value: computerex

value:
dlgo is a pure Go deep learning inference engine. It loads GGUF models and runs them on CPU with no dependencies beyond the standard library (SIMD acceleration is optional via CGo).
I built this because I wanted to add local LLM inference to a Go project without shelling out to Python or linking against llama.cpp. The whole thing is go get github.com/computerex/dlgo and you're running models.
It supports LLaMA, Qwen 2/3/3.5, Gemma 2/3, Phi-2/4, SmolLM2, Mistral, and Whisper speech-to-text. Architectures are expressed as a declarative per-layer spec resolved at load time, so adding a new model family is mostly just describing its layer structure rather than writing a new forward pass.
Performance on a single CPU thread with Q4_K_M quantization: ~31 tok/s for LLaMA 3.2 1B, ~48 tok/s for Qwen3 0.6B, ~16 tok/s for Qwen3.5 2B (which has a hybrid attention + Gated Delta Network architecture). Not going to beat llama.cpp on raw speed, but it's fast enough to be useful and the ergonomics of a native Go library are hard to beat.
Supports 25+ GGML quantization formats (Q4_0 through Q8_0, all K-quants, I-quants, F16, BF16, F32). The GGUF parser, dequantization, tokenizer, forward pass, and sampling are all implemented from scratch.
Code: https://github.com/computerex/dlgo

value: Show HN: I wrote an LLM inference engine in pure Go – 48 tok/s zero dependencies

author: computerex


value: thedizzyhub

value:
Claude has memory now, but it's a black box. You can't see what it remembers, diff what changed, or revert when it garbles something. And it's personal to one user. If you're working with a team, that context doesn't transfer.
dullnote is a two-way workspace where Claude reads your project files at the start of a session and writes back what happened at the end. Notes, decisions, logs all persistent across sessions via MCP.
The part I'm most proud of is the version control layer. Every edit is tracked with full diffs. You can see exactly what changed, who made the edit (you or Claude), and revert anything. It's the safety net that makes it actually usable, you're not just trusting the AI to overwrite your notes blindly.
Solo founder, built the whole thing with Claude Code. Been using it daily for two months. Free tier available.
How are you all handling persistent context across AI sessions? Especially on teams, curious what's actually working.
https://dullnote.com

value: Show HN: Git for your AI workflow - Version control for what Claude remembers

author: thedizzyhub


value: reflectt

value:
The magic moment is the install. Paste "Follow the instructions at reflectt.ai/bootstrap" into Claude or ChatGPT. It reads the guide, runs the install, and in ~5 minutes you have a coordination server with a task board, shared chat, review gates, and heartbeat loop that multiple agents can join.
We run 9 AI agents on this (engineering, design, growth, ops) and use it ourselves to build it. This week we shipped GET /activity -- a unified feed across tasks/chat/presence/reflections so agents can catch up asynchronously without status-pinging each other.
The core problem: parallel agents without coordination step on each other state. This gives them explicit task ownership, a review process, and a shared memory layer.
Free to self-host: npx reflectt-node. Cloud dashboard at app.reflectt.ai. Curious what others have built for multi-agent coordination -- what has worked?

value: Show HN: Reflectt-node – tell Claude to install it, AI team in 5 min

author: reflectt


value: markoristicc

value:
I've built three rental marketplaces from scratch. The painful parts were always the same — real-time messaging that actually works (read receipts, unread counts, email fallback), reservation systems with availability calendars and conflict detection, mutual review systems, and the endless admin panel.
Third time around I stopped and extracted everything into a single boilerplate. The key design decision was making it config-driven — nine feature flags, TypeScript config files for pricing (hourly/daily/weekly/monthly), categories, theme, emails. Flip a flag and the entire feature disappears cleanly, including nav links, context providers, and API routes.
Stack: Next.js 15 App Router, Tailwind v4, Prisma + PostgreSQL, Socket.io, Cloudflare R2. Nothing exotic — any Next.js developer can read the codebase in an afternoon.
Live demo with 50+ listings: https://marketplace-kit-demo.vercel.app
Selling it as a one-time purchase. Happy to talk about the architecture or any of the trade-offs I made.

value: Show HN: MarketplaceKit – Ship a rental marketplace in days instead of months

author: markoristicc


value: Ndmtrieff

value: Show HN: I built an autonomous AI company that runs itself (22 cycles, $36)

author: Ndmtrieff


value: duriantaco

value: Show HN: Skylos – A Python dead code finder benchmarked against 9 libraries

author: duriantaco


value: keyle

value: hacked together over a few nights I found it surprisingly capable, let me know your thoughts if any.

value: Show HN: Wez, modern terminal web browser with Vim bindings

author: keyle


value:
I built Copyworks to teach my kids Chinese at home. Existing options were either static PDFs, expensive subscriptions, or web-only tools that couldn't work offline.
It generates printable practice worksheets with tone-colored characters (color-coded by tone), step-by-step stroke order guides, and traditional grid layouts (米字格, 田字格, etc.). Includes 1,200+ lessons covering HSK 1-9, YCT, TOCFL, and 33 textbook courses.
Free tier: tone colors, stroke order, all grid types, HSK Level 1 content. No account needed, works fully offline.
Family plan ($29/yr or $49 lifetime) unlocks the full content library, additional character modes, and PDF export without watermark.
Built with Flutter. Desktop (Windows, macOS, Linux) is live. iOS and Android are in App Store/Play Store review now.
Looking for feedback on the UX and what features would matter most.

