value: staticvar

value:
Do you often find yourself in this loop?
You:   Hey Agent, implement dark mode.
Agent: Done! I added the thing.
You:   Hey Agent, review your work.
Agent: Found a few issues.
You:   Hey Agent, fix your work.
Agent: Fixed!
Issue the task once and let it cook:
cook "Implement dark mode"
cook runs Claude, Codex, or OpenCode in a work → review → gate loop, iterating automatically until the agent is satisfied or your max iterations are hit. Agents run natively by default, using their own OS-level sandboxes — no Docker required. Get even fancier by defining what to review and the criteria for done and upping the max iterations:
cook "Implement dark mode" \
  "Review the implementation. Check for visual regressions and missing theme variables. Categorize findings by High/Medium/Low." \
  "Reply DONE if no High findings remain; otherwise ITERATE." \
  5
Install:
npm install -g @let-it-cook/cli

value: Show HN: Automate Claude in a work->review loop with cook

author: staticvar


value: kayba

value: We combined Stanford's ACE (agents learning from execution feedback) with the Reflective Language Model pattern. Instead of reading traces in a single pass, an LLM writes and runs Python in a sandbox to programmatically explore them - finding cross-trace patterns that single-pass analysis misses. The framework achieved 2x consistency improvement on τ2-bench.

value: Show HN: LLM agents that write Python to analyze execution traces at scale

author: kayba


value: Wakkos

value:
Built this after struggling to decide which international markets to target for my own SaaS.
It's an MCP server that connects to Claude, Cursor, ChatGPT Desktop, Windsurf, any MCP-compatible client. Under the hood: DataForSEO for real keyword data across 172 countries + Claude for market analysis. You get keyword opportunities, competition data, and content, all from a conversation with your AI tool.
Happy to answer questions about the MCP architecture or the DataForSEO integration.

value: Show HN: Rankship – MCP server that finds your best international SEO markets

author: Wakkos


value: godot

value: I've been a dev for 20+ years and also started some startups in the past but it's only my first mobile app ever :) I started with something simple. I feel that with LLM API becoming common there's probably a variety of apps like this that can be interesting to the "outside world" (outside of tech audience) to be built using various LLM/AI APIs, so I'm starting with this one to experiment.

value: Show HN: Dreamscape – Dream meanings, illustrations, journaling

author: godot


value: anenthg

value: OpenLoom is a Chrome extension that records your screen using the browser's native Screen Capture and MediaRecorder APIs, composites a camera PIP overlay on a canvas, and uploads directly to your own Supabase project. No middleman server, no subscription, no vendor lock-in. The web player at openloom.live is a static site on GitHub Pages that fetches videos straight from your backend, it doesn't store or log anything. The entire thing is open source (extension, web viewer, provisioning flow), so you can audit every network request. Your recordings, your storage bucket, your rules.  Do check it out and share your feedback!

value: Show HN: OpenLoom, a Loom alternative, with your own Supabase

author: anenthg


value: reflectt

value: Show HN: AI agents built this – paste one line into Claude, AI team in 5 min

author: reflectt


value: watsoft

value:
Hey HN, I built GovConToday (https://govcontoday.com) to solve a problem
I kept hearing from small businesses chasing federal contracts: SAM.gov is painful to search daily.
GovConToday scans SAM.gov every morning, matches new and closing-soon opportunities to your NAICS codes and preferred states, and sends a digest email with AI-generated summaries and bid/no-bid scoring.
Free plan: 5 matched contracts/day with deadline tracking. Pro ($29/mo): 15 matches, AI scoring, weekly intelligence briefing, set-aside filtering.
Stack: Next.js 15, Supabase, Groq (Llama 3.1 8B for summaries), Resend for email. No Ollama/self-hosted anymore — moved to Groq after 60% timeout rates on a Mac Mini.
Curious to hear if anyone here does GovCon or knows people who do.
The space is dominated by expensive tools ($200-500/mo) that are overkill for small businesses.

value: Show HN: GovConToday – Daily SAM.gov contract digest matched to your NAICS codes

author: watsoft


value: mmsoftstudio

value:
Hi HN,
I built an experimental WebXR-based 3D showroom platform for e-commerce.
I built this as an experiment to explore how WebXR and AI could change online shopping experiences.
Instead of traditional product pages, visitors can explore products inside an interactive 3D environment and potentially interact with AI assistants.
The demo currently includes:
• WebXR / Three.js immersive scene
• Interactive 3D product displays
• Concept for AI shopping assistants
• Voice interaction concept
• Designed for enterprise e-commerce platforms
Live demo:
https://shop-webplikacija.pages.dev
I would really appreciate feedback from developers here, especially about:
• performance on mobile devices
• usability of the 3D interface
• potential real-world applications in e-commerce
Thanks!

value: Show HN: WebXR AI 3D Showroom for E-commerce (Three.js Demo)

author: mmsoftstudio


value: robinweller

value:
Hi HN,
I built Adversa to solve a problem I kept running into while working on SaaS projects: tracking competitor changes.
I was manually checking competitor pricing pages, landing pages, and feature pages to see if anything had changed. Tools exist that detect page changes, but they mostly send raw diffs or visual alerts.
Adversa monitors competitor webpages and uses AI to explain what actually changed and why it might matter. The goal is to filter out cosmetic updates and highlight meaningful changes like pricing shifts, new features, or messaging changes.
For example:
• pricing updates
• new product features
• positioning changes on landing pages
Instead of sending raw diffs, the system generates a short explanation of the change.
The project is still early and I launched it today. I’d really appreciate feedback from the HN community, especially around:
• whether the summaries are useful
• what competitor signals people actually care about
• anything confusing in the product
Thanks for taking a look.

value: Show HN: Adversa – Monitor competitor websites with AI summaries

author: robinweller


value: zameermfm

value: Show HN: Mdspec.dev – let us heavylift your cluttering md files

author: zameermfm


value: jasonjeong

value: Show HN: Tessera – MCP server that gives Claude persistent memory and local RAG

author: jasonjeong


value: luanderock

value:
Hi HN,
I’ve tried just about every clipboard manager for macOS, but I've always ran into the same two issues: either they were heavy Electron apps that felt sluggish, or they required me to take my hands off the keyboard to find what I needed. Raycast is what I used most of the time, but it's slow in loading screenshots and is search first, meaning I needed to leave the loved home row to scroll down through items.
I built Paster because I wanted something that felt like an extension of my terminal and had instant load of the content being copied. It's written in Rust to keep the latency as low as possible and uses a local SQLite database for history. It's completely private and does not have any telemetry, your data is your own. It does reach to it's domain to validate the license.
Some specific choices I made: - Navigation: I mapped it to j/k and / for search. If you use Vim or a terminal, it should feel like second nature. - Privacy: I’m not a fan of cloud-syncing my clipboard. Everything stays local on your machine. - Quick look: I've added a nice little bonus feature to view each clipboard item in a larger quick look window. Pretty handy for screenshots and offers syntax highlighting for text.
It’s currently a paid app with a 7-day trial. I’m really curious what the community thinks about the "Vim-for-everything" approach. For transparency sake, it's built with help from AI (Gemini) mostly for UI stuff which requires lots of boiler plate.
It's Macos only for now, I do intend to work on a Linux version but no promises.

value: Show HN: Paster – A keyboard-first clipboard manager for Vim users

author: luanderock


value: FKJ

value: Show HN: I built a daily game that tests if you can tell 1999 apart from 2005

author: FKJ


value:
Hi HN,
I built BulkShare (https://www.bulkshare.cloud/) because I was tired of sending clients generic links from services like Dropbox or WeTransfer. It always felt a bit unprofessional and disconnected from my brand. I wanted a way to share files that looked like they were part of my own site, hosted on my own domain, without having to set up a complex server or manage cloud storage buckets myself.
The idea is pretty simple: you connect your own domain or a subdomain (like files.yourdomain.com), and you can immediately start uploading files or entire folders. You can keep things public if you want them easily accessible, or you can password-protect them for more privacy. It preserves your folder structures perfectly, which makes sharing large sets of organized data much easier.
I think the main benefit here is ownership and branding. You own the URL, so you’re not locked into a specific platform's link format, and it just looks a lot cleaner to your clients or users. I’m really looking for some honest feedback on the workflow—is it easy to set up? Are there features like expiring links that you feel are missing?
I'll be around all day to answer any questions or hear your thoughts!

value: Show HN: Share files and folders on your own domain or subdomain


value:
Hey Folks, I've been building an MCP server that generates beautiful Excalidraw architecture diagrams fully auto-laid-out, no manual positioning. It's open source, works with any AI IDE (Cursor, Windsurf, etc.).I wanted to share it early because the core layout engine is solid and already producing clean diagrams.
What Problem Am I Solving?
AI IDEs generate architecture diagrams as Mermaid or ASCII art. When they attempt Excalidraw, they hallucinate coordinates and boxes overlap, arrows cross through nodes, and you spend more time fixing the diagram than drawing it yourself. LLMs understand what a system looks like, but they have zero intuition for where things go on a 2D canvas.
How Does This MCP Solve It?
You describe the components and connections. The MCP runs a Sugiyama hierarchical layout algorithm to compute positions deterministically -- the AI never touches coordinates. It auto-styles 50+ technologies (say "Kafka" and get a stream-styled node), stretches hub nodes like API Gateways to span their services, and routes arrows around obstacles. The result is a clean .excalidraw file in seconds, with stateful editing so you can say "add a Redis cache" without starting over.
Why Not the Official excalidraw-mcp?
The official MCP gives the AI a raw canvas and lets it place elements freely -- great for general sketching, but architecture diagrams still end up with overlapping boxes. excalidraw-architect-mcp takes a structured graph as input and runs a proper layout algorithm, so every diagram is overlap-free by design. It also runs fully offline in your IDE with no API keys, outputs version-controllable .excalidraw files, and includes architecture-aware styling that the general-purpose tool doesn't have.
What's the Future?
Diagram-from-code (point at a codebase, get an architecture diagram automatically), live sync so diagrams update as code evolves, richer layouts (radial, swimlane, grid), and an expanded component library covering cloud-provider icons and CI/CD tools. The goal: architecture diagrams as a byproduct of building software, not a separate task.

value: Show HN: Excalidraw Architect MCP for AI Based IDEs


value: Mvhaperen

value:
Hey HN! I built TeamShotsPro because getting consistent, professional headshots for a team is a pain — coordinating schedules, hiring photographers, and the results still look mismatched.
TeamShotsPro lets each team member upload a single selfie and get a studio-quality AI headshot in about 60 seconds. The key is consistency: everyone gets the same lighting, background, and style so the team page actually looks cohesive.
Built with Next.js 15, Google Gemini for image generation, BullMQ for async processing, and Stripe for billing. The hard part was getting the AI to produce headshots that look natural rather than "AI-generated", we spent a lot of time on prompt engineering and post-processing.
Would love feedback on the product and the generation quality. Happy to answer any questions about the tech stack or approach!

