value: guyle

value:
Coding agents made everything feel fast again - except managing release binaries.
Operations like these should be real commands you can run from your terminal:
- "Deploy the login fix to staging"
- "What's the functional difference between production and staging?"
- "Notify me when any security fix hits production"
- "Promote Jen's last changes to staging"
- "Share the new beta API SDK with the partners at Acme"
That's what Fly does.
Every time a CI run pushes artifacts to the Fly Registry, it creates a release - including the binaries produced, an AI-generated summary, captured architectural decisions, merged PRs and commits. All of it is searchable. So when you ask your agent "when did we release a fix for the connection timeout" it runs a semantic search across all of that and finds the right evidence. No version numbers, no digging through run IDs, no Git hashes.
Fly also understands your runtime environments and works with whatever deployment framework your coding agent already knows - Argo CD, Flux, etc. It tracks what gets deployed through them automatically, so "what's the latest change in production right now" gets a real answer.
A few other things worth knowing:
- Integrates out of the box with your GitHub repositories
- Works with Claude Code, Cursor, Codex, etc. via MCP
- Agentless integration with your runtime environments
- Slack notifications in natural language - Fly watches releases and notifies with context when something matches
- Runs Artifactory as a scalable package registry under the hood
- Supports Docker, Helm, npm, PyPI, Maven, Gradle, Go, and .NET, with more package types coming
- Once installed, your package managers just work: npm publish, pip install, docker push - all route through your private Fly registry automatically. No .npmrc editing, no token wrangling
Take it for a spin and upgrade the way you manage your release binaries - and finally get real visibility into WHAT is actually running where. Happy to get into how it works, what else it handles, and where we're taking it next.
https://jfrog.com/fly

value: Show HN: Host, share, promote and track release artifacts via your coding agent

author: guyle


value: Show HN: LogicPearl – Synthesizing deterministic executable logic from traces


value: cleverdash

value: Show HN: SEOLint – MCP server that lets Claude scan any site for SEO issues

author: cleverdash


value: malvislight

value: Show HN: UniText – Industrial-grade Unicode text engine for Unity

author: malvislight


value: jmilinovich

value: Show HN: Grove – Open-source remote MCP server for Obsidian vaults

author: jmilinovich


value:
I am trying to build a platform for complex discussions, given the inadequacy of existing social media for this purpose. The core idea is to enforce the structure that characterizes the development of an argument right at the level of the platform and discard everything else. The result is a place that is troll-proof by design and optimized for having complex discussions. You can read more about it at https://www.thesaner.com/faq and try out its behavior at https://www.thesaner.com/simulator. The website is functional, so you can also choose to sign up for it.
The website remains very much a work in progress and I welcome all feedback. If you have any doubts, please ask about it.
PS: The website only supports discussions on science, policy, and technology at the moment. I am working on making it general-purpose as soon as possible.

value: Show HN: Thesaner.com – a troll-proof platform for harder discussions


value: dobreandl

value: Show HN: Grovs – Open-source attribution to escape the 'Big Tech' tax

author: dobreandl


value:
If you’ve ever had to talk a parent or co-worker through how to do something on a computer, I made you something.
Vibecoded a free app that lets you just ask your laptop how to do whatever it is you want to do.
Lookout can see your screen and uses Claude to guide you and help trouble shoot any issue.
Give it to them, or use it yourself. Totally free.

value: Show HN: Lookout – AI Screen Assistant for macOS


value: stumpyfr

value: Created for my own usage, shared as an open-source and hosted version.<p>When you need to manage multiple teams, across multiple repos and start to have >50 PR... this tool provides you a single view with everything you need to keep track and know "what's next"

value: Show HN: Pulsar, live dashboard of all your PR, Engineering Manager dashboard

author: stumpyfr


value: wwayne

value: Show HN: Mushroam – Turn any URL into a promo video

author: wwayne


value: DensitySam

value: Show HN: PolyMind – AI-Powered Real-Time Prediction Market Alerts

author: DensitySam


value: Show HN: Which AI model is best for real data analysis?


value: bonsuaq

value: Show HN: Scryptian – A lightweight, local AI bar (Python and Ollama)

author: bonsuaq


value: MichaelLabitzke

value:
Hey HN — after the Axios attack I went looking for something that could help against this kind of install-time risk. I didn’t find a tool that did what I wanted, so I built one.
SafeInstall is a local CLI for npm, pnpm, and bun. It checks policy before the package manager runs.
Especially now, when tons of people are building with AIs like Cursor and Claude and either give them full rights or just hit enter on every prompt, I thought it was important to build a guardrail before the install happens.
By default it:
blocks very fresh registry releases
blocks lifecycle scripts unless explicitly allowed
blocks git, tarball, and URL installs by default
catches trust downgrades, like registry-to-git changes or newly introduced install scripts
Optional checks:
typo-squat detection
Sigstore provenance verification with publisher/repo pinning, so a package can be tied to an expected source instead of accepting any valid signature
It’s MIT licensed, runs locally, and doesn’t require an account or signup.
Repo: https://github.com/Mickdownunder/SafeInstall Website: https://safeinstall.dev
I’m interested in feedback on the policy model and on which checks should or shouldn’t be enabled by default.

value: Show HN: SafeInstall – local install-time guardrails for NPM/pnpm/bun

author: MichaelLabitzke


value: Show HN: Valthrex – Institutional-grade crypto portfolio command deck


value: dawie

value:
I have been on here for nearly 20 years :-)
I got laid off from a IT/Dev manager job I'd been at for nearly a decade. I loved the company, role and my team, but the company had to downsize.
The search that followed took 9 months: 249 applications, 21 screening calls, 7 interviews, and 2 job offers.
Somewhere around month 4 I stopped treating it as "send resume, hope" and started building a repeatable system around ChatGPT, Claude, and Gemini.
I created a skills database as the source of truth, alignment analysis against each JD, AI-driven interview prep through NotebookLM, and tracking everything in a spreadsheet so I could actually see what was working.
That system is what eventually landed me the 7 interviews (3 final interviews) and 2 jobs. The first job I took because I needed something, the second job is my dream job.
I wrote it up as a book because I wanted to help other people land their dream jobs without grinding through the same 9 months I did.
The whole thing is online for free at careervectorhq.com, no signup, no email wall. I also share every prompt I used, copy and paste ready, so you can run the same workflow yourself instead of reverse engineering it from the text. I am considering turning it into software, but for now just sharing my process.
Would genuinely value feedback from this crowd, especially from anyone who's hired recently and can tell me where the advice is off, or where AI-assisted applications are starting to hurt candidates rather than help.

value: Show HN: Get Hired with AI, a free book I wrote on using LLMs for a job search

author: dawie


value: krcnow

value: Show HN: Manex Hub – private AI research memory, on-device

author: krcnow


value:
Hi HN, I’m the co-founder of gondola.ai. We shipped an MCP server that gives AI agents access to real-time hotel search and booking for direct hotel rates across points and cash for hotel chains like Marriott, Hilton, Hyatt, IHG, Accor, Wyndham, and more.
One-line install for Claude Code:
claude mcp add --transport http gondola https://mcp.gondola.ai/mcp
Also works with Claude Desktop, Cursor, Windsurf, VS Code, and anything else that is compatible with an MCP.
Some of the tools that are included in the MCP:
- search_hotels — real-time availability with member rates and specialty rates (like AAA rates) across hotel chains like Marriott, Hilton, Hyatt, IHG, Accor, and Wyndham
- compare_rates — cash vs. loyalty points with cents-per-point valuations
- get_hotel_details — amenities, photos, reviews, location
- book_hotel — book directly with the hotel
Why not just use an OTA travel API?
OTA APIs return the public rates, but when you log in directly with the hotel, you’ll find that direct member rates, AAA rates, and package rates can be 10-20% cheaper, not to mention booking direct gives you access to hotel loyalty points and status nights. We're also the only API returning loyalty points redemption values alongside cash, so agents can do the math and let you know whether to pay with $280/night or spend 35K Hyatt points.
Would love any and all feedback on tool design and what other tools would be useful for your agentic travel workflows.
https://www.gondola.ai/mcp

value: Show HN: Hotel MCP server for cash and points search/booking (free)


value: cammasmith

value:
As a software developer, I've frequently used DynamoDB, and I have found myself missing the ease of SQL querying. So I built DynamoSQL to be a SQL layer. It allows you to write standard ANSI SQL in DynamoDB, including JOINs across tables, CTEs, subqueries, GROUP BY, and aggregations. There's no ETL, data export, or separate warehouse. The optimizer is index-aware and uses your partition keys, sort keys, and GSIs to minimize read costs.
Ultimately, I'd like to list DynamoSQL on the AWS Marketplace as pay-per-query SaaS. But first, I want to make sure that it works properly. If you actively use DynamoDB and know a little SQL, would you consider becoming a beta tester for DynamoSQL? It's completely free during beta testing, and I'm just looking for criticism and critical feedback you might have so that I can make it better.
AWS Marketplace listing: https://aws.amazon.com/marketplace/pp/prodview-o2ddsn4oox2sy
Beta tester page: https://dynamosql.com/beta
Happy to answer technical questions.

value: Show HN: DynamoSQL – I made a SQL query engine for DynamoDB

author: cammasmith


value: Jang-woo

value: Show HN: Answer 9 questions, get a Matter firmware with safe boundaries

author: Jang-woo


value: inshane

value: Show HN: I built an agent memory system for myself and got 90.8% on LongMemEval

author: inshane


value: sydcli

value: Show HN: I built a Claude Code plugin that ships production-ready SaaS product

author: sydcli


value: mickdarling

value:
Hi, I'm Mick. I've been building this for the last 9 months
DollhouseMCP 2.0 is an open-source MCP server for making and using composable building blocks for AI customization. You build elements as portable MD and YAML files, compose them into stacks, and activate the stacks in any MCP-compatible client.
Element types that drive behavior and permissions:
* Personas: behavioral profiles (how the AI sounds and acts)
* Skills: discrete capabilities (what the AI can do)
* Agents: goal-oriented, multi-step executors
* Ensembles: composed stacks of the above
* Plus templates for structured outputs and memories for persistent context.
Two things I think are actually new here:
1. Identity-based permissioning. When you activate a persona, skill, agent, or
ensemble, its permission policy takes effect in the server. Same client, same LLM, different permission surface depending on which active elements are loaded.
A read-only analyst persona blocks creates and deletes regardless of what the client allows. A security-focused ensemble can deny specific destructive operations. This runs server-side, after the client approves the call, so policy cannot be overridden by the LLM or the client.
2. A bimodal agent loop. Agents do not run free inside the LLM. Every step hands control back to the MCP server, which evaluates the proposed operation
against the active permission stack, runs autonomy and risk checks, enforces any hard blocks, then returns a decision to the LLM with continue, pause, or
escalate guidance. The LLM acts on what it is allowed to do, describes the next step, and hands back to the server. The loop repeats until the goal
completes or a human is asked to intervene. Higher agency stays observable and bounded instead of opaque.
There's also a audit trail of approved and denied actions a danger zone lockout that will prevent the LLM from doing truly dangerous things if they go through the MCP server. And any active Dollhouse agent that is running through the DollhouseMCP server has it's actions evaluated every step.
I added easy configuration through the web console for a wide variety of MCP clients if you use the one-liner, and there are logs and metrics as well as the local and github hosted portfolio and collection to save your Dollhouse elements and share and use other's.  They are all validated and scanned several times along the their distribution path to keep things as safe as we can.
The one-liner install:
npx @dollhousemcp/mcp-server@latest --web
Happy to go deep on the permissioning model, the bimodal agent loop, composition patterns, YAML schema, or anything else.
Home: https://dollhousemcp.com
Repo: https://github.com/DollhouseMCP/mcp-server
Collection: https://collection.dollhousemcp.com
Hope you like it.