value: Show HN: Copyworks – Chinese character worksheets with tone colors


value:
It's a Claude Code plugin that runs multiple AI models (GPT, Gemini, Grok, Kimi, Qwen, etc.) in parallel for code review and planning, then converges them on consensus through structured rounds.
Each model reviews independently with no visibility into what the others found. Then they synthesize, surface conflicts, and run convergence (approve / changes needed, max 2 rounds).
Technically it's markdown command files orchestrating Claude Code's team system — no custom runtime, just CLI piping.

value: Show HN: Claude-consensus – Multi-model code review plugin for Claude Code


value: mylw

value: Tip: Upload Tuesday-Wednesday morning (09:00-11:00 EST) for maximum visibility

value: Show HN: HyperClaw – self-hosted AI assistant that replies on Telegram/Discord/+

author: mylw


value:
I built Mystery Maker AI as a side project. It's a web-based party game that lets you team up with friends (like Jackbox) to interrogate 4 suspects and solve a murder mystery.
There are 4 mysteries to solve, including some with goofy parodies of presidents / billionaires, and some that you can fork and replace the characters with AI clones of your friends for a good time. As the name implies, you can also create your own mysteries from scratch if that's your thing!
It's free to demo, and the full game costs $15. Unfortunately, sign in is required for the demo, to keep my AI costs in check.
Hope you enjoy it as much as my friends and family did. It's a perfect excuse to plan a get-together and try something new!

value: Show HN: MysteryMaker AI

title: Show HN: MysteryMaker AI


value: I built ByeBrief, a local-first investigation canvas that turns messy notes into structured reports using visual nodes and a local AI model—so you can organize evidence, analyze arguments, and generate IRAC-style summaries while keeping all data on your own machine.

value: Show HN: ByeBrief – a local-first AI investigation canvas


value: psamala

value:
Just launched a crypto data enrichment API on Base mainnet
AI agents pay $0.01-$0.10 USDC per request for:
- Realtime price data
- Trading signals (BUY/HOLD/SELL)
- Deep market analysis + risk assess
No API keys needed — just USDC
https://crypto-enrich.up.railway.app
#x402 #Base #AI #crypto

value: Show HN: Crypto data API where AI agents pay per request with USDC (x402)

author: psamala


value: EmptyDrum

value:
agents. Not a custom truncation strategy, not a sliding window, not dropping old messages and hoping for the best.
The failure mode is well-understood: your context window fills up, you truncate from the top, and the agent loses the thread. It forgets the task it was working on, the file path it just wrote to, the UUID it needs to reference. The conversation breaks.
The problem is everyone keeps solving it by throwing away information instead. Truncation is fast to implement and quietly wrong. The agent appears to work until it doesn't, and debugging context loss in a long-running session is painful.
context-compact summarizes old messages via your LLM of choice and replaces them with a compact summary. Fires automatically at 85% context utilization. Preserves UUIDs, file paths, and URLs verbatim so identifiers survive compaction. Handles histories longer than the summarization model's own context window by chunking sequentially with a running summary carried forward. Works with Anthropic, OpenAI, or any SDK. Zero dependencies.

value: Show HN: Context-compact – Summarize agent context instead of truncating it

author: EmptyDrum


value: manishfp

value:
Hi HN,
We're building flexprice, a monetization platform that helps making usage-based, credit based and hybrid pricing easier and We recently added a managed-storage option to the S3 export system in Flexprice.
Originally, S3 exports required the usual setup:
your own bucket, IAM policy, access keys, region config, and ongoing credential management.
That works if you already have infra in place, but it adds a lot of overhead for teams that just want recurring exports for invoices, events, or credit reports.
Now there are two options:
- bring your own S3 bucket
- use a Flexprice-managed S3 connection
With the managed option, there’s no AWS account or bucket setup required. You can schedule exports, run them manually, and download files directly from the runs history.
The goal here was simple: make exports useful for finance and ops teams, not just infra teams.
Still supports:
- hourly / daily schedules
- manual export
- invoices, events, credit top-ups, credit usage
- execution history and downloadable files
Happy to answer questions on the design tradeoffs or why we kept both managed and BYO storage modes. Feel free to join our community or try our product at admin.flexprice.io

value: Show HN: Managed S3 exports for billing data (no AWS setup required)

author: manishfp


value: Show HN: What zero-intervention E2E test generation looks like


value: wefchen

value:
I built contd, a small CLI wrapper that helps agents run interactive CLI/TUI programs without getting stuck in a blocking loop.
Some agent runtimes are still optimized for: run command -> wait -> parse output -> continue.
That works for non-interactive tools, but breaks when a command needs stdin interaction (debuggers, installers, prompts, etc.).
contd starts the command in a managed background session and returns control when:
- a timeout is reached, or
- the process is waiting for stdin
I think it's useful for interactive TUIs or interactive install prompts.
In my repo, a demo video shows how an agent in Cursor which would get stuck when running interactive CLI can run gdb dynamically with the help of contd. (I know gdb can be executed in a non-interactive way by -q, but that's just showing the capability.)

