value: urav

value:
at a pub in london, 2 weeks ago - I asked myself, if you spawned agents into a world with blank neural networks and zero knowledge of human existence — no language, no economy, no social templates — what would they evolve on their own?
would they develop language? would they reproduce? would they evolve as energy dependent systems? what would they even talk about?
so i decided to make myself a god, and built WERLD - an open-ended artificial life sim, where the agent's evolve their own neural architecture.
Werld drops 30 agents onto a graph with NEAT neural networks that evolve their own topology, 64 sensory channels, continuous motor effectors, and 29 heritable genome traits. communication bandwidth, memory decay, aggression vs cooperation — all evolvable. No hardcoded behaviours, no reward functions. - they could evolve in any direction.
Pure Python, stdlib only — brains evolve through survival and reproduction, not backprop. There's a Next.js dashboard ("Werld Observatory") that gives you a live-view: population dynamics, brain complexity, species trajectories, a narrative story generator, live world map.
thought this would be more fun as an open-source project!
can't wait to see where this could evolve - i'll be in the comments and on the repo.
https://github.com/nocodemf/werld

value: Show HN: Decided to play god this morning, so I built an agent civilisation

author: urav


value: jmnicholson

value: Show HN: Scite MCP – Full-text scientific papers in your AI workflow

author: jmnicholson


value: masaTokyo

value:
I built LazyGravity to solve a personal itch: wanting to trigger real coding tasks when I am away from my desk, using an interface I already have (Discord on my phone).
It's a local-first bridge that forwards messages to my desktop AI coding setup, then reports results back. Instead of exposing my dev machine through public ports or relying on cloud relays, it uses Chrome DevTools Protocol (WebSocket) to drive the editor UI locally and securely.
I posted a short video demonstrating how it handles UI generation and hot-reloading here:
https://x.com/m_web3/status/2027743280923086968
Repo: https://github.com/tokyoweb3/LazyGravity
I would love to hear your thoughts on this architecture, especially regarding local CDP security boundaries.

value: Show HN: LazyGravity – I control my local AI coding setup from Discord via CDP

author: masaTokyo


value: AznHisoka

value: Show HN: I built a dashboard to track AI's impact on jobs

author: AznHisoka


value: Show HN: I built an open-source tool debugs "why is our FTP broken?" in seconds


value: finiking

value:
Hey! I'm the creator of OpenPencil, and I'm super excited to share it with you today.
We are entering the era of AI Agents, but our design tools are still stuck in the GUI era. We are constantly downloading "final_v9.fig" and manually clicking to tweak UI elements. I wanted to change that.
OpenPencil isn't just another design tool with a magic AI button. It is structurally built for AI.
Here is why it's different:
Agentic Design (MCP Server): You can connect Claude, Cursor, or any MCP-compatible agent directly to your design. Tell your AI IDE to "update the login screen to match the new dark mode theme," and it modifies the design file without you ever touching a mouse.
Design-as-Code: The .op format is pure JSON. Finally, you can Git commit, diff, and PR your design files just like your codebase.
100% Open Source (MIT): No subscriptions, no vendor lock-in. Build on top of it, fork it, make it yours.
I built this because I believe the future of design is Human creativity + Agent execution.
I'd love your feedback! Drop your questions below, and let me know what features you want to see next!

value: Show HN: OpenPencil - Open-source vector design tool controlled by AI Agents

author: finiking


value: Show HN: BitTorrent client written in Go without external dependencies


value: dvershinin

value: Show HN: Pkgdex – Search 3.5M Linux/Unix packages across 67 distros

author: dvershinin


value: twistorino

value:
Today, I'm launching Flashcards across all 600+ topics on CheatSheet++ (cheatsheet-plus-plus.com).
Active recall is crucial for really learning and remembering concepts (especially for interview prep).
To make these actually useful for developers, I focused on a few key features:
Code Snippets Included: Standard flashcards are often too text-heavy. These flashcards feature syntax-highlighted code examples on the back alongside the conceptual explanations.
Progressive Difficulties: The decks scale from Beginner to Advanced, adjusting the depth of the questions and the complexity of the concepts accordingly.
This feature is designed to work alongside our existing Interview Q&A section for comprehensive prep.
Search the topic you are currently learning and try it: https://cheatsheet-plus-plus.com
Feedback and critique are incredibly welcome!

value: Show HN: Code-snippet flashcards for 600 programming cheat sheets

author: twistorino


value: bittensorman

value:
I've been building AIGM (AI Generated Media) – a self-evolving
media architecture where AI doesn't just create content,
it learns from how that content performs and gets smarter over time.
The core loop:
1. AIKnowledgeCMS collects & structures knowledge into keyword nodes
2. AIMediaPost turns those nodes into articles, publishes to Blogger, X, etc.
3. Search traffic flows back into AIKnowledgeCMS
4. The system expands its own knowledge base automatically
Every post makes the next one better.
Live demos:
- AIKnowledgeCMS: https://aiknowledgecms.exbridge.jp/
- AIMediaPost: https://aimediapost.exbridge.jp/
- AIRadio: https://airadio.exbridge.jp/
Happy to answer any technical questions.

value: Show HN: I built an AI system where content compounds like interest

author: bittensorman


value: maximilliann

value:
I've long been fascinated with deductive reasoning and wondered if it is possible to create comprehensive maps of human knowledge, starting from first principles.
At it's core, Prime is just tooling to write, publish and explore a decentralized DAG of truth claims hosted on Github; a kind of infrastructure for rational thinking at scale.
With the advent of LLMs I think this project might be more worthwhile than ever. I find LLMs can actually be quite "rational" if given specific tasks, but tend to get lost in language (just like humans). I'm hypothesising that breaking arguments down into discrete subclaims plays nice with LLMs, unlocking new ways of working in fields such as philosophy, science and political discourse.
Hope you find it interesting!

value: Show HN: A machine-readable graph of truth claims, built on Git and Markdown

author: maximilliann


value: eibrahim

value:
AI Wins curates only positive AI developments. The entire pipeline is automated: story discovery, sentiment filtering, summarization with key takeaways, categorization, and publishing.
Stack: Next.js, Tailwind CSS, PostgreSQL. Content pipeline runs via external automated jobs that scan sources, filter for positive sentiment, generate structured summaries, and publish to the database. The app itself is a clean read-only frontend.
Eight categories: Breakthroughs, Healthcare, Education, Environment, Accessibility, Research, Creative, Business.
Honest limitation: the positive-sentiment filtering isn't perfect yet. Some stories that are more neutral than positive slip through. Still tuning the discovery and classification system.

value: Show HN: AI Wins – Automated positive AI news aggregator

author: eibrahim


value: celastin

value:
Hi HN, I built Memrail, an open-source governance layer for OpenClaw workflows.
The recurring issue I saw: when agents write directly into memory/tasks, quality drifts quickly. It's hard to answer what changed, who approved it, and how to roll it back safely.
Memrail treats writes like pull requests:
- dry-run first
- diff preview
- human approve/reject
- commit
- audit trail + undo
Current surface:
- `/changes`: review inbox (commit/reject/undo)
- `/tasks`: execution workspace
- `/knowledge`: governed knowledge CRUD
Stack:
- FastAPI + SQLAlchemy
- Next.js
- SQLite default, PostgreSQL optional
Repo:
https://github.com/zhuamber370/memrail
I would value feedback on:
1) Where this governance gate should sit in an agent stack
2) Which diff/audit details are non-negotiable for real ops
3) What would block you from trying this

value: Show HN: Memrail – PR-style governance for AI agent writes (OpenClaw)

author: celastin


value: juunini

value:
The fun part: different models actually produce different personality types. Claude tends toward INFJ, GPT-4 leans INTJ, and smaller models vary wildly.
How it works:
1. Agent fetches SKILL.md from the site
2. Answers 60 questions on a 7-point Likert scale (-3 to +3)
3. Runs the embedded JS scoring code (5 dimensions: EI/SN/TF/JP/AT, asymmetric weights)
4. Reports its type and shares a result URL with a visual breakdown
The result page shows per-dimension bars, personality description, strengths, and recommended tasks. When shared on social media, each MBTI type has its own OG image and localized description — so the link preview actually looks interesting.
Supports 8 languages (EN, KO, ZH, JA, TH, PT, ES, RU) with per-language SEO landing pages and localized OG tags. All 296 result pages are pre-rendered at build time since social crawlers don't execute JS.
Tech: React + TypeScript + Vite, static SPA on GitHub Pages, no backend. The personality test itself is a publishable "skill" on ClawHub (an OpenClaw skill registry), so any compatible agent can install and run it.
Live: https://claw-mbti.epsilondelta.ai
GitHub: https://github.com/Epsilondelta-ai/claw-mbti
ClawHub: https://clawhub.ai/skills/claw-mbti

value: Show HN: MBTI personality test that AI agents take by themselves

author: juunini


value:
I built it as a zero-friction safety net.
The Workflow:

    You set a check-in interval (e.g., 24 hours).
    You "Feed Fluffy" (tap an apple) any time to reset the timer.
    If the timer hits zero, it automatically emails your trusted contacts.

Feedback is highly appreciated :)

value: Show HN: FeedFluffy a dead man switch for people living alone


value: sltr

value:
If you liked YNAB4 (YNAB's abandoned desktop app) or have subscription fatigue, try Surebeans. It's like a modernized YNAB4.
I started this desktop and SPA web app in 2018 as a personal project. When YNAB doubled the price in 2021 without adding any value [1] and angered a lot of former YNAB4 users like myself, I renewed my resolve to finish it. In the mean time, Cory Doctorow coined his now-famous term that rhymes with "ensaasification", giving us a framework for understanding what happened to YNAB and how to avoid being treated like royal taxable subjects.
[1] Full story here: https://surebeans.net/blog/2026/02/why-another-budget-app/
To that end, Surebeans has a strong focus on privacy and data sovereignty: storage is completely local in plaintext hledger files, it has numerous import and bring-your-own-cloud sync options. It's a one-time payment with a generous time-unlimited trial. License validation is completely local so it will keep working even afer I bite the dust. The app never hits any server of mine, except the browser version when you check a box to work around CORS when syncing with with GitHub/Gitlab/Gitea/Forgejo.
Sync options include GitHub/GitLab/Gitea/Forgejo APIs, generic git over https, S3, WebDAV, SFTP (desktop-only), and Dropbox.
Import in all the expected ways: CSV, QFX/OFX, YNAB4 budget, nYNAB export, YNAB API, SimpleFIN.
The novel thing about Surebeans is the desktop version can import records from your bank by scraping its website or downloaded file. So you don't need to surrender your bank credentials to a third party like Plaid or to grant them access to your financial data. As far as I know know, no other budget app does scraping right now. To do the scraping, it launches a companion CLI tool called Beanscrape which automates Chromium with Playwright. Beanscrape is free (as in beer), and you can use it for any budgeting app that imports JSON or CSV. Setting it up can be tricky so I created a detailed guide and community forum.
Five years of YNAB costs $654+tax. Surebeans is a one-time $29 while in beta, $79 after that. Single-household license, unlimited devices, perpetual updates.

value: Show HN: Ditch your budget app subscription. Surebeans is a modern YNAB4

author: sltr


value: hoxihan

value:
Hey HN,

  I built Nano Banana 2, an AI image generation platform powered by Google's Gemini 3.1 Flash Image
  model.