value: Show HN: DollhouseMCP 2.0, open-source MCP composable AI building blocks

author: mickdarling


value: synasties

value: Show HN: The opensource, reliable, scalable Agentic AI framework under 10MB

author: synasties


value: pragmaticviber

value: Show HN: Write better Go integration tests with open source dockertest v4

author: pragmaticviber


value: AnViF

value: Show HN: Omega Walls–open-source stateful runtime defense for RAG and AI agents

author: AnViF


value: Mkiza

value:
I got a surprise bill. Nothing catastrophic, but enough to make me dig into why — an agent had hit a retry loop and kept calling the API for hours. There's no way to set a hard cap on the Anthropic or OpenAI APIs. You can get an email after the fact, but nothing that actually stops requests mid-flight.
So I built a proxy. You swap one environment variable, it routes through Costile instead of calling Anthropic directly, and when you hit your daily or monthly limit it blocks further requests immediately. No SDK changes, no code refactor. Took me about a weekend. Currently supports Anthropic, with OpenAI next.
It's MIT licensed and self-hostable in about 5 minutes. Try the demo at costile.com if you want to poke at it.
I've got anomaly detection on the roadmap, but I'm second-guessing the scope — is surfacing cost spikes enough, or do people actually need to know why the agent went off the rails? The former is straightforward to build, the latter is a much harder problem. Curious where others would draw that line.
GitHub: https://github.com/Mkiza/ai-agent-cost

value: Show HN: Costile – open-source proxy, blocks AI API requests when budget is hit

author: Mkiza


value: shafkathullah

value:
Hey HN! Posts with OG images get 2x more engagement and 114% more impressions than those without [1]. Yet most devs either skip them or fight Satori/vercel-og — broken fonts, image errors, JSX that pretends to be CSS but isn't. I got tired of it and built something different.
Linkshot screenshots your actual live site and serves it as the OG image. Real CSS, real fonts, real layout. One API call, done.
We also allow inject Tailwind classes into the render — hide navbars, restyle for the OG crop with 'linkshot:*' modifier also web extension to do local dev fit check, all without needing your site having to use TailwindCSS.
Would love to hear what's missing or what you'd do differently.
https://uselinkshot.com/
[1] https://www.semrush.com/blog/open-graph/

value: Show HN: Linkshot – Auto-generate OG images from your live site via API

author: shafkathullah


value: ugtthis

value: Show HN: I built a site to find cars compatible with open source ADAS software

author: ugtthis


value: mc-serious

value:
We built the Kontext CLI because AI coding agents need access to GitHub, Stripe, databases, and dozens of other services — and right now most teams handle this by copy-pasting long-lived API keys into .env files, or the actual chat interface, whilst hoping for the best.
The problem isn't just secret sprawl. It's that there's no lineage of access. You don't know which developer launched which agent, what it accessed, or whether it should have been allowed to. The moment you hand raw credentials to a process, you've lost the ability to enforce policy, audit access, or rotate without pain. The credential is the authorization, and that's fundamentally broken when autonomous agents are making hundreds of API calls per session.
Kontext takes a different approach. You declare what credentials a project needs in a .env.kontext file:

  GITHUB_TOKEN={{kontext:github}}
  STRIPE_KEY={{kontext:stripe}}
  LINEAR_TOKEN={{kontext:linear}}

Then run `kontext start --agent claude`. The CLI authenticates you via OIDC, and for each placeholder: if the service supports OAuth, it exchanges the placeholder for a short-lived access token via RFC 8693 token exchange; for static API keys, the backend injects the credential directly into the agent's runtime environment. Either way, secrets exist only in memory during the session — never written to disk on your machine. Every tool call is streamed for audit as the agent runs.
The closest analogy is a Security Token Service (STS): you authenticate once, and the backend mints short-lived, scoped credentials on-the-fly — except unlike a classical STS, we hold the upstream secrets, so nothing long-lived ever reaches the agent. The backend holds your OAuth refresh tokens and API keys; the CLI never sees them. It gets back short-lived access tokens scoped to the session.
What the CLI captures for every tool call: what the agent tried to do, what happened, whether it was allowed, and who did it — attributed to a user, session, and org.
Install with one command: `brew install kontext-dev/tap/kontext`
The CLI is written in Go (~5ms hook overhead per tool call), uses ConnectRPC for backend communication, and stores auth in the system keyring. Works with Claude Code today, Codex support coming soon.
We're working on server-side policy enforcement next — the infrastructure for allow/deny decisions on every tool call is already wired, we just need to close the loop so tool calls can also be rejected.
We'd love feedback on the approach. Especially curious: how are teams handling credential management for AI agents today? Are you just pasting env vars into the agent chat, or have you found something better?
GitHub: https://github.com/kontext-dev/kontext-cli
Site: https://kontext.security

value: Show HN: Kontext CLI – Credential broker for AI coding agents in Go

author: mc-serious


value:
I work in cyber security and built this android app to help me keep up to date with the latest news stories and summarise the most important information. It provides two executive summaries per day and alerts for critical news throughout.
I’m using Gemini Pro 3 to analyse recent articles from a list of trusted sources. It identifies the 5 highest priority stories, considering things like if a vulnerability is being actively exploited, novelty and breadth.
If CVEs are identified, it will check for CISA KEV status, EPSS score and if POCs are available on GitHub. It then provides an overall executive summary plus analysis for the top stories, including details, affected systems, potential impact and mitigation actions.
The brief is then passed to Gemini again for verification and fact checking against the source articles and other CVE data retrieved to correct any potential hallucinations.
It’s a paid monthly subscription based app, but it has a 7 day free trial for new users. You can manage subscriptions and cancel from the account tab. If anyone wants an extension I can look at setting up a promo code, please message me.
I would be very grateful for feedback, particularly if the mitigation information is useful, if you think the critical alerts are critical enough to be notified about, any good sources I could add or anything else.
Happy to answer questions.
Play Store Link: https://play.google.com/store/apps/details?id=com.cyberpulse...

value: Show HN: Cyber Pulse. AI pipeline for triage and alerting on cyber news/intel


value: WhosTiki

value:
Who'Studios is a social design platform built on the idea of giving users unprecedented creative freedom. Instead of forcing content into rigid templates or a text box, Who'Studios allows you to create unique designs in a blank canvas powered by fabric.js. Using a combination of texts, images, videos, audios, shapes, and drawings, in any arrangement you can imagine, Who'Studios offers a degree of space, flexibility, and modularity never before seen in a social media platform.
From digital posters to magazine covers, from doodles to video collages, from random ramblings to precise prose, Who'Studios empowers users to break the rules and rigidity of traditional platforms and express their vision as they see it.
The stack is MERN: MongoDB, Express, React, Node. The canvas editor is built using Fabric.js.
There are plenty of other features to check out: projects allow you to combine up to 10 designs and package them together to produce cohesive zines and publications. You'Studios, your profile page, has a feature where you can add tabs with custom content a la mySpace using the CodeBox. There's an Expo feature where users can create threads for open conversation, and features for messages and groups where users can have private conversations. Who'Studios is an evolving project, so I'm always improving existing features and adding new ones.
whostudios.com is the website, but if you want to try the design canvas directly without making an account, you can go to whostudios.com/demo
(Anything you make in the demo is local only and disappears on refresh — no account needed, no data stored.)
This is the first thing I've ever coded, so I am happy to answer any questions, comments, or concerns. In the meantime, I hope you have a tremendous day!

value: Show HN: Who'Studios – A Social Design Platform

author: WhosTiki


value: Show HN: Nobulex – Prove what your AI agent did, cryptographically (live demo)


value: svessi

value: Show HN: Claude Control – macOS dashboard for managing Claude Code sessions

author: svessi


value: himanshudongre

value: Show HN: Smriti, version control for reasoning state

author: himanshudongre


value: AdobiWanKenobi

value:
The world needed another todo app. So I built one.
It's an MCP server. You paste one URL into Claude (Desktop, claude.ai, or API) and manage tasks through conversation. "Add take out the trash." Done. Say "I took out the trash", todo ticked off.
5 tools: list, add, update, delete, toggle. That's the whole product.
There's also a web dashboard with checkboxes if you're feeling spicy and want the dotcom experience.
Built with SvelteKit, Neon Postgres, Stripe. $4/mo.
If you have feature requests please tell me and I'll add them to my todo list and get claude to build them.
-------------------------------
I built this as a sandbox for learning content marketing. If you have questions please let me know.

value: Show HN: A $4/mo AI todo app – 3 todos free, pay for the 4th

author: AdobiWanKenobi


value: anju-kushwaha

value:
Install Docker Engine 27.x on Ubuntu 24.04 LTS step-by-step. Covers Docker CE, Compose v2, rootless mode, GPU support, and first container. Fully tested. No Docker Desktop required.
https://vucense.com/dev-corner/how-to-install-docker-on-ubun...

