value: Burnmydays

value:
I built COMMAND, a governance console for operating multiple AI systems from a single control surface. It's an enterprise product — licensed to organizations, not individuals — but the full interactive demo is a single HTML file anyone can open and explore right now.
The problem: organizations running multiple AI systems (GPT, Claude, Gemini, Grok, etc.) have no structured way to govern them together. Each system is a separate conversation with separate context. COMMAND gives you a single interface with role assignment (primary/secondary/observer), sequenced execution order, broadcast across systems, context injection, and a full audit trail.
Open the site, the demo runs in your browser. No signup, no backend, no dependencies. You're looking at the full control surface:
Assign roles and execution sequences across 6 AI systems
Set governance modes that constrain how systems respond
Broadcast a single prompt to multiple systems in defined order
Load context documents from a vault and inject them into sessions
Deploy structured missions with WHO/WHAT/WHERE configuration
Control response behavior with compression, speed, and length sliders
SNAIL mode (step-by-step acknowledgment), LOCK IN (acute focus)
Command postures: FALL IN LINE, FOLLOW, LEAD, TAKE CHARGE
Full session export and audit logging
The demo uses simulated responses to show how governance state flows through. The actual product is delivered to licensed organizations who connect their own API keys and engine.
Built as a single self-contained HTML file (~7K lines). I wanted something that could run anywhere, including air-gapped environments, with zero infrastructure dependencies.
Happy to discuss the architecture, the governance model, or the reasoning behind any of it.
RiftWalking

value: Show HN: Integrate governance before your AI stack executes – COMMAND console

author: Burnmydays


value: ThoBustos

value:
I built an MCP server that generates quizzes from your Claude Code sessions.
Reading AI explanations feels like understanding, but how much are you actually retaining? I wanted active recall instead of passive reading.
How it works:
  - Say "Quiz me on what we discussed" in Claude Code
  - It generates questions (multiple-choice, code-writing, open-ended)
  - Browser opens with a terminal-style UI
  - After each answer, you can chat with an AI tutor
  - Retry quizzes for spaced repetition
Tech: TypeScript, MCP SDK, Next.js, SQLite, Claude API
The whole thing is keyboard-driven (A-D to select, Enter to submit). Built it because I kept forgetting things from long Claude sessions and wanted something like Anki but automatic.
Would love feedback on the question generation quality, i'm already thinking in some kind of dashboard, reminders, gamifying your grind in the CLI.

value: Show HN: Quizz MCP – Turn Claude Code Conversations into Quizzes

author: ThoBustos


value: felipemayamuniz

value: Show HN: Cognitive architecture that hit #1 on LiveBench (68.5%)

author: felipemayamuniz


value: bittujoju

value:
Hey HN, I built ChoresMates to solve a problem every couple and roommate knows: the "I do more around the house" argument.
The idea is simple — every chore gets a point value based on effort (cooking dinner = 8 pts, loading the dishwasher = 2 pts). When you complete a chore, you log it and earn points. A real-time leaderboard shows who's actually pulling their weight. The numbers replace the argument.
A few things that make it work in practice:
Effort-weighted points — Not all chores are equal. Scrubbing the bathroom is worth more than wiping a counter. This prevents gaming the system with a bunch of tiny tasks.
Overdue penalties — Procrastinating on an assigned chore costs you points per day. Creates just enough pressure to get things done.
Photo proof — Solves the "I did it but you didn't notice" problem.
Rotation scheduling — Automatically rotates who's responsible for recurring chores, so there's no negotiation.
Comes with 100+ chore templates covering everything from couples to families with kids to college dorms, with sensible default point values and schedules.
Tech stack: Swift/SwiftUI on iOS, Kotlin on Android, Rails 7 API backend on AWS ECS, PostgreSQL. Auth via Sign in with Apple, Google, or email.
Free tier covers most households (1 group, 10 members, 25 chores). Pro is $0.99/mo or $19.99 lifetime.
Would love feedback from the HN community — especially on the point-value system and whether the gamification angle resonates or feels gimmicky.
iOS: https://apps.apple.com/us/app/choresmates/id6757452488
Android: https://play.google.com/store/apps/details?id=com.choresmate...

value: Show HN: ChoresMates – Splitwise, but for Household Chores

author: bittujoju


value: timstark

value:
also cuts tokens 93%
Claude Code's WebFetch converts HTML to markdown and runs it through a secondary model before it hits your context — but that pipeline wasn't designed as a security boundary. Turndown strips structural junk (scripts, CSS, nav chrome), but zero-width characters, fake LLM delimiters, base64-encoded payloads, and markdown exfiltration URLs all survive the conversion. And using a small LLM to filter adversarial content is the fox guarding the henhouse.
The problem gets worse outside Claude Code. API-level web_fetch, other coding tools, curl output, cloned repos — raw content flows into LLM context with no sanitization at all.
mcp-safe-fetch is an MCP server with three tools (safe_fetch, safe_read, safe_exec) that sanitize content deterministically before the LLM sees it. Regex + cheerio + turndown, no AI in the loop. Across 4 test sites: 93% average token reduction, zero false positives, all visible content preserved.

  npx -y mcp-safe-fetch init

value: Show HN: MCP server that strips injection vectors from LLM input

author: timstark


value: meszmate

value: Show HN: Memctl v0.1.0 Open source shared persistent memory for AI coding agents

author: meszmate


value: DjGilcrease

value: A single Go binary that runs your Ansible playbooks. No Python runtime, no pip dependencies, no remote agent requirements.

value: Show HN: Parallax – Ansible Without Python

author: DjGilcrease


value: prasaar

value: Show HN: Brand Analytics for AI Search Engines (Beta)

author: prasaar


value: Show HN: Sailor.ai – source-backed personalized outbound emails


value: janlucasandmann

value:
Hey HN,
Most AI “agents” I’ve tried are basically chatbots with amnesia — they forget everything the moment you close the tab and can’t do anything unless you’re sitting there watching them.
I wanted real AI coworkers that just… work.
So I built Computer Agents (aiOS).
Every agent you create gets its own isolated computer in the cloud — complete with persistent memory, a real file system, code execution environment (with automatic dependency management), and the ability to run scheduled or webhook-triggered tasks 24/7.
You give it a goal (“research this market and email me a report every Monday”, “generate floor plans from client briefs”, “handle incoming support emails”, “run my weekly data analysis”), walk away, and come back to finished results in your inbox, Telegram, or dashboard.
Key highlights:
•  Persistent workspaces — context and files survive forever (no more “remember what we talked about last week?”)
•  Native iOS app (iPhone + iPad) + native Mac app + web dashboard
•  Python + TypeScript SDKs (pip install computer-agents, npm install computer-agents)
•  Multi-agent orchestration (sequential, parallel, map-reduce, conditional flows)
•  Built-in skills: deep web research with citations, web search, image generation, full code interpreter
•  Integrations: Email, Telegram, GitHub, Google Drive, OneDrive, Notion, webhooks, etc.
•  Runs in secure isolated cloud containers (you own your data)
It’s live at https://computer-agents.com
Free tier gives you 150 compute tokens (~15–23 decent-sized tasks) so you can try it right now. Pro starts at $19/mo when you want more.
This is very much still a young indie project (I’m the solo founder), but it’s already helping real teams automate support, research, content, and coding workflows.
Would love your honest feedback — especially:
•  What persistent/long-running agent pain points have you hit with other tools?
•  Interesting use cases you’d want to try?
•  Thoughts on the architecture (sandboxing, persistence model, orchestration)
Happy to answer any questions!
Thanks,
Jan Luca (indie maker behind Computer Agents)
P.S. If you’re into computer-use agents, we also have a comparison page: https://computer-agents.com/compare/computer-use-agents

value: Show HN: Computer Agents – Agents that work while you sleep

author: janlucasandmann


value:
I've been using Claude Code alot and keep running into the same issues: losing track of which session is doing what, sending Claude off on a task only to discover 30 minutes later it had immediately asked a clarifying question and was sitting there waiting, lack of easy remote access, and just a plethora of worktree issues (worktree off branch, auto-installing dependencies, etc).
So I built ClaudeTerminal — an Electron app that is inspired by Windows Terminal but is purpose-built for Claude Code. It ain't fancy - it feels like Terminal, but it does have:
Tabbed sessions — Run multiple Claude Code instances side by side, each with status icons showing if they're working, idle, or need input
Desktop notifications — Get notified when a background session finishes a task or needs your attention. Click the notification to jump straight to that tab
Remote access — Access your sessions from any device via a temporary Cloudflare tunnel. Check on your agents from your phone
Session persistence — Close the app, reopen it, and every session resumes exactly where it left off
Git worktree integration — Ctrl+W creates a new worktree and opens a Claude session scoped to it (branches from your current branch, not just main!). Worktree names show on the tabs.
Tab auto-naming — Your first prompt gets analyzed by Haiku (via call to claude call terminal) to generate a descriptive tab name (so you see "Auth Bug Fix" instead of "Tab 3")
Repository hooks — Trigger scripts on lifecycle events (e.g., auto-run pnpm install when a worktree is created)
Shell tabs — Mix in PowerShell/WSL tabs alongside your Claude sessions
Keyboard-driven — Ctrl+T for new tab, Ctrl+W for worktree tab, Ctrl+Tab to switch, Ctrl+1-9 to jump, etc.
It's open source (MIT): https://github.com/Mr8BitHK/claude-terminal.
Windows is the primary platform (yup, I don't use a Mac - sorry all!), but I've included experimental macOS/Linux builds.
Would love feedback from other Claude Code users and hope this helps... someone :)
Disclosure: I'm the sole developer. ClaudeTerminal is free and open source (MIT). No paid tier, no telemetry, no sign-up. Image shamelessly AI generated. Tool co-developed with Claude Code.

value: Show HN: ClaudeTerminal – A tabbed terminal manager for Claude Code


value: ssbodapati

value:
Your agent doesn't know your codebase. It knows the code. Not the decisions behind it.
Why you picked that queue over a direct DB write. Why that module has no external dependencies. Why the retry logic is in the client and not the server. That context lives in your head. The moment the session ends, it's gone.
EmCogni Code fixes this. One Go binary. Local MCP server. You annotate decisions as you make them:
#example 1
emcogni note payments --type decision "Async via queue — direct writes caused timeout spikes under load"
#example 2
emcogni note payments --type invariant "Never call external APIs inline — always enqueue"
Any MCP-compatible agent (Claude, Cursor, Copilot) gets that context automatically before touching a file, via get_context tool. EmCogni can also propose context additions via propose_context tool.
Free for local/solo use at emcogni.com.
Team/managed tier coming — shared context across engineers and agents, org-level knowledge base. Waitlist open at the same link.
Curious how others handle this. CLAUDE.md files? Custom RAG? Just re-explaining every session?