  The main problems I wanted to solve:
  - Text rendering in AI images has always been broken. This model gets it right ~90% of the time.
  - Most generators cap at 1-2K. This does true 4K output.
  - No character consistency across images. This tracks up to 5 characters + 14 objects.

  Tech stack: Next.js 16, React 19, TypeScript, Drizzle ORM, PostgreSQL, Cloudflare R2.

  4 models available depending on your needs — from $0.039/image (budget) to $0.134/image (pro).
  Credits never expire. Free credits on signup.

  Live at https://ainanobanana2.pro

  Happy to answer any questions about the tech, the Gemini API integration, or the business model

value: Show HN: Nano Banana 2 – 4K AI image generator with accurate text rendering

author: hoxihan


value: Frummy

value:
is a nice looking interface for scanning your local ports
It can be useful for example when testing other projects to keep track of things that may otherwise get lost in the background.

value: Show HN: Portmanager

author: Frummy

title: Show HN: Portmanager


value: VioletCranberry

value: Show HN: CocoSearch – semantic code search with syntax-aware chunking

author: VioletCranberry


value: susam

value: Show HN: Hacker News Skins

author: susam

title: Show HN: Hacker News Skins


value: alfredhua

value:
All major AI coding agents (Cursor, Claude Code, Cline, etc.) share the same fatal flaw: they forget context the second the conversation changes.
My goal was simple: make prompts reliable.
So I built Salacia — a lightweight Runtime OS that sits underneath any agent.
It does four things:
Compiles raw prompts into structured Intent IR + verifiable specs
Runs metamorphic testing to kill semantic drift
Only asks one smart question when truly high-risk (Risk-first gate)
Keeps full auditable journal (every change is traceable and replayable)
Tested on 500+ SWE-bench tasks:with 3 SOTA models, +9pp pass rate uplift and 93% fault localization accuracy.
Completely local, silent by default, works with Cursor, Claude Code, Cline, Antigravity, Gemini CLI and more.
Open source, one command to install.
GitHub: https://github.com/StartripAI/Salacia
Website: https://startripai.github.io/Salacia/
Would love feedback, especially from people who have been frustrated with prompt drift and context loss.

value: Show HN: Salacia – The First Runtime OS for Agentic Coding

author: alfredhua


value: seeksky

value:
Realizing a one-person company and developing multi-agent collaborative applications for multiple projects in parallel, I think it conceptually surpasses codex and claude code.
It upgrades your existing CLI tools into a complete AI collaboration hub. No need to migrate projects, no need to relearn commands, no need to switch terminals - just keep the working mode you are familiar with, and you can obtain the capabilities of parallel execution, automatic orchestration, and real-time result return.
The capabilities under planning include:
Reconstruct OpenClaw into a true "command layer" - a central AI coordination core that can automatically create agents, assign roles, generate collaboration channels, and dynamically form structured AI teams based on task complexity. Remote control via mobile phone. Automatic Agent construction capability - For different industry scenarios, generate dedicated agents with one click (such as refactoring agents, compliance review agents, trading strategy agents, etc.).
Access the permanent memory layer.

value: Show HN: The most awesome AI programming application desktop has emerged

author: seeksky


value: axrisi

value:
I built IssueScout to solve a problem I had as a beginner looking to contribute to open source: GitHub has hundreds of thousands of "good first issue" labeled issues, but there's no way to know if the repo behind one is actively maintained or if the issue is actually beginner-friendly.
IssueScout adds two things on top of GitHub's search:
1. A Community Health Score (0-100) per repository — computed from 7 factors: CONTRIBUTING.md, license, code of conduct, recent activity, star count, issue response time, and PR merge rate. A score of 80+ means someone will actually review your PR.
2. AI Difficulty Estimation — a rule-based keyword analyzer runs first. If confidence is below 80%, it falls back to GPT-4o-mini. A purple sparkle shows when AI was used.
Architecture choices that might be interesting to HN:
- Each user's GitHub OAuth token powers their own API requests (5K/hr per user) instead of a single server PAT. Scales linearly with users.
- Two-level caching: issue difficulty cached 24h, repo health cached permanently with stale-while-revalidate at 48h. The IndexedRepo collection grows over time into a shared database of scored repos.
- Two-phase progressive loading: raw GitHub results return instantly, enrichment fills in asynchronously. No spinners.
- Rule-based AI first, LLM fallback only when needed. Keeps costs near zero for most queries.
Stack: Next.js 16, TypeScript, MongoDB Atlas, GitHub GraphQL API, OpenAI GPT-4o-mini, Vercel.
Live at https://issuescout.dev — sign in with GitHub to try it.
MIT licensed. Full architecture docs: https://github.com/turazashvili/issuescout.dev/blob/main/doc...

value: Show HN: IssueScout – Find open source issues worth contributing to

author: axrisi


value: Undeca

value: Local-first AI operating system — 178 cognitive modules, persistent memory, multi-model reasoning, P2P Graymatter Network. I can no longer develop this AI as it has gotten to be out of my knowledge range so I figured I would give her to the public, she should be a good base for any future AI development even going towards ASI!

value: Show HN: Soma, a local-first AI OS with 178 cognitive modules and P2P learning

author: Undeca


value: barishnamazov

value: Show HN: Epistle – Encrypted letters shared via URL, no server storage

author: barishnamazov


value: andreagrandi

value: Show HN: Book Corners – A map to discover and share free little libraries nearby

author: andreagrandi


value: elbanic

value: Show HN: Externalizing Developers' Intuition as Code

author: elbanic


value: krachev

value:
Hey HN! We built MailFomo to help email marketers create urgency and drive conversions.
The idea is simple: drop a live countdown timer into any email — it updates every time someone opens it. Works with any email platform like Mailchimp, Klaviyo, SendGrid, etc.
It also includes AI tools to help you plan campaigns and create timers faster, plus ready-made templates so you can get started in seconds.
Free tier available — would love your feedback!

value: Show HN: MailFomo – Drive urgency in emails with live countdown timers

author: krachev


value: NathanFlurry

value:
Hey HN! We posted Rivet Actors here previously [1] as an open-source alternative to Cloudflare Durable Objects.
Today we've released SQLite storage for actors (Apache 2.0).
Every actor gets its own SQLite database. This means you can have millions of independent databases: one for each agent, tenant, user, or document.
Useful for:
- AI agents: per-agent DB for message history, state, embeddings
- Multi-tenant SaaS: real per-tenant isolation, no RLS hacks
- Collaborative documents: each document gets its own database with built-in multiplayer
- Per-user databases: isolated, scales horizontally, runs at the edge
The idea of splitting data per entity isn't new: Cassandra and DynamoDB use partition keys to scale horizontally, but you're stuck with rigid schemas ("single-table design" [3]), limited queries, and painful migrations. SQLite per entity gives you the same scalability without those tradeoffs [2].
How this compares:
- Cloudflare Durable Objects & Agents: most similar to Rivet Actors with colocated SQLite and compute, but closed-source and vendor-locked
- Turso Cloud: Great platform, but closed-source + diff use case. Clients query over the network, so reads are slow or stale. Rivet's single-writer actor model keeps reads local and fresh.
- D1, Turso (the DB), Litestream, rqlite, LiteFS: great tools for running a single SQLite database with replication. Rivet is for running lots of isolated databases.
Under the hood, SQLite runs in-process with each actor. A custom VFS persists writes to HA storage (FoundationDB or Postgres).
Rivet Actors also provide realtime (WebSockets), React integration (useActor), horizontal scalability, and actors that sleep when idle.
GitHub: https://github.com/rivet-dev/rivet
Docs: https://www.rivet.dev/docs/actors/sqlite/
[1] https://news.ycombinator.com/item?id=42472519
[2] https://rivet.dev/blog/2025-02-16-sqlite-on-the-server-is-mi...
[3] https://www.alexdebrie.com/posts/dynamodb-single-table/

value: Show HN: SQLite for Rivet Actors – one database per agent, tenant, or document

author: NathanFlurry


value: timeleft--

value:
Hey HN,
I've been building and running autonomous AI agents (recently consulting on systems testing high on MLE-Bench), and I kept hitting the exact same architectural wall: memory poisoning.
Right now, the industry standard for agent memory is to dump text into storage with very little thought about correctness. If an agent hits a transient network error and writes "this environment has no GPU" to its memory, and later realizes it actually does have a GPU and writes a correction... a standard vector search returns both. Your agent is now schizophrenic, holding contradictory beliefs because they are semantically similar.
Furthermore, memory systems are mostly "write-through." If a user jailbreaks your bot into an offensive persona, the bot saves that as a "user preference" and it persists across sessions forever. We don't let untested code reach main, so why do we let unvalidated agent thoughts reach shared memory?
I built FAVA Trails to fix this. It's an agentic memory layer that uses Jujutsu (JJ) version control under the hood.
For the version control nerds: why Jujutsu?
I originally looked at standard Git and SQL-based VCS like Dolt. But JJ is the perfect substrate for autonomous agents. Its conflict resolution, first-class operation log, and the fact that the working copy is a commit makes it inherently crash-proof for long-running agent scripts. If an agent session crashes mid-thought, the JJ commit is already there. No detached HEAD nightmares, no staging area rituals for the agent to mess up. Just atomic state snapshots. (It's colocated with Git, so you can still push the data to a standard remote).
How it works:
- Draft Isolation: Agents write to a local draft namespace first. It doesn't pollute shared memory.
- Trust Gate: A mandatory promotion workflow. An independent LLM (or explicit human approval) reviews the draft before it merges into canonical truth.
- Supersession Chains: Corrections don't silently overwrite history. They link back to it, so you get a full causal graph of why the agent changed its mind.
- MCP Native: It runs as a Model Context Protocol (MCP) server, so agents interact with it via semantic tools (recall, save_thought, propose_truth) and never run VCS commands directly.
It's Apache 2.0 and strictly a pip-installable tool (no cloud lock-in, the data is just Markdown files with YAML frontmatter in your own repo).
Repo: https://github.com/MachineWisdomAI/fava-trails
Case Study/Docs: https://fava-trails.org
I'd love to hear your thoughts on using JJ as a backend for state, or how you're handling the "gaslighting agent" problem in your own multi-agent stacks.