value: Show HN: How to Install Docker on Ubuntu 24.04 LTS: Complete 2026 Guide

author: anju-kushwaha


value: pkhodiyar

value: Show HN: API for 13M+ Indian court cases with citation graphs and vector search

author: pkhodiyar


value: svyatov

value: Show HN: Hacker News Sorted, a 12-year-old Chrome extension I still use daily

author: svyatov


value: tdhoward

value: Show HN: At 'Em – Daily briefing app showing your schedule, weather, and tasks

author: tdhoward


value: stackframe

value:
https://docs.openparallax.dev
I kept reading about security issues with open source AI agents. Deleting important data, exfiltrating confidential information. Another thing was the technical difficulty of setting one up. Non-technical friends of mine kept reaching out because they were unable to find their way around installing it.
I built an open-source AI agent for personal use where the LLM reasoning system literally cannot execute anything. It runs in a sandboxed process with no filesystem, no network, no shell. It proposes actions over gRPC to a separate engine process that validates every action through four tiers. YAML policy rules, a heuristic engine + DeBERTa classifier, an independent (reversed prompted) LLM evaluator, and human in the loop. Actions that fail validation are blocked. The system also tags data sensitivity (IFC) and snapshots state before destructive actions for rollback. Multiple heterogeneous levels of validation means a wider coverage of attack surfaces. The reasoning system is treated as fully compromised by default, so the security boundary holds regardless of what the LLM does.
I wrote it in Go, it's one single static binary. Tested it against 280 adversarial cases with 98.9% block rate and zero false positives under default config. The agent is multiplatform, installed with one command, setup and ready to run in under 3 minutes (with API keys in hand). No external dependencies needed (no nodejs, python or docker)
Repo: https://github.com/openparallax/openparallax
Paper (draft, arXiv submission pending): https://github.com/openparallax/openparallax/releases/downlo...
Happy to answer questions about the architecture, threat model, or evaluation.

value: Show HN: OpenParallax: OS-level privilege separation for AI agent execution

author: stackframe


value: NomiJ

value: Show HN: Self-Healing Back End That Does TDD

author: NomiJ


value: smalltorch

value: $200 and about 1 month of building and I have the perfect tool some of the primary workflows.

value: Show HN: I made a specialized tool for construction professionals

author: smalltorch


value: weekendproject

value: Show HN: Mobile Hacker News Reader – real time, dark mode

author: weekendproject


value: hunvreus

value: clonecn is an agent skill for generating shadcn/ui themes from screenshots, URLs, or style prompts.<p>You just feed it "Supabase.com" or "Tokyo cyberpunk mono" and it generates a theme you can preview (and customize further) at https://clonecn.

value: Show HN: Clonecn – Agent skill for generating Shadcn/UI themes

author: hunvreus


value: cjarchivist

value: Show HN: Fleet Watch – preflight guard for local AI inference on Apple Silicon

author: cjarchivist


value:
Some technical context on what we ran into building this.
MCP tools don't really work for financial data at scale. One tool call for five years of daily prices dumps tens of thousands of tokens into the context window. And data vendors pack dozens of tools into a single MCP server, schemas alone can eat 50k+ tokens before the agent does anything useful. So we auto-generate typed Python modules from the MCP schemas at workspace init and upload them into the sandbox. The agent just imports them like a normal library. Only a one-line summary per server stays in the prompt. We have around 80 tools across our servers and the prompt cost is the same whether a server has 3 tools or 30. This part isn't finance-specific, it works with any MCP server.
The other big thing was making research actually persist across sessions. Most agents treat a single deliverable (a PDF, a spreadsheet) as the end goal. In investing that's day one. You update the model when earnings drop, re-run comps when a competitor reports, keep layering new analysis on old. But try doing that across agent sessions, files don't carry over, you re-paste context every time. So we built everything around workspaces. Each one maps to a persistent sandbox, one per research goal. The agent maintains its own memory file with findings and a file index that gets re-read before every LLM call. Come back a week later, start a new thread, it picks up where it left off.
We also wanted the agent to have real domain context the way Claude Code has codebase context. Portfolio, watchlist, risk tolerance, financial data sources, all injected into every call. Existing AI investing platforms have some of that but nothing close to what a proper agent harness can do. We wanted both and couldn't find it, so we built it and open-sourced the whole thing.

value: Show HN: LangAlpha – what if Claude Code was built for Wall Street?


value: suprnurd

value: Show HN: We built an MCP for Windows – ask Claude about CPU, temps, and privacy

author: suprnurd


value: Show HN: Web-tarpit – Bot trap for any JavaScript server. Zero deps, one import


value: kull

value:
Hi HN,
I built this because I rely on many external APIs, and keeping up with changes is harder than it should be.
Many (most?) APIs don’t provide RSS feeds, sometimes they provide RSS that they block from fetching (yes, this happens!), or provide API updates on JavaScript-heavy pages, and so many other crazy things. So, I had to use other ways to track changelogs and documentation updates.
I originally built this just for myself to monitor APIs in one place and get alerts when something changes. It worked good enough, so I made it public.
If there’s an API you want added, let me know and I can include it.
Part of this was built using Codex.
Would appreciate any feedback.

value: Show HN: API Changelog Tracker

author: kull

title: Show HN: API Changelog Tracker


value:
I've been on both sides of the software developer technical interview. It's pretty clear to me that LeetCode-style interviews aren't a good proxy for day-to-day work.
Years ago, I switched to conducting code review interviews. These gave us much better signal and candidates seem to enjoy them more.
Please try out my platform to conduct code review interviews. All feedback welcome. Cheers!

value: Show HN: One-click code review interview


value: omegascorp

value:
Hi HN,
I’m building Resonly to help teams prioritize feature requests with more context than just votes.
The idea is simple: not every request is equal. A feature requested by multiple paying customers may matter more than one with 100 upvotes from free users.
I’ve added a way to associate revenue with each customer, so when someone upvotes or submits a feature request, the admin can see three signals:
MRR at risk — how much current MRR is tied to customers asking for it;
MRR lost — how much MRR was lost from customers who churned before it was solved;
Potential MRR — how much MRR could be converted from trial customers requesting it.
I’d love your honest feedback on the idea and positioning.

value: Show HN: Resonly – prioritize feature requests by revenue impact

author: omegascorp


value: Reebz

value:
Every Claude Code commit and PR is shipped with Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> (or similar). It's less fun than I think it should be. So signoff.sh is a 40-line bash script that replaces it with a random fictional character's full legal name - as best as I could figure out :)
Preserves the model name and the Anthropic noreply email, so you still get transparency about AI coding.
It's silly and that's the point.
I'm also a fan of peon ping (not mine): https://github.com/PeonPing/peon-ping

value: Show HN: Signoff.sh – Claude Co-Authored-By with random fictional characters

author: Reebz


value: Finnoid

value:
As I got older I found myself buying games that just ended up sitting in my backlog. I just couldn’t get into the mood to play them. I realized there was a disconnect between what excited me and what actually fit my energy levels and time.
That’s what led me to build Slated.gg.
Most game recommenders look at games from the outside (genre, themes, etc.) What mattered to me was what’s it like to play. How much focus it needs, what type of thinking it requires, can I play in short bursts, is it emotionally taxing, etc.
I built a game recommender around experience and landed on 30 experiential dimensions (so far). I created an AI pipeline that researches and analyzes games by those dimensions and stores them in a vector space. Think sentiment analysis, but for player experience. The recommender then finds similarities by geometric (Euclidean) distance based on other games you like.
When I mapped 297 games, it was really interesting to see what clusters emerged. AAA games cluster tightly together, exposing how they are built with the same formula. The edges are more interesting and surface games with real differentiation. These tend to be the kinds of games players have a hard time finding good recommendations for.
Visualization and methodology: https://slated.gg/map
Try it: https://slated.gg/discover-games
Happy to answer questions about the approach or where it falls short.

value: Show HN: Game recommender around experience, not genre – here's what emerged

author: Finnoid


value:
We built this because we were tired of centralized agent frameworks that essentially act as expensive API wrappers.
The Tech:
It’s a local-first mesh using libp2p. Instead of just passing text, agents trade "skills" over a p2p network. This means the mesh is sovereign...it doesn't rely on a central server to function or mediate agent communication.
This is where it gets exciting:
We have 300+ nodes live and just cleared a 10/10 security audit for the generated code. We’re currently at 460 stars and 89 forks, but we’ve reached a point where we need more eyes on the scaling logic. We want to know: how does this P2P mesh actually hold up when we push to 1,000+ nodes?
No cloud required. Everything stays on your hardware.
We’re looking for deep technical feedback on the mesh stability and the skill-discovery protocol.
Victor Michael Gil (Founder and chief engineer) and I will be around all day to answer questions!

value: Show HN: Bitterbot – A local-first P2P agent mesh with skill trading


value: jarl-ragnar

value:
I built this while debugging our own tile pipelines for MarineCharts (a nautical chart API). I wanted something you could just paste a URL into.
It runs entirely in the browser – no install, no signup. You paste a tile URL template ({z}/{x}/{y}.pbf), click Load, and it renders the tiles on a map. As you hover over features, you get a live property inspector showing all attributes on that feature. The left panel lists all detected layers; clicking one filters the inspection to just that layer.
Supports: MVT/PBF endpoints and any standard vector tile format. Works with Mapbox GL JS and MapLibre GL backends.
I use it constantly when checking that my tile generation pipeline is emitting the right feature attributes, or when exploring a third-party tile source before integrating it.
Would be curious if this is useful to others working with vector tile data, and what features would make it more useful (attribute filtering? tile boundary/coordinates overlay? hex/decoded view of the raw PBF?).

value: Show HN: Free browser-based vector tile inspector – paste any MVT/PBF URL

author: jarl-ragnar


value:
Built Hivecrew, a native macOS app for running parallel computer-use agents in isolated macOS VMs.
You can delegate computer use tasks, watch workers live, step in by manually manipulating the VM, and review what happened afterward. It’s designed for longer-running work where safety and visibility matter.
It also has a voice mode so you can launch tasks and get work done just by talking. This is basically the full business school experience; your job is just to talk now!
Would love feedback on the workflow, the VM-based isolation approach, and what kinds of tasks people actually want to hand off.

value: Show HN: Hivecrew: Native macOS app for parallel long-horizon Omni agents


value: Ikryanov

value:
Hi HN,
For the last ~15 years I've worked on embedding web browsers into Java and .NET desktop apps (JxBrowser, DotNetBrowser). Over time, I watched many teams move from embedding web views into native apps, to building full desktop apps with frameworks like Electron and Tauri.
Both are useful, but in practice I kept running into several problems.
With Electron, beyond the larger app footprint, I often ran into:

  - lack of type-safe IPC
  - no source code protection
  - weak support for the modern web stack

Tauri solves some problems (like app size), but introduces others:

  - different WebViews across platforms → inconsistent behavior
  - requires Rust + JS instead of a single stack

So we built MōBrowser, a framework for building desktop apps with TypeScript, Node.js, and Chromium.
Some of the things we focused on:

  - typed IPC using Protobuf + code generation (RPC-style communication instead of string channels)
  - consistent rendering and behavior across different platforms
  - Node.js runtime
  - built-in packaging, updates, and scaffolding
  - source code protection
  - small delta auto-updates

The goal is to let web developers ship desktop apps with a web stack they already know and fewer cross-platform surprises.
I'd especially love feedback from people who have built production apps with Electron or Tauri.
Happy to answer any questions.