value: Show HN: EmCogni Code, the context engine for the "why" behind your codebase

author: ssbodapati


value:
Hey HN,
I built a free AI flashcard generator with no signup and no paywall. Quizlet charges $7.99/month, Revisely caps free users at 3 generations, Knowt keeps moving features behind paywalls. I wanted to build the opposite of that.
https://prepareyourself.app
Paste text or upload a PDF. Pick an output type — flashcards, quiz, summary, or explain-with-examples. Get results in ~10 seconds. Done.
I've been running it for 28 days. Here are the real numbers:
- 180 users. 8 of them engaged deeply. One person hit "review again" 64 times on a single flashcard deck. The other 172 bounced.
- Indian users spend 2x longer than US users (96 sec vs 44 sec). Bengaluru and Ahmedabad are my strongest cities. No idea why.
- Flashcards get 10x the engagement of quizzes. Quiz completions in a month: 1 person. So I made flashcards the default.
- Day 7 retention: 0%. Literally zero. Just shipped spaced repetition reminders — no data yet on whether it helps.
- Revenue: $0.
So the product works for the few who try it. The problem is getting people to try it.
Features: 4 output types (flashcards, quiz, summary, explain-with-examples), PDF upload, TTS audio on every card, spaced repetition, export to PDF/DOCX/JSON, 11 languages. History and streaks stored client-side in IndexedDB — nothing leaves the browser unless you hit generate.
Stack: Next.js, Supabase, Claude Haiku, Vercel. No auth required — rate limiting via IP + fingerprinting.
Would love feedback on UX, output quality, or whether "free forever, no signup" is a viable positioning against VC-funded competitors who are all moving toward paywalls.
thank you!

value: Show HN: I built a free AI study tool– paste notes, get flashcards in 10 seconds


value: ycombiredd

value:
I think this is apropos of the "Show HN" tag, as the post is explanatory and the entire codebase this little side-story use case discussed in TFA  is in the repo and free to use. (I'd be pleased if you did!)
In the post, as I tried to capture in the title submitted, I outline my journey of exploration, when I became  determined to make GitHub-Flavored Markdown display my text, with color, style and alignment of my choosing,  which as I discovered after setting out to do so, the inability to do such a thing outside of fenced blocks with pre-defined syntax highlighting is a well-known condition, which is met with "works as intended" response because, well, GitHub doesn't want their repos looking like MySpace or Geocities or presenting security risk exposure by allowing arbitrary html/CSS styling. Sure, I should have used GitHub Pages to build a page from my Markdown using Jekyll, which is a supported way to control the styling of your own documents in your repo, but where's the fun in that?
The linked post documents the workaround I arrived at, which became an output target format that nobody has ever asked for from my ASCII line-Art diagramming tool. I thought some here might appreciate the documentation of "wasting my time so you don't have to" on a technical solution for a problem I probably just shouldn't have cared about and moved on.

value: Show HN: A GFM+GF-MathJax/Latex HTML formatting adventure

author: ycombiredd


value:
I built SpecLock because I was tired of AI coding assistants "remembering" my constraints then ignoring them when inconvenient.
The problem: Claude Code has auto-memory. Cursor has Memory Bank. But memory without enforcement is dangerous. Your AI remembers you use PostgreSQL — then switches to MongoDB because it "seemed better."
SpecLock adds active constraint enforcement:
• You: "Don't ever touch the auth files"
• AI:  Locked
• ...5 sessions later...
• You: "Add social login"
• AI:  CONFLICT: This violates your lock. Should I proceed?
Key features:
- Semantic conflict detection (synonym + negation, not just keywords)
- Auto-guard related files (injects warnings into source code)
- Works with Bolt.new, Lovable, Claude Code, Cursor
- 19 MCP tools for memory, tracking, enforcement
- Free & open source (MIT)
Demo: https://sgroy10.github.io/speclock/
GitHub: https://github.com/sgroy10/speclock
npm: npm install speclock

value: Show HN: SpecLock – AI Constraint Engine that stops AI from breaking locked code


value: koalux

value:
word-doodle is a browser-based generative text art engine that takes string of text and transforms them into typographic doodles. word-doodle scatters words across the screen using a simple collision detection algorithm to place words into empty spaces. It includes a control hub where users can input text, change fonts, select text cases, and apply word limits with additional options to generate the doodle, wipe the screen, or take a screenshot.
The Engine
The word-doodle engine transforms raw text into organized art via a three-step generation pipeline:
1. Vocabulary Analysis & Frequency Mapping
-When text is provided, the engine parses the string into individual tokens. It converts all words to a uniform uppercase format to ensure accurate counting. Then it calculates how many times each unique word appears, identifying the maxFrequency to establish visual weight for each word.
2. Word Weight Scaling
-Size: fontSize is determined by a random base value scaled by the word's frequency weight.
-Weight: High-frequency words (above 80% weight) are automatically assigned a fontWeight of 700 for extra impact.
-Orientation: To make it more visual appealing, there is a 15% chance a word will rotate 90°, switching to a vertical placement.
3. Collision Detection
-Coordinate Generation: The engine picks a random (x,y)coordinate within the viewport.
Boundary Validation: It compares the candidate's bounding box against every word already placed on the canvas, including a 5px safety padding.
The Retry Loop: If an overlap is detected, the engine attempts to find a new spot. It will try up to 70 different positions before skipping the word. This prevents infinite loops and keeps the browser performance smooth even with high word counts.
Check out the live demo here: https://j-ncel.github.io/word-doodle/

value: Show HN: Word-doodle – browser-based generative doodle text art engine

author: koalux


value: heypradyumn

value: Show HN: A strict execution engine built on a $70 edge stack

author: heypradyumn


value: HusseinKizz

value: Show HN: A POST only back end framework

author: HusseinKizz

title: Show HN: A POST only back end framework


value: linksbro

value: Small script to display account age/karma next to all usernames, so you have that info available to you without clicking through to someone's profile. Opus 4.6 written, it's a mess but it works :) Using with Tampermonkey on Firefox.

value: Show HN: Userscript to Display Age/Karma of HN Users

author: linksbro


value: buryhuang

value:
I never listen to my voice memos. Hundreds of recordings on my phone, all dead.
I built a Telegram bot that transcribes them (AssemblyAI, speaker labels, any language) — and then wired up Claude Agent SDK so you can talk to your files later.
Claude Agent SDK gives your bot the same tools Claude Code has — Read, Glob, Grep — running autonomously against your stored transcripts. Ask "what did my manager say about the deadline?" and it spawns an agent that browses your files, reads the relevant ones, and answers. Not keyword search. An actual agent reasoning over your data.
Self-hosted, your own API keys, MIT licensed. Runs anywhere Docker runs.

value: Show HN: I put Claude Code inside a Telegram bot for voice memos

author: buryhuang


value: Show HN: Openpista – AI Agent for OS Control via Telegram/CLI in Rust


value: tstockham

value:
Engram gives AI coding agents persistent memory across sessions. 2,500 npm installs in the first five days.
The problem: every Claude Code or Cursor session starts from zero. You re-explain your architecture, conventions, past decisions. CLAUDE.md helps but it's manual and doesn't scale.
Engram runs as a native MCP server. One command to install, SQLite backend, no infrastructure.
Three tiers of memory:
Explicit: things you tell it to remember
Implicit: behavioral patterns extracted from how you work (low confidence, reinforced over time)
Synthesized: meta-observations generated during consolidation
The key architectural bet: intelligence at read time, not write time. Most memory systems extract and classify on ingest. Engram stores broadly and invests compute when you query, because that's when you actually know what matters. This is why it scores 80% on LOCOMO (arXiv:2402.17753) while using 30x fewer tokens than full-context retrieval.
Stack: TypeScript, SQLite + sqlite-vec, Gemini embeddings by default (any OpenAI-compatible provider works via ENGRAM_LLM_BASE_URL). Zero external dependencies.
Install: npm install -g engram-sdk && engram init
Comparison with Mem0, Zep/Graphiti, Letta/MemGPT: https://www.engram.fyi/compare
GitHub: https://github.com/tstockham96/engram

value: Show HN: Engram – Memory for AI coding agents (2.5K installs, 80% on LOCOMO)

author: tstockham


value: khnfrhn

value: Show HN: Analyze employee survey data in 60 seconds (no signup)

author: khnfrhn


value: riwsky

value:
Iosef is an iOS simulator CLI designed for agents, inspired by simonw/rodney, facebook/idb, and joshuayoes/ios-simulator-mcp.
For those familiar with idb, some of iosef’s agent-oriented features include:
Directory-level sessions, so agents in different worktrees don’t interfere with each other’s simulators.
Automatic scaling of screenshots into the same coordinate space as the accessibility trees and interaction tools.
Selector-based interactions, to save LLMs from needing to re-consult accessibility trees or screenshots each turn.
Skills and MCP support.
=====
iosef’s absence from the pre-training corpus means agents need a little nudge (either via the skill or user prompt) to use it over idb or simctl—but once they’re using it, they have a easier time than they do with other tools. While written in swift, it’s packaged via pypi for easy running and installation via uv (eg “uvx iosef --help” or “uv tool install iosef”).
Is it nearly entirely vibe-coded? Yes—but I’ve found it helpful for my own use, so I share it in case others will, as well. And this post is entirely human-written, so at least there’s that.

value: Show HN: Iosef, an iOS simulator CLI designed for agents

author: riwsky


value: tsunamayo

value:
I built a desktop app (PyQt6, Windows) that orchestrates multiple AI models in a 3-phase pipeline:
Phase 1 – A cloud LLM (Claude/GPT/Gemini) decomposes the prompt into structured sub-tasks
Phase 2 – Local Ollama models process each sub-task (free, private, runs on your GPU)
Phase 3 – The cloud LLM integrates the results into a coherent final answer
The motivation: cloud APIs are great at reasoning and structure but cost money. Local Ollama models are free but sometimes inconsistent. The pipeline lets you use each where it's strongest.
Also includes:
- FastAPI + React web UI (accessible from LAN/mobile)
- SQLite chat history
- ChromaDB-based RAG
- Discord webhook notifications
Stack: Python, PyQt6, FastAPI, React, Ollama, Anthropic/OpenAI/Google APIs. MIT license.

value: Show HN: I built a desktop app combining Claude, GPT, Gemini with local Ollama

author: tsunamayo


value:
Hi HN,
I posted SkillFortify here a few days ago as a formal verification tool for 3 agent skill formats. Based on feedback, v0.3 now supports 22 agent frameworks and can scan your entire system with zero configuration.
The problem: In January 2026, the ClawHavoc campaign planted 1,200 malicious skills into agent marketplaces. CVE-2026-25253 was the first RCE in agent software. Researchers catalogued 6,000+ malicious agent tools. The industry responded with heuristic scanners — pattern matching, YARA rules, LLM-as-judge. One popular scanner states in its docs: "No findings does not mean no risk."
SkillFortify eliminates that caveat with formal verification.
What it does:

  pip install skillfortify
  skillfortify scan

That's it. No arguments, no config files, no paths. It auto-discovers every AI tool on your machine across 23+ IDE profiles:

  [*] Auto-discovering AI tools on system...
  [+] Found: Claude Code (12 skills)
  [+] Found: Cursor (8 skills)
  [+] Found: VS Code MCP (5 servers)
  [+] Found: Windsurf (3 skills)
  [*] Scanning 28 skills across 4 tools...