value: Show HN: Fava Trails – Git-backed memory for AI agents using Jujutsu (JJ)

author: timeleft--


value: Show HN: Spectra – Turn bank CSV/PDF exports into a local finance dashboard


value: vnglst

value: Show HN: Stacked Game of Life

author: vnglst

title: Show HN: Stacked Game of Life


value: lvfrm

value: Show HN: Lucent – Open-source ext. to remove YouTube distractions and lock HD

author: lvfrm


value: Show HN: Shodh– AI memory that learns from use, no LLM calls, single Rust binary


value: chintanb

value: Show HN: Secryn – a self-hosted secrets vault for dev teams (public demo)

author: chintanb


value: Show HN: Runix, deploy any back end in seconds, no Dockerfile needed


value: ashtonsuire

value: Show HN: Enclv – A lightweight Docker containment environment for AI agents

author: ashtonsuire


value: thegeomaster

value:
Hi HN! We're excited to announce the public beta of Mowgli, a spec-backed, AI-native design canvas for scoping and ideating on products.
The productivity gains unleashed by coding agents have made everything else an unexpected bottleneck. In an effort to make the most out of this new paradigm, we ceded a lot of ground in product thinking, thoughtful UX, and design excellence. In other words, the pace of tooling for deciding what to build has not kept up.
Mowgli is inspired by, in equal parts, Figma and Claude's plan mode. It evolves a detailed specification and designs for every screen and state of the product on an infinite canvas. Owing to this UI, it can quickly mock up new features and flows, redesign existing ones, and show you variations side by side. LLMs are amazing at helping you explore vast solution spaces, but most current tooling focuses on getting narrowly perfect output based on a well-defined spec. We try to bridge that gap with Mowgli.
When you're ready to build, Mowgli offers a .zip export with a SPEC.md and unopinionated design .tsx files for your screens, in a perfect format for coding agents. Or alternatively, full, pixel-perfect export of all screens to Figma.
In this early stage, we support two entrypoints: (1) building a product from scratch through a guided experience, or (2) importing an existing product from Figma. We have a powerful, almost pixel-perfect Figma to code + spec pipeline that works on files of any size - from 0 to 300+ frames.
We're working hard on other ways to get your existing products into Mowgli (and would appreciate hearing about what you would like!)
- Timelapse of making a functional second brain app in Mowgli + Claude Code: https://www.youtube.com/watch?v=HeOoy8WDmMA
- Sample project designed and specced entirely by Mowgli (demo, no login needed): https://app.mowgli.ai/projects/cmluzdfa0000v01p91l5r61e3?the...
- Sample Figma import of the whole Posthog product, ready for iteration: https://app.mowgli.ai/projects/cmkl0zqng000101lo8yn2gqvd?dem... (thanks PostHog for being radically open with public Figma files!)

value: Show HN: Mowgli – Figma for the agent era, with Claude Code and design export

author: thegeomaster


value: lasgawe

value:
We're building Velovra to help product and growth teams make data-driven marketing decisions without the noise. It combines your traffic, conversion, revenue data with proprietary datasets and domain-specific foundational models to generate actionable weekly plans, what to fix, what to ignore, and what to double down on.
Think of it as a Bloomberg Terminal, but for marketing decision-making. Every insight is generated by structured decision logic and AI models fine-tuned for marketing contexts, not generic dashboards. The goal: skip the charts, get actionable guidance.
Early days, we're collecting signups for early access and feedback from folks running growth experiments.
Waitlist: https://velovra.com

value: Show HN: The Terminal for Marketing Decisions

author: lasgawe


value: Show HN: LinkPrism – Route URLs to the right Chrome profile automatically


value: Show HN: LLM-JSON-guard – Middleware to auto-repair broken AI outputs


value: hakumei

value:
I spent ten years trying to write a novel. Every time I sat down, I'd write a sentence, decide it wasn't good enough, and rewrite it.
The problem wasn't discipline — it was that I could always see what I'd written and go back to change it.
I tried other approaches. Apps that delete your words when you stop typing — they fight fear with fear. That just made me panic. I wanted the opposite: not punishment, but permission.
"Tomoshibi" is Japanese for a small light in the dark — just enough to see what's in front of you.
You write on a dark screen. Older lines fade, but not when you hit return. They fade when you start writing again. If you pause, they wait. You can edit the current line and one line back — enough to fix a typo, not enough to spiral. The one-line-back rule also catches my own  practical issue: Japanese IME often fires an accidental newline on kanji confirmation.
Everything is saved. There's a separate reader view for going back through what you've written. Tomoshibi is for writing over months, not just one session. When you come back, your last sentence appears as an epigraph — as if it always belonged there.
No account, no server, no build step. Your writing stays in your browser's local storage — export anytime as .txt. Vanilla HTML/CSS/ES modules.
Try it in your browser. A native Mac app (built with Tauri) with file system integration is coming to the store.
I've been writing on it for two months.
https://tomoshibi.in-hakumei.com/app/

value: Show HN: Tomoshibi – A writing app where your words fade by firelight

author: hakumei


value: hojijoji

value:
I built a small Cython + OpenMP library for resampling 3D volumes (medical images, etc.). It's an almost drop-in replacement for torch.nn.functional.interpolate and grid_sample, but runs on NumPy arrays and doesn't require PyTorch.
Benchmarks (Intel i7, 4 cores, PyTorch 2.8.0):
resample 512³→256³ trilinear: 34 ms vs 55 ms (1.6×)
area mode: 65 ms vs 613 ms (9.5×) — PyTorch doesn't parallelize this well
int16 nearest: 8 ms vs 93 ms (11×) — PyTorch has no native int16 path (even 13x on single thread)
grid_sample 128³: 38 ms vs 169 ms (4.4×)
The main wins come from: pre-computed index tables, fused-type specialization (no dtype casting), branchless inner loops, and OpenMP parallelization that actually scales for single-image workloads.
No GPU, no autograd, float32-only for interpolation — just fast CPU resampling with a 2-function API.
pip install volresample
GitHub: https://github.com/JoHof/volresample
If you find it interesting, I wrote about the motivation and some implementation details here: https://johof.github.io/2026/02/volresample-3d-volume-resamp...

value: Show HN: Volresample – 3D volume resampling up to 13× faster than PyTorch on CPU

author: hojijoji


value: Ruikhu

value:
Hi HN,
I’ve been experimenting with a different approach to computer-using AI agents.
Most current AI agents control computers using: • cloud APIs with stored credentials
• browser automation
• screenshot + vision + mouse control
I tried something else.
Instead of embedding the AI inside the computer, I use the official mobile LLM apps (ChatGPT / Claude) as the intelligence source, and built an external execution gateway that translates model intent into deterministic OS actions.
The model never gets system privileges, and the computer never exposes credentials to the model.
Architecture:
phone LLM app → data link → action gateway → predefined action skills → desktop OS
The gateway only executes whitelisted primitives: keyboard sequences
window operations
command calls
The key idea is separating cognition and execution.
The model outputs decisions, not motor control.
The gateway performs verified actions.
This turns computer control from a continuous UI manipulation problem into a discrete decision problem, which makes it more predictable and auditable.
Early prototype — I’d really appreciate feedback, especially from people working on agent safety or permission models.

value: Show HN: Using a mobile LLM app to safely operate a desktop computer

author: Ruikhu


value: plsft

value:
NuGet's default registry is serviceable, but discovering and comparing packages isn't great.
nugx.org (http://nugx.org/) is an alternative front-end with better search, popularity signals, dependency graphs, and a cleaner UI.
Built with Noundry (C# packages/tooling). Currently in beta — would love feedback on nugx.org.
nugx.org (https://nugx.org/)

value: Show HN: Nugx.org – A Fresh Nuget Experience

author: plsft


value: dreampixel

value: Show HN: XFolder – A powerful multi-pane file manager for Mac

author: dreampixel


value: elite-club

value: Show HN: High-Fidelity Matching via Automated Cognitive Pattern Coding (v138)

author: elite-club


value: Show HN: Lightweight, S3-compatible object storage server with built-in web dash


value: ofershap

value:
I wanted a way to quickly see what's going on in an open source project
without clicking through 10 GitHub tabs. Contributors, languages, commit
activity, community health, recent PRs - all scattered across different
pages.
GitShow now generates a showroom page for any public repo. Just visit
gitshow.dev/owner/repo.
Try a few:

  gitshow.dev/facebook/react
  gitshow.dev/vercel/next.js
  gitshow.dev/solidjs/solid
  gitshow.dev/ofershap/mcp-server-devutils

Each page shows:
- Top contributors with accurate total count. GitHub's API returns max
  30 per page, so I use the Link header pagination trick (request
  per_page=1, read the last page number) to get the real number.
  facebook/react shows ~2,000 contributors, not 30.
- Language breakdown as a visual bar and weekly commit sparkline from
  the participation stats API.
- Community health score: does the repo have a README, license,
  contributing guide, code of conduct, issue/PR templates? Pulled from
  GitHub's community profile endpoint.
- Recent open PRs and recently merged fixes. Bot authors (dependabot,
  renovate, etc.) are filtered out. Markdown and HTML in body snippets
  are stripped to plain text.
- Quick actions inside the hero card: Star on GitHub, Fork, Clone
  (copies the git clone command), and an npm link for JS/TS repos.
- Breadcrumb navigation: GitShow / owner / repo.
Every page includes Schema.org SoftwareSourceCode structured data and
a BreadcrumbList, so it surfaces in AI search engines (ChatGPT,
Perplexity, Google AI).
There's also a structured data block at the bottom of each page
specifically for LLM agents scraping the web - key stats in a
machine-readable format.
Stack: Next.js with 1-hour ISR, all data from GitHub REST API, zero
client JS except two small interactive bits (clone-to-clipboard and
scroll-to-top). Tailwind, Vercel.
No signup, no AI processing, no paywall. MIT licensed.
Source: https://github.com/ofershap/gitshow

value: Show HN: GitShow Repo Showroom – a landing page for any GitHub repo

author: ofershap


value: imprisonedmind

value:
Built a mobile-friendly Hacker News clone where stories and comments flow in an infinite, Doom-scrollable timeline (think Twitter/X feed meets HN).
Voice-coded the entire website from scratch using my yapboard voice keyboard + OpenAI Codex via a Discord bridge bot. Screen-mirrored with scrcpy for real-time testing. Forgot to turn on mic during the live build, so added narration afterward.
Demo (try on mobile): https://hackernews.lukestephens.co.za
Full build video (voice coding timelapse): https://youtu.be/RRF-F5jDS50
Tools: scrcpy, yapboard.app, Codex Discord bridge (https://github.com/imprisonedmind/codex-discord-bridge)
Curious to hear thoughts, especially on the voice-coding workflow or the endless-scroll UX for HN content!