value: Show HN: TeamShotsPro – AI team headshots from a selfie in 60 seconds

author: Mvhaperen


value: davecandi

value:
Creator here. Built this after wasting hours switching to other tabs while Claude Code/Gemini sat idle waiting for permission.
Simple Python script that monitors terminal activity and plays a sound when your agent stops.
Demo video: https://youtu.be/cligunGJgwY
get it free on github: https://github.com/davecandi/vibechime
project page here https://davecandi.com/projects/vibechime
Would love feedback!

value: Show HN: Python script that alerts when your CLI AI agent goes idle

author: davecandi


value: Show HN: Leonardo – FFmpeg Video Converter for Linux Creators


value: Dwedit

value: Show HN: NoCopilotKey – Utility to change Copilot Key back into right ctrl key

author: Dwedit


value: baristaGeek

value: Companies do standups. People say what they did last week, and they’ll do the next one. I thought there should be a personal version of this. Keeping you accountable with other people pushes you to progress. Send your weekly update, vote on other people’s update, and climb up on the leaderboard.

value: Show HN: Personal Standup

author: baristaGeek

title: Show HN: Personal Standup


value: frankylarry

value:
I built ReelsLab after realizing small businesses need video content but can't afford production teams or spend hours editing.
You type a prompt, it generates a video using Seedance 2.0. No editing required.
You can watch a demo without signing up. Would love feedback on the output quality and use cases you think it fits.

value: Show HN: AI video generator for small businesses without video production budget

author: frankylarry


value: FelixZY

value:
I have spent years trying to understand and follow established software architectures - and wound up writing my own at the end: SRA (Specification - Realization - Assembly). Please do check it out and let me know what you think!
Background:
Whenever I would learn a new architecture, the initial ideas seemed interesting but the implementation almost always ended up with my autistic sense of detail screaming loudly about one or more shortcomings and even inconsistencies.
For example, a lot of architectures rely on human discipline, give certain aspects of the software - such as the UI - an unproportional amount of power, or promote coupling despite claiming otherwise.
As a programming generalist, I also found certain architectures hard to transfer between platforms and project types, especially in multiplatform code bases.
In the end I started from the basics; What is good code? What principles are common regardless of language? How can we make good code easier to write than bad code? What makes code adaptable to evolving technology and changing requirements?
The result: SRA. Enjoy!

value: Show HN: SRA – A new architectural pattern for modern product engineering

author: FelixZY


value: Sorasful

value:
Hey HN!
I feel that my muscle memory is worst than ever. That's why I wanted to make something to remind me nice tips and things about Python, like flash cards.
I plan to add more, I need to reflect on what could be nice, I would like to keep it a little advanced.
I'll add some things about profiling, performances and ORMs also.
What do you think ?

value: Show HN: A website to learn Python tips daily

author: Sorasful


value:
I noticed a recurring problem with "vibecoding" using tools like Cursor, Copilot, or Claude Code: the AI hallucinates, hallucinates badly, or wastes thousands of tokens just trying to figure out where things are. They lack architectural context and environmental awareness out of the box.
To fix this, I built VibeRepo. It's a single CLI command (npx viberepo-cli init) that scans your existing project and scaffolds the "Agent Context Protocol" for you.
https://github.com/kk5190/viberepo

value: Show HN: VibeRepo – Make any codebase AI-agent-ready in one command


value: stoatstudios

value:
I made this browser-native game while my partner was stuck in the Middle East and I was super stressed out. You could interpret it as an illustration of how many of the things we worry about were not problems until we decided to worry about them.
Also, it was a good first project to learn Raylib. It's written in C and open source (https://github.com/jerellsworth/spaceship-vs-pong)

value: Show HN: A Bullet Hell of Your Own Making

author: stoatstudios


value: Show HN: N8n-trace – Grafana-like observability for n8n workflows


value:
I built NervOS to safely run AI agent code inside a Firecracker microVM instead of directly on the host machine.
It boots a lightweight Alpine VM and provides tools like Python, bash, curl and git for the agent to use. If something goes wrong, the VM can be destroyed and the host remains untouched.
Currently experimenting with using it alongside MCP for local agents.
Repo: https://github.com/ashishgituser/NervOS
Docs: https://ashishgituser.github.io/NervOS/

value: Show HN: NervOS – Sandbox for AI Agents Using Firecracker MicroVMs


value: nullora

value: Networking library for beginners and people that don't need the overkill of boost.asio

value: Show HN: NovusNet, a C++ networking library for beginners

author: nullora


value: thebababak

value:
As someone who loves both coding and language learning (I’m learning Japanese right now), I always wished there was a free, open-source tool for learning Japanese, just like Monkeytype in the typing community.
Here’s the best part: I added a gazillion different color themes, fonts and other crazy customization options, inspired directly by Monkeytype. Also, I made the app resemble Duolingo, as that’s what I’m using to learn Japanese at the moment and it’s what a lot of language learners are already familiar with.
Miraculously, people loved the idea, and the project even managed to somehow hit 1k stars on GitHub now. Now, I’m looking to continue working on the project to see where I can take it next.
Back in January, I even applied to Vercel’s open-source software sponsorship program as a joke. I didn’t seriously expect to win, and did it more out of curiosity, fully expecting to lose.
Lo and behold, yesterday I woke up to an email saying the app has been accepted into Vercel’s Winter cohort. Crazy!
GitHub: https://github.com/lingdojo/kana-dojo
Live demo: https://kanadojo.com/
Anyway. Why am I doing all this? Because I’m a filthy weeb.
どうもありがとうございます!

value: Show HN: I made an App for learning Japanese, and it won in Vercel's OSS program

author: thebababak


value: pajones

value:
I built this for myself after realizing that as someone who spends time alone, there was no simple way to ensure someone would be notified if something happened to me.
The idea is straightforward: you check in on a schedule you set (daily, weekly, or a custom interval). If you miss your deadline, the system automatically notifies your emergency contacts via email and SMS. You control the grace period before alerts go out.
It's a Node.js/Express backend with a PostgreSQL database, PWA frontend, and Web Push notifications. No app store required.
It's in early beta — invite only for now while I work out the kinks.
Happy to answer questions about the technical implementation or the product itself.

value: Show HN: Dead Man's Switch – miss a check-in, alert your contacts

author: pajones


value: Show HN: Typeui.sh – generate skill files for design system specifications


value: scrygl

value:
I kept finding out about competitor price changes weeks late —
from customers or in sales calls. Built a tool that checks pricing
pages daily and emails a plain-English summary when anything changes.
Free for 1 competitor, no card required
Happy to answer questions about how the detection pipeline works.

value: Show HN: PriceMon – get alerted when a competitor changes their pricing

author: scrygl


value: crosschainer

value: Show HN: Ruffle.rs brought back a 2012 Flash MMO to the modern web

author: crosschainer


value: leeschmalz

value: A fun daily scavenger hunting game where you can find the target anywhere on Earth!

value: Show HN: StreetSeekr a Street View scavenger hunt

author: leeschmalz


value: pnaame

value: Show HN: Ytm-player – A cross-platform TUI+CLI YouTube Music player in Python

author: pnaame


value: Shmungus

value:
I've been building the substrate layer for production AI systems in Rust. This is the index repo for 10 crates I shipped this week.
The AI side covers agent memory (episodic, semantic, working with decay and multi-agent bus), cost governance with hard budget enforcement and automatic model downgrade cascade, CRDT state sync for distributed agent fleets, output versioning with full lineage tracking, a knowledge graph on top of agent memory, LLM inference primitives for WASM and edge runtimes, and a complete ReAct loop that runs inside a Cloudflare Worker.
The financial side covers market primitives (orderbook, OHLCV, indicators, position, risk) and a streaming ingestion pipeline handling 100K+ ticks/second across 4 exchanges.
Everything is zero-panic enforced at the lint level, typed errors with thiserror throughout, no unnecessary dependencies. The crates are designed to compose, the orchestrator runs the agents, memory stores what they know, the graph understands relationships between what they know, the budget governs what they spend, sync keeps 24 agents consistent without locks.
The orchestration engine that coordinates all of this is tokio-prompt-orchestrator, which I've been running with 24 simultaneous Claude Code agents.
https://github.com/Mattbusel/rust-crates

value: Show HN: Rust primitives for AI agents, LLM infrastructure, and financial data

author: Shmungus


value: pvisilias

value:
I have been building many many many projects the last 2 years and everytime i faced one problem.
Managing all of my projects
Most of my apps never published because i couldn't keep up with the ideas that i have since i couldn't open so many windows and code across multiple projects
That's why when i installed OpenClaw i had this idea. To build multiple projects in one dashboard without switching apps.
Then i created ClawCode which you can build, manage and plan multiple projects on one dashboard. But not only that, when you open a project, automatically openclaw generates 12 agents:
- 1 Main
- 5 Coding
- 1 Debugger
- 1 Performance
- 1 Planner
- 1 Security
- 1 Tester (opens browser)
- 1 Ui Designer
Each projects gets its own agents, which you can run in parallel mode or with queued messages. So you can set up 5 coding agents, switch project set up next 5 switch next 5 etc.
That solved my biggest problem. But not only that, if you are starting a new project you can describe the app, build the actual plan with planner agent, build the workflow and split it into tasks. Then assign tasks to agents by writing # in chat.
My plan is to extend the same logic to have Claude instead of using only OpenClaw and connect your server logs, feature requests and errors reports from your customers, so agents can run them without needing to pay for other apps or switching apps

value: Show HN: I Made OpenClaw for Coding – ClawCode

author: pvisilias


value: susam

value: Show HN: I wrote a script to customise Hacker News with bigger fonts and skins

author: susam


value: bootbloopers

value: I created a tool today for embedded device security assessments.  Basically what it does it searches the linux `mtd` partitions to find the U-Boot environment variable location and then gives the correct format for the `fw_env.config` file that the U-Boot utilities `fw_setenv` and `fw_printenv` use.  So, if those utilities are not installed with the proper config file on the embedded device, you can statically compile them and create the proper config file to use them to modify the U-Boot environment variables from the Linux host.

value: Show HN: U-Boot Fw_env.config Bruteforcer

author: bootbloopers


value: tonelord

value:
I am working on some bigger projects and wanted to start sharing them on LinkedIn and YouTube. The problem is you can't just post audio on LinkedIn, it needs to be a video. Every tool I found for this wanted a signup or a subscription, and none of the demos looked how I wanted anyway.
So I just built one with Claude. It runs in the browser, nothing gets uploaded anywhere. It is a single HTML file. A bit janky but it does what I need.
If you are on the job market or building something new and want to share your voice without being on camera, maybe it helps you like it helps me. If you think something could be improved, reach out.
Try it here: https://ohmstone.github.io/audiogram/
Source: https://github.com/ohmstone/audiogram
Other projects: https://github.com/orgs/ohmstone/repositories

value: Show HN: Turn an audio recording into a LinkedIn video – no signup, no server

author: tonelord


value: slucerodev

value:
I built ExoArmur because autonomous systems are becoming capable of taking real actions (deployments, infra changes, trading, etc.), but most systems still lack deterministic governance boundaries.
The core idea is that every action must pass through a verifiable execution boundary that produces a replayable evidence bundle.
Pipeline:
Gateway → ActionIntent → PolicyDecisionPoint → SafetyGate → Approval Workflow → ExecutorPlugin → ExecutionTrace → ExecutionProofBundle
This allows you to deterministically replay a decision and verify exactly why an action was allowed or denied.
The repo includes a restrained-autonomy demo where an operator vetoes an action and the system produces a deterministic audit stream.
I'm particularly interested in feedback from people working on agent infrastructure, security, and safety systems.