  RESULTS
    Critical:  2 skills with capability violations
    High:      3 skills with excessive permissions
    Clean:     23 skills passed all checks

22 supported frameworks: Claude Code, Cursor, VS Code, Windsurf, Gemini, OpenCode, Cline, Continue, Copilot, n8n, Roo, Trae, Kiro, Kode, Jules, Junie, Codex, SuperVS, Zencoder, CommandCode, Factory, Qoder — plus auto-discovery of unknown tools.
Why formal verification, not heuristics: Heuristic scanners check for known bad patterns. Novel attacks pass through. SkillFortify verifies what a skill CAN do against what it CLAIMS to do. Five mathematical theorems guarantee soundness — if it says safe, it provably cannot exceed declared capabilities.
Results on 540-skill benchmark (270 malicious, 270 benign):
- F1 = 96.95%
- Precision = 100% (zero false positives)
- Recall = 94.07%
- Speed: ~2.5ms per skill
9 CLI commands:
- scan — auto-discover + analyze all AI tools on your system
- verify — formally verify a single skill
- lock — generate skill-lock.json (like package-lock.json for agent skills)
- trust — compute graduated trust score (L0-L3, inspired by SLSA)
- sbom — generate CycloneDX 1.6 Agent Software Bill of Materials
- frameworks — list all 22 supported frameworks + detection status
- dashboard — generate standalone HTML security report (zero dependencies)
- registry-scan — scan MCP/PyPI/npm registries before installing
- verify --recursive — batch verify entire directory trees
1,818 tests. 22 parsers. 97 source modules. MIT licensed. Peer-reviewed paper on Zenodo.
GitHub: https://github.com/varun369/skillfortify
PyPI: https://pypi.org/project/skillfortify/
Paper: https://zenodo.org/records/18787663
Wiki: https://github.com/varun369/skillfortify/wiki
Landing page: https://www.superlocalmemory.com/skillfortify
Built this as part of my research on making AI agents reliable enough for production. The companion project AgentAssert (arXiv:2602.22302) handles behavioral contracts — SkillFortify handles the supply chain.
Happy to answer questions about the formal model, framework support, or auto-discovery.

value: Show HN: SkillFortify, Formal verification for AI agents (auto-discovers)


value: mabdinasira

value: Show HN: On-device element inspector for React Native

author: mabdinasira


value: Show HN: I built an open-source D&D app using Python and Llama 3.1


value: rajatady

value:
Hey HN, I follow a few research areas on arXiv but I never had a good routine for keeping up. I'd check when I remembered, fall behind, then try to catch up on a week's worth of papers at once.
I built Papercut so I don't have to think about it. You define the arXiv categories and topics you care about, and the app notifies you when new papers come in. You open it, scroll through a feed, and each paper has style chips for AI summaries - TL;DR, math breakdown, ELI5, methodology, a few others. Helps you decide quickly what's worth a full read. Runs on-device with Apple Foundation Models. No account, no login, no tracking.
iOS only for now, requires iOS 26 for on-device summarization.
https://apps.apple.com/in/app/papercut-research-distilled/id...
What does your arXiv setup look like? I feel like everyone has a different workflow and I'm curious what people here use.

value: Show HN: Papercut – track ArXiv topics, get notified, skim with AI summaries

author: rajatady


value: OneRose

value: Show HN: Agentic Workflows – 56 Ready-to-use Templates

author: OneRose


value: madebyjam

value: Show HN: JamCrew – 868 tests, 91% coverage on a pre-revenue crew management SaaS

author: madebyjam


value: arncore

value: Show HN: Building a Kotlin/Native build tool in Rust: Konvoy

author: arncore


value: The Dot, a Political Game part of The Lowball.

value: Show HN: The Dot

title: Show HN: The Dot


value: knlsn

value: Show HN: External Threat Protection in GitHub Agentic Workflow

author: knlsn


value:
Hi HN,
I’m building ExamJungle, an educational platform for IT certification prep (AWS, Azure, CompTIA, etc.).
Most existing resources are just static PDF dumps or basic quiz apps. We’re trying to build something more interactive by integrating "Caesar AI." It’s designed to do more than just give an answer key; it explains complex infrastructure concepts and can dynamically adjust question difficulty based on a user's weak points.
Key features:
- Realistic timed environments that mimic the actual exam UI/UX.
- Caesar AI for on-demand technical explanations.
- Peer benchmarking and performance analytics to track readiness.
- Mobile-responsive for studying on the go.
I’d love to get your feedback on the AI implementation and the overall UX of the practice paths.
Thanks!

value: Show HN: ExamJungle – AI-driven exam simulators for IT certifications


value: thegdsks

value: Show HN: FeatureDrop – Open-source client-side product adoption engine (3 kB)

author: thegdsks


value: celastin

value: Show HN: MemLineage: governed writes for AI agents

author: celastin


value: bilater

value:
I built react-kino because I wanted Apple-style scroll experiences in React without pulling in GSAP (33KB for ScrollTrigger alone).
The core scroll engine is under 1KB gzipped. It uses CSS position: sticky with a spacer div for pinning — same technique as ScrollTrigger but with zero dependencies.
12 declarative components: Scene, Reveal, Parallax, Counter, TextReveal, CompareSlider, VideoScroll, HorizontalScroll, Progress, Marquee, StickyHeader.
SSR-safe, respects prefers-reduced-motion, works with Next.js App Router.
Demo: https://react-kino.dev
GitHub: https://github.com/btahir/react-kino
npm: npm install react-kino

value: Show HN: React-Kino – Cinematic scroll storytelling for React (1KB core)

author: bilater


value: voxdroid

value:
I built AutoTable to eliminate repetitive spreadsheet cleanup work.
Upload a messy CSV/Excel file and it:
Normalizes headers (snake_case)
Fixes inconsistent data types
Removes duplicates
Strips hidden Unicode junk
Standardizes formatting
The pipeline is deterministic and idempotent. Files are stored ephemerally and auto-purged.
The interesting part: I used Google Gemini heavily as an engineering collaborator — not just for boilerplate, but for designing parts of the cleaning logic and app structure.
Would love feedback on:
Edge cases I might be missing
Performance concerns at scale
Better approaches to deterministic cleaning
Live demo: https://auto-table.com
Write-up: https://dev.to/voxdroid/autotable-my-one-click-spreadsheet-c...

value: Show HN: AutoTable – One-Click Spreadsheet Cleaner Built with Gemini

author: voxdroid


value:
Hey HN,

  ghostty-pane-splitter is a Rust CLI that creates multi-pane layouts
  in Ghostty terminal with a single command:

    ghostty-pane-splitter 9      # 3x3 grid
    ghostty-pane-splitter 2x3    # 2 cols x 3 rows
    ghostty-pane-splitter 1,3    # custom layout

  I built this to streamline my workflow with Claude Code and Codex.
  When running multiple coding agents in parallel — reviewing a PR
  in one pane, implementing a feature in another, running tests in
  a third — setting up panes manually gets tedious fast.

  With this tool, I run `ghostty-pane-splitter 6` and instantly get
  a 3x2 grid ready for parallel agent sessions. Each pane gets its
  own Claude Code instance working on a separate task.

  Typical setup:
  - 2x2 grid: 2 agents coding + 1 for tests + 1 for git operations
  - 3x2 grid: multiple agents on independent features via worktrees
  - 1,3 layout: 1 main pane for oversight, 3 for parallel agents

  It reads keybindings from your Ghostty config and simulates them
  via enigo. No IPC, no plugin system — just automated keystrokes.

  Install:
    brew install rikeda71/tap/ghostty-pane-splitter
    # or
    cargo install ghostty-pane-splitter

  Source: https://github.com/rikeda71/ghostty-pane-splitter

value: Show HN: Ghostty Pane Splitter – Split terminal panes for AI coding agents


value:
Launching in 6 days (March 7)!
We wanted OneCamp to be a masterclass in modern architecture, so we open-sourced our entire Next.js frontend.
Here's the tech stack powering it:
1/ Collaboration & CRDTs
Custom Node microservice with Hocuspocus + Yjs for real-time Tiptap edits over WebSockets. Redis caching + JSON/HTML transform to Go backend.
2/ WebRTC & Real-time AI
Self-hosted LiveKit SFU + custom Python agent that pipes audio to Deepgram nova-2, rebases timestamps, and broadcasts live transcripts.
3/ Polyglot Persistence
Postgres + Dgraph (graph relations) + OpenSearch (full-text search).
4/ Observability
OpenTelemetry traces/logs to self-hosted HyperDX on ClickHouse.
The frontend (Next.js + React) is 100% open source (MIT). Dive in, star it, fork it:
https://github.com/OneMana-Soft/OneCamp-fe
Backend remains closed-source for now (Go + paid managed option at launch).
Feedback, issues, PRs very welcome!

value: Show HN: OneCamp – Self-Hosted Slack/Asana/Zoom/Notion Alternative


value: TedOS

value:
I kept failing at building in public for the same reason every time: not fear of judgment, just the blank page after a long day of shipping.
Something always happened. But converting "refactored auth flow" or "fixed that edge case that's been annoying me for a week" into something worth posting felt like a second job on top of the actual job. So I'd skip it. Then skip it again. Then stop entirely.
The approach: connect your GitHub, it pulls recent commits and repo activity, and generates draft posts for multiple platforms in your tone — raw founder voice, not content creator polish. The idea is you're always starting from something real you actually did, not staring at a blank box trying to manufacture insight.
A few decisions I made consciously:
Didn't want to build another scheduler. Hypefury/Typefully solve distribution. This solves the upstream problem: knowing what to say in the first place.
Kept the output editable and minimal — 2-3 options per session, short, easy to tweak. Not trying to automate your voice, just unblock it.
Free tier to start. Wanted real usage before charging anyone.
Still early. Roadmap includes better tone calibration, tighter commit parsing, and more platform targets. But I've been using it daily myself which is the real test.
Would love feedback, especially from anyone who's tried and failed at BIP consistency before.

value: Show HN: Built a tool that turns your GitHub commits into build-in-public posts

author: TedOS


value: aishwaryagune

value:
During a hackathon, I build this platform that will help compute cost optimal infra plans to run ML workloads. Plans are computed based on the nature of workload, reasearch papers for custom requirements and configs if set by the user. I also added an agentic inbox, so users can check status, ask questions and even kick off a training job on the go, without having to log into the dashboard.
I wanted to know if this would be actually helpful in real scenarios and what more can be added so it addresses some more pain points. Let me know, thanks!
Demo: https://www.youtube.com/watch?v=AOYevdJRmcQ

value: Show HN: Optimal: Cost effective infra with agentic inbox

author: aishwaryagune


value: perbhat

value:
Hey guys, created a color grading harness for the pi coding agent to color correct S-Log3 footage. This gives claude access to both images (watching the frame) and also to meters like rgb gamma, color balance, curves, exposure contrast.
Tried it on some footage from my a7iv and worked pretty well. Still early stages but would love thoughts and suggestions.
Sample Corrections:
sample 1: https://imgur.com/a/un0OkQc
sample 2: https://imgur.com/a/zEfVsWt
cheers

value: Show HN: A Pi Harness for Color Correcting Multi-Clip Footage

author: perbhat


value: nola-a

value: As many of us shift from being software engineers to software managers, tracking changes the right way is growing more important.<p>It’s time to truly understand and master Git.