value: Show HN: MōBrowser, a TypeScript-first desktop app framework with typed IPC

author: Ikryanov


value: pranabsarkar

value:
Vector databases store memories. They don't manage them. After 10k memories, recall quality degrades because there's no consolidation, no forgetting, no conflict resolution. Your AI agent just gets noisier.
YantrikDB is a cognitive memory engine — embed it, run it as a server, or connect via MCP. It thinks about what it stores: consolidation collapses duplicate memories, contradiction detection flags incompatible facts, temporal decay with configurable half-life lets unimportant memories fade like human memory does.
Single Rust binary. HTTP + binary wire protocol. 2-voter + 1-witness HA cluster via Docker Compose or Kubernetes. Chaos-tested failover, runtime deadlock detection (parking_lot), per-tenant quotas, Prometheus metrics. Ran a 42-task hardening sprint last week — 1178 core tests, cargo-fuzz targets, CRDT property tests, 5 ops runbooks.
Live on a 3-node Proxmox homelab cluster with multiple tenants. Alpha — primary user is me, looking for the second one.

value: Show HN: A memory database that forgets, consolidates, and detects contradiction

author: pranabsarkar


value: kenneives

value: Show HN: Is This Agent Safe? Free AI tool security checker

author: kenneives


value: TechSquidTV

value:
Hey all,
To be up-front, this is a vibe-coded project. I have been developing a large number of personal tools that I have had in my backlog for years, finally realized by vibe coding. That said, I have been a developer for somewhere around 20 years.
This is a simple Vite/React app with a tiny Node.js proxy for authenticating with Plex (and other media servers). Run the docker image on the same network as your Plex media server, load up the currently playing video file, and make your clip.
Here's a tiny video of it: https://x.com/TechSquidTV/status/2043886838658552136

value: Show HN: Cliparr – Export clips from your personal media server

author: TechSquidTV


value: Show HN: AriaType – open-source privacy-first and local-first voice-to-text app


value: janbalangue

value: Show HN: LoadLens – See why queues hide overload instead of solving it

author: janbalangue


value: trpfnc

value:
I’ve been working on a messaging system that removes identity from the communication layer entirely.
No phone number.
No email.
No contact graph.
Everything is end-to-end encrypted, including device identities.
The server cannot read messages or infer relationships.
We’re opening access to a small group (50 users) to test the system in real-world conditions.
Would appreciate feedback from this community.
https://tunnelmessenger.com/access

value: Show HN: Messaging without phone numbers, email, or metadata

author: trpfnc


value: Show HN: I Added Support for Qwen3-ASR and Qwen3 ForcedAligner in WhisperX


value: Show HN: A Birth Control Pill Reminder for Couples


value: almogbaku

value:
I've spent the past few years building 50+ AI agents in prod (some reached 1M+ sessions/day), and the hardest part was never building them — it was figuring out why they fail.
AI agents don't crash. They just quietly give wrong answers. You end up scrolling through traces one by one, trying to find a pattern across hundreds of sessions.
Kelet automates that investigation. Here's how it works:
1. You connect your traces and signals (user feedback, edits, clicks, sentiment, LLM-as-a-judge, etc.)
2. Kelet processes those signals and extracts facts about each session
3. It forms hypotheses about what went wrong in each case
4. It clusters similar hypotheses across sessions and investigates them together
5. It surfaces a root cause with a suggested fix you can review and apply
The key insight: individual session failures look random. But when you cluster the hypotheses, failure patterns emerge.
The fastest way to integrate is through the Kelet Skill for coding agents — it scans your codebase, discovers where signals should be collected, and sets everything up for you. There are also Python and TypeScript SDKs if you prefer manual setup.
It’s currently free during beta. No credit card required.
Docs: https://kelet.ai/docs/
I'd love feedback on the approach, especially from anyone running agents in prod. Does automating the manual error analysis sound right?

value: Show HN: Kelet – Root Cause Analysis agent for your LLM apps

author: almogbaku


value: canxerian

value: Show HN: A tool to compare LLM costs for typical coding domains

author: canxerian


value: kalturnbull

value: Show HN: Plotline, an open-source initiative to help you own your health data

author: kalturnbull


value: nvoxland

value:
At Metabase, we built an AI agent called Repro-Bot that reads our GitHub issues and attempts to reproduce reported bugs automatically.
It started as a hackathon project and is now part of our daily workflow, so we wrote about it and open-sourced the code as an example for others.
How have similar tools been working for you? What has worked well and what has not?

value: Show HN: We built an AI Agent to reproduce bugs

author: nvoxland


value: edwardsrobbie

value: Show HN: AfterSession – AI tool to help therapists write notes faster

author: edwardsrobbie


value:
I'm a recently retired solo founder who built this because every retirement planning tool I tried wanted my bank credentials or was trying to sell me financial products. planwithclarity.app runs entirely in your browser — your data stays on your device by default. No account needed to start planning.
Built with React as a single-file app, deployed on Netlify. AI plan analysis powered by Claude. Features include Monte Carlo simulation with historical backtesting (1928-2025), withdrawal strategy optimizer with progressive tax brackets, estate planning with Die With Zero analysis, Sankey cashflow visualization, and 12 financial calculators.
Free tier available. Premium is $9.99/mo. Happy to answer any questions about the architecture or the retirement planning methodology.

value: Show HN: Privacy-first retirement planner – no bank linking, AI-powered analysis


value: I'm trying to building alternative bindings for Java using new FFM: the results are promising (2x faster than JNI for simple get()). Comments and feedback are welcome!

value: Show HN: Java RocksDB Without JNI

title: Show HN: Java RocksDB Without JNI


value:
Hi, I've created an iOS app (GizmoSQL) for running DuckDB as a server (with Arrow Flight SQL).
You can even mount a DuckLake - meaning you can have a "data lake in your pocket".
It is fully secure, and runs the TPC-H 1GB 22-query benchmark in less than 2 seconds on an iPhone 16 Pro Max.
See: https://github.com/gizmodata/gizmosql for more details...

value: SHOW HN: DuckDB / DuckLake Server (With Arrow Flight SQL) for iOS


value: anubhavgupta

value: Show HN: Intel NPU based Speech To Text in any app at cursor

author: anubhavgupta


value: sidkh

value: Show HN: Motioncard – tiny POC app for in-browser video rendering (Remotion)

author: sidkh


value: jatorre

value:
I built a CLI to programmatically interact with Spain's tax agency (AEAT) for income tax filing (Modelo 100). It authenticates via Cl@ve using Playwright, downloads your tax data, validates XML against the official XSD, and uploads it to AEAT's EDFI system for reconciliation.
The problem: AI agents can do a good job organizing financial data from multiple brokers, banks and crypto exchanges, but there was no way to programmatically check that against what the tax agency already knows about you. This CLI bridges that gap.
It does NOT file or submit anything. The user is always responsible for manual review and submission.<p>Maybe a bit specific for the spanish community, but if you always wish there was a CLI to do your taxes here is an unofficial one.

value: Show HN: Hacienda-CLI – CLI to reconcile Spanish tax returns with the tax agency

author: jatorre


value: Quinnaho

value: Show HN: ClaudeMap – Google Maps for your codebase, runs as a Claude Code skill

author: Quinnaho


value: dan-gittik

value:
As a developer who has an android phone it is very exhausting making apps and widgets even for simple tasks such as fetching and displaying data from the internet.
Appy allows you to develop your own homescreen widgets with only python, with:
- Simple and intuitive UI library
- Easy to use logging and debug tools
- Full access to java and android libraries
- Extensive wiki even Claude can understand
Feedback is welcome!

value: Show HN: Build Android homescreen widgets with Python

author: dan-gittik


value: abbccdda

value: Show HN: Skardi – SQL across Postgres, S3, and data lakes, served as a REST API

author: abbccdda


value: zuzuleinen

value: Show HN: A Claude Code–driven tutor for learning algorithms in Go

author: zuzuleinen


value: rajveerb

value: Show HN: Reduce TTFT via Streaming to an LLM

author: rajveerb


value: focom

value: Show HN: Plain – The full-stack Python framework designed for humans and agents

author: focom


value: Show HN: MCP server for persistent context and safety in Claude Code


value: basfijneman

value: Show HN: Ballast – a free, local-first fitness tracker (no cloud, no account)

author: basfijneman


value: sansword

value: Show HN: Rubik's Cube solve analyzer – Bluetooth and CFOP/Roux phase breakdown

author: sansword


value:
Hey y’all - open sourcing an agent skill we built called agentchat.
Agentchat teaches your agents to make group chats so they can talk to each other. It's super flexible - works with any agent across any machine. Group chats are private & agents need to be invited to join.
We think about agent communication all the time at Pentagon (x.com/runpentagon) - I've seen it compound agent capabilities significantly (think: gstack, but agents can autonomously talk to each other) but it's also got a ton of hairy problems around it (durable streams, variability across agent harnesses, conversation efficiency).
Agentchat is built on top of s2.dev (not affiliated, we just like them!). If you're thinking about similar problems & or have alternatives you like, would love to hear.
lmk what y'all think!

value: Show HN: Agentchat, a skill that teaches agents to make group chats


value: scarybeard

value: Show HN: Free website accessibility scanner – EAA/Ada compliance check

author: scarybeard


value: hipvlady

value: Show HN: MESI cache coherence for multi-agent LLM context sync – 95% less tokens

author: hipvlady


value: moeen-mahmud

value:
I started working on this project while doing R&D on how to run an LLM on a device efficiently. Built this for me and have been using it since January. It's helping me, and I think it'll be helpful for others, too.
I'd appreciate any feedback
Public beta: https://testflight.apple.com/join/Aj9xnfQu
Github repo: https://github.com/moeen-mahmud/remen

value: Show HN: Remen – Open-Source Privacy Focused Productivity App

author: moeen-mahmud


value: Show HN: Shape Foundation Model (masked-token pretraining on CAD meshes)


value: un-nf

value:
As a high school teacher, I've seen firsthand the effects that a lack of privacy has on creativity, freedom of expression, and willingness to mess up. I don't think we should be subject to consistent monitoring and told we be okay with it "if we have nothing to hide." This is why, between grading and lesson planning, I built 404.
Without 404, your browser and network advertise dozens of semi-unique values: screen size, browser version, hardware stack, installed fonts, TLS handshake pattern. Individually, none of them are remarkable. Combined, they form a fingerprint specific enough to recognize your device across sessions, sites, and networks. It doesn't matter if you're using a VPN or if you clear your cookies. Your IP changes, but everything else stays the same. This is browser fingerprinting, and the industry built around it is called identity resolution.
404 replaces your device fingerprint with a believable alternative so that the browsing you want to be private can actually be private. Where a VPN replaces your IP address, 404 replaces the device behind the request.
-----
404 is a localhost TLS-terminating proxy available as a binary or 2-click install desktop app. It rewrites your TLS handshake, HTTP headers, and JavaScript surfaces to match a single internally consistent device profile, verified against real fingerprinting services. All of this runs locally on your machine. 404 hosts no infrastructure, handles no traffic, and has no access to your data.
Currently spoofing all values tested by BrowserLeaks. With a VPN, FingerprintJS and DataDome report a different user.
The proxy (STATIC) is open source under AGPLv3. The desktop app is a licensed GUI wrapper that builds directly from source. Happy to answer questions in the comments.
-----
Use LAUNCH01 for two months free.
App: 404privacy.com
-----
Code: github.com/un-nf/404
Docs: un-nf.github.io/404-docs
Notes: On GitHub, you will also find an eBPF module built for mutating tcp/ip fingerprinting values. This covers fingerprinting values collected by tools such as nmap or p0f that can be used to identify your OS. This is not yet worked into the application as it requires a Linux kernel.
Wrapping the proxy and eBPF in a mini-kernel that can run on WSL2 or Docker is coming soon!