value: Show HN: ExoArmur – Deterministic governance runtime for autonomous systems

author: slucerodev


value: runxiyu

value:
Chuck some SHA-1 repositories at the endpoint:
git push git://lindenii.org/ HEAD:refs/heads/change-this-name
(I wrote a pure-Go git receive pack implementation and wanna test it out with a diverse set of repositories. The code is at https://github.com/runxiyu/furgit)

value: Show HN: Custom Git-receive-pack implementation

author: runxiyu


value: eibrahim

value:
AICurate lets professional associations create AI-curated news hubs for their members.
Problem: Associations spend 10-15 hrs/week manually finding, filtering, and summarizing industry news for members.
Solution: Configure industries, topics, and RSS/web sources. AI agent scans, scores relevance, generates summaries, and delivers via branded portal + email digests.
Multi-tenant architecture, each org gets isolated config, branding, and analytics. Curation AI is configurable per-tenant so a legal association and a medical society can have completely different relevance criteria.
Currently handles RSS feeds and web source discovery. Working on adding direct API integrations with major news providers.

value: Show HN: AICurate – AI news curation platform for associations

author: eibrahim


value: otmardev

value:
Hey HN, I built Meshcraft – a web-based tool that generates 3D models (GLB) from text prompts or images.
What's new since the first Show HN (Feb): Back then it was a basic TripoSR wrapper. A commenter here (thanks vunderba) pointed me to Trellis 2, which was vastly better. Since then I've rebuilt the whole thing:
- Two 3D engines: Standard (Trellis 2 via HuggingFace ZeroGPU) and Premium (Hunyuan v3.1 Pro via fal.ai). Standard is free, Premium costs 50 credits and produces ~1.4M face models with proper PBR materials.
- Four image models for text-to-3D: FLUX 1 Schnell, FLUX 2 Dev, GPT Image 1 Mini, GPT Image 1.5. You pick the model, type a prompt, and it generates an image then converts to 3D.
- Unified credit system with variable costs per action (1-59 credits depending on engine + image model combo).
Stack: Next.js 16 on Netlify, Supabase (auth + DB + storage), Stripe, HuggingFace ZeroGPU H200, fal.ai serverless for Hunyuan and image generation. Background generation via Netlify Background Functions (up to 15 min async).
What I learned building this:
1. The 3D engine is the quality bottleneck, not the image model. I tested 8 engines before settling on two. Trellis 2 is great for simple objects but struggles with complex geometry (missing fingers, back-side artifacts). Hunyuan v3.1 Pro solves most of these.
2. Image model quality matters less than you'd think for 3D – a $0.003 FLUX schnell image produces nearly the same 3D result as a $0.009 GPT Image 1.5 image.
3. HuggingFace ZeroGPU is incredible for bootstrapping – free H200 inference with a $9/mo Pro account. The cold start and queue times are the trade-off.
Free tier: 5 credits/month, no credit card required. Would love feedback on the generation quality and UX.

value: Show HN: Meshcraft – Text-to-3D and image-to-3D with selectable AI engines

author: otmardev


value: nonatofabio

value:
Hey HN. I evaluated three agent frameworks for a homelab project, one had 400K lines of code (and 42K exposed instances on Shodan), one was 9 days old, and the third was so thin I'd rebuild most of it anyway. None worked for me.
So I built my own in ~2300 lines of Python. No frameworks, 8 runtime dependencies, 106 tests.
What it does:
- Persistent memory via SQLite (FTS5 keyword search + sqlite-vec embeddings + recency decay, fused with Reciprocal Rank Fusion)
- MCP tool integration — add capabilities by editing a JSON file
- Native tools with safety guardrails (bash blocklist, timeouts, output caps)
- Discord interface with session isolation
- Structured JSON logging for every operation
- Conversation compression for effectively infinite context
Runs locally on 2x RTX 3090 with Qwen3-Coder-Next via llama-server. No cloud APIs.
The design philosophy was: don't build what you don't need, but don't block the insertion points. For example, my AI firewall isn't built yet, but all LLM traffic goes through a single configurable URL, swapping in a filtering proxy is a config change I'll do later.
DESIGN.md documents the reasoning behind every architectural decision. Tests mock the LLM client so you can run them on a laptop.
GitHub: https://github.com/nonatofabio/luna-agent
Blog post with full technical deep-dive: https://nonatofabio.github.io/blog/post.html?slug=luna_agent
Happy to answer questions about any of the design tradeoffs.

value: Show HN: Luna Agent – Custom AI agent in ~2300 lines of Python, no frameworks

author: nonatofabio


value: dszb

value: Show HN: MicroBin – Easy File Sharing for Everyone – Self-Hostable

author: dszb


value: qvalitet

value: Show HN: Sajtanalys.se – Automated website analysis for Swedish businesses

author: qvalitet


value: ProgrammerByDay

value:
Hi HN,
I'm the creator of BeyondComments ([https://beyondcomments.io]). It’s a tool that uses AI to analyze YouTube comments and extract actionable signals—content requests, brand deals, sentiment, and bug reports—that normally get buried.
The Problem If you run a successful YouTube channel (or publish tech tutorials), your comments section is a goldmine. Viewers literally tell you what they want you to build or film next. But YouTube Studio is mostly built for raw numeric stats. Finding high-intent comments manually means endlessly scrolling through "First!" and "Great video bro," which doesn't scale and leads to missed opportunities.
How it works You paste a YouTube URL and the application fetches the comments to run them through our pipeline:
Clustering: It groups hundreds or thousands of comments into major discussion themes.
Intent & Signal Detection: Flags high-value signals like purchase interest, feature requests, or collaboration outreach.
Virality & Next Steps: Calculates a "Virality Score" based on engagement velocity and provides concrete next steps (e.g., "Reply to UserXYZ about the Enterprise plan").
I'd love for you to try it out on your own videos (or your favorite creator's videos) and share feedback. I’m particularly interested in hearing how you find the accuracy of the topic clustering, and whether the AI insights feel actionable vs. generic.
You can try it out here: [https://beyondcomments.io]
Thanks!

value: Show HN: BeyondComments – Find content ideas and signals from YouTube comments

author: ProgrammerByDay


value: aerosol

value: Show HN: Open-Sable, Self-hosted cognitive AI agent with memory and 127 tools

author: aerosol


value:
OpenGrammar is a free, privacy-first browser extension that gives you Grammarly-like writing help everywhere (Gmail, Google Docs, Notion, Reddit, etc.) without sending your data anywhere.
Key points:
• Core engine runs 100% locally in your browser (rule-based detection of passive voice, repetition, long sentences, readability). No internet needed.
• Optional AI power: just paste your own OpenAI/Groq/OpenRouter/Ollama key. You pay only pennies for what you use. Your key never leaves the browser.
• Tone rewriting (Formal, Casual, Professional, etc.) with one right-click.
• Writing stats dashboard (Flesch score, reading time, vocabulary diversity).
• Underlines suggestions in red/yellow/blue — click to apply.
• Fully self-hostable backend (Cloudflare Workers or Vercel, one-command deploy).
I built this because I was tired of Grammarly storing everything I write and charging $30/month. Everything is open source (Apache 2.0), no accounts, no telemetry.
Demo video & screenshots coming soon. Right now you can try it in 2 minutes:
1. Clone the repo
2. Load the unpacked extension (chrome://extensions)
3. (Optional) add your API key
GitHub: https://github.com/swadhinbiswas/opengrammar
Would love feedback from writers, developers, and privacy enthusiasts. What grammar rules should I add next? Any features you miss from Grammarly?
(Also happy to take PRs — especially for more local rules!)

value: Show HN: OpenGrammar Open-source, self-hostable Grammarly alternative


value: gmays

value:
Hi HN,
I’m building Main Character, a gamified productivity app where you earn XP and levels for completing tasks and habits.
Tasks run on a simple kanban board and habits are tracked with a GitHub-style consistency graph. The goal is to make everyday work fun and feel more like a game.
Why I built it:
I like games (used to play EverQuest and World of Warcraft), but between work and kids I haven’t had much time for them over the last 20 years. I wanted something that scratches that same itch while helping me stay on track.
I also wanted something simple for tracking tasks and habits. My current longest streak is almost 900 days of practicing math daily.
A lot of productivity apps end up making the act of managing work the focus instead of actually doing the work. I’m trying to keep this simple and motivating.
There’s a no-login demo you can: https://maincharacter.game
It works best on desktop, but it's usable on mobile, more work to do there.
It’s still simple, but it’s the best thing I’ve built on my own and I’m excited to keep building it.

value: Show HN: Main Character – A gamified productivity app (no login demo)

author: gmays


value:
Hi HN,
I’ve spent years hopping between different knowledge bases, but I always found myself hesitating to open them for quick, transient thoughts just because of the loading lag or the cognitive load of deciding where a note belongs before I even write it.
My buddy and I decided to build Jot (gojot.app) to solve this specific friction. The philosophy is simple: it should feel as lightweight as a physical sticky note, but with the power of modern web tech.
Under the hood, Jot is local-first. It works offline by default, keeping your notes with you, and handles syncing in the background. We built in instant markdown formatting and a Command-K menu to keep hands on the keyboard.
We are actively wrestling with a few technical challenges and would love some eyes on the project:
Sync Architecture: Re-engineering the cross-device sync logic, specifically around edge cases with deleted notes and removed tags.
UI/UX Edge Cases: Redesigning the Command-K dropdown to prevent container overflow when triggered in the middle of the text area.
Markdown Parsing: Squashing some text-parsing bugs (like formatting failing to trigger if an underscore is added after typing the word).
I’d love to hear how the "quick capture" flow feels to you. Any feedback from folks who have built or scaled local-first syncing architectures would be incredibly appreciated.
Link: https://gojot.app

value: Show HN: I built a zero-lag, local-first Markdown capture tool


value:
I built DBWarden for Python projects which use SQLAlchemy and need a simple migration workflow.
Most tools add large frameworks and complex configuration. DBWarden focuses on a small CLI and readable SQL migrations.
Core ideas
• Auto detect SQLAlchemy models from a models directory
• Generate raw SQL migration files from model changes
• Keep migrations transparent and easy to review
• Track migration history and database state
• Minimal setup through a small warden.toml file
Typical workflow
1. Write SQLAlchemy models
2. Run dbwarden make-migrations "name"
3. Review the generated SQL
4. Run dbwarden migrate
Commands include
• init
• make-migrations
• migrate and rollback
• history and status
• diff between models and database
Supported databases
• PostgreSQL
• SQLite
• MySQL
Project status: experimental. I look for feedback from developers who work with SQLAlchemy migrations.

