recruiting firms for a long time, and one pattern I kept seeing is that small recruiting agencies often end up using software designed for much larger organizations.
Tools like Bullhorn are powerful, but they can be heavy and complex for smaller teams. Many of the agencies I’ve worked with (typically 3–10 recruiters) mostly need to:
 • post jobs
 • track candidates through a pipeline
 • submit candidates to clients
 • search past applicants
Instead they often end up with systems that require a lot of configuration and administrative overhead.
I built Hire Gnome to explore a simpler approach: a lightweight ATS focused on the core workflows small recruiting teams use every day.
A few ideas behind it:
 • simple candidate pipelines
 • easy job posting
 • straightforward candidate submissions
 • minimal setup and configuration
 • email parsing via webhook so recruiters can forward resumes directly into the system
It’s still early, but I’m interested in feedback from anyone who has worked with recruiting software or built niche SaaS products.
https://hiregnome.com
Built with: Next.js, Node, MySQL, Postmark

value: Show HN: Hire Gnome – a lightweight ATS for small recruiting agencies

author: harrywynn


value: pdrgds

value:
Canon doesn't provide a working macOS driver for the PIXMA G3010. I was stuck using Canon's iPhone app for all printing and scanning.
I pointed Claude Code at a packet capture from the iPhone app and it reverse-engineered Canon's proprietary CHMP protocol, wrote a pure Rust eSCL-to-CHMP bridge daemon, and built a .pkg installer. My role was the physical parts: capturing packets, testing on the printer, confirming Image Capture worked.
The protocol docs in docs/ are probably the first public documentation of Canon's CHMP protocol.

value: Show HN: Canon PIXMA G3010 macOS driver, reverse-engineered with Claude

author: pdrgds


value: JaredCampbell

value:
I got frustrated with Gmail's lack of customization, and built an extension for myself.
It doesn't do much other than modify how Gmail is displayed in Chrome. Does it as minimally as possible (that I can engineer). Done locally.
Currently built the following settings:
Conversations:
* Show newest message first
* Show every message in a thread (sometimes they're collapsed into numbered bubbles)
* Remove AI overviews (retches)
Layout/UI:
* Minimize the search & top bar
* Minimize the far left "rail" (Mail/Chat/Meet)
* Hide a bunch of extraneous icons/buttons/items
* Adjust width of Folders/Labels column (still in-process)
Mini-Automations:
* Automatically open to inbox on page start/refresh
* Automatically select first message if the reading pane is open (unless unread, or previous message already selected)
* Add a recurring reminder (if wanted) to use Gmail's new Manage Subscriptions folder/feature
Bunch more features I'd like to add. Let me know if you think I should change anything. Or if you have any code/bug feedback... :)
jared2campbell@gmail.com

value: Show HN: I Made a Tool to Cleanup Gmail

author: JaredCampbell

title: Show HN: I Made a Tool to Cleanup Gmail


value: Forsheeezy

value: Show HN: MurmurCast – Daily briefs from your podcasts, YouTube, and newsletters

author: Forsheeezy


value: quintes

value:
Hey hey
I'm launching this on product hunt and I did a show many months back but prfrm is way better now
prfrm - by ArchitectFWD, is a performance management platform. It is a platform for Teams, Startups & Organizations and also Individuals, Solo Founders & Families to organise and track goals, set plans and review periods to stay on top of development plans and set out the path for success.
Typically uses for Review periods, performance plans, goals
Also just added Team OKR
The Goals AI assistant can create meaningful goals linked to the OKR or to individual goals and plan outcomes
I included a journal to track progress
The AI assistant can go over the journal for next steps, talking points for the next meeting or check in
An a Kanban style schedule tracking
---
I built prfrm by ArchitectFWD because I was tired of traditional performance management
Spreadsheet.. blank cell ..what is next..
No more. I can set myself up, set a period, set the plan and outcome and use the AI assistant to help generate meaningful goals. I can track how I’m going and plot my path to success.
With the addition of team OKR (objectives and key results) the goals can be mapped to team objectives as well, strengthening goals to real business goals
https://prfrmhq.com goes to https://prfrm.architectfwd.com
There's a silent video on the landing of how it works in mobile view
If you want to comment on product hunt that's welcome too at https://www.producthunt.com/products/prfrm-by-architectfwd/e...
Lastly, want to see video's? They're on https://www.youtube.com/playlist?list=PLBYzijBKDTJVrBzOlYuU0...