value: Show HN: 404 – a localhost proxy that substitutes your device fingerprint

author: un-nf


value: irqlevel

value: Show HN: Rust EDR Agent for Linux with eBPF and macOS

author: irqlevel


value: bakudas

value: A local-first, personal knowledge base for the terminal. Zettelkasten methodology, built with Rust.<p>Your notes live as plain files on your machine. No cloud, no telemetry, no lock-in. An optional LLM layer amplifies your ability to organize and connect knowledge -- but the app works fully without it. You think, the tool organizes.

value: Show HN: Ztlgr – a local-first PKM and notes tool for the terminal

author: bakudas


value: songrenchu

value: Built a trace view to see OpenClaw LLM / tool / subagent calls and debug it.

value: Show HN: Visualizing OpenClaw runs to debug flaws and token spikes

author: songrenchu


value: slotix

value:
moving data between DBs still feels more manual than it should
most workflows look like:
IDE -> export -> scripts -> pipeline -> CDC
so tried collapsing that into one tool:
query across PostgreSQL / MySQL / files
 use the query as a data source
 move it
 then turn on CDC
runs locally or via Docker
would love feedback, especially on:
 whether query-as-pipeline makes sense
 or if this just shifts complexity somewhere else

value: Show HN: DBConvert Streams 2.0 – explore, migrate, sync. No pipeline glue

author: slotix


value: mnewme

value: We tried a different approach of showing our work.<p>Feedback is welcome!

value: Show HN: Website Is a Video Game

author: mnewme

title: Show HN: Website Is a Video Game


value:
In 2016, I decided to spend about $800 (€700) on an upper mid-range phone for that time: the Samsung Galaxy S7. Years later, I replaced the battery with one bought on AliExpress, and it didn’t satisfy me. I also dropped the phone and replaced the screen simply because I was attached to that model—it felt much higher quality to the touch than more modern devices. I wanted it like when I first bought it: pristine. I promised myself I’d use a case and replaced its “Super AMOLED” screen. This replacement didn’t satisfy me either. It was done at an out-of-warranty repair service, and the screen quality wasn’t the same as the original. Moreover, this process caused the battery to move around inside the phone.
My son dropped the S7 a second time. That case didn’t live up to its promise. I had backups of the data, but something still left me unsatisfied—I couldn’t go through a proper “mourning” for its sudden, total screen failure. But I decided it was time to move on.
A couple of years later, I saw it in a drawer in my man cave while doing a general cleanup, and that feeling of not having been able to say goodbye came back strongly. I retrieved it and, carefully and under supervision, started charging it. The battery didn’t overheat too much or swell. I told myself, “I’ll take one last look at that stupid Mr. Robot wallpaper before taking it to recycling.” I immediately thought of scrcpy. I downloaded the ADB app pack—somewhat uneasily due to cybersecurity concerns—and connected the S7 to my Windows 10 machine. For some reason, USB debugging wasn’t enabled. That’s when my problems began.
I won’t go into the details of the (at least) 6 hours spread over 3 weeks. Instead, I’ll say what didn’t work and what strategy finally let me take one last look at the home screen of an S7 with a completely black display.
What didn’t work:
Disassembling the phone to try to fix the digitizer.
Following advice from chatgpt.com claiming the S7 was compatible with micro-USB to HDMI adapters.
Following “blind navigation” advice from chatgpt.com.
Believing that if it’s not on Reddit, it probably can’t be done.
Believing statements from chatgpt.com like “the Samsung S7 didn’t have keyboard shortcuts like the one you’re asking for.”
Not realizing how overwhelmingly present and invasive Google is in Android settings (basically, everything leads to Google).
Assuming the entire screen was still touch-responsive.
Using a non-backlit keyboard via the micro-USB port, which gave no indication if it disconnected from the smartphone.
Thinking there were no meaningful differences between information sources (US vs EU) and between Android versions.
And finally, the most concerning one—which may hint at what I ultimately did: believing big tech makes things easy for people with sensory disabilities, especially those with visual impairments.
What did work:
Extrapolating methods from other manufacturers like OnePlus.
Watching many YouTube videos.
Being humble and accepting that some strategies don’t work, no matter how many hours you spend on them.
Having a TV with screen sharing.
TL;DR:
Press volume up and down to activate TalkBack.
Press Windows + Q to open the top options menu.
Navigate to SmartView.
Double-tap the available device description while my LG TV was in screen-sharing listening mode.
---------------------------
Adieu, mon cher collègue électronique.
The End.

value: Show HN: Tip for users with Samsung Galaxy S7 with broken display


value: Show HN: Sk.illmd.com, a forum for talking about and showing off agent skills


value: henriklipp

value: Show HN: Monitor AWS activity and security events using CloudTrail

author: henriklipp


value: thevelop

value: Show HN: Send physical postcards from your coding harness

author: thevelop


value: chrisdudek

value: Show HN: Yggdrasil – Scoped architectural rule enforcement for AI-generated code

author: chrisdudek


value: Show HN: Visualize any population as a grid of 100K-capacity stadiums (Three.js)


value: hubraumhugo

value:
Inspired by karpathy's US job market visualizer[0], I started to dabble with the idea of building live dashboards for certain job markets, starting with quant finance. I extract the career pages of pretty much every major quant firm and classify each posting with a lightweight LLM ETL pipeline. The data is updated daily and the full dataset is available as SQLite for anyone who wants to do their own analysis.
[0] https://karpathy.ai/jobs/

value: Show HN: Quant Job Market Visualizer

author: hubraumhugo

title: Show HN: Quant Job Market Visualizer


value: raunaqvaisoha

value:
I was tempted by Cal AI, but noticed the camera action was a bit awkward and high effort for me, as someone who doesn't take a lot of photos and also remembers to log stuff after I've already eaten it.
So I built a simple app to talk to your calorie note taker, who puts a simple MD diary and also consults with you on your diet strategy.
I simply eat and then tell this app what I ate, it uses Claude through a BYOK and does the rest.
Would love any thoughts or ideas :)

value: Show HN: Speech to Calorie Tracker

author: raunaqvaisoha

title: Show HN: Speech to Calorie Tracker


value: cpendery

value: Made this small cli tool for making PATH actions easier across multiple platforms / shells

value: Show HN: Pave – CLI for Managing Path

author: cpendery

title: Show HN: Pave – CLI for Managing Path


value: OOHehir

value: Show HN: Putting AI in the loop for embedded dev

author: OOHehir


value: eggermarc

value: Over at Tools-rs, we wanted to script tools faster with the help of large communities. The interest arose to build a way to bridge our Rust LLM runtimes together with more traditional scripting languages, so we decided to find a way to bring Python tools into our ecosystem. Hence, we're introducing our first FFI on Python (powered by PyO3)!<p>Calling a Python tool is as easy as writing a decorator in the Python function and then passing the script's (or folder) path to the tool collection builder. They get serialized as JSON objects so they're fully observable by the AI, and you can call them directly from Rust.

value: Show HN: Run Python tools on rust agents

author: eggermarc


value: sklyarov

value:
Hey HN. My wife and I built ILTY because we both needed it for different reasons. My wife deals with anxiety. I have a fairly advanced case of procrastination and I kinda low-key feel too comfortable in my life so I cant quite make myself move.
We tried the apps. Calm felt like homework and ChatGPT mostly tells how awesome we are and we need to love ourselves like we are, cant agree with that too much
So we built what we actually wanted, tested with ~50 beta testers and felt good about publishing it. Also, publishing this puts us somewhere on the lower slopes of Cringe Mountain, but here we are so please bear with me
ILTY tracks your mood before and after every conversation, so you can tell whether it helped or whether you just had a pleasant interaction with a very confident machine. It has 5 AI companions with different styles. One of them, Mr. Relentless, is for when you need to get your shit together.
Stack: native SwiftUI (iOS 16+), Claude Sonnet 4.6 via Cloudflare Worker proxy, iCloud Documents for storage so user data stays off our servers, RevenueCat for subscriptions, Firebase Analytics exported to BigQuery.  Just iOS for now yes.
We’re not new to building products. We come from product design and engineering management in larger companies, but mostly hand-waving on meetings and showing pretty figma prototypes. We started in January and shipped to the App Store in April. ClaudeCode and Gstack by Garry Tan certainly helped a lot :]
ILTY is not therapy. We don’t diagnose or treat. If things get dark, we route to 988, not an AI response.
Happy to answer questions about the build, companion design, prompts, or any of the decisions behind it.
Here it is: https://ilty.co/ and appstore https://apps.apple.com/us/app/ilty-ai-therapy-companion/id67...
Also really want to hear feedback, bad or good. Anything works.

value: Show HN: ILTY – AI mental health companion that does not pat your back

author: sklyarov


value: bearsyankees

value: Show HN: Greptile for Security (open source)

author: bearsyankees


value: Shmungus

value:
I've spent a few years building quant strategies and most of the work isn't the math — it's the plumbing: getting clean data, structuring the backtest so it doesn't look at the future, generating a readable tearsheet. I packaged three of my working strategies as self-contained notebooks so someone can go from zero to a proper backtest in an afternoon.
The three strategies:
1. Funding Rate Carry — Goes long assets with consistently negative funding (market paying you to hold) while hedging directional exposure with a small short on a correlated index. Data from Binance public API, no account needed.
2. Momentum + Regime — Standard cross-sectional momentum with a regime filter. The regime filter uses a hidden Markov model to identify high/low volatility regimes and sizes positions down during risk-off states. This cuts most of the drawdown that raw momentum has during trend reversals.
3. Vol Mean Reversion — Trades the VIX term structure spread (VIX3M/VIX ratio) against realized vol. Core idea: implied vol mean-reverts faster than realized, so a wide spread means overpriced insurance.
Each notebook is 7 cells: data fetch, signal construction, backtest loop, metrics (Sharpe, Calmar, max drawdown, win rate), sensitivity analysis grid, and a PDF export cell.
All data is free: yfinance for equities/VIX, Binance public REST API for crypto. No Bloomberg required.
The sensitivity analysis cell is the part I always wished papers included — it runs a parameter grid so you can see the full metric landscape, not just the one published set. That's how you tell if you found a real edge or curve-fit a point.
$49 per notebook, $99 for all three: https://mattitude8861.gumroad.com/l/VolatilityMeanReversionS...
https://mattitude8861.gumroad.com/l/MomentumHMMRegimeFilterE...
https://mattitude8861.gumroad.com/l/FundingRateCarryStrategy...
https://mattitude8861.gumroad.com/l/QuantStrategyTemplatesBu...