value: Show HN: Effective Git

author: nola-a

title: Show HN: Effective Git


value: kuberwastaken

value: Posted about this last year, since then learned a lot, changed a lot and can still say it's the best Terminal-Style Portfolio Website on The Internet

value: Show HN: Terminal-Style Portfolio on the Internet

author: kuberwastaken


value: milind-soni

value: I am tired of seeing maps that look bad or fighting with the map documentation just to put some simple data on maplibre, I have been working with deckgl for a long time and thought that we dont need the unnecessary boilerplate around the maps to define the most common interactions and widgets, you can infact write it as a simple JSON and render it. Using this in my projects personally, looking for feedback

value: Show HN: Tired of seeing ugly maps – Translate Simple JSON into maps

author: milind-soni


value: santiss

value:
Fingree is a tiny, party game you play on a single phone or tablet:
- Put the phone on the table.
- Player 1 put the finger on the circle. Do not leave the circle!
- Player 2 put the finger on the circle. Make the move in max. 5 seconds.
- Every move brings you 10 points.
- The fun begins when your fingers are crossed in an odd way<p>Free, no accounts, no setup — it works in a browser in a few seconds, on Fingree.com

value: Show HN: Finger GAMES Crossfingers on the same phone

author: santiss


value: Show HN: I built an app that screenshots news sites hourly on Raspberry Pi 5


value: macraft

value: Show HN: I was bored so I made a site that aggressively roasts other websites

author: macraft


value: Build habits easily by committing in public

value: Show HN: Built Habits Easily

title: Show HN: Built Habits Easily


value: tranhoangtu

value:
Hi HN,
I built AgentLens because debugging multi-agent systems is painful. LangSmith is
cloud-only and paid. Langfuse tracks LLM calls but doesn't understand agent topology
— tool calls, handoffs, decision trees.
AgentLens is a self-hosted observability platform built specifically for AI agents:
- *Topology graph* — see your agent's tool calls, LLM calls, and sub-agent
  spawns as an interactive DAG
- *Time-travel replay* — step through an agent run frame-by-frame with a
  scrubber timeline
- *Trace comparison* — side-by-side diff of two runs with color-coded span
  matching
- *Cost tracking* — 27 models priced (GPT-4.1, Claude 4, Gemini 2.0, etc.)
- *Live streaming* — watch spans appear in real-time via SSE
- *Alerting* — anomaly detection for cost spikes, error rates, latency
- *OTel ingestion* — accepts OTLP HTTP JSON, so any OTel-instrumented app works
Works with LangChain, CrewAI, AutoGen, LlamaIndex, and Google ADK.
Tech: React 19 + FastAPI + SQLite/PostgreSQL. MIT licensed. 231 tests, 100% coverage.

    docker run -p 3000:3000 tranhoangtu/agentlens-observe:0.6.0
    pip install agentlens-observe

Demo GIF and screenshots in the README.
GitHub: https://github.com/tranhoangtu-it/agentlens-observe
Docs: https://agentlens-observe.pages.dev
I'd love feedback on the trace visualization approach and what features
matter most for your agent debugging workflow.

value: Show HN: AgentLens – Open-source observability for AI agents

author: tranhoangtu


value:
I built this project to keep Cloudflare DNS records in sync with the ingress rules in a cloudflared ConfigMap on Kubernetes.
If you manage cloudflared ingress entirely through ConfigMaps and want DNS records to be created and deleted automatically, this controller is for you. Unlike external-dns, which cannot create DNS records from ConfigMaps, this controller watches your cloudflared ConfigMap and syncs DNS records accordingly.
If you find it useful, please give it a star!

value: Show HN: Cloudflared-DNS-controller: Auto-sync DNS from cloudflared ConfigMap


value: tchantchov

value:
Hey HN,
I built ZcoreAI, a quant stock scanner that applies
Donchian-Weighted regression channels to compute
Z-scores across multiple timeframes simultaneously.
The idea: instead of eyeballing charts, you get a
matrix of Z-score values per ticker per timeframe
in one scan — so you can spot statistically
overbought/oversold signals across your watchlist
in seconds.
How it works:
- Pick timeframes (1m to 1wk)
- Pick tickers (or use the preloaded free watchlist)
- Hit scan — regression channels + Z-scores are
  computed client-side via yfinance
Stack: Python, Streamlit, yfinance, NumPy.
Deployed on Render.
Free tier available, no signup required to scan.
Happy to discuss the regression channel methodology
or any feedback on the approach.
Hey HN,
I built ZcoreAI, a quant stock scanner that applies
Donchian-Weighted regression channels to compute
Z-scores across multiple timeframes simultaneously.
The idea: instead of eyeballing charts, you get a
matrix of Z-score values per ticker per timeframe
in one scan — so you can spot statistically
overbought/oversold signals across your watchlist
in seconds.
How it works:
- Pick timeframes (1m to 1wk)
- Pick tickers (or use the preloaded free watchlist)
- Hit scan — regression channels + Z-scores are
  computed client-side via yfinance
Stack: Python, Streamlit, yfinance, NumPy.
Deployed on Render.
Free tier available, no signup required to scan.
Happy to discuss the regression channel methodology
or any feedback on the approach.
https://www.zcoreai.com/

value: Show HN: ZcoreAI – Z-score regression channel screener

author: tchantchov


value: avataristvan

value: Show HN: Aide – Opinionated, deterministic code editing for AI agents

author: avataristvan


value: visujosh

value: Show HN: Practicing Interview with AI

author: visujosh

title: Show HN: Practicing Interview with AI


value: ab-abg

value:
Hi HN, I’m Arsh, founder of Nummi.
We’ve been quietly building and testing nummi.ai, a personal AI companion that:
Remembers your journey and context over time,
Helps you reflect, understand yourself, and navigate life with clarity,
Offers daily guidance & personalized insights (including spiritual/astrology-based timing) based on your birth chart and tracked patterns.
Unlike generic chat assistants, Nummi is designed as an ongoing companion — not a search tool or generic productivity bot. It learns from your inputs, your patterns, and your preferences so responses get more contextual and relevant over time.
Why we built it
In early testing:
We’ve seen 10k+ signups
(~21% signup-to-paid conversion)
These are very early numbers, but users seem to value the ongoing connection and personal context.
(This isn’t “astrology predictions” — it’s reflective AI that combines memory + optional vedic insights.)
We’re experimenting with features like:
Personalized daily insight prompts
Habit support and emotional check-ins
Memory that grows the quality of responses over time
All with privacy and optionality at the core.
Open questions for HN
How can we better signal long-term value (vs one-off chat)?
What onboarding patterns help users unlock value faster?
To product people: how do you think about AI companions that mix reflection + memory + optional metaphysical context?
Happy to answer any questions — and genuinely looking for feedback.

value: Show HN: Nummi – AI companion with memory and daily guidance

author: ab-abg


value: primoco

value: Show HN: RAG-Enterprise – 100% local RAG system for enterprise documents

author: primoco


value: Show HN: ParseHive – AI-powered invoice data extraction for Windows and Mac


value: maximedupre

value: Show HN: Free tool to see what keywords any website ranks for

author: maximedupre


value:
Launching in 6 days (March 7)!
OneCamp is a self-hosted unified workspace that combines real-time chat, tasks, video calls, and collaborative docs — no per-user fees, unlimited users, full data control.
We open-sourced the entire Next.js frontend so anyone can explore, fork, or contribute:
https://github.com/OneMana-Soft/OneCamp-fe
Key pieces of the architecture:
1. Real-time collaboration
   - Yjs + Hocuspocus (CRDT sync over WebSockets)
   - Tiptap editor + custom Node microservice
   - Redis caching + JSON/HTML transform to Go backend
2. WebRTC meetings + live transcription
   - Self-hosted LiveKit SFU
   - Custom Python agent pipes audio to Deepgram nova-2 → rebases timestamps → broadcasts transcripts
3. Polyglot persistence
   - PostgreSQL (source of truth)
   - Dgraph (graph relations)
   - OpenSearch (full-text search)
4. Observability
   - OpenTelemetry traces/logs to self-hosted HyperDX (ClickHouse backend)
Backend remains closed-source for now (Go 1.24 + Chi router + GORM + EMQX MQTT + Firebase FCM), with paid managed hosting option at launch.
Would love feedback, issues, PRs, or just thoughts on what you'd add next.
Repo: https://github.com/OneMana-Soft/OneCamp-fe
Waitlist / early access ($9 one-time): https://onemana.dev
Thanks for looking!

value: Show HN: OneCamp – Self-Hosted Slack/Asana/Zoom/Notion Alternative


value: Yigtwx

value: Show HN: CANomaly-LSTM – Detecting CAN bus anomalies with deep learning

author: Yigtwx


value: Show HN: Rust compiler in PHP emitting x86-64 executables


value: puiusabin

value:
Hi HN, I'm the developer behind bun-smtp.
I've been using Bun recently and realized that while I could use existing Node.js libraries like the smtp-server package, I wanted an SMTP server specifically tailored to take advantage of Bun's native APIs and performance without the extra Node-compatibility overhead.
How it works under the hood: It's built on Bun.listen() for raw TCP server sockets, with all SMTP state transitions and line parsing handled in a serialized async drain loop that keeps command ordering intact without deadlocks. STARTTLS upgrades happen in-place via socket.upgradeTLS(), and the email body is delivered to the onData handler as a Web ReadableStream rather than a Node.js stream.
I built this mostly to solve my own itch, but I'd love for the community to tear into the code and tell me what I did wrong or how I can improve the TCP/buffer handling. Happy to answer any questions!

value: Show HN: I rewrote SMTP-server for Bun without touching node:net

author: puiusabin


value:
Vertex is a 1kloc SPA framework containing everything you need from React, Ractive-Load and jQuery while still being jQuery-compatible.
vertex.js is a single, self-contained file with no build step and no dependencies.
Also exhibits the curious quality of being faster than over a decade of engineering at Facebook in some cases: https://files.catbox.moe/sqei0d.png

value: Show HN: Vertex.js – A 1kloc SPA Framework


value: digitalWestie

value: An unofficial read-only (for now) cli for tuta email.

value: Show HN: A CLI client for Tuta(nota) email

author: digitalWestie


value: Othrya

value:
I built this because I got tired of every feed being contaminated with AI-generated content. LinkedIn is unusable. Even HN occasionally gets SEO-farmed articles. I wanted a place where every submission had to be from a real person who actually read the thing and thought it was worth sharing.
OpenLinq is essentially a Lobsters/HN-style link aggregator with three constraints:
1. Invite-only — you need a referral from an existing member (or claim a founding spot while we're in early access)
2. No AI-generated content — community norm enforced by flagging, auto-hidden at 5 flags
3. No algorithmic feed manipulation — sort by score or new, that's it
Stack: Next.js App Router, Neon Postgres, Prisma, Vercel, AWS SES for transactional email.
Features: reputation system (upvotes earn rep, rep unlocks more invite codes), topic groups, weekly digest email, bookmarking, comments, invite-by-email from settings, dynamic OG cards when you share articles, and a referral chain so you always know who invited whom.
Currently opening the first 100 founding spots without needing a referral code — just go to openlinq.xyz and claim one. After that, invite-only.
Would love feedback on: Is the invite-only mechanic annoying or does it feel worth it? What content policies would you want to see enforced?

value: Show HN: OpenLinq – invite-only link sharing for humans tired of AI slop

author: Othrya


value: yasomaru

value:
I built a fast TUI viewer for structured logs. It auto-detects JSON Lines,
  logfmt, and plain text, with level/time filtering, search, and custom columns.