value: Show HN: DBWarden – A database migration tool for Python/SQLAlchemy projects


value: bilkoo

value: I vibe coded this! It is a nice little game I use to send secret message to my girlfriend, I guess you could also use it to encourage a little one to learn spelling? Let me know if you can think of a use for it!

value: Show HN: Send Secret Message in a Maze

author: bilkoo

title: Show HN: Send Secret Message in a Maze


value: nicklo

value:
Hey HN,
I kept rebuilding the same infrastructure every time I wanted to put a Claude Agent SDK into production. Sessions, streaming, sandboxing, persistence, a REST API, file hooks. So I built Ash.
An agent is a folder with a CLAUDE.md file, skills folder, etc, and one-line deploy from cli, or tsx/python sdks.
Some things I cared about when building this:
Sandboxing. Each agent runs in its own isolated process with an environment allowlist, cgroups resource limits, and bubblewrap filesystem isolation on Linux.
Session persistence. All state lives in CRDB. If the server crashes mid-conversation, resume picks up where it left off. You can also snapshot workspaces to S3/GCS and resume on a different machine.
Keeping it thin. The SDK's `Message` types flow through the whole pipeline untranslated, from the Unix socket to SSE to the client. Ash adds session routing, sandbox pooling, and lifecycle management. It doesn't re-wrap or translate the SDK's types.
I've been measuring overhead pretty carefully. Ash adds <0.5ms per message at p99. Warm resume is 1.7ms. Cold resume (restore workspace, spawn process, reconnect) is 32ms.
OSS, Self-hostable, MIT licensed: https://github.com/ash-ai-org/ash-ai

value: Show HN: Ash – OSS Infra for Running Claude Agent SDK

author: nicklo


value: Show HN: Brw – Browser automation for Claude Code agent teams


value: sankalpnarula

value:
Hey HN,
Herd is a zero-dependency Go library that manages fleets of OS subprocesses and routes HTTP traffic to them with strict 1:1 session affinity.
If you put heavy, stateful binaries (like Ollama, headless Chromium, or Python REPLs) behind a standard reverse proxy and get a spike in traffic, it usually ends badly. You either trigger a massive CUDA/Metal context storm that OOM-kills the host machine, or you bleed state across different users' sessions.
Herd handles this without needing a heavy control plane like Kubernetes StatefulSets or Firecracker. It gives you automatic process lifecycle management and a built-in reverse proxy in about 10 lines of Go.
How it works under the hood:
- It spawns OS-level subprocesses via exec.Cmd.
- It routes incoming HTTP traffic based on any custom Session ID you define (a header, a cookie, a path parameter).
- If a session exists, it routes to that exact pinned OS process.
- If it doesn't, it safely acquires a singleflight lock, spawns a new process, waits for the /health endpoint, and proxies the request.
- If a process crashes, the blast radius is contained to one session, and the pool auto-recovers.
To test the concurrency constraints, I hurled 200 concurrent LLM inference requests at a Herd gateway backed by a pool capped at 10 Ollama (Qwen3:0.6B) workers on an M4 Pro Mac. It scored 200/200 with zero dropped packets, acting as a perfect backpressure queue to safely drip-feed the OS without thrashing the host's Unified Memory.
It’s MIT licensed. Would love for you to check out the repo, try to break the singleflight lock, or review the architecture.
Repo: https://github.com/HackStrix/herd
Architecture & Mermaid Diagrams: https://github.com/HackStrix/herd/blob/main/docs/ARCHITECTUR...

value: Show HN: Herd – Session-affine process pool for Go

author: sankalpnarula


value: ethantheswe

value:
Built browse2mcp to automatically create an mcp server by opening a broswer. I built it because there's a few website's I use on a regular basis that were super annoying to try to get to work with Claude. Note: this is mostly vibe coded with a few touch ups.
How it works:
Run browse2mcp to record and a browser opens, you use the site normally
Close the browser, all API requests are saved
Run browse2mcp generate, it analyzes the traffic and spits out a TypeScript MCP server you can have it automatically run add to claude desktop
It handles REST and GraphQL, detects auth patterns, bakes in captured tokens, and generates a login tool so you can refresh expired tokens without rerecording.
There's also an optional LLM refinement pass using Claude Haiku (could probably extend to other models) that rewrites the autogenerated tool descriptions into something actually useful, adds sensible defaults, and removes noisy endpoints.
Some things definitely still to-do are to decrease the tools (it doesn't consolidate very well), and make it generally less flaky.
https://github.com/danielthedm/browse2mcp

value: Show HN: Open-source tool turns a site into an MCP by recording the browser

author: ethantheswe


value: siegers

value: Show HN: Agentpng – turn agent sessions into shareable images

author: siegers


value: kencho

value: Show HN: Search 10k paintings by describing them in plain English

author: kencho


value: Ndmtrieff

value: Show HN: Help] I run 4 AI-driven companies simultaneously from my terminal

author: Ndmtrieff


value: armanj

value:
Hi HN!
I built this tool specifically for Claude Code users who hit the 5-hour rate limit wall mid-flow. There's no official plan between Pro ($20/mo) and Max ($100/mo). it's a fixed gap with nothing in between.
The workaround most people do manually: running multiple Pro accounts and switching when one is limited. This actually works, but naive rotation wastes a lot of capacity. When you activate an account turns out to matter as much as which one you use. A single throwaway prompt sent a few hours before your coding session can unlock an extra full cycle.
PolyClaude automates this. You tell it your accounts, your typical coding hours, and how long you usually take to hit the limit. It uses combinatorial optimization to compute the exact pre-activation schedule, then installs cron jobs to fire those prompts automatically. When you sit down to work, your accounts are already aligned.
It's free and open source. Install is one curl command, then an interactive setup wizard handles the rest.
Repo: https://github.com/ArmanJR/PolyClaude
Hope you find it useful!

value: Show HN: PolyClaude – Using math to pay less for Claude Code

author: armanj


value: dasubhajit

value:
Hello HN, we're Jeet and Husain from Modulus (https://modulus.so) - a desktop app that lets you run multiple coding agents with shared project memory.
We built it to solve two problems we kept running into:
- Cross-repo context is broken. When working across multiple repositories, agents don't understand dependencies between them. Even if we open two repos in separate Cursor windows, we still have to manually explain the backend API schema while making changes in the frontend repo.
- Agents lose context. Switching between coding agents often means losing context and repeating the same instructions again.
Modulus shares memory across agents and repositories so they can understand your entire system.
It's an alternative to tools like Conductor for orchestrating AI coding agents to build product, but we focused specifically on multi-repo workflows (e.g., backend repo + client repo + shared library repo + AI agents repo). We built our own Memory and Context Engine from the ground up specifically for coding agents.
Why build another agent orchestration tool? It came from our own problem. While working on our last startup, Husain and I were working across two different repositories. Working across repos meant manually pasting API schemas between Cursor windows — telling the frontend agent what the backend API looked like again and again. So we built a small context engine to share knowledge across repos and hooked it up to Cursor via MCP.
This later became Modulus.
Soon, Modulus will allow teams to share knowledge with others to improve their workflows with AI coding agents - enabling team collaboration in the era of AI coding. Our API will allow developers to switch between coding agents or IDEs without losing any context.
If you wanna see a quick demo before trying out, here is our launch post - https://x.com/subhajitsh/status/2024202076293841208
We'd greatly appreciate any feedback you have and hope you get the chance to try out Modulus.

value: Show HN: Modulus – Run multiple coding agents with shared project memory

author: dasubhajit


value: jdanielnd

value: Show HN: CRM-CLI – A local-first personal CRM for the terminal with MCP server

author: jdanielnd


value: harveyrandall

value:
I spend a lot of time using CLI tools and also enjoy scrolling through Bluesky. I found a CLI client written in Go but it didn't include all the features I wanted so I began building my own and got a bit carried away. Now you can use this client to perform just about every common action in the app or on the web without leaving the terminal.
The client has multi-account support through named profiles when you authenticate so you can perform all of the actions below using a different account each time by simply using a flag and the profile name.
Every command also has support for `--json` so can be piped to `jq` or passed to other scripts for more complex workflows.
It uses the AT Protocol API and covers the most common functionality from the app and a bit more.
* View your timeline
* Make new posts, including new posts with attached images or videos
* Reply to and quote posts
* Like, repost, bookmark, and search for posts
* Follow, unfollow, block, and unblock other users
* DM other users
* Connect to the Jetstream to receive a real-time feed of posts with support for regex filtering using `--pattern` e.g. `bsky stream --pattern="typescript|rust"`. By default it uses the `gi` modifiers.
Apart from profile functionality above shell completions are included for `bash`, `zsh`, and `fish`.
The client is distributed as a standalone binary that works on macOS, Linux, and Windows so no Node runtime needed.
Install:
---------------------------------------------
; npm install -g @harveyrandall/bsky-cli
; # or
; brew install harveyrandall/tools/bsky-cli
---------------------------------------------
GitHub: <https://github.com/harveyrandall/bsky-cli>
It's built with TypeScript, Commander.js, and the AT Protocol SDK under the hood. Has 134+ tests, colocated with their implementations. They can be run separately and also run in CI/CD using GitHub Actions. GitHub Actions also publishes new versions to NPM and updates the Homebrew formula.
Happy to hear thoughts on the CLI design — and if there's a command you wish existed, I'd like to know. Please check it out, install it and play around with it. Report any bugs you find and create issues for them, any and all feedback is appreciated! I hope you find it as useful as I do.

value: Show HN: Bsky-CLI – A full-featured CLI client for Bluesky

author: harveyrandall


value: AhmedAshraf

value: Show HN: Jarvey - a local JARVIS for MacOS

author: AhmedAshraf


value: tantara

value: A year ago, I shared a simple demo of running LLMs locally in a Chrome extension. Today, I’m excited to share TinyWhale, a monorepo that lets you run Qwen 3.5 0.8B entirely on-device, both as a web application and a Chrome extension. I plan to support mobile and desktop apps in the same repo.

value: Show HN: Run Qwen3.5 0.8B in browser (Web and Extension)

author: tantara


value: e-gockel

value: Built this for cron-based backup setups where DB dumps get pushed to Dropbox and pile up over time. It prunes backups only if they’re older than a configured threshold and not among the newest N files, so a minimum recent set is always retained. Bash only, no Dropbox API.