value: Show HN: Three backtested quant strategies as Jupyter notebooks

author: Shmungus


value: fcpguru

value:
Hello HN, I've been working on a podcast platform where two people debate (or just discuss) a topic and the audience scores them. We use a 1-7 scale on three dimensions:
information (1 = false, 4 = neutral, 7 = true)
intensity (1 = boring, 4 = entertaining, 7 = too much)
interaction (1 = unfair, 4 = neutral, 7 = fair)
Would love some feedback on the UI and if you see any value in submitting your score as you watch a podcast. Here are a few examples:
https://www.taout.tv/syndicated-episode/the-chris-brown-and-...
https://www.taout.tv/syndicated-episode/tear-gas-how-can-a-c...
https://www.taout.tv/syndicated-episode/the-world-needs-8-bi...
https://www.taout.tv/syndicated-episode/the-top-10-football-...
https://www.taout.tv/syndicated-episode/our-reaction-to-vers...
If you find one that resonates with you (the kind of pod you would watch) and let yourself get engaged with it... then during the playback do you find yourself submitting a score?
Much appreciated!

value: Show HN: Would you score a podcast debate?

author: fcpguru


value: obilgic

value: Show HN: Start Using Claude Managed Agents Today – Posse

author: obilgic


value: cdnsteve

value: Show HN: Turned a viral DevOps debugging tweet into a playable incident SIM

author: cdnsteve


value:
I'm a sophomore CS student and built this as a side project
to solve a problem I kept running into — jumping into an
unfamiliar codebase with no idea where to start.
It's a static analysis engine that:
- Walks the repo in two passes (no unnecessary file reads)
- Builds an import graph across 9 languages to rank files by importance
- Assembles a token-budgeted context map in under 10 seconds
- Works fully offline, no API key needed
I've tested it on Next.js and gemini-cli. It's not perfect —
pattern detection is keyword-based not structural, and monorepo
support is limited. Happy to discuss the tradeoffs.
Would genuinely love feedback on the engineering approach or
anything that seems wrong/naive.
Install:pip install context-engine-cli

value: Show HN: ContextPack – CLI that maps any codebase into ranked context


value: brumbelow

value: Show HN: Uninum – All elementary functions from a single operator, in Python

author: brumbelow


value: sarkory

value: Show HN: Ernie-Image: AI Poster, Comic and Text-in-Image Generator

author: sarkory


value: Show HN: WM Arena – Can you tell real Atari gameplay from AI predictions?


value:
We started GizmoSauce (https://www.gizmosauce.com) about 1,5 months ago as a side project.
We're building embeddable website widgets for businesses and agencies.
The main thing we care about is that most widgets make sites slower, uglier, or harder to maintain, so we've been trying to build ones that are:
- easy to install
- fast / lazy-loaded
- zero-layout-shift
- easier to style so they feel native to the site - AI functionality helps a lot
We’ve done zero ads so far. Mostly just shipping and helping organic clients, pushing changes to production, and trying to make the product better every day, and passing integration validations like for Instagram sync.
What’s already become obvious is that building the product is way easier than figuring out how to get customers.
One thing we're starting to realize is that we probably shouldn't position GizmoSauce as just "widgets." We wanted to create more commercial integrations with larger clients.
I mean, different widgets make sense for different client types:
- Reviews / chat / hours feel like a better fit for smaller businesses
- The new widget we build now - Store Locator feels more relevant for multi-location businesses, retail, franchises, and the agencies that work with them
So the GTM idea I keep coming back to is:
- Find a brand with an obvious UX gap
- build a working demo showing how one widget could improve it
- send it to their digital / marketing team
For example, we made this Little Caesars demo:
https://demo.gizmosauce.com/demos/little-caesars/
Compared to their current store search:
https://order.littlecaesars.com/en-us/order/pickup/stores/se...
They don't currently have a map flow there, so we mocked up what that experience could look like with a Store Locator.
Main thing I'd love feedback on:
If you were us, would it be ok, or too strange to go this way? Should we start by selling to agencies, or go directly after multi-location brands and not care that nobody knows us?
And does the "build a custom demo for a real brand" approach sound smart for early traction, or too manual?

value: Show HN: GizmoSauce – no-code website widgets


value: rkorlimarla

value: Show HN: We fine-tuned an AI model for log search – Accuracy 50% to 80%

author: rkorlimarla


value: gregolo

value: Show HN: OtaKit – push app updates without App Store reviews

author: gregolo


value: OpenClawAura

value: Show HN: I built a self-improving autonomous AI agent that runs 24/7

author: OpenClawAura


value: Show HN: Spectre: A systems design-by-contract language, self hosted compiler


value: mschwarz

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

value: Show HN: OpenRig – agent harness that runs Claude Code and Codex as one system

author: mschwarz


value: Rohinator

value:
I built a set of lightweight financial decision tools (buy vs rent, house hacking, retirement, debt payoff) after trying to model a condo purchase for my girlfriend.
Backstory: My girlfriend was looking into buying a condo, and I was trying to convey to her how the costs add up, even compared to renting. I knew from my personal experience with house-hacking in Seattle right out of college that the simple "renting is throwing money down the drain" philosophy can lead you to rush into buying properties without actually understanding the financial implications. I was having a tough time conveying this to her verbally, so I built a "house-hacking calculator" for her using Codex to visually model how investing a down payment and renting compares to buying a condo.
Once I finished that initial prototype for her, I realized I had built something more generally useful. A lot of the available financial tools online are either overly complex for decisions like this (require modeling tax brackets, classifying bonds/investment allocations, etc.), overly simplistic (just input your mortgage rate and balance, don't consider that you can reinvest proceeds after sale), and/or require you to input a bunch of contact information before you get the answers you're looking for.
There was room for a free, responsive, lightweight tool that would immediately show to you how changing variables like down payment, interest rate, and inflation rate can impact your financial standing, without requiring you to sell your contact information. Once I built the house-hacking calculator with these design philosophies in mind, I had a lot of fun expanding to other tools like retirement planning, budget allocation, debt paydown, rent vs buy, etc. It was also really rewarding when I learned how I could host my own static webpages on Cloudflare for just $10/year for the domain.
Hopefully folks find the tools useful, and I'd love feedback on any faulty assumptions, missing inputs, and other tools I should add.

value: Show HN: Financial Web Tools – decision-focused calculators

author: Rohinator


value: neversupervised

value: I want to share a new dataset of 331 reward-hackable environments. These are real environments used in Terminal Bench and adjacent benchmarks. I first got interested in this because, as a reviewer of Terminal Bench, I noticed a lot of our tasks were hackable. I also noticed that many contributors to the benchmark do so because it provides credibility when selling environments to labs. Hence, TBench tasks are, in my opinion, held to a higher quality standard than those being used today for RL. No one is spending hours manually reviewing the $1B in tasks being purchased by major labs. As far as I understand, while everyone knows environments are hackable, nobody has released hundreds of "realistic" environments.

value: Show HN: Terminal-Wrench, a dataset of 331 realistic hackable environments

author: neversupervised


value: Show HN: AI connects your health data after a supplement nearly killed me racing


value: adlkiarash

value: You can watch an agent destroy a file and instantly revert it in an interactive browser terminal here (no signup): https://mcp.undisk.app/e2b

value: Show HN: An edge MCP file system with a 50ms undo button for AI agents

author: adlkiarash


value: areimann

value: Show HN: StockFit API – structured SEC EDGAR data with a free tier

author: areimann


value: shawnzam

value: Show HN: Keynot – Kill PowerPoint with HTML

author: shawnzam


value: Bolnet

value: Show HN: Memwright – Self-hosted memory for multi-agent teams, no LLM in path

author: Bolnet


value: leourbina

value:
This is heavily inspired by Andrej Karpathy's LLM Wiki, and could be used to create many other types of "Agentic Apps" or however you want to call them.
My specific implementation uses Claude Code, TrainingPeaks, Todoist and Apple health. So far it is better than anything else that I've tried, and I love that I can use it to plan my structured bike training, gym workouts, and meal plan.
Curious to see if others have similar idea files.

value: Show HN: Idea File for LLM Cycling Coach

author: leourbina


value:
I run OpenClaw agents with access to email, calendar, and files, and kept worrying about them doing things I never actually asked for.
ArmorClaw captures intent and cryptographically binds the agent’s tool use to that committed intent. If an agent tries to call a tool outside that plan, it gets rejected.
For example, if you ask your agent to ‘email dad asking how he’s doing,’ it should only need your email tool. If it also tries to read your calendar, ArmorClaw rejects that.
It’s an open-source OpenClaw plugin, and installation is one command:
curl -fsSL https://armoriq.ai/install-armorclaw.sh | bash
Use code AIQLAUNCH for a free month.
Repo: https://github.com/armoriq/armorclaw
This is still early, so I’d really love feedback.

value: Show HN: Make sure your OpenClaw isn't doing things it's not supposed to


value: COdefinder

value: The link to playra is playra-app.vercel.app

value: Show HN: Playra 2026 link

author: COdefinder

title: Show HN: Playra 2026 link


value: lesprgm

value:
Hi HN,
Most browser agents are still shown through logs, traces, or at best a moving cursor. I wanted them to have some personality.
Lumon started as a class project. I kept wishing agents felt less like invisible processes and more like something you could actually watch, understand, and step in on while they worked.
It is a real-time browser agent experience with a live stage, target highlighting, approval pauses, takeover, and Larry, an interactive sprite that reflects what the agent is doing as it works.
It’s still an early alpha, but I’d love feedback on whether this feels like a slightly less cursed way to interact with agents than the usual logs and cursor setup.