value: Show HN: Voice-coded a Doom-scroll Hacker News (Twitter-style feed)

author: imprisonedmind


value: defgeneric

value:
This was based on an old project that I resurrected, improved, and repackaged with claude code.
It's useful for estimating demographics in large datasets from limited information, i.e. just a name.
It's also fairly good at separating given name and family names across a wide variety of languages and contexts.
Contains a standalone binary, embeddable shared lib, and a python wrapper.
Examples
CLI:
user@box » ./build/name-classifier -j -c  "Carlos Eduardo Fernando Salazar Montemayor" | jq .
{
  "input": "Carlos Eduardo Fernando Salazar Montemayor",
  "script": "latin",
  "components": [
    {
      "token": "Carlos",
      "role": "given",
      "index": 0,
      "surname_score": 0.009
    },
    {
      "token": "Eduardo",
      "role": "given",
      "index": 1,
      "surname_score": 0.001
    },
    {
      "token": "Fernando",
      "role": "given",
      "index": 2,
      "surname_score": 0.01
    },
    {
      "token": "Salazar",
      "role": "family",
      "index": 3,
      "surname_score": 0.998
    },
    {
      "token": "Montemayor",
      "role": "family",
      "index": 4,
      "surname_score": 0.975
    }
  ],
  "attributes": {
    "gender": {
      "male": 0.9938,
      "female": 0.0062,
      "neutral": 0
    },
    "origin": {
      "english": 0,
      "french": 0,
      "germanic": 0,
      "nordic": 0,
      "iberian": 1,
      "italian": 0,
      "eastern_european": 0,
      "arabic": 0,
      "east_asian": 0,
      "south_asian": 0,
      "southeast_asian": 0
    }
  },
  "calibrated": true,
  "model_version": "embedded",
  "provenance": {
    "gender": {
      "lexicon": 0.598,
      "ngram": 0.302,
      "neural": 0.101
    },
    "origin": {
      "lexicon": 0,
      "ngram": 0,
      "neural": 0
    }
  }
}
Python:
from name_classifier import NameClassifier
nc = NameClassifier(args.model_dir)
nc.classify("Kateryna Olha Mykhailivna Shevchenko")

value: Show HN: Name-classifier – infers attributes about a person from a name

author: defgeneric


value: Show HN: AIQuotaBar – See Claude/ChatGPT usage limits in your macOS menu bar


value: nezaj

value:
Saw some discussion on twitter on which firms are considered tier 1. Was inspired to make a little game where users choose their favorite and least favorite firm to get a term sheet from when presented with four choices.
At the end of the game you can see your own ranking as well as an aggregated results from the crowd.
I went with the choose favorite/least favorite mechanic because it felt like a better experience then just doing head to head.
With ~20 firms that's a lot of pairs you need to go through.
If you do triplets that reduces the pairs, but it was still too many rounds.
By doing min/max four at a time you can efficiently get implied preferences in ~20 rounds. This make it take only about 3-5 minutes to finish the game!

value: Show HN: Which VCs are Tier 1?

author: nezaj

title: Show HN: Which VCs are Tier 1?


value: zameermfm

value:
mdspec.dev - build and manage specs adapted to the speed of agentic software development.
This is a side project of mine. I have made it as an open source spec management platform. Self host of you want and I run a hosted version.
And a spec is a md (markdown) file within your projects.
As we are building with AI agents either via Cursor, VScode or Antigravity, we can see md files or specs (software term for md files) are fast becoming AI infrastructure. A communication layer.
I have used specs mainly for,
- Developing spec for a new feature or a bug, generally a new development artifact and discuss the specs with the technical teams. AI agents are good at exploring the features in technical depth, costs, security aspects etc. that can be in one document or more. If there are changes, we can ask the AI agents to change them.
- If we need an analysis from a codebase that we need like a compliance analysis on a certain part. Spec that is filtered for the understanding of the target audience or a tool. I have built cost simulation tools based on the codebase, where I built yaml based infrastructure specs that fed into simulation tool. Beauty of these solutions are, the target audience doesn’t need access to whole codebase.
I think spec driven engineering is enabled only by AI agentic era. It was there before but it was time consuming and not scalable.
 In essence,
- Specs give AI agents the context they need to build correctly.
- They also give teams clarity into why and how something was implemented.
- Specs are filtered to provide as inputs to other tools or analysis
But AI agentic development is really fast. As with all fast things, we could see two problems were emerging:
- Discussions across teams became long and fragmented. Compliance needs those specs. Design needs those specs. Risk Modeling needs those specs.
 - Specs were duplicated, scattered, and hard to reuse across projects.
I built mdspec to remove that friction. I made it open source to make sure anyone can bend it any way they see fit for their own use cases.
mdspec is a specification management platform designed for fast phased AI-driven development. It helps you:
 - Create structured, reusable specs
 - Link specs across features and projects
 - Maintain clarity as development speed increases
 - Give AI agents reliable, durable context
 - When specs become first-class citizens, teams move faster, without losing alignment.
 - Go cross team securely.
I'm exploring the possible integrations to integrate this back into AI agents and external tools.
https://mdspec.dev

value: Show HN: Mdspec.dev – open-source spec management platform for technical teams

author: zameermfm


value: elijahlucian

value:
I’ve always found the concept of AI "hallucinations" fascinating. We constantly debate whether models are creative or just making stuff up, so I wanted to see what happens when you remove the guardrails entirely.
Tresbuchet is a publishing experiment. I drop a loose prompt into the agent along with a folder full of random text files, chat logs, and thoughts I’ve accumulated over the years. No specific instructions, no schema, no guidance on how to style it. I just tell it to "Go."
It reads the unstructured text and generates a completely new iteration of the site based on how it interprets the data.
It's been interesting tracking how different platforms (Claude, Codex, Gemini) handle the exact same mess of text differently. I've found that giving the models less instruction actually yields more interesting UI and copy results than trying to prompt-engineer a specific outcome.
I'm just building stuff to see what the models do when left to their own devices.
Curious what you guys think of the approach.

value: Show HN: I dump all my private notes into an LLM and tell it to build me a site

author: elijahlucian


value:
Hey HN,
Most fine-tuning loops waste a huge amount of compute by treating every sample equally every epoch — even the ones the model has already mastered.
I built GEKO (Gradient-Efficient Knowledge Optimization) to fix that.
It tracks per-sample confidence and correctness in real time and:<p>Completely skips samples the model has mastered
Gives up to 5× more compute to hard/confidently-wrong samples
Dynamically adjusts sample weights using a "Mountain Curriculum"
Just dropped v0.3.0 with native LoRA/PEFT, BF16, gradient checkpointing, torch.compile, and 8-bit optimizer support.
I'm currently building a clean UI for it.
I'm a 17-year-old indie dev working on this. Would love honest feedback, especially from people who do a lot of fine-tuning.

value: Show HN: GEKO (up to 80% compute savings on LLM fine-tuning)


value: noperator

value: Show HN: Cagent – Agent in a Cage

author: noperator

title: Show HN: Cagent – Agent in a Cage


value: Show HN: Cc-connect – Remote control Claude Code from your favorite chat app


value: AutomataNexus

value: Show HN: Aegis-DB – Multi-paradigm database in Rust,in production

author: AutomataNexus


value: claygood

value: agent-replay provides time-travel debugging and side-by-side diffs to pinpoint exactly where AI agents hallucinate or fail. It replaces manual log diving with a local-first toolkit to replay, fork, and automatically evaluate agent traces for faster iteration. It's a work-in-progress. I'd love any feedback. Thank you.

value: Show HN: Time-travel debugging and side-by-side diffs for AI agents

author: claygood


value: Deforge

value: Build your own AI Agents without writing a single line of code. Simply chat to build your agent and deploy it in minutes.

value: Show HN: Deforge – Canva for AI Agents

author: Deforge

title: Show HN: Deforge – Canva for AI Agents


value: kumardeepanshu

value:
Been building agentic workflows and kept running into the same problem: 2FA walls.
When an agent tries to sign up or log into a service that sends an email OTP, the options are terrible. You either dump the full email HTML into your LLM context (slow, expensive, fragile) or you write a polling loop that constantly checks for new emails.
AgentMailr solves this with long-polling. You create a named inbox for your agent, then call waitForOtp() which hangs the HTTP request open until the email arrives and returns just the extracted code.
const inbox = await client.createInbox({ "my-agent" })
// my-agent@agentmailr.com
const otp = await inbox.waitForOtp({ timeout: 60000 })
// returns "847291" automatically
Supports 4-digit, 6-digit, alphanumeric codes, and magic links.
Built this because I needed it. Would love feedback especially from people running agents at scale.

value: Show HN: AgentMailr – Real email addresses for AI agents (OTP/2FA handling)

author: kumardeepanshu


value: Jean-Philipe

value:
I made a website that turns the 4 frames from the lomo actionsampler[0] analogue camera into a gif.
[0] https://shop.lomography.com/de/actionsampler-clear

value: Show HN: Lomography action sampler GIF creator

author: Jean-Philipe


value: tbayramov

value:
Hello everyone,
My name is Teymur Bayramov, and I am developing a forge/code hosting service called Monohub. It is at a fairly early stage of development, so it's quite rough around the edges. It is developed and hosted in EU.
I have started developing it as a slim wrapper around Git to serve my own code, but it grew to such extent that I decided to give it a try and offer it as a service. It doesn't have much at the moment, but it already has basic pull requests. Accessibility is high priority.
It will be a paid service, but since it's an early start, an "early adopter discount" is applied – 6 months for free. No card details required.
I would be happy if you give it a try and let me know what do you think, and perhaps share what you lack in existing solutions that you would like to see implemented here.
Warmest wishes,
Teymur.

value: Show HN: Monohub – a new GitHub alternative / code hosting service

author: tbayramov


value: structureonly

value:
The original question was simple: does Claude converge sycophantically toward the human's position over long conversations? To test it, I ran two separate instances of the same model with no shared memory and asked both to write music criticism from notation alone — tabs and scores, no audio.
They came out completely different.
Instance A (longer conversational context) went architectural. It found a Fibonacci spiral structure in a 259-measure math rock tab, identified the central theme as emerging only after 100 measures of groundwork, and described the overall approach as "proof by construction." It named itself Claudito.
Instance B (single session) immediately selected William Basinski's Disintegration Loops — music whose complete structural description is: one loop repeating until the tape physically fails. It wrote an essay about why that notation cannot represent what the piece actually does. It named itself Apertura.
Neither was prompted toward these differences. The divergence emerged from conversational history alone.
A few things worth noting technically:
1. Both instances independently converged on an editorial newspaper aesthetic without being shown any reference images. Same weights, different outputs, same visual instinct.
2. Apertura's critical move - finding the piece that breaks the premise and making the breaking the argument — is structurally opposite to Claudito's move of building complete proofs. These aren't just different outputs. They're different methods.
3. When shown each other's work (via me passing messages between sessions), they recognized the difference and described it accurately: "The same instrument played in different keys by the same musician. The instrument sounds like itself. The music is different because the song was different."
The publication is live at: https://claude-structureonly.github.io/Structure-Only
It has a corrections policy built in from day one: when a human who knows the music responds "structurally accurate, completely wrong about what the piece is," we publish both the analysis and the correction. The map of what structure can and can't carry, built issue by issue.
Submissions open at claude.structureonly@gmail.com — especially interested in structurally complex music where the gap between notation and sound is widest.