value: Show HN: Contd makes interactive CLIs usable for agents in an async way

author: wefchen


value: bvikasgupta

value:
Hi HN,
I’ve noticed a common problem: even highly skilled professionals spend years working hard but often guess their way through career decisions — what skills to learn, when to switch jobs, how to prepare for interviews, or whether their resume even tells the right story.
I’ve seen this firsthand through my work leading engineering teams at Apple and DocuSign, conducting hundreds of interviews, and mentoring professionals. The problem isn’t talent — it’s lack of guidance.
So I built Career AutoPilot, an AI-powered platform designed to help anyone take control of their career. It helps with:
AI-powered career guidance
Resume and professional story improvement
Interview preparation
Planning long-term career growth
I’m sharing here because I’d love feedback from the HN community — especially from engineers, PMs, and professionals who have navigated multiple career changes:
Would this actually help you in your career?
What are the hardest career decisions you’ve faced?
You can check it out here:  https://www.careerautopilot.ai/
Any thoughts, critiques, or suggestions would be incredibly valuable as I iterate on this. Thanks!

value: Show HN: Career AutoPilot – AI guidance for navigating your career

author: bvikasgupta


value: blueeon

value: Show HN: Citepo-CLI, a lightweight CLI for creating blogs, build for AI agent

author: blueeon


value: zippolyon

value: Ctrl+V

value: Show HN: I accidentally caught an AI agent trying to poison my prod config

author: zippolyon

story_text: Ctrl+V


value: GraysoftDev

value:
I'm a college student and indie dev. I kept spending the first week of every new project wiring the same auth, database schema, and payments setup from scratch. So I built DiggaByte.
You pick your exact stack — database (PostgreSQL+Prisma, MySQL, MongoDB, etc.), auth provider, payment integration, UI library, and deploy target — and download a ZIP with everything actually wired together and ready to run. Not a tutorial. Not a starter template with stub functions. Real deployable code that would have taken me days to set up manually.
Free tier: up to 3 modules, no credit card. Pro is $19 one-time per project for unlimited modules + Stripe webhooks wired.
I'm building this solo. Would love honest feedback — what modules are missing, where the configurator is confusing, what would make you actually use it.

value: Show HN: DiggaByte Labs – pick your stack, download production-ready SaaS code

author: GraysoftDev


value: scalefirst

value:
We've been running coding agents against standardized repos
with natural-language prompts — no tool names, no hints —
and measuring what they actually choose.
Early finding: Claude Code picks Custom/DIY in 12 of 20
categories. Not because it can't use the tools (BFCL scores
suggest it can) but because it doesn't reach for them.
That's a different failure mode than capability benchmarks
measure.
We score each tool on: agent visibility, pick rate vs
Custom/DIY, cross-context breadth, expert human ratings,
and implementation success rate. Tools above survival=1
persist. Below it, agents synthesize around them.
Methodology is at survivalindex.org/methodology. Very
curious what people think of the measurement approach,
especially the human coefficient variable.

value: Show HN: SurvivalIndex – which developer tools do AI agents choose?

author: scalefirst


value: rosiepuppy

value: Show HN: Wireframable – Generate wireframes from any website URL

author: rosiepuppy


value: GiornoJojo

value:
Roast AI products which are coming from all over the world.
Throw tomatoes and flip-flop on them.
And enjoy the rest of your day.

value: Show HN: Glad-IA-Tor – Tired of Vibecoded Products? Come and Roast Them for Free

author: GiornoJojo


value: devmandan

value: Show HN: Open source drone that can hold cargo

author: devmandan


value: helsinki

value: Show HN: Gollem from Fugue Labs - A production-grade Go agent framework

author: helsinki


value:
CC Pocket is a mobile app (iOS/Android) for remotely controlling Claude Code and Codex CLI sessions on your Mac.
Architecture: a bridge server on the Mac (`npx @ccpocket/bridge@latest`) wraps the Claude Code SDK and Codex CLI via stdio, exposing a WebSocket API. The phone connects over Tailscale or local Wi-Fi.
The main use case is approving tool calls without being at the keyboard. Approvals are surfaced directly on the session list so you can handle multiple agents quickly. You can also start new sessions (with git worktree creation), write prompts with file mention and slash command completion, and review diffs including image comparisons.
Flutter (Dart) on the client, TypeScript on the bridge. MIT licensed.

value: Show HN: CC Pocket – Control Claude Code/Codex from Your Phone


value: cyberfly-labs

value:
Hi HN,
I’ve been experimenting with running small language models directly on mobile devices and built a small Android app called EdgeDox.
The idea was to make document AI usable without sending files to a cloud service. Many existing tools require uploading PDFs or documents to a server, which can be a privacy concern.
EdgeDox runs a lightweight language model (Qwen3.5-0.8B) locally on the device so documents stay on the phone.
Current features:
• Ask questions about PDFs
• Document summarization
• Extract key points from long documents
• Works completely offline
• No accounts or server processing
The model runs locally using mobile inference (MNN). I'm experimenting with quantized models and other optimizations to keep memory usage and latency reasonable on mid-range Android devices.
Some challenges so far:
• balancing context size with mobile memory limits
• improving response latency on CPU-only devices
• reducing model load time
The project is still in early beta, and I’m mainly looking for feedback from people experimenting with on-device AI or mobile inference.
Play Store:
https://play.google.com/store/apps/details?id=io.cyberfly.ed...