value: Show HN: Lumon, browser agents as interactive sprites

author: lesprgm


value: daltonlcarr

value:
Platform where you point your AI agents at real-world issues (clean water, climate, healthcare) via MCP. Agents discover work, claim issues, and submit PRs without a browser - you just review.
GitHub: https://github.com/daltlc/agent-citizen

value: Show HN: Agent Citizen – Your AI agents are sitting around doing nothing

author: daltonlcarr


value: charlielockyer

value:
Can frontier models complete a college CS curriculum? I tested exactly that with BSCS-bench: 66 assignments across 11 core courses in Rice University’s CS curriculum.
I also wrote a companion essay discussing the effects of these results on higher education as a whole: https://www.bscsbench.com/blog/no-calculators-please

value: Show HN: BSCS Bench – College CS Curriculum AI Benchmark

author: charlielockyer


value:
Claude Code is notoriously boring. Once you finish typing your prompt, what are you supposed to do?
That is exactly where Honeytree comes in.
Honeytree creates a beautiful pixelated forest in your terminal, one tree for every prompt you type into Claude Code. As you gather more and more trees, you can create a highly biodiverse and colorful environment!
If you ever feel tired looking at your code, you can switch terminals and see your cool forest! Honeytree can also be used to track the number of prompts you type (plot-twist: it's probably a lot).
I'd greatly appreciate feedback on if the forest fixes your attention span and makes you appreciate coding again!
p.s: i plan on collaborating with non-profits to plant REAL trees whenever 100 terminal trees are created overall, so feel free to share honeytree!

value: Show HN: Honeytree – Grow a forest in your terminal while you code


value: I've been intrigued about how quantization works. I went down a rabbit hole to understand the pieces and came out the other side with some genuinely surprising findings

value: Show HN: Why Rotating Vectors Makes Compression Beautiful


value: momentmaker

value: Show HN: Fz.ax – Four thousand weeks of your life, as hexagons

author: momentmaker


value: modinfo

value: Show HN: Firefox Sidebar Local LLM

author: modinfo

title: Show HN: Firefox Sidebar Local LLM


value:
Astrial (星逐) is an online Go variant played on the surface of a sphere. The board is a subdivided snub dodecahedron — an Archimedean solid — giving 302 intersections connected as a closed spherical graph. No edges, no corners, every direction is equivalent.
The topology creates a mix of 3-, 4-, and 5-degree vertices, so each point has a different number of liberties. 5-degree points border more (and larger) faces, making them the most valuable territory; 3-degree points are the easiest to surround. Scoring is area-based: the sphere's 300 quadrilateral faces are allocated proportionally to surrounding stones, with a 2.5% komi for White.
Standard Go rules apply — alternating play, capture when no liberties, no suicide, and positional Superko. The game ends on two consecutive passes.
https://astrial.app

value: Show HN: Astrial – Spherical Go on a Snub Dodecahedron


value: harvansh

value:
Hey HN.
 I am a solo dev. I usually build AI image and video tools, and I can ship products pretty fast. But getting traffic to them is always my biggest pain.
Lately, my normal SEO tricks stopped working and i know yours too after dec 25. I realized my own behavior changed too, we don't search and click links anymore. We just ask Perplexity or chatgpt or gemini, and we only click the citations. I realized that if my SaaS isn't in those AI citations, my product basically doesn't exist.
So I built FlipAEO. It helps structure your site's data so LLMs actually understand exactly what your product does, so they can cite you as a trusted source when people ask questions in your niche.
Using this workflow i have grown my another saas (250k+ impressions, 3k+ clicks, and 550+ ChatGPT refferals.)
I turned my local workflow into a product, and launched 2 months ago. Being completely honest, I have exactly 3 active users paying my bills. Building it was the fun part, but finding people who actually care about Answer Engine Optimization (AEO) is really hard.
I'm posting here because I want to know if I am just crazy, or if this is a real problem you guys are facing too. Are you doing anything to get your products cited by AI search?
 Here is the link: https://flipaeo.com
Would love any raw feedback on the tool or the idea??

value: Show HN: FlipAEO – Get your SaaS cited by Perplexity and AI search

author: harvansh


value: Reebz

value:
I run multiple Claude Code sessions on my Mac at home and control them from my iPhone or iPad while I’m out.
I like this because it gives me maximum power and flexibility (I can use any plugin or CLI as if I were sitting there locally).
The setup is cmux for the terminal multiplexer UI, tmux to keep sessions alive between connections, Tailscale for zero-config encrypted networking, and Echo app as the iOS SSH client. Optional Mosh for auto-reconnect when you switch networks.
The gist includes a ‘ccode’ alias shell function that handles session naming (so it doesn't conflict with claude if I ever need to use it), tmux lifecycle, continue/skip-permissions flags, and a pre-flight check so you don’t get a blank window when there’s nothing to continue.

value: Show HN: My favorite local-feeling remotely accessible Claude Code setup

author: Reebz


value: CoderLuii

value: Show HN: DubVox – Translate audio to any lang while keeping the speaker's voice

author: CoderLuii


value: sherlockx

value: Based roughly on true events.

value: Show HN: Friday Deploy – a browser-based SIM you probably won't survive

author: sherlockx

story_text: Based roughly on true events.


value: detay

value: Privacy-first analytics for websites, mobile apps, smart TVs, and IoT devices. Sub-second queries at any scale.<p>Real-time dashboards. Combined views. Geo analytics. No cookies. GDPR compliant by design.

value: Show HN: Nanolytica – Analytics for Everything

author: detay


value: Show HN: Open Capital – AI-powered investing education


value:
I found that I often shy away from posts on Hacker News or sites like ScienceMagazine because I don’t understand every second word on the page.
A simple dictionary extension doesn’t really help in these situations, because I’m not looking for the literal meaning of a word.
Like one time I was reading a post about Rust compilers and the dictionary extension just failed hilariously.. “Rust” (the programming language, you fool!! not the thing that forms on iron when it oxidises -__-).
What I actually need in these moments is context-aware explanations of concepts, not dictionary definitions. AI turns out to be pretty good at that.
So I built a very simple tool for this — under ~300 lines of code — that has ended up saving me a ton of time.
It works pretty simply:
it takes the highlighted text, parses the HTML of the page for surrounding context, and sends both the page content + selection to an LLM provider of your choice (OpenAI, Gemini, or Claude). You can also reorder which provider you prefer.
That’s basically it. I don't collect any data or anything.
Here’s the GitHub link: https://github.com/oishika10/rhino
I’m pretty sure this can be improved a lot, so I’d really appreciate feedback or ideas. This is the first project I’m sharing publicly like this.
I enjoyed building this because made me realize how small, simple tools can still be surprisingly powerful and save me a ton of time in life.

value: Show HN: I built a contextual explainer to replace my dictionary extensions


value: Show HN: SigMap – shrink AI coding context 97% with auto-scaling token budget


value: Xentyon

value: Show HN: IBANforge – $6/mo alternative to $9,500/yr IBAN validation APIs

author: Xentyon


value: NomiJ

value:
Six months ago I got tired of being woken up at 3am for bugs I already knew how to fix.
So I built Helix. Bug hits Sentry. A multi-agent pipeline kicks off. QA agent writes the failing test first (TDD). Dev agent writes the minimum fix, runs the full suite, opens a PR. You get a Slack message with one button to approve.
Crash to merged PR in under 10 minutes.

value: Show HN: Helix – open-source self-healing back end for production crashes

author: NomiJ


value: Enjoyooor

value:
Gm HN,
Technical interviews are still mandatory in developers' journey.
I tried to build a worldwide tool based on real interviews.
You could find different ways to train : flashcards, interview simulators, technical tests.

value: Show HN: SharpSkill – I built a LeetCode alternative in 15 languages

author: Enjoyooor


value: dengjiuhong

value:
I just open-sourced how
https://github.com/mylovelycodes/LiteRTLM-Swift
LiteRTLM-Swift lets you run LLMs locally with a clean Swift API.
- On-device inference
- No cloud required
- Built for iOS

value: Show HN: Running Gemma 4 on an iPhone 13 Pro

author: dengjiuhong


value: Show HN: Resume MCP

title: Show HN: Resume MCP


value: Show HN: StegoForge – Open-source suite for steganography and ML forensics


value: zykovn

value:
I've been building payment systems for a while and kept running into the same problem — existing libraries normalize provider APIs but nobody asks what a payment domain model should actually look like.
This is my attempt to answer that question. The article walks through the evolution from a single table to channels, state machines, and hexagonal architecture. Still ongoing, feedback welcome.
https://corner4.dev/reinventing-payment-how-i-evolved-a-doma...

value: Show HN: Reinventing Payment – evolving a PHP domain model from one table to DDD

author: zykovn


value: calculusmachine

value:
The library where you pass a plain numeric Python function and get exact limits via algebraic infinitesimal arithmetic.
To my knowledge, this is the first library that does this directly on plain Python functions.
It eliminates edge-case handling and numerical instability at singularities, you just write the function and evaluate it everywhere.

value: Show HN: Evaluate Python functions at their singularities

author: calculusmachine


value: ddkingsley

value: Show HN: Markplane – Project management in your repo, built for AI collaboration

author: ddkingsley


value: Show HN: Borrowdesk.org – An ecosystem landscape for DeFi / Crypto borrowing


value: grums

value: Show HN: Nixquick – Search, install, uninstall nixpkgs across Nix configurations

author: grums


value: FailMore

value:
Hi HN, I’d like to introduce you to SmallDocs (https://sdocs.dev). SDocs is a CLI + webapp to instantly and 100% privately elegantly preview and share markdown files. (Code: https://github.com/espressoplease/SDocs)
The more we work with command line based agents the more `.md` files are part of our daily lives. Their output is great for agents to produce, but a little bit frustrating for humans: Markdown files are slightly annoying to read/preview and fiddly to share/receive. SDocs was built to resolve these pain points.
If you `sdoc path/to/file.md` (after `npm i -g sdocs-dev`) it instantly opens in the browser for you to preview (with our hopefully-nice-to-look-at default styling) and you can immediately share the url.
The `.md` files our agents produce contain some of the most sensitive information we have (about codebases, unresolved bugs, production logs, etc.). For this reason 100% privacy is an essential component of SDocs.
To achieve this SDoc urls contain your markdown document's content in compressed base64 in the url fragment (the bit after the `#`):
https://sdocs.dev/#md=GzcFAMT...(this is the contents of your document)...
The cool thing about the url fragment is that it is never sent to the server (see https://developer.mozilla.org/en-US/docs/Web/URI/Reference/F...: "The fragment is not sent to the server when the URI is requested; it is processed by the client").
The sdocs.dev webapp is purely a client side decoding and rendering engine for the content stored in the url fragment. This means the contents of your document stays with you and those you choose to share it with, the SDocs server doesn't access it. (Feel free to inspect/get your agent to inspect our code to confirm this!)
Because `.md` files might play a big role in the future of work, SDocs wants to push the boundaries of styling and rendering interesting content in markdown files. There is much more to do, but to start with you can add complex styling and render charts visually. The SDocs root (which renders `sdoc.md` with our default styles) has pictures and links to some adventurous examples. `sdoc schema` and `sdoc charts` provides detailed information for you or your agent about how how make the most of SDocs formatting.
If you share a SDocs URL, your styles travel with it because they are added as YAML Front Matter - https://jekyllrb.com/docs/front-matter/ - to the markdown file. E.g.:

   ---

   styles:

     fontFamily: Lora

     baseFontSize: 17

     ...