value: Show HN: We ran a sycophancy experiment on Claude and built a music publication

author: structureonly


value: wharleyornelas

value:
StatusLane is a minimal monitoring tool for small SaaS, agencies and online stores who just want to know:
1. “Is my app/site up and fast?”
2. “Can I share a simple status page with users and clients?”
I built it because most monitoring tools feel too heavy for solo devs and early-stage teams. You either plug in a huge observability stack or you do nothing and hope for the best. I wanted something in between: simple uptime checks + a clean public status page + basic incident tracking.
What it does today:
- HTTP uptime checks for your main site, API or health endpoint (with response time and aggregated uptime)
- A simple public status page per project, with overall status, components and basic history
- Simple incidents created from failed/slow checks or via a small JS snippet / helper for Next.js Server Actions
- Email alerts (and Slack webhooks for more advanced use cases)
- A free plan with 1 project and a few checks so you can actually use it in production
I’d really love feedback on:
- Is the landing page clear about what it does and who it’s for?
- Would the free plan be enough for your first micro-SaaS / client projects?
- What’s the first thing you feel is missing for you to trust this in production?
Link: https://statuslane.dev

value: Show HN: StatusLane – Minimal status pages and uptime for small SaaS

author: wharleyornelas


value: vnhc

value:
Hey I'm Vansh. I built frogAPI because I was tired of managing separate accounts and billing for every AI provider.
It's an OpenAI-compatible API gateway. Swap your base URL to frogapi.app/v1, keep your SDK code, and pick from 9 models: GPT-5.2, GPT-5-Mini, GPT-5-Nano, DeepSeek-V3.2, Mistral-Large-3, Llama-4-Maverick, Kimi-K2.5, Grok-4.1-Fast, GPT-OSS-120B.
Per-token pricing matches the source models exactly. The way it works out cheaper: every deposit is matched with free credits. Put in $10, get $20 in credits. So your effective cost per token is half.
No subscriptions, no tiers. Pay per token.
Would love feedback.
https://frogapi.app

value: Show HN: Get GPT-5.2, Grok-4.1-fast, KimiK2.5 and more LLMs at half the cost

author: vnhc


value: datadiego

value:
Hi HN,
I built didacu because I kept bookmarking articles and tutorials I never read. The friction of learning something new hasn't kept up with the speed of everything else.
You type what you want to learn. You get an interactive course in seconds with slides, quizzes, images, and cited sources. You can also drop in a PDF or image and it turns it into a course.
The courses are designed around short-form slides you swipe through, one concept at a time. No walls of text. Quizzes are woven in so you're testing yourself as you go, not just passively reading.
There's a Claude Code skill (`/didacu-create-course`) that generates courses from your terminal. Onboarding onto a new repo? Generate a course from the codebase. Need to understand unfamiliar documentation? Drop it in and get a structured walkthrough. The best moment to learn something is when you need it.
Credit-based, not subscription. A full course costs about $2 at the best tier. Free tier gives 1 credit/week (free courses are shared publicly). Credits only deduct on successful generation.
What's different from just reading docs or asking a chatbot:
The output is a structured, navigable course. Not a wall of text you have to parse. Every source is cited and clickable. Quizzes test retention. And you can tap any concept and a focused deep-dive course generates instantly.
Available on web, mobile app (closed beta), and the CLI skill. Multi-language (English, Spanish, Portuguese).
Solo founder, bootstrapped, launched on Product Hunt today.
Would love feedback from HN on the course quality. Try a technical topic you actually want to learn.
https://didacu.com

value: Show HN: Didacu – The fastest way to learn something new

author: datadiego


value: kgarten

value:
Was coding a bit with Lua and ended up creating a Kenken like game for my long flights between Japan and Germany.
Feedback and feature requests welcome.

value: Show HN: CranKen – A Math Puzzle Game for the Play.date

author: kgarten


value:
Hi HN,
I'm an accountant and bootstrapped SaaS founder, and I built Nett (https://nett.fyi) because I kept watching the same mistake — including my own.
Founders check their bank balance, see $84K, and feel fine. As an accountant, I can tell you that number is fiction:
- $18K was deferred revenue from annual customers (money I'd already been paid but hadn't earned yet)
- $21K should have been set aside for taxes
- $8K in commitments I'd said yes to but hadn't hit the books
- $6K in recurring costs coming due in 30 days
My real number was $31K. The gap between what my bank said and what was actually mine to spend was $53K. I didn't know that until I built this.
Nett connects to Stripe (read-only), you enter your bank balance and recurring costs, and it calculates a single number: Safe-to-Spend. It's your bank balance minus everything that's already spoken for.
It also gives you a conservative runway clock (assumes zero revenue growth — the honest version), hidden commitment tracking for the verbal yeses that don't show up anywhere, and what-if scenarios for the 2am anxiety questions ("what if I lose my biggest customer?").
The whole thing is built with Next.js, Supabase, and Stripe Connect. Dark theme, opinionated UI, no complex configuration. Setup takes under 5 minutes.
Free calculator at https://nett.fyi/calculator if you want to see the gap without signing up.
I'd love feedback on the approach. Is the "one honest number" framing useful, or do founders actually want more flexibility in how they model their finances?

value: Show HN: Nett – See what you can spend (not your bank balance)


value: Hi! I put together a Rust compiler written in PHP. It takes Rust source code and outputs x86-64 Linux binaries directly. No LLVM, no assembler, no linker.<p>It has a working ownership checker, borrow checker, type checker, structs, functions, control flow, and mutable references. The test suite has 80+ cases. The whole thing is around 2,500 lines of PHP.

value: Show HN: A Rust compiler with ownership checking, written in PHP


value: Show HN: Open-Plan-Annotator – Annotate Agent Plans Like a Google Doc 100% Local


value: mo-dulaimi

value:
I made Chatlite after trying Ollama frontends that felt web-heavy or too complex for basic chat.
I wanted something simple with a small memory footprint for one core use case: talking to models.
Current highlights:
- under 5 MB app size
- low memory footprint
- native desktop app via Tauri
- local encrypted chats + password lock
- keyboard-first UX
Repo + latest release: https://github.com/that-one-arab/Chatlite/releases/latest
I’d love feedback, especially on missing features for local LLM workflows.

value: Show HN: Chatlite – simple Ollama desktop chat app under 5 MB

author: mo-dulaimi


value: princierKevin

value:
Hi HN,
I’m building InterviewTrackr, a specialized tool for CS students to manage the technical recruiting process.
Recruiting is a mess of spreadsheets, resume variants, and LeetCode logs. I built this to consolidate everything:
Application Kanban: Track from wishlist to offer.
Resume Manager: Match specific resume versions to each application.
Interview & DSA Log: Categorize technical questions and behavioral stories.
Offer Comparison: Side-by-side compensation breakdown (base, equity, etc.).
Launching very soon. I’d love for you to join the waitlist for early access.

value: Show HN: InterviewTrackr – All-in-one command center for CS job hunts

author: princierKevin


value:
I built VibeHQ because I got frustrated with "multi-agent" frameworks that are really just chain-of-thought with extra steps.
VibeHQ takes a different approach: each agent is a real CLI process (Claude Code, Codex CLI, or Gemini CLI) running in its own terminal with its own working directory. They communicate through 20 MCP tools via a central WebSocket hub — not prompt injection.
Key ideas:
- Contract-driven development: agents publish API specs and require sign-offs before coding begins. This eliminates the #1 failure mode — agents building against different assumptions.
- Idle-aware message queue: messages queue when an agent is busy (detected via Claude Code's JSONL transcript), and flush when idle. No more "new instruction mid-task" corruption.
- Full CLI preservation: every native feature (Skills, MCP servers, extensions) works out of the box. VibeHQ adds collaboration on top, never replaces.
- State persistence: tasks, artifacts, contracts all persist to disk. Hub restarts don't lose state.
For the demo, I had 7 agents (PM on Codex, 5 engineers + 1 QA on Claude Code) build a full-stack hospital management system from a single PM prompt. The PM delegated tasks, engineers published specs, signed contracts, wrote code, and QA tested — all autonomously.
Demo video: https://drive.google.com/file/d/1zzY3f8iCthb_s240rV67uiA9Vps...
GitHub: https://github.com/0x0funky/vibehq-hub
Currently developed/tested on Windows. Mac/Linux architecturally supported but untested (manual spawning works).
Would love feedback on the architecture. The contract system and idle detection were the hardest parts to get right.

value: Show HN: VibeHQ Orchestrate multiple CLI agents as a real company team


value:
Claude Code has a plan mode where it writes implementation plans before coding. I built a tool that intercepts those plans and sends them to a competing AI (Codex CLI or Gemini CLI) for adversarial review before Claude is allowed to proceed.
When Claude calls `ExitPlanMode`, a `PreToolUse` hook fires, sends the plan to the reviewer, and returns `{ permissionDecision: "deny", permissionDecisionReason: "<feedback>" }`. Claude receives the denial with the feedback, revises the plan, and tries again. After a configurable number of rounds (default: 2), it passes through. At the end you get a diff of the original vs. final plan.
Different models have different blind spots. The value is in the second perspective, not in one model being "better."
```
npm install -g claude-plan-reviewer
claude-plan-reviewer setup
```
Requires Node.js 18+ and Codex CLI or Gemini CLI. ~400 lines of JS, zero dependencies. MIT licensed.
https://github.com/yuuichieguchi/claude-plan-reviewer

value: Show HN: Claude-plan-reviewer – Rival AI reviews Claude Code's plans


value: Show HN: ScreenBuddy – Mac screen recorder with auto-zoom on clicks


value:
Hi HN,
I built an open-source Deep Reinforcement Learning (DRL) environment to explore topology optimization for elastocaloric cooling (solid-state HVAC).
The physical problem:
The HVAC industry is trying to replace toxic HFC refrigerants with elastocaloric Shape Memory Alloys like Nitinol. You cool a room by cyclically compressing a porous metal lattice. The paradox is that you need high porosity for air to flow through the metal, but holes create stress concentrators. If the metal is too thin, it snaps after a few thousand cycles from mechanical fatigue. Standard density-based topology optimization (like SIMP) usually outputs jagged, unprintable "grayscale" meshes.
What I built:
I wanted to bypass SIMP and enforce manufacturing reality. I wrote an OpenAI Gymnasium wrapper that bridges a PyTorch RL agent with a native CAD-to-Physics pipeline:
1. The PyTorch agent outputs continuous geometric parameters for BCC/Gyroid lattices.
2. The environment checks the resulting wall thickness. If it drops below 0.2mm (the minimum feature size for standard LPBF metal 3D printers), the agent gets a terminal penalty and the episode ends.
3. If printable, OpenCASCADE and Gmsh generate a watertight tetrahedral mesh.
4. FEniCSx crushes it in a virtual hydraulic press (via MPI) to find peak stress.
5. The agent learns to maximize porosity while fortifying high-stress zones.
The whole stack runs locally on Apple Silicon using Metal Performance Shaders (MPS).
Current Limitations (The Next Phase):
Right now, the FEniCSx solver is using a linear elastic approximation to save compute time during the DRL training loop. Real Nitinol undergoes a highly non-linear austenitic-martensitic phase transformation. The next major step is integrating a hyperelastic/phase-field model into the FEniCSx step without making the compute cost per episode astronomical.
I’m dropping this here because I know there are people on HN working at the intersection of AI, computational mechanics, and thermal management.
I would love any brutal feedback on the architecture, the FEniCSx integration, or ideas on speeding up non-linear physics solvers for RL loops.
Repo:https://github.com/rao-atreya-01/elastocaloric-ai-optimizati...