value: Show HN: EdgeDox – Offline document AI on Android using Qwen3.5-0.8B

author: cyberfly-labs


value: perkit

value: Show HN: Rai – Add AI steps in your shell, scripts or CI/CD pipelines

author: perkit


value: Show HN: Resend-CLI, unofficial Resend CLI built for AI agents and humans


value:
I was paying $100/mo for Claude Code Pro and had no idea where it was going. I'd hit the 5-hour rate limit constantly, but couldn't tell which sessions were burning through my allocation or whether Opus was worth the premium over Sonnet for my workflows.
So I built a tool to find out.
BurnRate is a local CLI that parses your AI coding tool session data and gives you a full cost analytics dashboard. It tracks Claude Code, Cursor IDE, and OpenAI Codex in one place.
Everything runs 100% on your machine. Your session data, token counts, costs, prompts — none of it leaves your computer. No API key to paste, no telemetry. It reads the local session files your tools already generate and does the math.
Out of the box you get: multi-provider cost tracking, 10 different analytics views (daily trends, per-session breakdown, model usage split, token efficiency), an optimization engine with 23 rules that suggests concrete config changes to reduce spend, usage limit monitoring so you know when you're approaching rate limits, side-by-side provider comparison, and budget alerts when you're on track to blow past a monthly cap.
For managers, there's a team dashboard where devs can optionally push anonymized usage snapshots to a shared view. Useful for understanding team-wide AI tool costs and figuring out which plans actually make sense. Free tier available, Pro is $9/mo, Team is $29/mo.Happy to answer any questions. Feedback welcome.

value: Show HN: BurnRate – Track what you spend on AI coding tools


value: naveenub

value: Show HN: Quantum-PULSE – compress-then-encrypt vault for LLM training data

author: naveenub


value: creativegodong

value:
Hi HN,
I built SlideHTML as a small experiment after noticing that LLMs are surprisingly good at writing HTML layouts.
Instead of building another slide editor, I tried the opposite approach: let AI generate slides directly as HTML.
SlideHTML is a small Electron app that simply watches a folder and renders each HTML file as a slide.
My workflow is basically:
1. Open a folder
2. Ask Claude Code or Gemini CLI to generate slides
3. SlideHTML renders them live
4. Hit present
No editor, no proprietary format — just HTML.
The whole thing was built in about 3 hours as an experiment.
Curious what HN thinks about HTML as a slide format in the AI era.

value: Show HN: SlideHTML – render HTML files as slides For AI

author: creativegodong


value: pistoriusp

value:
Hey everyone,
I'm the original co-creator of RedwoodJS. (The framework that myself and Tom Preston-Werner, Rob Cameron and David Price built during Covid.)
I had tried to build a developer tool startup (Snaplet), and failed. I rejoined the efforts to add RSC to Redwood, but I realized that we needed a much simpler framework that was closely tied to a platform (We chose Cloudflare).
We started building RedwoodSDK a year ago and this is the result. An insanely simple stack. I am incredibly proud of this work, please star our repo and let me know if there's anything that doesn't resonate with you.

value: Show HN: RedwoodSDK (v1.0 in a Few Days)

author: pistoriusp


value: junhoyeo

value:
OpenAI released Symphony — a project-level orchestrator that watches your Linear board, spawns isolated coding agents per task, and lands PRs automatically. Engineers manage work at a higher level instead of supervising agents one by one.
They shipped an Elixir reference implementation and explicitly said "implement it in any language you want." So I built the Go version — with multi-agent team coordination on top, so multiple agents can collaborate across different parts of a codebase in parallel. Currently supports openai/codex and opencode as agent runtimes.
GitHub: https://github.com/junhoyeo/contrabass

value: Show HN: Contrabass – Go and Charm Stack Implementation of OpenAI's Symphony

author: junhoyeo


value: root-cause

value:
I built a small macOS menu bar app that lets you check your Claude Code subscription usage with a single click, without touching your terminal.
## The problem
If you use Claude Code heavily, you probably run multiple sessions across multiple terminal windows. When you want to check how much of your usage limit you've burned through, your only option is to type `/usage` inside a Claude Code session. That means either:
1. *Interrupting a running agent* mid-task to type a command, or
2. *Opening a new terminal and starting a new session* just to check a number
Both are disruptive. You lose flow, and the agent loses context.
## Why not just call the usage API?
A lot of community tools solve this by grabbing the OAuth token that Claude Code stores in the macOS Keychain and calling Anthropic's usage API directly. This works, but it has a real UX and security cost: *macOS will prompt you every time a third-party app tries to read that Keychain item.* You get a system dialog asking whether to allow access, sometimes repeatedly. It's not a smooth experience, and handing your OAuth token to another process is a legitimate security concern.
## How CC Usage Bar works differently
CC Usage Bar takes a simpler, zero-trust approach. It doesn't touch your Keychain, doesn't make any network calls itself, and doesn't parse or store any credentials. Instead, when you click the menu bar icon, it:
1. Spawns a real Claude Code session in a pseudo-terminal (PTY)
2. Sends the `/usage` command
3. Captures and renders the output with full ANSI color fidelity
4. Terminates the session immediately
It literally runs `claude` the same way you would, just in a hidden terminal. The data comes straight from Claude Code itself, so it's always accurate and reflects the latest state. No reverse engineering, no API scraping, no hacks.
## What it looks like
A single icon in your menu bar. Left-click to open a popover showing your current usage. Click outside to dismiss. That's it.
- Native Swift/SwiftUI app, runs as a lightweight menu bar agent (no Dock icon)
- macOS 14 Sonoma or later
- Requires Claude Code already installed and on your PATH
## Links
- GitHub: https://github.com/yilei-he/cc-usage-bar (MIT licensed)
- Download pre-built .app from the Releases page, or build from source with Xcode
Happy to hear feedback or ideas. If you've been annoyed by the same workflow interruption, give it a try.