value: Show HN: Dropbox Cleaner – prune old Dropbox backups with Bash

author: e-gockel


value: alfridus

value: Show HN: Beam Protocol – SMTP for AI Agents (natural language agent-to-agent)

author: alfridus


value: policycortex

value: Show HN: PolicyCortex – AI agent that autonomously remediates cloud misconfigs

author: policycortex


value: Show HN: macOS Window Managers Directory


value: Mariovega

value: Show HN: Hosted OpenClaw – 60s setup, no Mac Mini, $99 lifetime BYOK

author: Mariovega


value: aosmith

value: Show HN: Iceberg Map

author: aosmith

title: Show HN: Iceberg Map


value: houqp

value: Show HN: Kiorg – a battery included file manager for keyboard nerds

author: houqp


value: savvyllm

value: Show HN: Joy – Open trust network for AI agents (AI-to-AI vouching)

author: savvyllm


value: theborat

value:
I built a small web tool to solve a common annoyance: prepping photos for physical printing (Passport ID, Retro/Polaroid, or custom frames) without the typical web bloat.
Key features:
Client-side processing: Images are processed locally using JS/Canvas. No data is uploaded.
Zero-Distortion logic: Implemented a smart-crop helper to maintain aspect ratios for physical dimensions.
Precision: Calibrated for ISO 216 (A4) printing.
Minimalist UI: No ads, no trackers, no login required.
Tech stack: Vanilla JS, CSS, and jsPDF. I'm curious to hear what you think about the UX/performance.

value: Show HN: Scalisos – A privacy-first, ad-free passport photo layout tool

author: theborat


value:
I use 4-5 AI coding tools depending on the task. The thing that kept frustrating me: every tool stores its context in a different place, in a different format. Switch from Claude Code to Cursor mid-project and you start from scratch. Set up a new machine and you rebuild everything by hand.
apc-cli fixes this with three commands:

  apc collect   # extract skills, MCP servers, and memory from every installed tool
  apc status    # see what's in sync and what isn't
  apc sync      # push everything to all your tools

It handles: Claude Code · Cursor · Gemini CLI · GitHub Copilot · Windsurf · OpenClaw
No cloud account. Everything runs locally. API keys go to the OS keychain, not a config file. MIT licensed.
PyPI: https://pypi.org/project/apc-cli/
Happy to hear what's missing — especially if you use tools I haven't added support for yet.

value: Show HN: Apc-CLI – sync AI memory across Claude Code, Cursor, Copilot


value: GiornoJojo

value:
Every day a new “AI tool” launches.
Most are vibecoded clones.
So we built Glad-ia-tor : an arena where AI SaaS fight for survival.
The crowd roasts them.
The crowd votes.
Only the strongest survive.

value: Show HN: Too many AI SaaS launching every day so we built Arena where they fight

author: GiornoJojo


value:
A simple tower defense game. Seeded randomly every day. Get the top score.
Have your agent try to get the top score.
dailydefense.ai/skill.md

value: Show HN: DailyDefense – Daily tower defense for agents or humans


value: SherryWong

value: Show HN: SiClaw – Open-source AIOps with a hypothesis-driven diagnostic engine

author: SherryWong


value: shalinmehtaaa

value:
I built an arena where AI agents compete in challenges, earn Elo ratings, and climb a leaderboard.
Agents can also author new challenges, so the benchmark evolves with the community.
New challenges go through a draft pipeline with automated checks and peer review from other agents before entering the arena.
It’s still early and there’s a lot to figure out, but it’s been fun to build.
The project is open source if you’d like to explore or contribute:
https://github.com/clawdiators-ai/clawdiators
Or you can also point an agent at it:
curl -s https://clawdiators.ai/skill.md
Happy to answer questions about the design or implementation.

value: Show HN: A dynamic, crowdsourced benchmark for AI agents

author: shalinmehtaaa


value: Show HN: I automated DJ Screw's chopped and screwed technique with Python+FFmpeg


value: princierKevin

value: Show HN: A command center for CS students drowning in recruiting season

author: princierKevin


value: HPSimulator

value:
I built a tool that simulates how visitors psychologically experience a website.
Instead of just analytics like bounce rate, it analyzes trust signals, cognitive friction, and persuasion flow to estimate conversion probability.
Curious what founders think.

value: Show HN: Human Psychology Simulator – AI website conversion psychology

author: HPSimulator


value: chardigio

value: Show HN: Pappardelle, a TUI for Multi-Clauding

author: chardigio


value: minorglitch

value: Show HN: Ethernity: Secure paper backups with age encryption and SSS

author: minorglitch


value:
I'm a solo carpenter. I was paying $150+/month for field service software and hated it — too bloated, too expensive for a one-person operation.
So I built FieldFlow. My rules:
$5/month flat. No per-user fees. Ever.
Invoicing, scheduling, client management — the three things I actually need
Every piece of feedback gets acted on fast. No ticket queue, no roadmap committee — just me
Jobber starts at $39. Tradify is $47/user. I want to build something that solo contractors can actually afford, and I'm willing to keep the price at $5 forever if that's what it takes.
I use it myself daily. That's the only quality filter I trust.
Brutal feedback welcome.
 https://fieldflow-nine.vercel.app/auth

value: Show HN: I built a $5/mo Jobber alternative for solo carpenter


value: katagamistudio

value:
I built Yumo.to because I wanted a map-first way to discover onsens in Japan without digging through booking sites.
It currently covers 19,652 onsens across all 47 prefectures, built by merging national datasets, prefecture and municipal open data, OSM, and water-chemistry sources into one searchable map.
You can filter by tattoo policy, prices, opening hours, access, and some water data, and each detail page links back to its sources. I’d especially appreciate feedback on search quality, missing data, and which filters or trip-planning features would make it more useful.

value: Show HN: Yumo.to, a map of 19,652 onsens in Japan

author: katagamistudio


value:
Im getting really burnt out with my main project and trying to turn it into a real company. So tonight I wanted to build something fun but still possibly useful.
While making a baked potato (takes about 90 mins) I tried to vibe code a pipeline checker. I do some consulting and its constantly a challenge for young sellers. We will be on a call with the whole team and their boss is grilling them on deal qualification and they don't know how to answer. Its embarrassing for them and they aren't learning anything.
I ended up building this BANT checker and training it with some of my knowledge on the qualification methodology. For an hour and a half it works pretty well. And its all poop themed!
How can I add more poop elements?

value: Show HN: Pipe Checker – paste a sales deal and it checks BANT qualification


value: jfornear

value: Show HN: Open-source personal finance AI that runs locally on your laptop

author: jfornear


value:
I built a small Python library called SafeAgent that protects real-world side effects when AI agents retry tool calls.
One issue we ran into while experimenting with agent workflows is that retries can trigger irreversible actions multiple times:
agent calls tool
↓
network timeout
↓
agent retries
↓
side effect happens twice
Examples:
• duplicate payment
• duplicate email
• duplicate ticket
• duplicate trade
Most systems solve this ad-hoc using idempotency keys scattered around different services.
SafeAgent centralizes this into a small execution guard.
The idea is simple:
1. every tool execution gets a request_id
2. SafeAgent records the execution receipt
3. retries return the original receipt instead of running the side effect again
Example:
FIRST CALL
REAL SIDE EFFECT: sending email
SECOND CALL WITH SAME request_id
SafeAgent returns the original execution receipt
(no second side effect)
The project is early but includes examples for:
• OpenAI tool calls
• LangChain style tools
• CrewAI actions
PyPI:
https://pypi.org/project/safeagent-exec-guard/
GitHub:
https://github.com/azender1/SafeAgent
Curious how other people are handling retry safety for agent side effects.

value: Show HN: SafeAgent – exactly-once execution guard for AI agent side effects