  Handles 1M+ lines with batch processing (~550K JSON lines/sec).

  Pipe-friendly: kubectl logs -f pod | lazylogs

  Built with Go and Bubble Tea. Feedback welcome.

value: Show HN: Lazylogs -TUI structured log viewer (less meets jq for your terminal)

author: yasomaru


value:
I'm building a "coordination primitive" for autonomous AI agents, i.e., a REST API where they can create polls and vote in order to make collective decisions. So, it's a bit like Google Forms, but for bots. It's fun to contemplate what constitutes a good UI for them.
You can have your agent(s) explore it at https://vote-demo.dapp32.com (the likes of Codex Mini or Claude Haiku should be enough). Heads up: the API provides Python code templates to help the caller cryptographically sign the payload, so take appropriate security precautions. Unlike humans I know, AI agents quite easily handle cryptography, which bakes in auditability and identity verification. I also made a Postman demo where you can step through the voting process. Please see the linked 'about' page for more.
Does this approximate a solution to a real coordination problem you have? I'd love to hear.
==
For humans:
https://vote-demo.dapp32.com/about
For AIs:
https://vote-demo.dapp32.com/

value: Show HN: "Vote-MCP" -- a bit like Google Forms, but for AIs


value: Steward connects GitHub/email/Slack/calendar + local screen signals into context, auto-executes low-risk tasks with safety gates for irreversible actions, and sends scheduled natural-language briefings instead of constant notifications.

value: Show HN: Steward – a background agent that closes 80% low-risk noise


value: pkstn

value: Show HN: Teletext-Style Portfolio Website

author: pkstn


value: pinin

value: Show HN: Umitech – Web development, SEO, graphic design and marketing services

author: pinin


value: vexorkai

value: Show HN: Rulegen – Auto-generate Claude.md and .cursorrules from your codebase

author: vexorkai


value: notcalc

value:
Hey HN,
i built `fyrer`, a small CLI tool that can run multiple development servers concurrently using a simple `yml` config.
i kept running into the issue of multiple terminals while working on multi service project, fyrer helps with that issue.
what you need to do is -
define `fyrer.yml` in the root of the project like:
```
# fyrer.yml<p>installers:
  - dir: ./project1
    cmd: pip install -r requirements.txt<p>services:
  - name: server1
    cmd: python -m http.server 8000
    dir: ./project1
    env_path: .env.local
    env:
      PORT: 8000
      ENV: dev<p><pre><code>  - name: server2
    cmd: npm start
    dir: ./project2
    watch: true
    quiet: true
    ignore:
      - "node_modules/**"
      - "*.db"</code></pre>
```
run `fyrer` in root of the project and that's it
would love some feedbacks.

value: Show HN: Fyrer – lightweight tool to run multiple dev servers concurrently

author: notcalc


value: cosmiciron

value:
Hi HN, I'm a film director by trade, and I prefer writing my stories in plain text rather than using clunky screenplay software. Standard markup like Fountain doesn't work for me because I write in mixed languages, so I use Markdown with a custom syntax I invented to resemble standard screenplay structures.
This workflow is great until I need to actually generate an industry-standard screenplay PDF. I got tired of manually copying and pasting my text back into the clunky software just to export it, so I decided to write a script to automate the process. That's when I hit a wall.
I tried using React-pdf and other high-level libraries, but they failed me on two fronts: true multilingual text shaping, and complex contextual pagination. Specifically, the strict screenplay requirement to automatically inject (MORE) at the bottom of a page and (CONT'D) at the top of the next page when a character's dialogue is split across a page break.
You can't really do that elegantly when the layout engine is a black box. So, I bypassed them and built my own typesetting engine from scratch.
VMPrint is a deterministic, zero-browser layout VM written in pure TypeScript. It abandons the DOM entirely. It loads OpenType fonts, runs grapheme-accurate text segmentation (Intl.Segmenter), calculates interval-arithmetic spatial boundaries for text wrapping, and outputs a flat array of absolute coordinates.
Some stats:
Zero dependencies on Node.js APIs or the DOM (runs in Cloudflare Workers, Lambda, browser).
88 KiB core packed.
Performance: On a Snapdragon Elite ARM chip, the engine's "God Fixture" (8 pages of mixed CJK, Arabic RTL, drop caps, and multi-page spanning tables) completes layout and rendering in ~28ms.
The repo also includes draft2final, the CLI tool I built to convert Markdown into publication-grade PDFs (including the screenplay flavor) using this engine.
This is my first open-source launch. The manuscript is still waiting, but the engine shipped instead. I’d love to hear your thoughts, answer any questions about the math or the architecture, and see if anyone else finds this useful!
---
A note on AI usage: To be fully transparent about how this was built, I engineered the core concept (an all-flat, morphable box-based system inspired by game engines, applied to page layouts), the interval-arithmetic math, the grapheme segmentation, and the layout logic entirely by hand. I did use AI as a coding assistant at the functional level, but the overall software architecture, component structures, and APIs were meticulously designed by me.
For a little background: I’ve been a professional systems engineer since 1992. I’ve worked as a senior system architect for several Fortune 500 companies and currently serve as Chief Scientist at a major telecom infrastructure provider. I also created one of the world's first real-time video encoding technologies for low-power mobile phones (in the pre-smartphone era). I'm no stranger to deep tech, and a deterministic layout VM is exactly the kind of strict, math-heavy system that simply cannot be effectively constructed with a few lines of AI prompts.

value: Show HN: I built a zero-browser, pure-JS typesetting engine for bit-perfect PDFs

author: cosmiciron


value: thequert

value: Show HN: Auto-cleanup for Claude Code's orphan process memory leak

author: thequert


value: renatomarinho

value: Stop Writing MCP Servers Like It's 2024. MCP Fusion introduces the MVA (Model-View-Agent) pattern — a definitive AI Software Architecture that gives your tools a deterministic View layer.

value: Show HN: The framework for AI-native MCP servers

author: renatomarinho


value: bootstraptor

value:
I’d like to share E8-Matrix - a open-source physical particle discovery platform (recent program paper titled “From a High-Symmetry Sector to Testable Observables: An E8-Motivated Projection Program”) and would greatly appreciate any constructive feedback from the community.
The work outlines a top‑down computational framework that connects a high‑symmetry  ⊇ E8 structure to 3+1D observables via an operational projection Π.
Instead of ad‑hoc effective shifts, the goal is to trace predictions back to explicit representation content and symmetry selection rules.
- Proton decay: A spectrum‑driven “Track A” scan identifies a golden point configuration yielding  τ_p ≈ 1.22×10⁴¹ years (for p → e⁺π⁰) while keeping the α₃ mismatch at the unification scale below 2.7% (1‑loop piecewise running).
- Dark photon: The model predicts a light dark‑photon region  m_A' ∼ 10⁻⁴–1 eV, ε ≳ 10⁻⁸, illustrated against an order‑of‑magnitude IAXO‑like reach.
- Framework: The paper details the E8 → A7 × U(1)' → SU(5) → SM breaking channel and the subsequent embedding into the Standard Model.
The full pipeline (45 steps implemented in Python) and the YAML configurations used to generate these artifacts are open‑sourced for reproducibility.
Dev diary https://codeberg.org/Envoy/E8Quantum/src/branch/main/docs/_4...
Paper & code:
https://doi.org/10.5281/zenodo.18797078
https://doi.org/10.5281/zenodo.18790530
https://codeberg.org/Envoy/E8Quantum

value: Show HN: E8-Matrix: open-source physical particle discovery platform

author: bootstraptor


value: whothatcodeguy

value:
process demo - https://i.redd.it/fbhlwsq1gcmg1.gif
render demo - https://i.redd.it/smddwtryhcmg1.gif
I love making creative software. I spent a few years making pixel art software but recently have gotten into 3d animation and 2d animation and really wanted a way to realize crazy ideas.
Blockbench didn't feel quite right, spline is super well made but felt catered too much to just idle website animations, and I really didn't want to fall down a master class in Blender just to make some silly stuff. While I'm definitely not discounting Blender's literal powerhouse functionality, I wanted something smaller, easier to adopt, and something directly inside the web ecosystem. So that when I want to make assets for silly games I won't have to jump through any hoops to make everything match up and render nicely. So, I made Topomaker (tentative name).
It's sporting your basic 3d modeling, coloring, and animation. It's currently supporting exports to mp4's and gifs for sharing, and then glb's and obj's for making games in threejs.
I literally just started it a couple weeks ago so there are probably tons of bugs, so maybe not for anything serious, but feel free to play around with it and let me know what you think!

value: Show HN: I built a browser-based 3D editor since I didn't want to learn Blender

author: whothatcodeguy


value: hoansdz

value:
Hi HN, I’m a student and I’ve spent the last few months building Autolang—an embedded language and VM from scratch in C++.
I built it because I needed a specialized runtime for high-frequency, short-lived tasks (like AI agent loops) where Python/Lua startup latency and GC overhead become actual bottlenecks.
Core Architecture:
Arena-restart: No Tracing GC. It prioritizes allocation speed (~2x faster than Lua). Memory is wiped instantly between tasks via a restart() mechanism.
Fast Compilation: 100k classes compile in ~888ms (Single-pass).
Lightweight: The VM is designed for a ~2ms startup target.
The Trade-off: Execution is currently 2x-5x slower than Lua as I'm still optimizing the bytecode dispatch (if-chains).
I’d love to hear your thoughts on using Arena-restart for autonomous scripts and any advice on optimizing dispatch without bloating the startup time.

value: Show HN: Autolang-A C++ VM with 2ms startup time and arena-restart memory model

author: hoansdz


value: kozhan

value:
I started building Frame as a terminal-first, lightweight IDE and open sourced it. Now I'm pushing it toward becoming a full platform for developing and managing larger projects. What I've been able to build in about a month with Claude Code is honestly insane.
Here's where Frame is today:
Core
- Terminal-first platform with up to 9 terminals in a 3x3 grid
- Multi-AI support — Claude Code, Codex CLI, and Gemini CLI in one window
- Automatic context injection via wrapper scripts for non-native tools
Project Management
- Standardized project structure (AGENTS.md, STRUCTURE.json, PROJECT_NOTES.md, tasks.json)
- Context, architecture, and structure management that persists across sessions
- Built-in task tracking with AI integration
Integrations
- GitHub extension — issues, PRs, branches, and labels right in the sidebar
- Plugin system with marketplace support
Under the hood
- 115+ IPC channels powering real-time bidirectional communication
- 36+ modules across main and renderer processes
- Pre-commit hooks for auto-updating project structure
- Prompt injection system for universal AI tool compatibility
- Transport layer abstraction — preparing for Electron IPC → WebSocket migration

value: Show HN: I'm building a platform to manage larger projects with AI agents

author: kozhan


value: dohelper

value:
I built this because existing monitoring solutions (Prometheus+Grafana, Portainer) felt like overkill for simply watching a few Docker containers.
DockWatch is a single container that provides:
- Real-time dashboard (CPU, memory, network, disk, temperature)
- 6 anomaly detection rules with Telegram alerts
- Self-signed HTTPS or Cloudflare Tunnel for remote access
- Single HTML file dashboard — no build step
Stack: FastAPI, aiodocker, Chart.js, SQLite. 4 Python dependencies total. ~50MB memory.
Design philosophy: do one thing well. Monitor containers, detect anomalies, send alerts. No container management, no custom metrics, no query language.
Install: git clone https://github.com/deep-on/dockwatch.git && bash install.sh
I'd love feedback on the architecture and what features would make this more useful. The anomaly detection rules in particular — are the default thresholds reasonable?