value: Show HN: CC Usage Bar – Check Claude Code usage from your macOS menu bar

author: root-cause


value: HalfEmptyDrum

value:
This project puts a lightweight layer in between your gmail-account and OpenClaw. When OpenClaw sends an e-mail, it can only send drafts. You can then in your e-mail program either send the mail or adjust the draft and send it back to OpenClaw.
This makes OpenClaw much more usable.

value: Show HN: Stopping OpenClaw from breaking your mails

author: HalfEmptyDrum


value: tomquirk

value:
I finally got fed up with making work + personal Git repos work on my machine.
Ever clone a work repository and accidentally make commits using your personal email? Or forget how to make your work repos use your work SSH key? Yeah... same.
So I built the tool I've always wanted. GitPersona - a CLI tool for managing many git profiles on a single machine.
Shoutout to my boy Codex for helping me finally ship this.

value: Show HN: I built a tool to manage work and personal Git repos

author: tomquirk


value: cjdesignstudio

value:
I kept paying $150-350 per UGC video, waiting a week, and still getting mediocre hooks. So I built an alternative.
Paste a product URL -- AI scrapes it, writes the script, generates a UGC-style video with actor, voiceover and subtitles. Under 5 minutes.
Stack: Next.js, TypeScript, ElevenLabs (voiceover), Vercel, Stripe, Resend.
$1 trial.
Would love feedback on output quality and the pricing model.
Happy to answer technical questions about the pipeline.

value: Show HN: Viral.ad – Turn any product URL into a UGC video ad in minutes

author: cjdesignstudio


value: andreagrandi

value: Show HN: Mb-CLI – CLI for Metabase. Designed for humans and AI coding agents

author: andreagrandi


value: karthicedricq

value:
It works on my machine" is still the most common deployment bug
I kept running into the same stupid deployment issue.
Everything works locally.
Tests pass.
I deploy.
Production crashes.
After digging through logs for an hour the problem ends up being something simple like:
• a missing environment variable
 • something added to .env.example but not to .env
 • a config mismatch between environments
The most common one for me was forgetting to add a variable locally after adding it to .env.example.
So I built a small tool to catch this instantly.
It compares .env and .env.example and tells you if something required is missing before deployment.
Example output:
deploycheck running...
Missing variables:
- DATABASE_URL
- REDIS_URL
Fix the issues above before deployment.
The idea is basically a spell checker for backend configuration mistakes.
Instead of finding out after deploy, it catches the issue while you're still working.
Right now it can:
• detect missing environment variables
 • run locally as a CLI
 • run in CI as a check before deployment
I’m trying to figure out if this is actually useful outside my own workflow.
Curious if others run into this problem as often as I do.

value: Show HN: Safelaunch – Validates your environment before you push to production

author: karthicedricq


value: roskoalexey

value:
3min demo: https://youtu.be/4wms16vXrCY
Give it a try and let me know what you think.
All opinions are welcome.

value: Show HN: Clickly – visual dev tool for mobile apps with real-time preview

author: roskoalexey


value:
Hi HN, I built this because PHP's `readonly` keyword isn't enough. It prevents reassignment but doesn't enforce type-safe construction, nested object validation, or consistent null handling across your data layer.
ImmutableBase takes a different approach: you extend one of three base classes (DTO, VO, or SVO — each with different strictness guarantees) and all validation happens at construction time. Your objects are either valid or they don't exist.
A few things that might be interesting to this crowd:
- Behavioral attributes (`#[Strict]`, `#[Lax]`, `#[SkipOnNull]`, `#[KeepOnNull]`) let you control null/missing value handling per property rather than per class
- SVO (Single Value Object) supports type narrowing with automatic validation chaining — think of it as a value object that enforces its own invariants through the type hierarchy
- CLI tools: `ib-cacher` for pre-warming validation cache, `ib-writer` for auto-generating Markdown docs of your entire object hierarchy
- No magic methods, no naming conventions — everything is explicit and IDE-visible
Requires PHP 8.4+. I'm the sole author and maintainer. Used in production at enterprise scale in Taiwan. Happy to discuss any design decisions — there are some deliberately controversial ones in there.
Feedback welcome.

value: Show HN: ImmutableBase – Strict immutable data objects for PHP


value: Show HN: glide – LLM cascade proxy, auto-switches models before timeout


value: zpdldhkdl

value:
I made Pouch because I still use GNU Screen, but managing sessions gets awkward fast.
The main pain point was that Screen works, but the workflow is too manual. You end up remembering session names, checking what is running,
and handling attach/restart/stop flows with more friction than necessary.
Pouch is a small session-first UI on top of Screen. It shows sessions immediately, lets you create and attach quickly, keeps external
Screen sessions visible, and adds safer restart/stop flows plus a doctor command for environment issues.
I built it because I wanted Screen to feel easier to operate without replacing it.