value: Show HN: Modern AI assisted goals and performance management

author: quintes


value: franrai

value: Show HN: Auto sketch prompt and AI renderings for architects

author: franrai


value: marctossip

value: 4 agents run simultaneously. Contradictions between outputs are the signal.

value: Show HN: 4D business analysis with parallel AI agents (AofA-inspired)

author: marctossip


value: hugs

value: Roadie is an open-source hardware KVM controlled via HTTP. HDMI capture in, USB keyboard/mouse/touch out, all from a browser.<p>Hardware KVMs with web UIs have existed for years (PiKVM, TinyPilot, JetKVM, etc.). Roadie adds two things they don't generally have: multi-touch support (so it works with phones and tablets) and a focus on agent-driven use: any browser automation tool can drive the /view page directly, or connect to the WebSocket endpoint for lower-level programmatic control.<p>~$86 in parts, including two CircuitPython boards, an HDMI-to-USB dongle, and a Go server running on the host. No software needed on the target.

value: Show HN: Roadie – An open-source KVM that lets AI control your phone

author: hugs


value:
Built a fun little Passover game while in the bomb shelter with my kids.
You play as Moses, trigger the 10 plagues, try to get out of Egypt.
Made it in Cursor in ~5–10 prompts. Burned through most my credits.
Is it AI slop? Yes.
Is it exactly the kind of AI slop I enjoy building? Also yes.
Happy Passover to anyone celebrating.

value: Show HN: Happy Passover – Passover Game

title: Show HN: Happy Passover – Passover Game


value: folli

value: A fully local Retrieval-Augmented Generation (RAG) implementation for querying 25 years of Swiss Teletext news (~500k articles in German language) — no APIs, no data leaving your machine.<p>Why? I thought it's a cool type of dataset (short/high density news summaries) to test some local RAG approaches.

value: Show HN: Local RAG on 25 Years of Teletext News

author: folli


value: mijkal

value: Show HN: Mycellm – BitTorrent for LLMs, pool GPUs into federated networks

author: mijkal


value:
I recently got a Lily58 split keyboard (from mechboards, if you need that information ;) ) and wanted to improve my typing speed and "proprioception".
Most typing tools (like Monkeytype) use random text / text, which didn’t feel very useful for programming.
So I built this to practice typing on real code instead.
You can train on code-like content and focus on patterns you actually use while coding.
Each code snippet come from real, know repo :) or one you add !
Would love feedback — especially from people who use split/ergonomic keyboards.

value: Show HN: A typing trainer that uses real code snippets


value: kasparnoor

value:
I kept deleting and redownloading Instagram because I couldn't stop watching Reels but needed the app for DMs. Tried screen time limits, just overrode them. So I built this.
Dull loads Instagram, YouTube, Facebook, and X and filters out short-form content with a mix of CSS and JS injection. MutationObserver handles anything that lazy-loads after the page renders, which is most of the annoying stuff since these platforms love to load content dynamically.
The ongoing work is maintaining the filters. Platforms change their DOM all the time, Instagram obfuscates class names, YouTube restructures how Shorts appear in the feed, etc. It's a cat-and-mouse thing that never really ends.
Also has grayscale mode, time limits, and usage tracking.
Happy to answer questions.

value: Show HN: Dull – Instagram Without Reels, YouTube Without Shorts (iOS)

author: kasparnoor


value:
Hi.
While tinkering with RAG ideas I've thoroughly processed the entire MDN Web Docs original content, pre-ingested it into LanceDB, uploaded the 50k+ rows dataset (https://huggingface.co/datasets/deepsweet/mdn) to HuggingFace, and published a RAG-MCP server (https://github.com/deepsweet/mdn) ready for semantic search with hybrid vector (1024-d) and full‑text (BM25) retrieval.
A screenshot is worth a thousand words: https://raw.githubusercontent.com/deepsweet/mdn/main/example...

value: Show HN: Offline-First MDN Web Docs RAG-MCP Server