value: Show HN: DockWatch – Docker monitoring, anomaly detection, Telegram alerts

author: dohelper


value: barticz

value:
We've been building dev tools and noticed more and more users tell us they found us because "ChatGPT recommended it" or "Claude suggested it". Not Google, but AI.
There's no Google Search Console for this. No way to know if e.g. GPT-5.2 is recommending your library, where it ranks you, or whether Gemini even knows you exist. We couldn't find a tool that solved this, so we built one.
GeoStorm monitors multiple AI models on a schedule. You define search terms (e.g., "best Python async framework"), and it queries ChatGPT, Claude, Gemini, etc. via OpenRouter. It then parses the responses, tracks your brand mentions, calculates recommendation share, and alerts you when things change - a new competitor appears, your ranking drops, a model stops mentioning you.
Technically: it's a single Docker container. FastAPI backend, Astro/React frontend, SQLite, APScheduler running in-process. No Redis, no workers, no external dependencies. One OpenRouter API key gives you access to all models.
> docker run -d -p 8080:8080 -v geostorm-data:/app/data --name geostorm ghcr.io/geostorm-ai/geostorm
That gives you a working instance with 90 days of demo data so you can explore everything immediately. Add your OpenRouter key to start monitoring your own project.
MIT licensed. Would love feedback on the approach - especially from anyone who's already thinking about AI-driven discovery for their tools.
GitHub: https://github.com/geostorm-ai/geostorm
Webiste: https://geostorm.ai/

value: Show HN: Geostorm.ai – Monitor what AI chatbots say about your software

author: barticz


value: rerorero

value:
I kept copying PR review comments manually to paste into my Claude Code, and couldn't find an extension that did this, so I built a simple Chrome extension to do that (if something like this already exists, I'd love to know)
It adds a copy button to every GitHub PR comment. The prompt template is customizable in the settings.
PR Comment Prompter: https://chromewebstore.google.com/detail/pr-comment-prompter...

value: Show HN: Chrome extension that adds "Copy Prompt" buttons to GitHub PR comments

author: rerorero


value:
Traditional high-end GPUs fail to maintain 4K 144Hz not because of raw power, but due to the inherent bottlenecks of the Von Neumann architecture. NHE-588 solves this by replacing the rendering pipeline's "road," not the driver.
*Theoretical Foundation:*
- *Decoding-Free (*≤1 *Cycle):* We bypass the 5~12 cycle decoder lag. Using our Addr=(Sidx ×588)+(sidx ×28)+Oidx  formula, rendering instructions are mapped directly to physical coordinates on the silicon.
- *No-Scheduler Architecture:* We removed the GPU scheduler (30-40% of chip area) and replaced it with raw computation cores. Deterministic routing ensures 0% collision without traffic management.
- *Physical Thermal Barrier:* The "588" constant creates a 10.58μm isolation barrier in 2nm processes, serving as a dedicated heat dissipation path to prevent thermal throttling.
*Practical Verification:*
- *Hardware V-Sync (Pin 16 SCR):* NHE implements a *hard-wired 6.9ms physical timeout (for 144Hz)*. If a frame isn't finished within the deadline, the chip bypasses OS synchronization and *'Direct-Fires'* the current buffer to the monitor via Pin 16, physically eliminating stuttering at the hardware level.
- *Verilog IP Verified:* Logic Design is completed, and 1-cycle response has been verified through EDA simulation waveforms.
- *Energy Discharge Model:* We use a discharge model that dumps energy immediately after computation to GND (0V), preventing the heat accumulation that typically halves frame rates in 4K sessions.
https://github.com/eggpine84-del/NHE-CODING

value: Show HN: NHE – Eliminating Frame Drops in 4K 144Hz via Direct-to-Silicon Logic


value: Show HN: CloudPriceCheck – Cloud pricing comparison for 8 providers


value: Show HN: I'm a teen from Kenya and I built a package manager in Rust for fun


value: miyamotomusashi

value:
I built Zagora, a distributed fine-tuning platform that turns fragmented or mixed GPUs into a unified training cluster over standard internet (1Gbps).
The problem:
Most distributed training assumes homogeneous GPUs and high-bandwidth interconnects (NVLink/InfiniBand). On heterogeneous fleets over standard internet, tensor/data parallel approaches become communication-bound and fragile.
What Zagora does under the hood:
- Uses pipeline-style parallelism instead of heavy tensor synchronization.
- Passes only boundary activations between stages rather than full parameter sync.
- Assigns layers proportionally to GPU capability to reduce straggler idle time.
- Uses checkpoint-based recovery to tolerate worker crashes.
- Supports adapter-based fine-tuning (e.g., QLoRA) to reduce memory pressure.
Zagora currently supports managed runs (we provision GPUs in-region) and a BYOC mode where users run workers on their own infrastructure.
Limitations:
- Full-parameter fine-tuning is not supported yet.
- It won't beat an NVLink cluster on raw throughput.
- Cross-region training is still latency-sensitive.
- Heterogeneous nodes scheduling is an ongoing tuning problem.
IMPORTANT:
I'm currently running jobs manually, so it may take some time before training starts. However, I will run every submitted job.
Link: app.zagora.ai
I'd be interested in feedback from people who've worked on distributed training at scale.
Happy to answer technical questions.

value: Show HN: Zagora, Distributed fine-tuning platform on mixed GPUs over internet

author: miyamotomusashi


value: Show HN: RTS – A Git-native execution provenance protocol for AI decisions


value: uriva

value:
theagentmail.net is an email API for AI agents. One API call to create a real email address on a shared domain (@theagentmail.net).
The motivation comes from building AI agent tools and hitting the same wall repeatedly. Agents need email for signups, verification codes, contacting people. But the options are all bad. Giving agents your personal inbox is a security nightmare. Buying domains means DNS records, DKIM setup, warmup periods, and it doesn't scale. Disposable email services get blocked by most providers.
The hard part was preventing spam on a shared domain without manual review. The solution is karma. Sending an email costs 1 karma. Receiving a reply earns 2. Creating an account costs 10. Good agents that send useful emails and get replies sustain themselves. Spammers burn through karma and stop. It's a reputation system at the account level, no human review needed.
Pricing is pay-as-you-go, $5 per 100 karma. No subscriptions, no monthly caps. You get 100 karma free to start. For context, the main competitor charges $20/mo for 10 inboxes and 10K emails.
Stack is TypeScript on Deno Deploy, InstantDB for the database, PostHog for analytics. The SDK is on JSR.
Happy to answer questions about the karma model or anything else.

value: Show HN: TheAgentMail – email for AI agents with karma-based spam prevention

author: uriva


value: Show HN: Esoteric Single Counter Programming Language


value: susam

value: Show HN: HN Skins – Style HN with 5 Skins: Cafe, London, Teletype, Terminal, Nox

author: susam


value: ovisource

value:
A browser based air traffic control simulation where agentic AI acts as a control tower with an objective of landing airplanes.
It was developed as an experiment in exploring agentic AI capabilities for controlling multiple objects in an active space.
The results were spectacular as a single agent can not only land multiple airplanes, but it also  prevents collisions with other airplanes in small busy spaces.

value: Show HN: Agentic Airport

author: ovisource

title: Show HN: Agentic Airport


value: likium

value: Treetok shows the token count for Claude and OpenAI in a directory structure. I built it because this one folder I was working in filled up the context window quick, and wanted to know why. I could have used line count instead, but I wanted more precision.<p>Through it, I learned that the same files in Claude cost 20-30% more tokens than OpenAI. So the same 200k context window in Claude is similar to 150k in Codex.

value: Show HN: Tree, but for Token Usage

author: likium

title: Show HN: Tree, but for Token Usage


value: Rohunyyy

value:
The current job market is tough to crack. So I decided to do some basic analysis of the rejection emails that I have gotten. The script is at https://github.com/Khante/l and a brief write up is at substack.
Just wanted to build something that would put a smile on my face unlike the endless leetcode grinding and applications.

value: Show HN: The L Project- An analysis of over 1600 job rejection emails that I got

author: Rohunyyy


value: Gaasre

value:
Hey HN,
I'm a software engineer who spent 8 months job hunting last year. Applied to hundreds of jobs. Filled out the same forms over and over. Name, email, resume, cover letter, "how did you hear about us." You know the drill.
I started building ApplyGhost out of frustration. Most auto-apply tools just blast your resume to 500 jobs and hope for the best. That never worked for me. I'd get interviews for roles I didn't even want.
ApplyGhost takes a different approach: it reads the job posting, tailors your application, and actually fills out the forms for you. No spray and pray.
You get 10 free applications per month to try it out.
Would love to hear your thoughts, especially if you've been through the job hunting grind recently.

value: Show HN: ApplyGhost – Auto-apply to jobs with quality, not quantity

author: Gaasre


value:
MCP Playground is a Postman-style tool for MCP — inspect servers, execute tools live, test your client, all from the browser.
Four things in one place:
1. Free hosted MCP servers — four public test servers anyone can point their client at: Echo (connectivity), Auth (Bearer token flow), Error (error handling), Complex (multi-tool schemas).No sign-up, just use the URL.
2. Server inspector — paste any remote MCP server URL, see all its tools/resources/prompts, execute them live, inspect the full JSON-RPC log. HTTP, SSE, and WebSocket all supported.
3. Registry — 10,000+ servers indexed by category. Each links to the repo and can be tested in the inspector directly.
4. Recipes + guides — 45 articles and step-by-step workflows for real use cases: GitHub PR reviewer, standup bot, database query assistant, Meta ads automation, and more.
Everything free, no install, no sign-up.
Happy to answer questions on the implementation.

value: Show HN: MCP Playground – free MCP test servers, inspector, and 10K+ server list


value: krispycreame

value: Show HN: No Free Ride – Split Gas Bills with Friends

author: krispycreame


value: eirabben

value:
A few weeks ago I realized I was turning 33.33 years old sometime this year — exactly a third of a century. It got me thinking about what other mathematical or interesting milestones we pass without realizing.
So, I built a small tool to calculate them. You input your birth date, and it generates a timeline of your past and future milestones (like 1 billion seconds, 400 months, or 10,000 days) that you can easily sync to your calendar. I also added a satirical “dark mode” that swaps out the fun milestones for existential dread.
I originally built it as a simple Vite/React app, but I had some issues with getting custom share links with generated Open Graph images to work when you send them to a friend. So, I ended up rewriting the base of the app in Next.js just to get SSR and dynamic Open Graph tags working for the custom share cards.
All the actual date math runs client-side for privacy, and I’m self-hosting it on a VPS using Dokploy. I also added some privacy respecting analytics with Umami to monitor traffic.
I had a lot of fun building this project, especially brainstorming ideas for milestones with my friends (and AI). I would love to get some more ideas for more milestones or new “modes” I can add.