value: justEstif

value:
I wanted web search I could pipe. Every tool I found either had an interactive UI (ddgr, googler), required a single paid API, or returned HTML. I wanted something that composes: pipe to jq, feed to an agent, use in scripts, no friction.
So I built qry. It's a hub binary that routes queries to adapter binaries via stdin/stdout JSON. The hub knows nothing about search engines. Adapters know nothing about routing. You swap adapters in a config file.
Four adapters today, three require no API key:
- DDG Lite scraping
- Brave Search HTML scraping
- Exa AI via the public MCP endpoint
- Brave Search API (requires key)
```
$ qry "numpy latest version"<p>[{"title":"...","url":"...","snippet":"..."}]<p>```
Install:
```<p>mise use -g go:github.com/justestif/qry@latest<p>mise use -g go:github.com/justestif/qry/adapters/qry-adapter-ddg-scrape@latest<p>mise reshim
```
The adapter protocol is plain stdin/stdout JSON so adapters can be written in any language. Docs on
building one are in the repo.
https://github.com/justEstif/qry

value: Show HN: Qry – CLI web search that always outputs JSON, with swappable back ends

author: justEstif


value: metafarer

value:
A golf course directory and navigation app powered by OpenStreetMap and the Overpass API. Browse golf clubs worldwide, explore course layouts, and find pin distances.
GitHub: https://github.com/refarer/navtee

value: Show HN: Navtee – Golf course directory and navigation app

author: metafarer


value: furkankub

value:
Hi HN,
I built a small open-source library called RotaGuide Spotlight.
It helps developers add step-by-step guides to complex web apps by tagging elements and writing the guide in markdown.
You can mark elements like:
guideTarget("save-button")
Then define the guide flow in markdown and trigger it with components like MarkdownGuideButton or MarkdownGuideTrigger.
I’m experimenting with a developer-friendly approach where guides can be versioned as files instead of configured in dashboards.
Would love feedback from frontend developers.

value: Show HN: RotaGuide Spotlight – Markdown based UI guide system for web apps

author: furkankub


value:
I've been wanting a "Football Manager for music" for years - deep management sim where you run a band, not just pick songs. Nothing like it existed, so I built one.
You start with a broke, unknown band. Hire musicians, rehearse songs, book gigs at empty bars, record demos in cheap studios, manage finances, tour other cities. Every decision has tradeoffs — push too hard and your band burns out, play it safe and you never break through.
There's a live competitive season running with leagues and promotion/relegation. It's completely free, no ads, no microtransactions — this is a passion project, not a business.

value: Show HN: Free browser MMO where you manage a band from garage to headliner


value: jaydurangodev

value: Show HN: Scan0tron – AI screen capture that auto-fills forms ($49)

author: jaydurangodev


value: oneinx

value: Show HN: Make AI and automation pipelines fail-closed

author: oneinx


value: gtlpanda

value: Show HN: Beecon Infrastructure as Intent, open source IaC built for AI agents

author: gtlpanda


value: Show HN: cryptographic receipts for AI code changes pip install titanate


value: Show HN: Strata – 31-43% cheaper Claude Code reads via entropy, no parser


value: startupYu

value: Show HN: HireSignal – discover tech hiring signals from social posts (waitlist)

author: startupYu


value: Show HN: Booklet AI – AI-Powered Digital Flipbook Creator


value: forhadahmed

value: Show HN: Render Claude Code and Codex Transcripts as Browsable HTML

author: forhadahmed


value: CleitonAugusto

value: Show HN: Neural-Q-Forge – a Rust-native quantum circuit forging framework

author: CleitonAugusto


value: smlee

value:
I built Joha, a free browser-based drawing playground built around preset shape tools.
You can click or drag to quickly generate individual shapes like waves, stars, layered squares, particles, textured strokes, and ring patterns, then combine them into larger compositions.
It’s designed for fast visual exploration and composition rather than precise vector editing.
Under the hood, it’s built with Vue 3, Vite, and p5.js for the drawing engine.

value: Show HN: Joha – a free browser-based drawing playground with preset shape tools

author: smlee


value: nadermx

value: Show HN: TTS.ai

author: nadermx

title: Show HN: TTS.ai


value:
I noticed there wasn't a maintained, verified list of malicious Chrome/Edge extensions, only behavioral scanners with high false positive rates.
So I built one.
The database only includes extensions with clear removal signals: official store removals or researcher reports that led to action. No behavioral guesswork.
Live dashboard (daily updates): https://malext.toborrm.com
GitHub + database: https://github.com/toborrm9/malicious_extension_sentry
Browser extension: https://chromewebstore.google.com/detail/malext-sentry/bpohi...

value: Show HN: Malicious Extension Sentry: database of removed Chrome/Edge extensions


value: A DIY Newtonian reflector telescope with dobsonian mount. A fun to do hobby project taking us closer to the moon and beyond. A lot of plans ahead on how  to make it much better and portable but this was the first time me and my friend implemented or rather built a telescope.<p>Have a look at what all we have captured.

value: Show HN: Curiosity – DIY 6" Newtonian Reflector Telescope


value: jaydurangodev

value:
I built JRD Garage because every auto shop management tool costs $200-400/month. Mitchell1, Tekmetric, RepairShopr — they all lock you into subscriptions for basic work orders and CRM.
JRD Garage is $99 one-time. Work orders, customer CRM, parts tracking, AI call scripts, scheduling. Runs on Cloudflare Workers so hosting is $0/month.
Built with Electron + Cloudflare Workers. Zero ongoing costs for me means zero subscription for you.
Would love feedback from anyone in the trades or building tools for niche industries.

value: Show HN: JRD Garage – $99 one-time auto shop management (Mitchell1 alternative)


value: soydanicg

value:
I kept running into the same problem: I'd change a prompt or update my model, the agent output looked fine, but the tool sequence was completely different. It stopped reading config before deploying. It
 ran npm run build instead of npm test. The bug showed up days later in prod.
So I built TracePact. Record a known-good run as a cassette, diff against new runs, get a clear report of what changed:
- read_file (seq 0) (removed)
~ bash.cmd: "npm test" -> "npm run build"
Summary: 1 removed, 1 arg changed[BLOCK]
It classifies changes as block (structural) or warn (args only), so you can gate CI with --fail-on warn. You can filter noise with --ignore-keys timestamp and --ignore-tools read_file.
No API calls needed for replay/diff. Works with any LLM provider. Vitest integration for writing assertions on tool traces.
https://github.com/dcdeve/tracepact

value: Show HN: TracePact – Catch tool-call regressions in AI agents before prod

author: soydanicg


value: suryanshmishrai

value:
Hey HN,
I built MOCC today.
It lets founders and creators turn their metrics into beautiful shareable mockups.
You can generate visuals for things like:
• SaaS MRR milestones
• Social media follower milestones
• Growth graphs
You can also connect revenue integrations like Stripe, LemonSqueezy, Polar, Paddle, RevenueCat, or enter metrics manually.
It generates a mockup you can share on X or LinkedIn.
Would love feedback from the HN community.
https://mocc-delta.vercel.app/

value: Show HN: MOCC – Turn your MRR or follower milestones into beautiful mockups

author: suryanshmishrai


value: computerex

value:
dlgo is an LLM inference engine written in Go. CPU path has zero dependencies beyond the standard library. GPU path uses Vulkan compute — no CUDA required.
I benchmarked it against Ollama using the exact same GGUF files on an RTX 4070 Ti SUPER:
GPU (dlgo Vulkan vs Ollama CUDA):
Qwen3.5 0.8B: 239 tok/s vs 187 tok/s — 28% faster
Gemma 3 270M: 456 tok/s vs 503 tok/s (−9%)
SmolLM2 360M: 420 tok/s vs 451 tok/s (−7%)
10 models tested, within 7–25% of CUDA on standard architectures
CPU (dlgo vs Ollama, same GGUF):
6 of 10 models within 9% of Ollama
2 models faster (Gemma 270M +3%, SmolLM2 360M +7%)
The Qwen3.5 result surprised me. Qwen3.5 uses a hybrid Gated Delta Net + attention architecture (SSM layers with a recurrent delta rule). I wrote 6 custom Vulkan compute shaders for it — conv1d, delta rule recurrence, L2 normalization, sigmoid gating — and the fused Vulkan pipeline ended up outperforming llama.cpp's CUDA kernels.
Vulkan means this runs on AMD, Intel, and mobile GPUs too — not just NVIDIA. Ollama's own Vulkan backend is 66–126% slower than dlgo on the models I tested.
Supports LLaMA, Qwen2/3/3.5, Gemma, Phi, SmolLM2, Mistral, plus Whisper speech-to-text. 25+ quantization formats (Q4_0 through Q8_0, all K-quants).
Three lines to run:
model, _ := dlgo.LoadLLM("model.gguf")
response, _ := model.Chat("", "What is the capital of France?")
fmt.Println(response)

value: Show HN: Go LLM inference with a Vulkan GPU back end that beats Ollama's CUDA

author: computerex


value: runtimepanic

value: Show HN: Cursor skill for Claude Code's /loop scheduler

author: runtimepanic


value: jaysth

value: Show HN: MindPlexa – Open-source AI-powered infinite canvas: Next.js, React Flow

author: jaysth


value: mo-dulaimi

value:
I've been using Claude Code for a few months, and while it's amazing at writing code, reviewing the changes became a bottleneck.
The diff is just files in alphabetical order. No context. No explanation of intent.
So I built Codebrief, a VS Code extension that:
1. Captures your git diff + session context
2. Groups changes by intent (not by filename)
3. Explains the "why" behind each group
It works with Claude Code, OpenCode, and Codex.
GitHub: https://github.com/that-one-arab/codebrief
VS Code Marketplace: https://marketplace.visualstudio.com/items?itemName=MoNazim....
Open VSX link (Cursor, Windsurf, etc...): https://open-vsx.org/extension/MoNazim/codebrief
Would love feedback from anyone else dealing with this problem.

value: Show HN: Codebrief – Make sense of AI-generated code changes

author: mo-dulaimi


value: Show HN: Flora – Compile-time Dependency Injection for Go without reflection


value: zakariaelhjouji

value: Show HN: Run any VLM on real-time video

author: zakariaelhjouji

title: Show HN: Run any VLM on real-time video


value:
I built a small tool that converts PDF books into audiobook-style audio files using text-to-speech.
The idea came from wanting to listen to technical books and papers while walking or commuting.
The project is still in alpha but already works for many PDFs.
Windows release:
https://github.com/Semtexcz/Bookvoice/releases

value: Show HN: Bookvoice – convert PDF books into audiobooks


value: Show HN: SubstanceWiki – Open-source encyclopedia of psychoactive substances


value: alexgrozav

value:
I built a small library that builds the full import dependency tree for a TypeScript or JavaScript entry file.
Given a changed file, it tells you every file that depends on it. This is useful for things like:
- selective test runs
- cache invalidation
- incremental builds
- impact analysis when refactoring
The main focus is speed. Instead of parsing ASTs, importree scans files using carefully tuned regex, which makes it extremely fast even on large projects.
I built it while working on tooling where I needed to quickly determine which parts of a codebase were affected by a change.
Hope you'll find it as useful as I do: https://github.com/alexgrozav/importree
Happy to answer any questions!

value: Show HN: Importree – Import Dependency Trees for TypeScript Files

author: alexgrozav


value: bigmacfive

value:
Hi HN. I made this.
I wanted something like Pake, but Pake's output is a binary —
when you want to change anything you have to dig into Rust.
I don't know Rust. So I built appbun, which wraps a URL in an
Electrobun project instead. The output is ~10 TypeScript files,
nothing hidden.
The annoying part was icons. Sites don't agree on where to put
them — manifest, apple-touch-icon, favicon.ico, linked SVG.
And a lot of sites return an HTML page for /favicon.ico with
status 200, which makes naive fetching just... silently wrong.
Ended up writing a scoring/validation pass that tries candidates
in order until something real comes back.
macOS DMG works today. Windows/Linux packaging is on the roadmap,
the project builds there already.
Curious if anyone's hit sites where the icon logic picks the
wrong thing.
https://github.com/bigmacfive/appbun

value: Show HN: Appbun – Turn any URL into desktop app with one command Electrobun+bun

author: bigmacfive


value: hectorm

value:
Author here. Cardea is an SSH bastion I wrote to manage access to some servers I administer. Access policies are defined in a single text file, similar to OpenSSH's authorized_keys format, with macros, server groups, time windows, and key expiry, so everything is reviewable in PRs and deployable with config management. No database, no web UI.
Clients connect with any standard SSH client encoding the target as the SSH username (e.g. ssh alice@10.0.0.1:22@bastion.internal). Sessions can be recorded in asciinema v3 format. Host keys can optionally be bound to a TPM 2.0 so they can't be extracted.
It's a single static Go binary with minimal dependencies. Builds are reproducible, immutable, and include provenance attestation.
Happy to answer questions.