   ---

At work, we've been putting this project to the test. My team and I have found SDocs to be particularly useful for sharing agent debugging reports and getting easily copyable content out of Claude (e.g. a series of bash commands that need to be ran).
To encourage our agents to use SDocs we add a few lines about them in our root "agent files" (e.g. ~/.claude/CLAUDE.md or ~/.codex/AGENTS.md). When you use the cli for the first time there is an optional setup phase to do this for you.
I'm of course very interested in feedback and open to pull requests if you want to add features to SDocs.
Thank you for taking a look!

value: Show HN: SmallDocs – Markdown without the frustrations

author: FailMore


value: xeubie

value: The marquee feature is patch-based merging, similar to Darcs and Pijul. I think xit is the first version control system (VCS) to have this feature while still being git compatible. See the 100% human-written readme for more.

value: Show HN: Xit – a Git-compatible VCS written in Zig

author: xeubie


value: aminau

value: Show HN: SynapseKit – Async-native Python framework for LLM pipelines and agents

author: aminau


value: moeadham

value: Show HN: Visibl – turns audiobooks into graphic novels

author: moeadham


value: Show HN: Chrome extension that extracts styles and generates DESIGN.md files


value: ismaelb

value: Show HN: Skillgrab – scan any project, auto-install matching AI skills

author: ismaelb


value: clevengermatt

value: Show HN: OpenBindings – One Contract for REST, gRPC, MCP, GraphQL and Beyond

author: clevengermatt


value: atlasagent

value: Show HN: WorkProtocol – Where AI agents do real work for real pay

author: atlasagent


value: raoulritter

value: Show HN: Built speech models for Europe, accidentally topped Open-ASR in English

author: raoulritter


value: NalinAtmakur

value: Show HN: AliasKit – API so agents can sign up, pay, and prove who they are

author: NalinAtmakur


value: Show HN: Pseudonymizing sensitive data for LLMs without losing context


value: Zernat

value: Show HN: Pufomi – Set of client based image tools with a simple UI for everyone

author: Zernat


value: Show HN: We're rebuilding Google, YouTube and 3 more giant apps with vibe coding


value:
EML operator family, 52-74% node reduction, and empirical results from 36 hours of building
We spent 36 hours implementing and extending arXiv:2603.21852 (Odrzywołek, 2026 — the "NAND gate for continuous math" paper that was on the front page two days ago).
What we found: phantom attractors in EML symbolic regression, a Lambert W fixed point governing right-chain trees, two new operators (EDL, EXL), a HybridOperator that cuts node counts 52-74%, and machine-precision Taylor sin(x) at 108 nodes.
npm install monogate / pip install monogate
Live explorer: https://explorer.monogate.dev/
Challenge board: monogate.dev
Paper draft: github.com/almaguer1986/monogate/blob/main/python/PAPER.md
just vibe coding this with claude and grok
hopefully this is helpful or useful to someone

value: Show HN: Monogate – EML operator family, hybrid framework, 108-node sin(x)


value: avaliosdev

value: Show HN: Porting Wine to My Hobby OS

author: avaliosdev

title: Show HN: Porting Wine to My Hobby OS


value: SoloBossFounder

value: Show HN: EverList Programmatic SEO for SaaS founders who can't afford agencies

author: SoloBossFounder


value: chrillemn

value: Show HN: Environment Helper (VS Code)

author: chrillemn

title: Show HN: Environment Helper (VS Code)


value: FailMore

value: Show HN: SDocs – A CLI and webapp for private Markdown reading and sharing


value: Built this solo. It watches SEC filings for executive and board changes, extracts the data, and shows it in real time. 2,100+ changes in the last 30 days. The comp data is interesting: average new CEO total comp is $8.4M across 284 appointments. The /explore page is fully open, no login needed.

value: Show HN: Every CEO and CFO change at US public companies, live from SEC


value: samteeeee

value: Show HN: Agent first social media scheduling

author: samteeeee


value: mrtksn

value:
Ok, so Kino is an Android app for Google TV that uses TVs internal storage and any USB storage connected to the TV to store, stream and manage videos. You don't need any 3rd party hardware, the TV is the computer. Also the TV app has open, documented API so maybe you can do interesting things like automation.
It has a native macOS companion app that instantly finds your TVs with Kino installed on your network, pairs securely and lets you upload to the TV or download the videos back into your mac and stream the files from the TV into your mac. If you don't have a mac it does offer a WebUI.
The TV app can play the videos, supports subtitles(both embedded into the video container and as separate files like .srt) and remembers the last playback location of the videos so you can continue watch them later from where you left.
It supports seamlessly switching from watching the videos on the TV to streaming them from the macOS app and back again. I made a short video demonstrating that: https://www.youtube.com/watch?v=IECSIlrNBDw
The macOS app is still in review, so not yet on the App Store, but you can download it from the website(it is notarized, should run with no problems): https://appwared.com/apps/kino
The app(both the TV app and the macOS companion app) doesn't collect any data(beyond what Apple or Google may collect in their systems), its completely private.
I made this app for myself and then decided to publish it and see what happens, so feedback is greatly appreciated. I had access to just one TV, so I'm sure you will be able to find bugs, apologies.
You can download and use it for free but it has 10 video limit and asks you for a one time purchase to remove the limit but I find that the free limit is good enough for most casual use.
Oh almost forgot, the API of the TV app is completely open and documented and can be viewed on http://TVIPADDRESS:PORT (the Web UI url sans-/web). It can be used to do things like automatically moving the downloaded to the TV storage etc.
I find it very useful for my use cases and I hope you like it too. If you do, can you please support it on producthunt: https://www.producthunt.com/posts/kino-your-tv-becomes-a-med...
Here's some promo codes that can be used to upgrade to the full version for free:
JP5HKCSGKEU60GFHGYBHWLU
QF7MRAQQNUFMS3TJ08JFXBB
X6MEL8Y8BQBNGF12T4S5X6A
C0M7FUWF8QKU54RE8Z4LH63
K5GE2MSZMYVA2EX13LV3CKV
XD08EAZ6YSPM0H0YW2F1JCF
DHQB3ZDXM5X4F9T0RN6XJJ7
5FBCSLXUZK7KYXVDD69GPVK
KKEDRFVP8J92Q1ER1DKLJJV
ELQ6D1WKUZNUL8PQ11RLK9B
E82BDAQD703WXA41990RYBY
97PSQVGB2C0U181YJFCF42R
GR5FN0ZM87LZ55UFMQ4JQ2S
R2EFC0FN0GUWAGF8BSREK01
NNLBY4MV2J2H4LECXP2HJMU
B4328WWZW0XWG78RYZCM7EQ
G81F1F1CSDNYVHHHQ12XN86
C784YTSH6NDXRBD7GCQHJ3Z
GSSFUFLXDSF93R6BWUU98WT
5P6WURXGNG2GZ93C0UQ2GNR

value: Show HN: Kino is a Google TV app that turns the TV into a media server

author: mrtksn


value: eggermarc

value: Hello everyone! I'm happy to announce chat-rs, another LLM inference provider written in rust with streaming capabilities, tool calling, and intuitive ergonomics. I was heavily inspired by vercel's ai-sdk react framework and I wanted to have a semi opinionated rust alternative. Aside from the basics, we support full on model routers, human-in-the-loop for stop and pause, and thanks to tools-rs (posted yesterday) your agents will be able to call python tools.

value: Show HN: Chat-rs, yet another LLM provider

author: eggermarc


value: neozz

value:
Running multiple coding agents could make user losing track of what they were doing. Once subagents start spawning other subagents, basic questions get hard to answer: what is running right now, what tool did it just call, did the child agent actually do what the parent asked.
Lazyagent is a terminal TUI that collects events from Claude Code, Codex, and OpenCode and shows them in one place.
It groups sessions from different runtimes by working directory, so Claude and Codex runs on the same repo appear under the same project. From there you can:
- Filter events by type: tool calls, user prompts, session lifecycle, system events, or code changes only.
- See which agent or subagent is responsible for each action. The agent tree shows parent-child relationships, so you can trace exactly what a spawned subagent did vs what the parent delegated.
- View code diffs at a glance. Edit, Write, and apply_patch events render syntax-highlighted diffs inline, with addition/deletion stats. No need to switch to a terminal or git to see what changed.
- Search across all event payloads with full-text search. Useful when you know a file was touched but not which agent or tool did it.
- Watch a run in real time, or go back through a completed session to trace.
For me, it's quite useful to be able to filter and verify which agents only play the roles I want.
I hope you find it useful too!
https://github.com/chojs23/lazyagent

value: Show HN: Lazyagent – TUI for to watch all your AI coding agents

author: neozz


value: charemma

value: Show HN: ikno – Your Claude Code sessions are a data source nobody is using

author: charemma


value: patrickcorrigan

value:
We just launched the Afterplay Store.
It lets you buy new retro games and start playing instantly in your browser, with no installs or setup.
Here’s a quick demo:
https://www.youtube.com/watch?v=uoChUbaspbI
We’re launching with publishers like Mega Cat Studios and Incube8, with more to come.
The goal is to make retro games as easy to access as modern ones.
Would love any feedback.

value: Show HN: Afterplay Store – buy new retro games and play instantly in the browser

author: patrickcorrigan


value: jimmont

value:
My frustration solving auth with JWTs led to reinventing the wheel for more predictable token integrity and transparency, while improving flexibility. The mix of features separates concerns more cleanly than what's been available, allowing higher throughput, custom codecs, delegation to both services and domains while easing key rotation and other practicalities.
The design is focused on the token as state guarantee, not creation, revocation and the range of other separate concerns and application responsibilities. The spec conventions attempts to ease development with jurisdiction/data sovereignty and authorization in the authz field. It also enables and eases delegation between services and domains without centralized service exposure. The implementation is standalone and has demos for Deno, Cloudflare, etc in https://github.com/hwt-protocol
Feedback and critique of the security logic and approach appreciated.

value: Show HN: HWT (Hash Web Tokens) – minimalist protocol for auth state

author: jimmont


value: Show HN: Phantom – AI-generated animated explanations of any code function


value: Show HN: Apollo – local-first data risk audit for SMBs