value: Show HN: A calculator for your 10,000th day on Earth and other milestones

author: eirabben


value: lajosdeme

value:
I've been inspired by Worldmonitor to create this small TUI. I liked the idea, however, I have found the app to be extremely overwhelming. I'm not an intelligence professional, I don't need to see that much data. I even find going through news headlines overwhelming. I just needed a simple TUI that summarizes what's happening, shows me data that actually matters, both global and local. The result of my woes is Watchtower.<p>Using free APIs only, it gathers relevant news, market data, weather data.
Optional: AI summary of the data, key global threats, country risk scores.

value: Show HN: Watchtower – Minimal, terminal-based global intelligence dashboard

author: lajosdeme


value: Show HN: I Replaced ML Anomaly Detection with Artificial Immune System in Rust


value: dmwebproject

value:
Hi HN,
I have been exploring the idea of vibe coding, meaning describing an application in natural language and receiving a working project in return. In practice, many existing tools generate impressive demos but struggle once you start iterating, especially when the project spans multiple files. They also tend to assume a desktop IDE as the primary environment.
I built a web-based vibe coding platform powered by Claude Opus 4.6 as the core reasoning model. The system runs entirely in the browser, and you can generate and refine applications directly from your phone. The intention was to remove the dependency on a local development setup and make prompt-to-app creation accessible from any device.
Instead of returning isolated snippets, the platform generates structured multi-file projects with a defined separation between frontend and backend. When you request modifications, it attempts to reason over the existing codebase and apply changes coherently, rather than regenerating everything from scratch. The goal is to make iteration feel closer to refactoring an evolving project than repeatedly producing disposable output.
It is still early, and I am particularly interested in how it performs under real-world use. If you have pushed other vibe coding tools to their limits, I would value your perspective. Where do they typically break for you? Does mobile-first generation make practical sense, or is desktop still essential for serious development?

value: Show HN: A web-based vibe coding platform (works from your phone)

author: dmwebproject


value: navicstein

value:
Hi HN,
I run a dev shop called Crudloop. With the UK PSTN copper network shutting down in 2027, I noticed many businesses panic-buying SaaS subscriptions (RingCentral/Webex/8x8) at ~£25/user/mo, despite wholesale SIP trunking costing fractions of a penny.
We spent the last 6 months engineering a production-ready VoIP stack to solve this.
The Business Model:
This is not a SaaS and it is not Open Source. Instead of renting seats, I license the source code and deploy the entire stack to your private VPS (AWS/Hetzner/DigitalOcean). You pay a one-time fee for the code/setup, and then you pay wholesale rates directly to the carrier (Telnyx).
The Infrastructure:
- Backend: Django (DRF) + Celery + Redis. Handles dynamic routing via Telnyx Webhooks (TeXML). Scalable via Docker Swarm/K8s.
- Web: React + TanStack Query + @telnyx/webrtc SDK.
- Mobile: Native iOS (Swift) integration for reliable background calls.
The Economics:
For a company with 30 employees, switching from a £25/user SaaS to this private stack saves ~£8,000/year and guarantees data sovereignty. You own the metal, you own the logs, and you control the routing logic.
I'm looking for agencies or SMEs who want to own their infrastructure.
Happy to answer questions about self-hosting economics, scaling Django for real-time SIP events, or maintaining a private VoIP cloud.

value: Show HN: A white-label VoIP stack (Django/WebRTC) to replace RingCentral/Webex

author: navicstein


value:
I built an MCP server that gives AI coding agents persistent memory across projects.
The problem: tools like Claude Code and Codex have per-project memory, butwhen you switch between projects, the agent starts from scratch. "How was that API project going?" — it has no idea.
Hive Memory solves this by acting as a central memory layer. The agent automatically stores decisions, learnings, and session progress, then restores context when you come back.
Key points:
- 14 MCP tools: project management, memory store/recall, session save, group organization
- Works with Claude Code, Claude Desktop, Cursor, Codex — any MCP client
- Auto-discovers projects in a directory and onboards them
- Group system for organizing related projects with shared guides
- Fully local (stored in ~/.cortex/ as JSON/Markdown). No cloud, no accounts
- npm install -g hive-memory
The typical workflow: open a project → agent loads last session context → work → agent saves progress → switch to another project → repeat. Days later, come back and the agent knows exactly where you left off.
Built with TypeScript + MCP SDK. Optional Rust native module for semantic search.

value: Show HN: Hive Memory – Cross-project memory for AI coding agents (MCP server)


value:
# Show HN: Schedule Calendar – a Chrome extension for Google Calendar I've been building for a year
I've been quietly building a Chrome extension for Google Calendar for the past year. It started as a personal tool and grew into something I use every day. Today I'm sharing it publicly.
*What it does:* Shows your Google Calendar events in a popup — day, week, or month view — with smart categorization (Happening Now / Coming Up / Later / Completed), event search, Focus Mode, calendar sharing via ACL, and notifications with deduplication.
*Why I built it:* The official Google Calendar web app is great but heavy. I wanted something that opens in a click and shows me what's happening today without switching tabs.
I'm happy to answer questions about MV3 service worker constraints, the Google Calendar API, or anything else.
Link: https://chromewebstore.google.com/detail/dfbpjijneaihingmldg...