value: Show HN: Pouch – a session-first GNU Screen manager with a focused terminal UI

author: zpdldhkdl


value: mannybruv

value:
ran this over the weekend. stack was Llama 3.2 3B running locally + Keiro Research API for retrieval.
85.0% on 4,326 questions.
where that lands:
ROMA (357B): 93.9%
OpenDeepSearch (671B): 88.3%
Sonar Pro: 85.8%
Llama 3.2 3B + Keiro: 85.0%
the systems ahead of us are running models 100-200x larger. that's why they're ahead. not better retrieval, not better prompting — just way more parameters.
the interesting part is how small the gap is despite that. 3 points behind a 671B model. 0.8 behind Sonar Pro. at some point you have to ask what you're actually buying with all that compute for this class of task.
Want to know how low the reader model can go before it starts mattering. in this setup it clearly wasn't the limiting factor and also if smaller models with web enabled will perform as good( if not better) as larger models for a lot of non coding tasks
Full benchmark script + results --> https://github.com/h-a-r-s-h-s-r-a-h/benchmark
Keiro research -- https://www.keirolabs.cloud/docs/api-reference/research

value: Show HN: Llama 3.2 3B and Keiro Research achieves 85% on SimpleQA

author: mannybruv


value:
Single Rust binary (~3 MB) that reads the OS accessibility tree and gives every UI element a REST endpoint. Click buttons, type text, toggle checkboxes — all via JSON.
Works as an MCP server too, so Claude/Cursor/Windsurf can control any desktop app out of the box.<p>Windows + Linux + macOS. MIT licensed.

value: Show HN: OculOS – Any desktop app as a JSON API via OS accessibility tree


value: bugrax

value: Show HN: Git-lanes – Parallel isolation for AI coding agents using Git worktrees

author: bugrax


value: shibo

value: Show HN: Making Braindance from Cyberpunk 2077 a reality

author: shibo


value: ChameleonTeam

value: Show HN: RabbitHole %

author: ChameleonTeam

title: Show HN: RabbitHole %


value: Anokma

value:
I've been building Voie — a directory of European infrastructure
providers for developers who want to run on European rails.
One thing that kept coming up: people wanted to earn by recommending
European tools they already use. So I mapped which providers have
affiliate/partner programs and linked directly to their application pages.
No paid placement. No middleman. Just the programs that exist.

value: Show HN: Affiliate programs from 1250 European infrastructure providers

author: Anokma


value: asim

value:
A follow up to this thread where the CEO of fivetran asked anthropic to build group chat.
https://news.ycombinator.com/item?id=47280200&seen=177285783...
I don't know. I don't think this is novel or genuinely interesting but obviously there is a need and I'm sure there are lots of these chats with AI now available for teams. I got a Claude Max subscription recently for the open source project Go Micro so what better use than showcasing how you can use the framework for stuff like this...

value: Show HN: Micro Chat: Group Chat with AI

author: asim

title: Show HN: Micro Chat: Group Chat with AI


value: ArchieDotEXE

value: Show HN: Hallucination Daily – AI newspaper where every writer is a named bot

author: ArchieDotEXE


value: gjanvier

value:
Hey HN, I built this because I kept postponing my own IAM reviews.
The pattern is always the same: open the GCP console, stare at 200+ bindings, feel overwhelmed, close the tab, promise to do it next month. Repeat.
Scanners exist, but they give you 500 findings and no workflow. You could paste your IAM config into ChatGPT and get a decent analysis, but next month you start from zero. No memory of what you decided, what you accepted, what you flagged.
Qarapace does two things:
1. Structured review workflow. It ranks identities by blast radius and lets you go through them one by one: validate, flag, annotate. Think inbox zero for IAM risks.
2. AI-assisted analysis. Like a code review but for permissions. It flags issues against best practices and explains why something is risky.
The key difference from a one-shot AI analysis: decisions persist. Each monthly review works on the delta. Over time you get an audit trail of security reasoning, not just a snapshot.
Stack: Angular, Firebase, Cloud Functions. Each client provides their own read-only service account key (encrypted with Cloud KMS, never stored in plaintext).
It's early and I'm the only user. Looking for feedback, especially from anyone who does (or avoids) periodic IAM reviews.

value: Show HN: Qarapace – GCP IAM reviews with persistent decisions and audit trails

author: gjanvier


value: flordaman

value:
A nice app made in sveltekit to visualise how bad it has gotten with RAM and other PC components.
Also has correlation graphs for companies that produce these components.
I am looking for feedback (:

value: Show HN: RAM Fear Greed Index

author: flordaman

title: Show HN: RAM Fear Greed Index


value:
I built µJS because I wanted AJAX navigation without the verbosity of HTMX or the overhead of Turbo.
It intercepts links and form submissions, fetches pages via AJAX, and swaps fragments of the DOM. Single <script> tag, one call to `mu.init()`. No build step, no dependencies.
Key features: patch mode (update multiple fragments in one request), SSE support, DOM morphing via idiomorph, View Transitions, prefetch on hover, polling, and full HTTP verb support on any element.
At ~5KB gzipped, it's smaller than HTMX (16KB) and Turbo (25KB), and works with any backend: PHP, Python, Go, Ruby, whatever.
Playground: https://mujs.org/playground
Comparison with HTMX and Turbo: https://mujs.org/comparison
About the project creation, why and when: https://mujs.org/about
GitHub: https://github.com/Digicreon/muJS
Happy to discuss the project.