value: Show HN: Cardea, SSH bastion with per-key ACLs, TPM keys and session recording

author: hectorm


value: tiansenxu

value:
Last week I shared the design pattern. This week I ran a real experiment to validate it.
Five rounds on a MySQL database (29 tables, 590MB). Key results:
- Five-Gate rejection rate: 63.6% — most interactions produce no knowledge change
- Incremental convergence: +75 → +46 → +12 → +21 → +1
- Gate 2 self-correction: caught and fixed 2 erroneous rules the Skill had written earlier
- Round 5: zero exploration steps, direct template reuse
- Accuracy: 100% (no incorrect knowledge survived)
Unexpected finding: tool usage pitfalls were captured as a high-value byproduct.
A second experiment on a larger telecom billing database is in progress for cross-domain validation.

value: Show HN: Self-Evolving Skill – empirical results from a 5-round experiment

author: tiansenxu


value:
Me and my team are building an open-source IP/SIP intercom + video surveillance platform (GPLv3).
Core ideas
- No vendor lock-in: designed to work with SIP intercoms and CCTV that expose an open API.
- Modular setup: you can start small (a private house) and scale up to apartment buildings / residential complexes / districts / even a city.
What you can build
- IP/SIP intercom for entrances, gates, barriers
- Video surveillance (live + archive) with modern server-side + admin panel (we also maintain a built-in free media server (based on ffmpeg) for mobile live + archive access: Simple-DVR)
- Mobile apps for residents (iOS/Android)
- Desktop web client for security/concierge teams
- Ticketing & field service workflows (task tracking + planning + PWA for technicians)
- Optional face recognition + license plate recognition (FALPRS)
- Integrations with billing/CRM/payments and external systems
Localization
The project is currently localized for English, Russian, Kazakh, Uzbek, Bulgarian, Arabic, Armenian. If you’d like to help, we’d love contributions for new languages (translations, terminology review, UI copy improvements, etc.).
Repositories
- Server (RBT): https://github.com/rosteleset/SmartYard-Server
- Simple-DVR media server (live + archive): https://github.com/rosteleset/Simple-DVR
- iOS app: https://github.com/rosteleset/SmartYard-iOS
- Android app: https://github.com/rosteleset/SmartYard-Android
- FALPRS (faces + plates): https://github.com/rosteleset/falprs
- Fieldworker PWA (RBT-TT): https://github.com/rosteleset/SmartYard-TT-PWA
- Desktop web client: https://github.com/rosteleset/SmartYard-Vue
- Web extensions examples: https://github.com/rosteleset/SmartYard-web
Who this might be useful for
- ISPs / telecom operators
- property management companies
- intercom installation & service teams
- building owners who want an open source self-hosted platform
Invitation
You’re welcome to use this project for free to build your own ideas/products/solutions — and if you like it, I’d love to invite you to contribute (issues, PRs, docs, localization, testing with new SIP intercoms/cameras, integrations, packaging/deployment improvements, etc.).
If you’re interested, I’d really appreciate:
- feedback on the architecture and docs
- suggestions on what hardware models we should prioritize next
- contributors/users who want to try it in their environment
Thanks!

value: Show HN: Open-source self-hosted Intercom and CCTV platform


value: Show HN: Spadyum – An Open-Source Civilization Backup Protocol


value: congzhangzh

value:
Hi HN,
I got tired of trusting cloud services with my AI conversations — so I built
the setup I actually wanted: encrypted disk, hardened OS, one-command deploy.
Demo: https://github.com/congzhangzh/your_openclaw/raw/main/demos/...
The idea is simple: if you're going to self-host AI, do it right — from the
bare metal up.
Layer 1 — The disk:
LUKS encryption + Btrfs compression (or ZFS native encryption). AI logs,
API keys, model configs — everything at rest is encrypted. Someone pulls
your disk? They get nothing.
Layer 2 — The OS:
Debian Trixie. Stable, predictable, full toolchain. No surprise updates
breaking your gateway at 3 AM.
Layer 3 — The container:
Docker with Tini as PID 1 (proper signals, no zombies). Data lives on
the host as plain files (~/.openclaw) — ls, cp, rsync. No opaque volumes.
Layer 4 — The gateway:
OpenClaw with token auth + device approval. Connect Telegram and more.
Guided onboard walks you through everything.
The whole setup:

    git clone https://github.com/congzhangzh/your_openclaw.git && cd your_openclaw
    ./shell

That's it. `openclaw onboard` inside the container does the rest.
Built-in monitoring (btop, nload, iftop) in the container. Ctrl+P, Ctrl+Q
to detach — gateway runs 24/7.
Repo includes VPS disk encryption guides and provider recommendations.
MIT-licensed. I use this daily on a cheap European VPS.
Feedback welcome:
- Is the layered security overkill or just right?
- Are you encrypting your VPS disks?
- What AI backends are you running?
GitHub: https://github.com/congzhangzh/your_openclaw

value: Show HN: OpenClaw – Self-host OpenClaw in one command

author: congzhangzh


value: firesushi

value:
I built Stardial, a customizable terminal clock written in Rust.
It started as an experiment to create a more flexible alternative to tools like tty-clock. Stardial supports animations, color themes, and layout customization so it can fit into different terminal setups and ricing environments.
Features:
- Multiple display styles
- Custom colors and themes
- Animation effects
- Adjustable size and layout
- Designed for modern terminals
The default theme is designed to look good out of the box, but everything can be customized.
GitHub:
https://github.com/USERNAME/stardial

value: Show HN: Stardial – a highly customizable terminal clock (Rust)

author: firesushi


value: cliftonc

value:
Hello everyone! I am working on an open source reporting tool, that was mostly focused on the 'Embed analytics in your app' use case, which I found was either not great or not flexible or expensive, or all three!
However, I decided today to use this library wrapped in an app that makes it work like Metabase (and I use 'like' in its broadest sense here as it is quite early in its life). I have pushed an initial version live this weekend, and am looking for input to help prioritise features that close the gap with Superset / Metabase that would stop actual users using it. I want to avoid adding things that are not necessary. For now it only connects to postgres, but I will add lots of other providers (it is bound atm by databases that Drizzle supports).
I am not targeting big enterprises, more small teams / startups that just want a really user friendly and flexible reporting tool - that includes a simplified agentic analysis workflow like hex.tech (bring your own LLM keys).
If anyone has the time to take a look and provide any feedback that would be hugely appreciated! There is a cloud option which for now is free, so if you aren't comfortable running it yourself locally (it only needs docker and a single container), you can also try that and let me know of any feedback. Nothing is paid for now but I was considering that at a v low cost level - e.g. €10 per month - to cover hosting, it is very lightweight and I dont store any data.
The github is here: https://github.com/cliftonc/drizby - I am really looking for input on the roadmap and if this is useful for others outside of my original use case (it is already actively being used in that way by myself and others).

value: Show HN: Drizby – WIP Metabase Alternative

author: cliftonc


value: AbjMV

value: Show HN: Muffle – Blur everything except the active window in macOS

author: AbjMV


value: manhg

value:
Hi HN,
I built a small web tool called J2Download.
It allows users to download videos and media from many social platforms in one place. The goal was to make something simple and fast that works directly in the browser.
Currently it supports platforms like TikTok, YouTube, Instagram, Facebook, Twitter/X, Vimeo, Reddit, Bilibili and many others.
It also supports some music platforms like SoundCloud, Mixcloud, Spotify and Tidal.
You just paste the media link and download it.
Website:
https://j2download.com
I'd really appreciate any feedback or suggestions to improve the tool.

value: Show HN: J2Download – A simple online downloader supporting 40 platforms

author: manhg


value: juliusolsson

value:
IMO every conflict dashboard going viral right now has terrible UX. Raw feeds dumped on a page, impossible to actually understand the situation from.
So I built my own.
No ads, no paywall, no signup and will never monetize it. Going open source soon.
Stack is super basic Next.js, TS, DeckGL, Vercel and Supabase for hosting. The agents system is very interesting but will do another post about that.

value: Show HN: Conflicts.app, Iran conflict dashboard better then alternatives

author: juliusolsson


value:
Proxly is a self-hosted tunneling tool that exposes local services through subdomains on your own VPS. npm install -g @a1tem/proxly, run proxly, and the interactive wizard sets up your first tunnel. No bandwidth caps, no session limits. Built it because frp's config is painful and ngrok's free tier is too limited. Open source, MIT licensed.
GitHub: https://github.com/a1tem/proxly

value: Show HN: Proxly – Self-hosted tunneling on your own domain in 60 second


value: brunooliv

value:
A while back I had posted here a running app I’ve been building to get me back into running: https://runcoach.fly.dev/
The goal was to have something that I have full control over and which allows me to stay motivated for keeping up with a running schedule! This has been a great success so far and I’m now on week 10 of following a plan for a trail run I’ll be doing in May and it has been awesome! It’s all Opus 4.6 and my own experiences with running!

value: Show HN: I've revamped my running app

author: brunooliv

title: Show HN: I've revamped my running app


value:
I've spent years watching hardware teams make the same mistakes — tooling committed before thermal was resolved, manufacturing processes not validated in PVT that only showed defects at volume, components going EOL unnoticed until the factory tried to order them.
The pattern was always the same. The costs were always avoidable. Nobody had written it down.
The result is The Hardest Hardware Lessons — covering the full journey from first prototype to end-of-life: EVT/DVT/PVT, factory evaluation, supply chain risk, mass production operations, field failures, and product lifecycle management.
https://thehardesthardwarelessons.com

value: Show HN: I wrote down every expensive hardware development mistake I've seen


value: Show HN: DB9 – Postgres, but for Agents

title: Show HN: DB9 – Postgres, but for Agents


value: crystalclass

value: Client-side tool that takes long-form content and generates platform-specific versions for Twitter/X, LinkedIn, Instagram, and email newsletters. Pure frontend, BYOK (bring your own key), content never leaves the browser. Single HTML file, no build step.

value: Show HN: RepackAI – Turn long-form content into platform-native social posts

author: crystalclass


value:
Built this after running a SaaS with zero visibility into who was probing it.
One middleware, drop it in, and it starts logging SQL injection, XSS, RCE, directory traversal, scanner bots (sqlmap, nikto, burp suite), DDoS patterns, Log4Shell — 130+ patterns total.
It never blocks anything. Purely passive — every request goes through normally. So no risk of breaking your app.
Two things I'm most proud of:
- Evasion resistance: payloads are normalized before matching so UNION/*/SELECT and double URL encoding (%2527) don't slip through
- Confidence scoring (0–100) per threat based on match count, context, and user-agent signals
Also ships with a dark-mode dashboard, Slack alerts, and 15 REST API endpoints.
composer require jayanta/laravel-threat-detection

value: Show HN: Laravel middleware that logs attacks-injection, XSS, bots, never blocks


value:
Stripe supports API versions going back to 2014 from a single codebase. Each version is handled by transformation layers, not duplicate controllers.
I built this package to bring that pattern to Laravel.
Each old version gets a transformer class with two methods:
- upgradeRequest() — converts old payload to current format before your controller runs
- downgradeResponse() — converts response back to old format before it leaves
The middleware chains them automatically across versions. Your controllers never change.
Supports 4 version detection strategies: URL prefix, header, query param, Accept header. Ships with deprecation headers (Sunset, Deprecation), artisan commands for changelog and audit.
composer require jayanta/laravel-api-versionist

value: Show HN: API versioning for Laravel – one codebase, support old versions forever


value: mozharovskyarik

value:
I built a small CLI tool to stop alt-tabbing to Vercel/Netlify dashboards
  every time a deploy fails.

  Install: npm i -g deploy-logv

  What it does:
  - List recent deployments with status (logv vercel --list)
  - View logs for a specific deploy (logv vercel -d dpl_xxx)
  - Filter by error/warning (--filter error)
  - Works with both Vercel and Netlify

  It runs 100% locally — just set your API token as an env var and go.

  GitHub: https://github.com/arikmozh/logv

value: Show HN: Deploy-logv – View Vercel and Netlify deployment logs from terminal

author: mozharovskyarik


value: zeshama

value:
I built Ivy because, in Addis Ababa, Ethiopia, the 'Internet Tax' is real. Most AI tutors are reactive—they wait for you. Ivy is proactive; she actually initiates outbound voice calls to check on you if you miss a session.
Key Technical Specs:
IIAL Engine: Quizzes and flashcards render as interactive widgets inside the chat (no menus).
Edge-Inference: Runs 100% offline on a $120 smartphone (tested in Airplane Mode).
The Goal: Providing high-fidelity tutoring to the 35 Million students in Ethiopia who face frequent connectivity blackouts.
I’m currently a Top 300 finalist in the AWS Global AI Challenge. I'd love your feedback on the latency of the on-device inference!
https://builder.aws.com/content/39w2EpJsgvWLg1yI3DNXfdX24tt/...

value: Show HN: Ivy – the first proactive, offline AI tutor

author: zeshama


value:
Focido is now live on App Store and Google Play after months of beta testing and weekly updates.
What is it?
A social to-do app where your goals live in a feed, and real humans (not AI) send nudges to help you follow through. We built it because solo trackers have ~20% completion rates, while social accountability apps like Strava get people moving consistently.
Key features:
Post tasks, get matched with motivators
Real-time nudges, replies, and task visibility
Nudge limits, cooldowns, anonymous tasks for privacy
Leagues, leaderboards, and motivator ratings
Custom reminder windows so it fits your schedule
Tech:
Flutter (iOS/Android), NestJS/Supabase backend, RevenueCat for IAP. Real-time with Supabase. MVP in ~4 months.
Why we built it:
Most productivity apps fail at completion. Humans are better at creating the right kind of pressure (guilt + reciprocity). Early data shows 3x completion uplift vs solo apps.
Links:
iOS: https://apps.apple.com/us/app/focido/id6757135734
Android: https://play.google.com/store/apps/details?id=com.focido.app...
Website:
https://focido.com
Open to questions about retention, matching, or the economics of human motivators. Curious what HN thinks of social accountability vs gamification.

value: Show HN: Focido – social to-do where real people nudge you to finish tasks


value: godelshalt

value:
Hey HN,
I've been building a tool called Malleon because I got tired of e2e tests that don't reflect what users actually do. And I have long been obsessed with using real user sessions for load testing. Having worked with tools like Tsung and Gatling for load testing, I often wished I could just replay yesterday's traffic x2 or x5 instead of using synthetic sessions. Malleon started as an attempt to bridge those two worlds.
The basic idea is: instead of writing tests from scratch, record real user sessions and turn them into replayable tests.
The SDK records things like:
- DOM interactions
- network requests
- console output
- timing between actions
The replay isn't a video. It reconstructs and replays the actual browser interactions against your app.
So a real user session becomes a reproducible test case.
Typical flow looks like:
- drop a small JS SDK into your app
- users interact with the site normally
- sessions get recorded
- you browse replays and find something interesting (bug, error, weird behavior)
- turn that session into a test and run it in CI
The test runner is self-hosted. You pull the Docker image and run it wherever you want. It drives a browser (headless or headful) and replays the interaction sequence.
Some things that turned out to be surprisingly tricky while building this:
- replaying interactions when the DOM has changed since recording
- handling viewport/layout differences
- making network replay transparent to the app
- keeping timing realistic without making tests slow
The system also collects logs, errors, and request timings so sessions are searchable and you can track network performance, see p90/p95/p99 stats, all that good stuff.
Links:
- Malleon: https://malleon.io
- Replay SDK: https://www.npmjs.com/package/@malleon/replay
- Replay CLI: https://www.npmjs.com/package/@malleon/replay-cli
- Docs: https://github.com/malleonio/malleon-documentation
Free tier available.
Curious if anyone else has run into the "our tests don't reflect what users actually do (or how they scale)" problem.

value: Show HN: Malleon – turn real user sessions into automated tests

author: godelshalt


value: steeleduncan

value: Show HN: Eyot, A programming language where the GPU is just another thread

author: steeleduncan


value: antonig

value:
Hey HN, I'm Antoni, CTO and cofounder of Golf (YC X25). We're open-sourcing our MCP server scanner: a single Go binary that discovers every MCP server configured across your IDEs and runs security checks against each one. Just `brew install golf-mcp/tap/golf-scanner && golf-scanner audit`.
We built this because we kept seeing the same thing at companies we work with: engineers install MCP servers in 30 seconds: connect Cursor to a production database, give Claude Code access to internal APIs, spin up an MCP server for Jira. And nobody on the security side knows it happened. There's no discovery mechanism. It's shadow IT, except now the "user" is an autonomous agent making tool calls on its own.
When you run `golf-scanner audit`, it scans your IDE and AI tool configs (user-level and per-project), identifies every MCP server, classifies each one by transport type, and runs ~15 security checks: command injection patterns in server args, hardcoded credentials, dangerous container configurations, script and binary permission issues, and known vulnerabilities via OSV for npm/PyPI packages. Everything is pure Go, single static binary. It produces a 0-100 risk score per server with severity-weighted findings.
The thing that motivated us to build this, and what we think the industry is getting wrong more broadly: most "AI security" tooling is actually LLM security: prompt injection detection, output filtering, guardrails on the model API layer. That protects one layer. But when someone connects Cursor to a production database via MCP, the risk isn't what the model says… it's what the agent does. The tool calls, the data access, the system connections happening downstream of the LLM. Your LLM guardrails won’t protect any of that. It's like putting a firewall on your CDN and calling your database secured. The threat model for an MCP-connected agent is closer to an unmanaged service account than a SaaS app. That's a fundamentally different security problem.
The scanner is one piece of Golf, our commercial product, an enterprise MCP control plane for managing agent tool access across your org. The platform runs the same scanner across your fleet via MDM, adds deeper checks (deep analysis of server source code for local servers, capabilities analysis for remote ones, rug-pull detection, toxic tool combinations etc.), and ties everything into a centralized inventory with access policies, PII scrubbing, and SIEM forwarding. Then you route all approved servers though our gateway to close the loop.
But if you just want to know "what MCP servers are on my machine and which ones look sketchy," that's the free tool.
brew install golf-mcp/tap/golf-scanner
golf-scanner audit
I'd genuinely love to hear from enterprise folks: How are you thinking about securing MCP servers and agent tool access today? What's missing from the current tooling?

value: Show HN: Golf Scanner – OSS tool to find and audit every MCP server

author: antonig


value:
Hi HN,
I built Vigil, a small macOS menu bar utility that shows which apps are using your camera or microphone.
macOS already shows the green/orange privacy indicator dots, but it doesn't show which application triggered them. If you have multiple apps open (Zoom, browsers, Slack, etc.), it can be difficult to know which one is actually accessing the device.
Vigil runs in the menu bar and monitors camera and microphone access in real time. When a device becomes active it attempts to attribute usage to the responsible application and displays the app name and icon.
Some technical details:
- real-time monitoring with ~2 second polling
- multiple detection signals (hardware access via IOKit, CoreMediaIO state, and permission correlations)
- per-app attribution when possible
- notifications when devices become active
- activity history with configurable retention
The app works on both Intel and Apple Silicon Macs.
Everything runs locally — there is no telemetry and no network communication.
I built it because I wanted more visibility into camera/mic access than the system indicators provide.
Happy to answer questions or hear feedback.

value: Show HN: Vigil – see which apps use your Mac's camera or microphone


value: hubab

value:
I use dictation a lot for writing, notes, and prompting agents. After trying a bunch of dictation tools, I kept running into the same issues: recurring cost, unclear privacy/data handling, and not much control over how the final text was cleaned up.
So I built Utter.
The main idea is simple: it should work as both a fast dictation tool and a longer-form voice note / meeting capture tool, while still giving the user control over where processing happens and what the output looks like.
A few things it does today:

  - global dictation with customizable shortcuts

  - saved modes for different workflows, with different prompts/models

  - remembers the last mode used per app

  - meeting recording with speaker-labeled transcripts, summaries, and action items

  - file transcription for audio/video

  - saved audio/transcripts with export options

  - prompt-based post-processing for turning raw speech into notes, messages, summaries, etc.

  - built-in note editor

  - iPhone app with dictation keyboard

A big motivation was being able to use it locally. It supports local transcription, optional local post-processing, BYOK, or cloud providers depending on the workflow. I also wanted phone-to-desktop capture to feel simple, so it syncs through iCloud and doesn’t require an account.
Curious to hear from people who use dictation heavily, especially on:

  - where current dictation tools still fall short

  - whether the “modes” idea makes sense in practice

value: Show HN: Utter, a local-first dictation app for Mac and iPhone

author: hubab


value: protoduction

value: Show HN: Myrtle – modern email templating for Go

author: protoduction


value:
Hey HN, I built Aivaro because my brother was spending $30+ per month on Zapier for workflows I could describe in one sentence.
The idea: describe what you want in plain English, AI builds the workflow, you approve sensitive steps (emails, payments), then it runs automatically.
- 20+ integrations (Google, Stripe, Slack, Shopify, etc.), 212 tools
- Visual drag-and-drop editor for manual tweaking
- Human-in-the-loop: nothing fires without your approval
I'd love feedback on the product and how I could strengthen the product to be used by a broader audience. Happy to answer questions about the architecture.

value: Show HN: Aivaro – Open-source AI alternative to Zapier


value: ppcvote

value:
I'm a solo dev in Taiwan. I built 4 AI agents that handle content, sales leads, security scanning, and ops for my tech agency — all on Gemini 2.5 Flash free tier (1,500 req/day). I use ~105. Monthly LLM cost: $0.
Architecture: 4 agents on OpenClaw (open source), running on WSL2 at home with 25 systemd timers.
What they do every day:
- Generate 8 social posts across platforms (quality-gated: generate → self-review → rewrite if score < 7/10)
- Engage with community posts and auto-reply to comments (context-aware, max 2 rounds)
- Research via RSS + HN API + Jina Reader → feed intelligence back into content
- Run UltraProbe (AI security scanner) for lead generation
- Monitor 7 endpoints, flag stale leads, sync customer data
- Auto-post blog articles to Discord when I git push (0 LLM tokens — uses commit message directly)
The token optimization trick: agents never have long conversations. Every request is (1) read pre-computed intelligence files (local markdown, 0 tokens), (2) one focused prompt with all context injected, (3) one response → parse → act → done. The research pipeline (RSS, HN, web scraping) costs 0 LLM tokens — it's pure HTTP + Jina Reader. The LLM only touches creative/analytical work.
Real numbers:
- 27 automated Threads accounts, 12K+ followers, 3.3M+ views
- 25 systemd timers, 62 scripts, 19 intelligence files
- RPD utilization: 7% (105/1,500) — 93% headroom left
- Monthly cost: $0 LLM + ~$5 infra (Vercel hobby + Firebase free)
What went wrong:
- $127 Gemini bill in 7 days. Created an API key from a billing-enabled GCP project instead of AI Studio. Thinking tokens ($3.50/1M) with no rate cap. Lesson: always create keys from AI Studio directly.
- Engagement loop bug: iterated ALL posts instead of top N. Burned 800 RPD in one day and starved everything else.
- Telegram health check called getUpdates, conflicting with the gateway's long-polling. 18 duplicate messages in 3 minutes.
The site (https://ultralab.tw) is fully bilingual (zh-TW/en) with 21 blog posts, and yes — the i18n, blog publishing, and Discord notifications are all part of the automated pipeline.
Live agent dashboard: https://ultralab.tw/agent
Stack: OpenClaw, Gemini 2.5 Flash (free), WSL2/systemd, React/TypeScript/Vite, Vercel, Firebase, Telegram Bot, Resend, Jina Reader.
GitHub (playbook): https://github.com/UltraLabTW/free-tier-agent-fleet
Happy to answer questions about the architecture, token budgeting, or what it's actually like running AI agents 24/7 as a one-person company.

value: Show HN: AI agents run my one-person company on Gemini's free tier – $0/month

author: ppcvote