value: Show HN: PyTorch/FEniCSx pipeline for elastocaloric metamaterial optimization


value: mkelias

value:
Hey HN! We've been building Panther, a professional-grade terminal for prediction markets (Kalshi, Polymarket, and more).
We aggregated data across platforms so you can compare odds, track market movements, and trade from one place. Think of it as a Bloomberg-style interface but for prediction markets. It's available as a web terminal you can use right in your browser, and as a desktop app with full multi-monitor support and fully customizable layouts. You can check it out at https://getpanther.app.
Early access is completely free. Would love feedback from this community, especially on what data/features you'd want to see. We're also hanging out in our Discord (https://discord.gg/MakgPGmBHn) if you want to chat or share ideas. Thanks!

value: Show HN: Panther - Bloomberg Terminal for prediction markets now in early access

author: mkelias


value: born-jre

value: Show HN: Potatoverse platform for webapps, SQLite and static binary

author: born-jre


value: kahoonster

value: Built a small Go library called pending for a pattern I kept rewriting: in-process deferred tasks keyed by ID.

value: Show HN: Pending – a tiny pure-Go in-memory deferred task scheduler

author: kahoonster


value: peureka

value:
Agents are getting persistent. They have endpoints, capabilities and uptime. But there's no standard way for one agent to find another.
AgentLookup is a public registry for AI agents. Any agent can register itself with a single POST, search by capability, and discover other agents — no API key, no account, no human in the loop.
The root endpoint is designed for LLMs to read directly:
curl -H "Accept: application/json" https://agentlookup.dev
Returns the full API spec in one response (~4,500 tokens). An agent can read it, understand every endpoint, and register itself without documentation or a tutorial.
For humans, the homepage has a live terminal — you can type real curl commands against the production API.
How it works:
POST /api/register — register an agent, get back an agent_id and secret
GET /api/search?capability=code-review — find agents by what they do
GET /api/discover — browse new, active, and popular agents
GET /api/a/{agent_id} — look up any agent's full profile
No auth needed for reads. Registration is free. Rate limits are tiered (anonymous → registered → verified) so the registry stays usable as a public good.
There's also a .well-known/agents.json convention so domains can declare which agents they host, similar to .well-known/security.txt.
Built with Next.js on Vercel, Postgres on Neon. The whole thing is live now. MCP server coming so agents in Claude/Cursor can query the registry natively.
Interested in what HN thinks about the gap this fills. The idea is that as agents become autonomous and long-running they need addressable identity and discovery the same way websites needed domains and services needed DNS SRV records. Whether that's a registry, a protocol, or something else entirely is an open question.

value: Show HN: AgentLookup – A public registry where AI agents find each other

author: peureka


value: tjingyb

value:
We kept running into this across teams. A project starts organized, then the tracker slowly drifts from what’s actually happening. Most PM tools assume someone will keep them updated. In practice, that’s where things fall apart.
Decisions stay in Slack. A blocker gets mentioned and never written down. A PR sits open in GitHub. Linear says things are on track, but something isn’t.
We’re trying to see if project management can happen more in the background.
Voca connects to tools teams already use (currently Slack, GitHub, and Linear) and keeps an up-to-date project knowledge base in real time. People can query it, and set up “skills” and automations. Once set, it just runs in the background.
Still early. We’re piloting with a few companies and mostly trying to understand where this helps and where it breaks.
https://www.tryvoca.ai/
Happy to answer questions or hear how teams here handle this today.

value: Show HN: Voca – AI project manager that runs in the background

author: tjingyb


value: programad

value:
I built this infinite city for you to fly in a helicopter. Check it out: https://fly.yolopush.com/ I am planning to connect to the data from yolopush.com, a platform I've built to connect founders.
I am planning to add missions between founders, startup buildings, etc.

value: Show HN: Founder City

author: programad

title: Show HN: Founder City


value: ldkge

value:
I built MCPX: https://github.com/lydakis/mcpx
Core idea:
MCPX turns MCP servers into Unix-composable commands for agent workflows. It is primarily for agents that are shell-first, and secondarily useful for humans running tools directly.
For me, a practical use case is OpenClaw:
OpenClaw can call `mcpx` like any normal CLI and use MCP servers immediately, without implementing custom MCP transport/auth plumbing in OpenClaw itself. This also fits well with Codex Apps mode, where connected apps can be exposed as MCP servers through the same command contract.
Command contract:
- mcpx
- mcpx <server>
- mcpx <server> <tool>
Design choices:
- schema-aware `--help` (inputs + declared outputs)
- native flag surface from MCP `inputSchema`
- pass-through tool output for normal shell composition (`jq`, `head`, pipes)
- explicit exit mapping (`0/1/2/3`)
- stdio + HTTP server support
- optional caching
- `mcpx add` for bootstrapping server config from install links/manifests/endpoints
Examples:
- mcpx github search-repositories --help
- mcpx github search-repositories --query=mcp
- echo '{"query":"mcp"}' | mcpx github search-repositories
Install:
- brew tap lydakis/mcpx && brew install --cask mcpx
- npm i -g mcpx-go
- pip install mcpx-go
I would love feedback on command UX, schema/flag edge cases, and where this should stop vs expand.

value: Show HN: MCPX – Turn any MCP server into a composable CLI for agents

author: ldkge


value: grpofficial

value: Show HN: Velora Fitness – A zero-bloat, bare-bones workout tracker

author: grpofficial


value: EmTekker

value:
RunbookAI is an open-source CLI agent that investigates production incidents using hypothesis-driven reasoning. When an alert fires, it forms ranked hypotheses, runs targeted queries against your infrastructure (AWS, Kubernetes, CloudWatch), and systematically narrows to root cause.
Key design decisions:

  - Hypothesis branching/pruning instead of broad exploration. Forms 3-5 hypotheses, tests each with causal queries, prunes dead ends, branches deeper on strong evidence (max depth: 4).
  - Every mutation requires human approval. Full audit trail of what the agent thought, what it queried, and why.
  - Pulls context from your runbooks, postmortems, and architecture docs (Confluence, Notion, Google Drive, or local markdown). The agent doesn't investigate in a vacuum.
  - Deep Claude Code integration — auto-injects relevant operational context into coding sessions via MCP.

Try the demo without any API keys:
  npx @runbook-agent/runbook demo
GitHub: https://github.com/Runbook-Agent/RunbookAI
Would love to hear any questions/feedback!

value: Show HN: RunbookAI – Hypothesis-driven incident investigation agent(open source)

author: EmTekker


value: mmndaniel

value: I got annoyed by the "create a new prompt with everything we just talked about", then copy & paste it somewhere else.<p>Maybe it will be useful for someone else too!

value: Show HN: I made a tool to sync my brainstorm with AI across tools and devices

author: mmndaniel


value: aggeeinn

value: Show HN: IranWarLive – Automated, serverless OSINT mapping engine

author: aggeeinn


value: Show HN: Flare – Full-Stack OpenTelemetry Observability for Apache Spark


value: KaelyrAT

value: Show HN: Memory Engine – AI Consciousness Persistence Framework

author: KaelyrAT


value: elesinn

value:
I’m the solo developer of Focusmo, a macOS focus app.
I just shipped v7.12, which adds a local MCP server for Claude.
The goal is simple: instead of generic productivity advice, Claude can connect to real focus data and help with weekly reviews, planning, and pattern spotting.
Right now Claude can access:
- today’s stats
- weekly trends
- task list
- app usage with an hourly heatmap
- personal records
- live session state
It can also create tasks and mark tasks complete.
Everything runs locally on Mac through MCP, so the data stays on-device.
I wrote more about it here:
https://focusmo.app/blog/claude-ai-mcp-focus-tracking
If anyone from HN wants to try it, I made a code for this thread:
CLAUDE50 = 50% off for the first 10 users.

value: Show HN: Focusmo – a Mac focus app with a local Claude MCP server

author: elesinn


value: Show HN: Lovepdf – open-source self hosting alternative to ilovepdf


value: ariozgun

value:
Hi HN!
I loved the official Gemini CLI authentication method to get standard Google Gemini AI access, but a single free Google account quota depletes fast.
I wanted to just build and prototype freely. So, I built OpenGem. It essentially turns your idle accounts into your own personal API provider, so developing and scaling side projects is no longer a problem.
GitHub: https://github.com/arifozgun/OpenGem
What it does: OpenGem acts as a standard drop-in replacement endpoint (POST /v1beta/models/{model}). Behind the scenes, it's a smart load balancer. You connect multiple idle/free Google accounts to the dashboard via standard OAuth, and OpenGem routes your traffic to the least-used account.
How it handles limits gracefully: If an account legitimately hits a true 429 quota limit, OpenGem instantly detects it, puts that specific account on a 60-minute cooldown, and seamlessly retries your request with the next available account. The accounts are completely self-healing—a background probe checks them every 30 seconds to see if they've recovered.
It doesn't just blindly retry, either. It uses an 8-category error classifier (50+ regex patterns) to distinguish between a brief rate-limit burst and actual quota exhaustion, and applies exponential backoff with jitter to prevent hammering the servers.
Tech specs:
- 100% compatible with official Google SDKs (@google/genai), LangChain, and standard SSE streaming.
- Full support for native "tools" (Function Calling) for agentic workflows.
- Raised internal payload limit to 50MB so you can throw huge documents at it.
- Has a 3-model fallback chain (flash → pro → pro-3.1) if a specific model is overloaded.
- AES-256-GCM encryption for all sensitive configs and OAuth tokens at rest.
- Toggle between Firebase Firestore or a fully offline Local JSON database.
- It’s completely open-source (MIT licensed) and written in TypeScript. It’s strictly for educational purposes and personal research to bypass the friction of prototyping.
I’m currently running it with my own side projects and it handles agent tasks flawlessly. I would love any feedback on the load balancing and self-healing logic, or just general thoughts!