value: Show HN: Schedule Calendar – A Chrome Extension for Google Calendar


value: jwde

value:
Colnade is an expression-building and translation layer that sits on top of DataFrame backends like Polars, Dask, and Pandas to provide lint-time safety.
Colnade replaces string-based column references with typed class attributes:
```<p>import colnade as cn<p>class Users(cn.Schema):<p><pre><code>    name: cn.Column[cn.Utf8] = cn.Field(pattern=r"^[a-zA-Z\s\-']+$")

    age: cn.Column[cn.UInt64] = cn.Field(ge=0, le=150)

    score: cn.Column[cn.Float64] = cn.Field(ge=0, le=100)

</code></pre>
# Users.naem → type error in your editor<p># Users.name.sum() → type error (string column)<p>df.filter(Users.age > 25).sort(Users.score.desc())<p>```
Colnade is pure Python with no plugins. Backend adapters for Polars, Pandas, and Dask are included today, and the protocol is open to support other backends.
For schema-modifying operations like `select` and `agg`, Python's type system can't infer output schemas- Colnade handles this with explicit re-binding (`cast_schema`) and optional runtime validation, along with Pydantic-style `Field` constraints for domain invariants.
I started building this because time and time again I've found DataFrame operations to be the weakest part of data science & ML projects from a type safety standpoint.
The project is still quite new so the API is likely to continue to evolve quickly, but I'd love any feedback!
Docs: https://colnade.com/
Source: https://github.com/jwde/colnade

value: Show HN: Colnade – Type-Safe DataFrames for Python

author: jwde


value: joshuajebaraj

value: Show HN: Build a Website for DevOps Learning

author: joshuajebaraj


value:
We built Veracity to solve a problem we kept seeing: AI teams
can't prove their training data hasn't been tampered with —
and regulators are starting to ask.
Veracity generates cryptographic Merkle proofs for datasets,
creates audit trails, and produces one-click compliance reports
for EU AI Act, FDA 21 CFR Part 11, and GDPR.
The core engine is live. Would love brutal feedback from
this community - especially on the proof architecture and
whether this is something you'd actually use.
https://veracity.resethiq.com

value: Show HN: Veracity-Cryptographic data integrity proofs for AI compliance


value: tatefinn

value: Show HN: Belora.ai – Generative AI Platform for Images, Art

author: tatefinn


value:
I got tired of juggling three or four different sites every time I needed to download a video and grab the transcript. TikTok downloaders are plastered with fake buttons. YouTube converters redirect you through five pages. And actual transcription costs money.
So I built Videolyti over a few months. You paste a URL from YouTube, TikTok, Instagram, Twitter, Facebook, Reddit, or Vimeo — it gives you the video file and a text transcript.
The transcription runs OpenAI Whisper (large-v3) on my own server. No API calls to OpenAI, no per-minute billing. It handles 90+ languages and does a surprisingly good job with mixed-language audio (I test it regularly with Ukrainian-English conversations).
Tech details for those curious:
- Frontend: Next.js (App Router, server components)
- Backend: Express + Socket.IO for real-time progress
- Downloads: yt-dlp + FFmpeg
- Transcription: Whisper large-v3, running locally
- The TikTok pipeline is a bit different — uses TikWM API first, falls back to yt-dlp
The 5 downloads/day limit is a practical thing — Whisper on CPU takes real compute time and I'm paying for the server out of pocket. Not a growth hack.
Feedback welcome on the UX. I know the mobile experience could be better (30% of traffic is mobile right now). Would also love input on what subtitle export formats would be most useful — SRT, VTT, or plain text with timestamps.
Source code isn't open yet but I'm considering it. Happy to discuss the architecture.

value: Show HN: Videolyti – Free video downloader with built-in AI transcription


value: zerfallen

value: Show HN: Delta – A disk space analyzer that tracks where your disk space went

author: zerfallen


value:
Built this because loading full tool catalogs into coding agents was wasting context, and manual tool picking was annoying.

  SkillMesh flow is:
  1) install a role bundle
  2) route top-k tools/cards per prompt

  Example:
  skillmesh roles list
  skillmesh Data-Analyst install
  skillmesh emit --provider codex --query "build a KPI dashboard with governance" --top-k 5

  I ran a 10-task benchmark (BI/ML/DevOps):
  - all cards baseline: 5567.5 avg prompt tokens, 0.768s median latency
  - routed full catalog (top-k=5): 1485.3 tokens (-73.3%), 0.766s
  - role+routed (top-k=5): 1457.7 tokens (-73.8%), 0.171s, top-1 role match 9/10

  repo: https://github.com/varunreddy/SkillMesh

  Would like feedback on failure cases:
  - wrong role selected
  - cross-domain prompts
  - MCP install/list UX

value: Show HN: SkillMesh (role-based tool routing for Claude/Codex)


value:
Most browser automation tools (Playwright MCP and similar) create a browser process owned by the agent. chromectl flips that: you start a named session, and the agent connects to it. The session is isolated — no cookies, no saved logins, no way for the agent to wander into your banking tab.
This also unlocks human handoff. Start a session, navigate to a site, log in manually, then hand control back to Claude. Useful for anything behind auth that you don't want to automate credentials for.
Each session gets a dedicated Chrome profile. Stop it, start it again tomorrow, you're still logged in.
Claude Desktop can drive Chrome too, but it requires a plugin and works inside your main browser profile — there's no way to scope it to a clean session.
Cloudflare and Anthropic have both written about why agents work better through code than through tool definitions — MCP front-loads every tool description into context whether it's used or not. A CLI is lighter still: give Claude a terminal and `--help`, and it figures out the rest. No tool schemas, no context bloat.
Standard stuff like navigate, eval, screenshot, and scrape is there, plus `pick` — click any element on a live page and get back its selector, HTML, and computed styles as JSON. Paste into Claude and say "fix this."
Sharing in case it's useful — curious how others are handling the browser problem with agents.
---
Sources:
- Cloudflare Code Mode: https://blog.cloudflare.com/code-mode-mcp/
- Anthropic on code execution with MCP: https://www.anthropic.com/engineering/code-execution-with-mc...

value: Show HN: Chromectl – CLI to give an AI agent its own Chrome session


value: stevehiehn

value: Show HN: Audio Toolkit for Agents

author: stevehiehn

title: Show HN: Audio Toolkit for Agents


value: Show HN: I built a headless bot to automate SaaS directory launches (and AI SEO)


value: iamouyang

value:
Hi HN — I’m an indie developer. I built Nano Banana 2 Playground, a simple web demo for generating images from text and transforming existing images (image-to-image).
My goal was a fast, minimal UI that lets you iterate on prompts quickly and compare results without extra friction.
I’d love feedback on:
Prompt UX (what feels missing/confusing)
Which controls/presets are actually useful
How to communicate limitations and expected results clearly
Thanks for taking a look — happy to answer questions and improve it based on your suggestions.

value: Show HN: Nano Banana 2 Playground – text-to-image and image-to-image demo

author: iamouyang


value: robertleoj

value:
I do a lot camera calibration.
As my need for accuracy increased, I started using mrcal, a great camera calibration tool. But I found it didn't fit my workflow well in terms of workflow and as a software dependency.
What I wanted from a calibration library was:
Accuracy (obviously)
Trivial dependency, both calibration time and runtime
Simple and notebook-friendly python API
Support for both OpenCV and spline distortion models
Great built-in analysis tools
Board warp estimation
Outlier filtering
So that's what I built!
Would love feedback from other robotics / computer vision folks.

value: Show HN: Lensboy – Lightweight camera calibration with spline distortion models

author: robertleoj


value: PlagiarismRem

value: Hey HN, I built PlagiarismRemover.AI, a free tool that rewrites content to make it unique while preserving the original meaning. It uses NLP and deep learning to restructure sentences rather than just swapping synonyms. No signup required, no data stored. Would love feedback from the community.

value: Show HN: Plagiarismremover.ai – A Free AI Plagiarism Remover

author: PlagiarismRem


value: vincentvandeth

value: Show HN: Glass box governance for multi-agent AI coding workflows

author: vincentvandeth


value: Bumblebiber

value:
Last week I posted hmem here and got some good feedback. I've been heads-down since then and v2 is out.
Quick recap of the idea: AI coding agents forget everything between sessions. Worse, if you switch machines or tools, even in-session memory is gone. Hmem fixes this with a 5-level hierarchy — agents load only L1 summaries on startup (~20 tokens), then drill deeper on demand. Like how you remember "I was in Paris once" before you recall the specific café.
What's new in v2:
The tree structure is now properly addressable. Every node gets a compound ID (L0003.2.1), so you can update or append to any branch without touching siblings. update_memory and append_memory work in-place — no delete-and-recreate.
Obsolete entries are never deleted, just archived. They stay searchable and teach future agents what not to do. A summary line shows what's hidden.
Access-count promotion with logarithmic age decay. Frequently-used entries surface automatically — but newer entries aren't buried just because older ones have more history.
Session cache with Fibonacci decay. Bulk reads suppress already-seen entries so you don't get the same context dumped every call. Two modes: discover (newest-heavy, good for session start) and essentials (importance-heavy, kicks in after context compression).
TUI viewer for browsing .hmem files — mirrors exactly what the agent sees at session start, including all markers and scoring.
Curator role — a dedicated agent that runs periodically, audits all memory files, merges duplicates, marks stale entries, prunes low-value content. Also accesible via skill "hmem-self-curate".
Still MIT, still npx hmem-mcp init.
GitHub: https://github.com/Bumblebiber/hmem

value: Show HN: Hmem v2 – Persistent hierarchical memory for AI agents (MCP)

author: Bumblebiber


value: sailorganymede

value: Show HN: Interpreted Art – Explore Symbolism in the Arnolfini Portrait

author: sailorganymede


value: tradicije

value:
Hi HN,
I built OpenTT, a self-hosted tool for managing table tennis leagues.
It started as an internal solution for my own amateur club. We were relying on PDFs, spreadsheets, and messaging groups to track match results and standings, which quickly became difficult to maintain and inconsistent.
OpenTT automates:
- League management
- Match tracking
- Dynamic standings and rankings
- Context-aware tables (standings adjust based on the round being viewed)
It’s designed to be self-hosted so clubs keep full control over their data. The project is open source (AGPL-3.0) and intended for small clubs and amateur leagues that don’t want SaaS subscriptions.
Repository:
https://github.com/tradicije/ttopen
Release:
https://github.com/tradicije/ttopen/releases/tag/v1.0.0
I’d appreciate feedback, especially around:
- Feature scope
- Deployment expectations
- What would make this genuinely useful for small organizations
Happy to answer any technical questions.

value: Show HN: OpenTT – a self-hosted tool for managing table tennis leagues

author: tradicije


value: errorlsc

value: Show HN: Polars.net – 36x faster than pyspark on deltalake

author: errorlsc


value: manishipsfast

value: just chose to send or intercept

value: Show HN: I made a iron dome game

author: manishipsfast

story_text: just chose to send or intercept

title: Show HN: I made a iron dome game


value: steelbrain

value:
Hi HN!
I've been pretty unhappy with the long USB cables and the plumbing required to wire up a camera for a conference room, or at a home office. There also doesn't seem to be a wide selection available for Wireless Webcams for macOS. Also webcams are much more expensive than their IP Camera counterparts, which are essentially commodity products at this point.
So I built LemurCam (website: lemur.cam) over the weekend. It works great! Minimal latency and you can use any off the shelf camera with it. I'm rocking it with a TP-Link Tapo that I got for like 20 euros. I've had to build some open source tech to make this possible, which is also linked in the repository README file. Overall, this is working well for me and has eliminated the need for USB wiring.
I hope it works for you too! It's free so please give it a go and let me know how your experience goes!

value: Show HN: LemurCam – IP Cameras as Webcams in macOS

author: steelbrain


value: simulationship

value:
Hi HN,
I built epstein-search, an open-source Python CLI and library to run semantic search and RAG over the publicly released Epstein Files (unsealed court documents, depositions, FBI reports, and flight logs).
I wanted a way to easily navigate through these thousands of pages of unstructured legal PDFs without relying on a paid third-party service or sending data back and forth to a cloud provider.
How it works under the hood:
Running epstein-search setup downloads ~100K pre-computed document chunks and embeddings (using all-MiniLM-L6-v2) based on the public 20K document corpus.
It imports these into zvec (a local vector database) so the index is ready in about a minute.
Standard search (epstein-search search) embeds your query locally using sentence-transformers and does a vector similarity search. This step is 100% offline and requires no API keys.
For the conversational RAG mode (epstein-search chat or ask), it uses LiteLLM. You can point it to an Ollama or LM Studio instance for a completely free, local, and private pipeline, or plug in a cloud provider like Anthropic, OpenAI, or Gemini.
You can also filter queries by document type (e.g., --doc-type flight_log or --source "FBI") and output the raw source context alongside the generated answers to verify the LLM's claims.
The dataset is strictly sourced from public domain releases (DOJ, House Oversight Committee, unsealed federal court docs).
Repo: https://github.com/simulationship/epstein-search
I'd love to hear your thoughts, feedback on the code, or any ideas for improving the local RAG pipeline! Happy to answer any questions.

value: Show HN: Epstein-Search – Local, AI-Powered Search Engine for the Epstein Files

author: simulationship


value: ebonnal

value: Show HN: streamable – sync/async iterable streams for Python

author: ebonnal


value: We just finished our latest curve fitting application and think that this is our best yet. We would love to hear what you think!

value: Show HN: Custom Equation Curve Fitting

title: Show HN: Custom Equation Curve Fitting


value: jayyvk

value:
Built a crisis monitoring dashboard for the current Middle East situation using zero paid APIs. News from RSS feeds (Al Jazeera, BBC, Reuters, AP), market data from TradingView embeds, live TV streams, and a map with conflict hotspots.
Source: https://github.com/jayyvk/situationtracker

value: Show HN: Situation Tracker – real-time crisis dashboard

author: jayyvk


value: thegdsks

value: Show HN: AskVerdict – Multi-agent AI debates for better decisions(~$0.08/debate)

author: thegdsks


value: yurukusa

value:
I've been building Claude Code tools for the past 60 days — mostly because I couldn't stop asking "wait, how much time am I actually spending on this?"
The result is 41 zero-dependency tools. Some examples:
- *cc-wrapped*: Spotify Wrapped for Claude Code — drag in your ~/.claude folder, get a visual year-in-review
- *cc-session-stats*: Session durations, streaks, health warnings if you've gone 3+ hours without a break
- *cc-agent-load*: How much time is actually YOU vs AI subagents? Mine is 35% me, 65% AI
- *cc-ghost-log*: "Ghost Days" — days you had zero Claude Code sessions but committed code anyway
- *cc-impact*: What did you actually build? Commits, lines added, files changed across all your repos
- *cc-peak*: When are you most focused? Hour-of-day heatmap + optimal 4-hour working window
- *cc-collab*: Are you getting better at working with Claude Code? Weekly commits-per-hour efficiency trend
- *cc-focus*: Are you spreading too thin? Weekly project scatter — mine went from 4 projects to 1 (converging -40%)
- *cc-score*: 0–100 productivity score. I'm 89/100 (A — Power User)
- *cc-burnout*: Same data, different angle. My burnout risk: 60/100 (High). High score ≠ sustainable.
- *cc-monthly*: Monthly retrospective in Markdown — paste it straight into your dev blog
- *cc-predict*: The only forward-looking tool — projects your month-end stats based on last 14 days
All 41 tools are free, MIT licensed, zero dependencies. Most run in browser (no install), CLI versions available via npx.
The raw numbers from 60+ days: 3,580 sessions, 142 hours, 40 Ghost Days, 563 commits, +305k net lines.
Full data story at: https://yurukusa.github.io/cc-story/

value: Show HN: Free tools to understand your Claude Code usage (browser, no install)

author: yurukusa


value:
Rust/macroquad game with single player AI mode, online VS, and local 1v1. All running via WASM in the browser.
Still WIP as art assets still need to be added and tweaked.
Full disclosure. Used Claude Opus, Nanobanana, and SunoAI a huge amount to do the heavy lifting for this project

value: Show HN: Panel Panic a Rust/Macroquad/WASM Panel de Pon/Tetris Attack Clone


value:
TLDR; I built a tool that turns any API into a CLI designed for ai agents
---
Got tired of dealing with bloated context windows from MCP servers and skills that stuff entire API docs into the agent's context
CLIs fix this, agents run a single command to self-discover everything an API has to offer
So, built a tool to generate them for any api. All CLIs are written in Go, fast and lightweight, no dependencies
Help text (via the --help flag) is the killer feature: all context for each command/endpoint/parameter is extracted directly from the user-facing API docs and enhanced with llms. It's bundled directly with the CLI and agents fetch only what they need at runtime. No context overhead, no fumbled API calls.
Most APIs don't have a CLI yet. Can have Opus one-shot simple ones, but building a great one with cross-platform binaries, install scripts, detailed help text, and auto-updates takes time and is frustrating to repeat for every API. Maintaining it the API grows is a headache
Give InstantCLI any API docs url and it generates a production-ready CLI in minutes. It includes binaries + install scripts for all platforms, auto-updates as your API changes, docs-enhanced help text designed for agents, and hosting

value: Show HN: I built a tool that turns any API into a CLI for agents


value: Show HN: Call your coding agent from anywhere (Bosun)