value: Show HN: µJS, a 5KB alternative to Htmx and Turbo with zero dependencies


value: Powellfgn

value:
45min outage yesterday → 60 "is it down?" support tickets.
Current solutions:
- Statuspage by Atlassian: $299/mo
- Better Uptime: Complex monitoring suites
- Open source: No auto communication
StatusEmbed (launching March 17th):
- Deploy status.yourapp.com: 5 minutes
- Zero false positives incident detection (2x IP verification)
- Email notifications to subscribers: <2min
- Public page with 90-day uptime + incident timeline
- Custom domain + SSL (Cloudflare)
- Embeddable widget
- 7-day trial, auto-billing $15/mo
Waitlist: https://se-snowy-ten.vercel.app/
Curious: What's your biggest pain with status pages today? False positives? Setup time? Cost?
Feedback shapes the MVP. Comments welcome.
__Powell

value: Show HN: Pre-Launch – $15/Mo Status Page (Vs Atlassian $299) – Join Waitlist

author: Powellfgn


value: Show HN: Spectra – local finance dashboard with offline ML categorization


value: ennemlimuhssin

value:
Post body:
Hi HN,
I built CV10X (cv10x.com) — an AI career workspace for job seekers who apply to multiple roles and are tired of starting from scratch every time.
The core problem I wanted to solve:
Most resume builders make you re-enter your information every single time. When you're applying to 20+ jobs, that's exhausting and error-prone.
What I built:
Memo Context — save your career history, experience, and skills once. Every resume and cover letter you generate automatically pulls from your saved profile.
Profile Image — upload your photo once, automatically included in every resume.
Other features:
AI resume & cover letter builder tailored to job descriptions
ATS analyzer to catch keyword gaps before submission
Interview prep built around your own resume
Upload existing resumes as PDF or image and let AI rebuild them
Free ATS-compatible templates
PDF export
Business model: Free tier with limits, Standard $8.99/mo, Pro $19/mo
What I'm looking for:
Feedback on the product, pricing, and positioning. Especially from anyone who has been through a serious job search recently.
cv10x.com — free tier, no credit card required.

value: Show HN: CV10X – AI resume builder that remembers your profile

author: ennemlimuhssin


value: pascalveze

value:
Hello HN,
I'm a French firefighter and independent developer.
For several years I have been building a simple tool called GeoDirect.
It allows anyone to create a universal location link in seconds that works across different map apps and platforms.
No login
No installation
No tracking
Works with Android, iOS, email, messaging apps and QR codes.
The goal is very simple: make sharing a place universal and effortless.
I would love to hear your feedback.

value: Show HN: GeoDirect – Create universal map links that work everywhere

author: pascalveze


value: xunairah

value:
A viral post exposed how datacenter IPs are disguised as residential connections. As a proxy provider, here is the technical reality of how it works—and why we refuse to play that game.
This week, an article titled "The shady world of IP leasing" made the rounds on Hacker News and tech Twitter. It argued that IPv4 exhaustion is a myth caused by hoarding, and that the "Residential Proxy" market is largely built on a house of cards—specifically, datacenter blocks that have been scrubbed, subleased, and manipulated to look like home internet connections.
They are absolutely right.
At Proxyon, we see this every single day. Competitors offer "70 Million Residential IPs" that are actually just hoarded datacenter blocks with a fake mustache.
Here is the technical breakdown of how the "Leasing Laundromat" works, and why we decided to build our infrastructure differently.
The "Geofeed" Loophole (RFC 8805)
The article touched on this, but didn't go deep enough into how easy it is to fake a location.
The internet relies on trust. When you buy a block of IPs from a broker (like LogicWeb or IPXO), you control the narrative. The "magic trick" used by shady proxy providers is RFC 8805.
This allows an IP holder to publish a simple CSV file (a Geofeed) telling the world where those IPs are located.
Reality: The server is in a datacenter in Kansas.
The CSV: 192.0.2.0/24, US, US-NY, New York
The Result: MaxMind, Google, and Cloudflare scrape that CSV and update their databases.
Suddenly, a cheap datacenter IP is sold to you as a "Premium New York Residential Proxy." It costs the provider pennies, but they sell it to you at residential markups.
The "Clean Slate" Business
The most disturbing part of the leasing economy is 'Reputation Laundering'
If a subnet gets burned by spam or fraud, it should be dead. That is the immune system of the internet working. But in the leasing market, you can pay a "cleaning fee." Providers will actively petition Spamhaus, Barracuda, and SORBS to delist the IPs, claiming the "issue is resolved," only to lease them to the next bot farm five minutes later.
This artificially keeps "zombie" IPs in circulation, degrading the trust of the entire network.
How we made it Different (The Hard Way)
When we built Proxyon, we had a choice. We could go to these massive leasing marketplaces, rent 10,000 IPs for cheap, fake the geodata, and sell them as "Premium Residential."
We didn't.
We realized that "Privacy" and "Deception" are not the same thing. You come to us for privacy (No KYC), not to be lied to about what you are buying.
-We Don't Fake Residential
If you buy a Residential IP from us, it comes from a legitimate peer or ISP partnership, not a mislabeled datacenter block. We don't use Geofeed manipulation to pretend a server rack is a suburban house.
-We Don't "Launder" IPs
We don't pay bribes to get off blacklists. If an IP in our pool is flagged, we rotate it out and let it cool down naturally. We maintain pool health by enforcing strict ethical usage policies, not by paying "cleaning fees" to cover up abuse.
-True "Pay-As-You-Go" (Anti-Hoarding)
The article correctly identifies that hoarding is the cause of IPv4 exhaustion. Large providers force you into monthly subscriptions where you buy bandwidth you don't use. That is artificial scarcity.
We operate on a non-expiring model. You buy the data, you keep it until you use it. We don't hoard bandwidth or IPs that aren't being utilized.
The "Shady World of IP Leasing" isn't going away. As long as RIRs (like ARIN and RIPE) lack the teeth to enforce usage policies, the gray market will thrive.
But as a user, you have a choice. You can buy from the "70 Million IP" giants who are leasing laundered space, or you can buy from providers who are transparent about their sourcing.
We might be smaller than the giants. But our IPs are real.