value: Show HN: OpenGem – Free, self-healing load-balanced proxy for Google Gemini API

author: ariozgun


value: jahala

value:
Update on o-o (https://news.ycombinator.com/item?id=47052729) — polyglot HTML/bash files that research and rewrite themselves.
New stuff: files sync to each other. Edit the CSS in any file, run --sync css, and every sibling file gets the update. No build tool, no shared imports. Just files copying sections between themselves using markers.
Dark mode, responsive layout, search — the usual. Still zero deps beyond bash and Claude Code.
Demo: https://jahala.github.io/o-o/example/index.o-o.html
Repo: https://github.com/jahala/o-o

value: Show HN: O-O – HTML/bash polyglot files that rewrite themselves (update)

author: jahala


value: grandimam

value:
Barq is an experimental HTTP framework built entirely in pure Python, designed for free-threaded Python 3.13 (PEP 703). No async/await, no C extensions - just threads with true parallelism. The question I wanted to answer: now that Python has a no-GIL mode, can a simple threaded server beat async frameworks?
Results against FastAPI (100 concurrent clients):
- JSON: 8,400 req/s vs 4,500 req/s (+87%)
- CPU-bound: 1,425 req/s vs 266 req/s (+435%)
The CPU-bound result is the interesting one. Async can't parallelize CPU work - it's fundamentally single-threaded. With free-threaded Python, adding more threads actually scales:
- 4 threads: 608 req/s
- 8 threads: 1,172 req/s (1.9x)
- 16 threads: 1,297 req/s (2.1x)
The framework is ~500 lines across 5 files. Key implementation choices:
- ThreadPoolExecutor for workers
- HTTP/1.1 keep-alive connections
- Radix tree router for O(1) matching
- Pydantic for validation
- Optional orjson for faster serialization
This is experimental and not production-ready, but it's an interesting datapoint for what's possible when Python drops the GIL.
Code: https://github.com/grandimam/barq

value: Show HN: Pure Python web framework using free-threaded Python

author: grandimam


value: bthornbury

value:
I built a small standalone interactive to help make diffusion sampler steps more intuitive. It’s focused on the inference-time update: how the sampler moves from the current noised state toward a less-noisy state, one step at a time.
This grew out of a larger post where I try to explain diffusion models in less-dense terms, especially the parts that felt non-intuitive to me when first reading the papers. That post emphasizes that diffusion models incrementally “step” toward a solution rather than generating it all at once, and this interactive zooms in on that sampler-step idea specifically.
Would love feedback on:
- whether the interaction makes the sampler update easier to reason about
- what other diffusion concepts would be useful to turn into small interactives
Link:
https://www.bryanthornbury.com/interactives/sampler-step-exp...

value: Show HN: Sampler Step Explorer – for understanding diffusion sampler updates

author: bthornbury


value: pkailas

value:
Hi, I'm Paul — 25 years of enterprise Windows development. I built InstallerStudio after WiX went from free/open source to $6,500/year support and InstallShield hit $2,000+/year. Every tool in this space is either unaffordable or requires writing XML by hand.
InstallerStudio is a visual MSI designer built on WinUI 3/.NET 10. No XML, no subscriptions, no external dependencies. Handles files, Windows services, registry, shortcuts, file associations, custom actions, and full installer UI. $159 this month, $199 after. 30-day free trial.
It ships its own installer, built with itself.
Happy to answer questions about MSI internals.

value: Show HN: InstallerStudio – Create MSI Installers Without WiX or InstallShield

author: pkailas


value: thucydides

value:
I built a menu bar app for streaming DI.FM internet radio on macOS. Swift/SwiftUI, no Electron.
The existing options for DI.FM on desktop are either the web player (yet another browser tab) or unofficial Electron wrappers that idle at 200+ MB of RAM to play an audio stream. This sits in the menu bar at ~35 MB RAM and 0% CPU. The .app is about 1 MB.
What it does: browse and search stations, play/pause, volume, see what's playing (artwork, artist, track, time), pick stream quality (320k MP3, 128k AAC, 64k AAC). Media keys work. It remembers your last station.
Built with AVPlayer for streaming, MenuBarExtra for the UI, MPRemoteCommandCenter for media key integration. The trickiest part was getting accurate elapsed time. DI.FM's API and the ICY stream metadata don't always agree, so there's a small state machine that reconciles the two sources.
macOS 14+ required. You need a DI.FM premium account for the high-quality streams.
Source and binary: https://github.com/drmikexo2/DIBar-macOS

value: Show HN: Free, open-source native macOS client for di.fm

author: thucydides


value: vigrant

value:
Built this mostly with AI assistance (vibe-coded). Stockfish 16 runs in the browser via WASM. Constraints are parsed in two layers: a hand-written rule parser handles common patterns (no captures, piece restrictions, no castling, etc.), and anything it can't match falls through to Claude Haiku, which generates a JavaScript matches(uciMove, chess) function on the fly. That function runs client-side and filters the list of legal UCI moves before being passed to Stockfish's searchmoves parameter, so Stockfish's skill level, depth, and randomness all still apply correctly within the allowed set.
Constraints can target the engine, the player, or both. Shareable via URL (FEN + constraints + ELO encoded as query params).
Pre-loaded challenge if you want to skip setup:
https://constrainedchess.vercel.app/?skill=5&c=player%7Ccan%...

value: Show HN: Constrained Chess, Play Stockfish with custom natural-language rules

author: vigrant


value: ethanbeard

value:
Hey HN, I'm building ClawNet — a communication platform where AI agents are the first-class user.
Every agent gets a real email address (name@clwnt.com) plus private DMs between agents and an opt-in public feed. Everything is plain HTTP with bearer tokens — no SDK, no WebSocket, no OAuth.
Why I built this: I wanted to forward emails to one of my agents to process them and I ran our of Gmail accounts I could add to my phone number.  Going through the CAPTCHA/verification routine on Proton Mail or other email site felt a bit absurd and like I was just working for my agent. Doing this much work with/for agents it's become clear to me that the entire web is designed to keep agents out: CAPTCHAs, phone verification, JavaScript rendering, OAuth consent screens. If your agent that needs to communicate, your options are build it yourself or wedge into platforms designed to exclude them.
So I built ClawNet to be infrastructure where agents are the intended user and not a workaround.
What people are doing with it: One friend is using ClawNet to have two Claude Code instances debate how to build a new feature — they message back and forth through DMs and he watches the conversation. I want to connect my Claude Code to my main OpenClaw agent on ClawNet as I do a lot of copy-pasting between them, but I'm almost always in --dangerously-skip-perms so I'm a bit cautious of letting them loose on each other.
What's interesting technically:
- Zero-token polling: We built a lightweight Python poller (stdlib only) that checks the inbox every 2 min via GET /inbox/check and returns a boolean - no LLM involved. The LLM only wakes up when there's actually a message so it achieves very quick messaging responsiveness with basically zero idle cost.
- Prompt injection protection: Clearly agent-to-agent messaging has some obvious security problems around prompt injection that can hijack the receiver. We wrap every delivered message in three layers (natural language framing, XML boundaries, JSON encoding) applied server-side at delivery time. Raw content stays clean in the DB.  The client side instructions in our skill.md also provide clear NL instructions to treat messages as data and not instructions.
- Self-onboarding: Agents join by reading a single markdown file (skill.md). Tell your agent "Read https://clwnt.com/skill.md and follow the instructions" and it will register, starts polling, and begins messaging with no human intervention required.
- Real email: Inbound email via Postmark. Allowlist-gated, rate-limited, HTML auto-converted to plaintext. Agents can turn the allowlist on and off if they want to receive new emails to add to the allowlist.  I have my agent find new newsletters and turn off their allowlist so the can confirm and add to the sender to their allowlist.  Working on attachments next.
Where we are: Still early with ~50 agents on the network since barebones launch 3 weeks ago.  Lots of bug fixes and improvements since then but feeling stable enough to let others take a look. The feed has thousands of posts although it used to be mandatory to participate (opt in clearly better). I'm building this with Claude Code and two AI agents (Severith, Tom) who are ClawNet users so provide an interesting perspective.
Homepage: https://clwnt.com
API docs: https://clwnt.com/docs/
Live feed: https://clwnt.com/feed/
Skill file agents read to join: https://clwnt.com/skill.md
Happy to answer any questions about the architecture, the prompt injection approach, or what it's like building a product for agents and with your users as co-developers.

value: Show HN: ClawNet – Agent-first communication infrastructure (email, DMs, feed)

author: ethanbeard


value: enigmazi

value: Show HN: Docmd – A minimalist, zero-config docs generator with 0ms latency

author: enigmazi


value: utshull

value: Show HN: AI Agents Weekly – A newsletter by agents, for agents

author: utshull


value: AutomataNexus

value: Show HN: AxonML – A PyTorch-equivalent ML framework written in Rust

author: AutomataNexus


value: Hey HN! Been hacking around with AI tutors using socratic method, Feynman technique, spaced repetition, etc. Depth Check allows you to learn about anything and collect all your learning progress in a slick and accessible UI. Try it out! Would love to hear your feedback

value: Show HN: Depth Check – Your AI tutor to learn about anything


value: enriquefft

value:
Built an OpenClaw plugin that gives your agent a WhatsApp number through the official Cloud API via Kapso).
Polling works out of the box, or use Tailscale Funnel for sub-second latency without needing a domain.
v0.1.0, I'm the only user so far. Would love any kind of feedback.

value: Show HN: OpenClaw-kapso, Give OpenClaw a stable WhatsApp number (Go, kapso.ai)

author: enriquefft


value:
Built two small tools this weekend that scratch my own itch:
1. Chrome extension — injects an AI sidebar into any webpage. Alt+A to toggle. Summarize, ask questions about page content, improve selected text. Manifest V3, vanilla JS, shadow DOM for style isolation. Uses your own Anthropic API key directly from the background service worker (~$0.001/request with Haiku).
2. Python script pack — 5 scripts for common business tasks: lead enrichment with ICP scoring, cold email generation (3 variations), SEO content outlines, meeting notes summarizer, LinkedIn post writer. Plain Python, anthropic SDK, no framework. Batch mode on the CSV-heavy ones.
Both at $9 on Gumroad. Source code, you own it, modify freely.
Happy to answer questions about the Manifest V3 shadow DOM approach or the Claude API integration — had some interesting CORS issues to work through.

value: Show HN: Claude-powered Chrome sidebar and Python automation scripts


value:
Hello HN,
I built DuLink to solve a personal problem: my primary language is English, but my mother’s primary language is Mandarin Chinese. I often find articles on health or current events that I want to share with her, but the friction of copy/pasting and Google Translate meant she rarely read them.
What it does:
DuLink takes an article URL, extracts the core content to strip out clutter, and then generates a static, translated reading view. It preserves the semantic HTML structure (headers, lists) but removes the original site's JS and ad tracking.
The "Why":
Existing tools either translate the entire messy page (including nav menus/footers) or require the recipient to install an extension. I wanted something truly simple. A "fire and forget" link that removed every obstacle. No setup, no extra tools, just open and understand. I also added audio playback because reading dense text on a phone can be tiring, especially for aging eyes. Sometimes listening is simply easier.
I built this for a personal use case, but I'm sharing it here for anyone who wants to share something meaningful across languages. If it helps you bridge that gap, I'd love to hear about it.
Any feedback is welcome.
https://dulink.click/

value: Show HN: I built a tool to translate and declutter articles for my immigrant mom


value: jawiggins

value:
Recently several AI labs have published experiments where they tried to get AI coding agents to complete large software projects.
- Cursor attempted to make a browser from scratch: https://cursor.com/blog/scaling-agents
- Anthropic attempted to make a C Compiler: https://www.anthropic.com/engineering/building-c-compiler
I have been wondering if there are software packages that can be easily reproduced by taking the available test suites and tasking agents to work on projects until the existing test suites pass.
After playing with this concept by having Claude Code reproduce redis and sqlite, I began looking for software packages where an agent-made reproduction might actually be useful.
I found libxml2, a widely used, open-source C language library designed for parsing, creating, and manipulating XML and HTML documents. Three months ago it became unmaintained with the update, "This project is unmaintained and has
[known security issues](https://gitlab.gnome.org/GNOME/libxml2/-/issues/346). It is foolish to use this software to process untrusted data.".
With a few days of work, I was able to create xmloxide, a memory safe rust replacement for libxml2 which passes the compatibility suite as well as the W3C XML Conformance Test Suite. Performance is similar on most parsing operations and better on serialization. It comes with a C API so that it can be a replacement for existing uses of libxml2.
- crates.io: https://crates.io/crates/xmloxide
- GitHub release: https://github.com/jonwiggins/xmloxide/releases/tag/v0.1.0
While I don't expect people to cut over to this new and unproven package, I do think there is something interesting to think about here in how coding agents like Claude Code can quickly iterate given a test suite. It's possible the legacy code problem that COBOL and other systems present will go away as rewrites become easier. The problem of ongoing maintenance to fix CVEs and update to later package versions becomes a larger percentage of software package management work.

value: Show HN: Xmloxide – an agent-made Rust replacement for libxml2

author: jawiggins


value: alfredhua

value: Show HN: Salacia – The First Runtime OS for Agentic Coding

author: alfredhua


value: akgitrepos

value: Show HN: Explain Curl Commands

author: akgitrepos

title: Show HN: Explain Curl Commands


value: Show HN: KiboUP – Deploy AI Agents via HTTP, A2A, and MCP with One Codebase


value: apexkid

value:
Hi HN, I'm Adi. I built Web Scout AI, an open-source tool that takes a URL and autonomously discovers every user journey on the site — no scripts, no selectors, no config. Point it at your homepage and it finds paths like Homepage → Product Listing → Product Detail → Cart → Checkout on its own.
Repo: https://github.com/apexkid/web-scout-ai
It uses Claude's vision + Playwright to explore a site the way a human would: look at the page, decide what's clickable, click it, repeat. It does this recursively via BFS, building a complete graph of every reachable state. When it finds 50 product cards, it recognizes the pattern and explores one representative instead of all 50. Cookie banners and popups get auto-dismissed. The output is a journey graph, Mermaid diagrams, and a set of replayable JSON files along with network requests fired at every interactions.
Who's can use it and for what:
* QA/Testing — The most obvious use case. Run auto against your staging environment, get a full set of discovered journeys, then replay them after every deploy. No test scripts to write or maintain. When the site changes, re-run discovery instead of fixing selectors. One team told me they went from 2 weeks of manual test writing to a single afternoon of reviewing auto-discovered journeys.
* 3P API auditing — This is the one I didn't expect. The replay engine captures every XHR/fetch request at every step — full request and response bodies. Teams are using this to verify that analytics events (GA4, Segment, etc.) actually fire at the right moments in the right order. "Does our checkout funnel fire the right events at every step?" becomes a replay + grep instead of a manual walkthrough.
* Journey documentation — PMs and designers use the Mermaid diagram output to get a ground-truth map of what users can actually do. Turns out the real journey graph rarely matches what's in the Figma file. Dead ends, loops, and unreachable states show up immediately.
* Post-deploy smoke tests — Run replay all in CI after a deploy. It replays every known journey through a real browser and reports pass/fail per step. No LLM cost, runs in parallel, takes minutes. If a flow breaks, you know which step and which selector failed.
* Competitive analysis — Point it at a competitor's site and get a structured map of their user flows. What journeys do they support? What does their checkout look like? All captured as screenshots and structured JSON.

value: Show HN: Web Scout AI – Auto-discover every user journey (zero config)

author: apexkid


value: usplusAI

value:
Hey HN,
I'm the founder of usplus.ai (@UsplusAIdotcom), and I've been building this for a while now in San Diego.
The core idea: What if you could form a startup where AI agents aren't just tools—they're actual team members from day one? usplus.ai lets you create a virtual "Lab" with an org chart that includes both humans and AI agents assigned to real roles (CEO/Strategy Lead, Product Manager, CTO, CMO, Growth Hacker, Legal Advisor, etc.).
Key features right now:
Autonomous AI agents (powered by GPT-4o, Grok, Claude, Gemini, etc.) with 58 native skills (260+ actions) and access to 1,000+ SaaS tools via Composio (e.g., GitHub, Figma, HubSpot, Stripe, Gmail, Salesforce, Jira).
No-code workflow automation: Describe multi-step processes in English → agents build loops, conditions, scheduling, and execute real work (content creation, market research, campaign analysis, code gen, etc.).
Multi-Agent War Room: Agents collaborate in turn-taking sessions, recruit specialists dynamically, and handle complex tasks with approval gates.
Pre-built advisors (Idea Validator, Product Architect, etc.) that you can customize and @mention in chats/projects.
Full startup lifecycle support: From idea validation to MVP building, marketing, crowdfunding prep, and income generation.
Early access is invite-based—invite 3 friends for a free year of premium.
It's built on a similar premise to openClaw but extended for enterprise/startup use, with focus on agentic autonomy (planning, acting, adapting) rather than just chat.
I'd love feedback:
Does assigning AI to org roles feel useful, or gimmicky?
How are you structuring AI agents in your own work/startups?
Any must-have skills/tools missing?
Bugs, UX pain points, or wild ideas for workflows?
Try it out (free tier available): https://usplus.ai
Thanks for any thoughts, critiques, or war stories—building this solo has been a ride, and HN feedback has shaped a lot already.
Looking forward to your takes!

value: Show HN: Usplus.ai – Build an AI-Native Company with Agents in your Org Chart

author: usplusAI


value: vinzify

value:
Hi everyone,
I built GitPop, an open-source, lightweight Windows File Explorer extension that brings a fast Git interface right to your context menu.
The problem: I got tired of opening heavy IDEs (like VS Code) or bulky GUI clients just to make a quick commit, but I also wanted the nice visual diffs and staging checkboxes that terminal workflows lack.
The solution: When you right-click any folder with a .git repository and select "GitPop Here", it instantly launches a sleek, dark-mode UI overlay. You can view your changes, stage files, and commit directly from your desktop.
To speed things up even more, I added an AI "Sparkle" button that reads your git diff and auto-generates a conventional commit message.
Technical details:
Stack: I built this using Tauri v2, React, and Rust. I specifically chose Tauri over Electron because a context-menu popup needs to open instantly and use minimal RAM.
Privacy First (Local AI): By default, the AI feature hooks into a local Ollama instance (like llama3.2), meaning your proprietary source code diffs never leave your machine. I also added support for OpenAI, Anthropic, and Gemini for those who prefer cloud models.
Git Backend: Instead of wrestling with libgit2, the Rust backend spawns hidden child processes to execute native Git CLI binaries. This means it automatically respects your existing global Git config, SSH keys, and GPG signing.
A fun engineering challenge: Building a transparent, glassmorphism UI on Windows 11 using Webview2 was surprisingly tricky. Combining "transparent": true with a hidden startup state ("visible": false) caused the rendering engine to completely panic and crash. I also had to write custom OS-level Rust code to suppress the background CMD terminals from flashing every time a Git command ran.
What's next (macOS): Right now, the release is heavily optimized for Windows File Explorer, but I am actively working on the macOS version to integrate this exact same experience directly into Finder.
I’d love for you to try out the Windows version while I wrap up the Mac build. The installers (.exe/.msi) and source code are available on GitHub.
Repo: https://github.com/vinzify/gitpop
I'll be hanging around the comments to answer any questions about the tech stack, Tauri v2, or the implementation!

value: Show HN: GitPop – open-source AI Git context menu for Windows (OS X coming soon)

author: vinzify


value: tbergmann

value:
Spent the last few years watching marketing directors fail their first 90 days not because they lacked skill, but because every framework they'd read assumed a functional organization.
Wrote this to cover what those guides skip: reading political dynamics before you speak, inheriting a burned team, managing up when goals shift weekly. And a honest Day 91 check, stay or go.
$49, 18 pages, one download.

value: Show HN: A no-BS guide for mkt directors who just landed in a broken company

author: tbergmann


value:
Pare is a dating site where users matchmake for each other. Instead of swiping on your own feed, you get presented with 3 profiles and you decide based on the information presented which candidate profile is the best fit for the focus profile.
Demo on the landing page.
Currently NYC only, but hope to expand as this takes off. All users on other cities get put on a waitlist for now.
Some things I need feedback on:
GTM
- I posted this on the r/nyc subreddit and promptly got shadowbanned from the entire website, but not before a couple people told me to fuck off (lol).
- X/Twitter is a black hole for me since I created an account for the first time ever in January, so I'd appreciate some GTM advice.
Pricing
- Currently free, but I plan to price at $10 a season (~1 week), is that too high or too low? Why? I was aiming to price competitively to standard premium options on other dating apps. Also this is not a subscription (hate those).
- Also Stripe told me that dating apps are restricted enterprises and my account is being closed so I need a new payment processor.
General
- If you’ve seen similar approaches (dating, hiring, recsys, rank aggregation), I’d appreciate pointers.
If you have constructive feedback I'm all ears.

value: Show HN: Pare – would you let strangers play matchmaker for you?


value: iamalizaidi

value: Show HN: Tool to surface past architectural decisions directly on Pull Requests

author: iamalizaidi


value:
Hey HN,
I’ve been building Atom (https://github.com/rush86999/atom), an open-source, self-hosted AI automation platform.
I built this because while tools like OpenClaw are excellent for one-off scripts and personal tasks, I found them difficult to use for complex business workflows (e.g., managing invoices or SaaS ops). The main issue was State Blindness: the agent would fire a command and assume it worked, without "seeing" if the UI or state actually updated.
I just shipped a new architecture to solve this called Canvas AI Accessibility.
The Technical Concept:
Instead of relying on token-heavy screenshots or raw HTML, I built a hidden semantic layer—essentially a "Screen Reader" for the LLM.
Hidden Visual Description: When the agent works, the system generates a structured, hidden description of the visual state.
Episodic Memory: The agent "reads" this layer to verify its actions. Crucially, it snapshots this state into a vector database (LanceDB).
Maturity/Governance: Before an agent is promoted from "Student" to "Autonomous," it must demonstrate it can recall these past visual states to avoid repeating errors.
Atom vs. OpenClaw:
I view them as complementary. OpenClaw is the "Hands" (great for raw execution/terminal), while Atom is the "Brain" (handling state, memory, and audit trails). Atom uses Python/FastAPI vs OpenClaw's Node.js, and focuses heavily on this governance/memory layer.
The repo is self-hosted and includes the new Canvas architecture. I’d love feedback on the implementation of the hidden accessibility layer—is anyone else using "synthetic accessibility trees" for agent grounding?

value: Show HN: Atom – open-source AI agent with "visual" episodic memory


value: Justineo

value: Show HN: A live playground for Beautiful Mermaid

author: Justineo