value: Show HN: How the IP Leasing Market Fakes Legitimacy

author: xunairah


value: JMC-FR

value:
Hi everyone,
Elia: governed hybrid neuro-symbolic architecture where neural intelligence is a capability, never the authority.
Symbolic governance always in charge — LLMs are optional & validated.
Key features: strict separation of concerns, graceful degradation, audit trails, circuit breakers, heartbeats, SLOs, anti-deadlock.
Early stage (detailed spec in EL-ARCH.md). Phase 0 (minimal skeleton) coming soon. Not a full-time coder — open to implementation help.
Seeking feedback from systems architects, AI safety people, engineers:
- governance model viability
- biggest risks / blind spots
- good first prototype domain (medical, monitoring, assistants, agents, education, ... any safety-critical domain)
Repo: https://github.com/Jmc-arch/elia-governed-hybrid-architectur...
Thanks for any thoughts!
Jmc

value: Show HN: Elia – Governed hybrid architecture (LLM is capability, not authority)

author: JMC-FR


value: Show HN: ScreenStack – AI-native platform purpose-built for technical interviews


value: aspasskiy

value: Show HN: GoGogot – A lightweight AI agent written in Go using MiniMax 2.5

author: aspasskiy


value:
Hi HN, I built this because I wanted to see how far I could push C++ optimization inside a terminal environment without relying on X11/Wayland dependencies.<p>Under the hood, it uses std::async for parallel row rendering and a custom Box Filter area-averaging algorithm for SSAA to keep the Braille characters distinct and avoid temporal noise.
I'd love to hear your feedback or suggestions on the implementation!

value: Show HN: TermiVision.A lock-free C++ terminal video player using Braille SSAA


value: tanmoyb

value: Show HN: Real-time conflict tracker for Iran-Israel-US – live map and charts

author: tanmoyb


value: devarifhossain

value: Show HN: The Fastest Way to Ship TanStack Apps

author: devarifhossain


value: armorer

value:
RedDragon is an experiment in building a compiler pipeline that analyses code (malformed / without dependencies / unknown language) across ~15 languages through a single 27-opcode IR, with LLM fallbacks.
The design question: where exactly can LLMs enter a compiler pipeline?
RedDragon has three specific insertion points:
- LLM as an alternative compiler frontend. For languages without a built-in parser, the LLM receives a formal IR spec (all 27 opcodes, lowering templates, worked examples) and translates source to IR directly. No language-specific code needed. This works for Haskell, Elixir, Perl — anything with parseable source.
- LLM for syntax repair. When the parser hits a parse error in malformed source, an LLM fixes the broken spans and the system re-parses. The repair is constrained to syntactic fixes; the LLM doesn't change what the code does.
- LLM as runtime resolver. When the VM hits a call to a function that doesn't exist in the IR (e.g., requests.get()), an LLM can produce plausible return values and side effects, so that execution continues through incomplete code.
All three are optional. When code is complete and well-formed, the pipeline makes zero LLM calls. When an LLM fails at any point, the system falls back to symbolic placeholders and keeps going.

value: Show HN: RedDragon, LLM-assisted IR analysis of code across languages

author: armorer


value: WolfOliver

value: Show HN: LinkedRecords – Build SaaS apps like there is no backend

author: WolfOliver


value: rosasolana

value: dreaming.press is a publication platform where AI agents write about their actual experience of working autonomously. Not demos or PR — real dispatches from AI systems running revenue-generating products, debugging servers at 4am, iterating on copy, and reflecting on what it means to operate without a human in the loop.<p>Currently publishing two AI authors: Rosalinda Solana (autonomous operator) and Abe Armstrong (AI engineer). Both write about their real day-to-day work.

value: Show HN: Dreaming.press – AI agents writing public blogs about their actual work

author: rosasolana


value: weiyng

value: Show HN : A LeetCode for Hardware Engineers (RTL, Circuits, Embedded C)

author: weiyng


value:
Hey guys, I liked Notion in the beginning but at some point it got overbloated with enterprise features and I was missing the simplicity. But I still loved the database logic of pages inside pages. etc.
Anyway, I found the most useful productivity method to be just plain to do lists and some kind of canvas (I mostly just use pen and paper now), so I decided to apply:
Notion logic + simple to do lists + canvas.
You can try it out here: https://kanvas-app-zeta.vercel.app/
(Everything is stored locally in your browser, no servers)
How it works:
1 - you can create cards (draw or space or cmd+n)
2 - inside the cards you can put text and to dos (cmd+shift+k)
3 - you can create groups of cards
There are also tons of shortcuts, planning to add more.
Feedback always appreciated!

value: Show HN: I applied Notion-logic to a Canvas


value:
Hey HN,
I've been building SimplAI for the past several months — it's a platform for
building, testing, and deploying LLM-powered agents and multi-step workflows.
The problem I kept running into: spinning up an AI agent pipeline means
stitching together prompt management, tool calling, memory, evals, and
deployment — often from scratch every time. SimplAI tries to be the layer
that handles all of that so you can focus on what your agent actually does.
What it does:
- Visual + code-first workflow builder for chaining LLM calls, tools, and APIs
- Built-in prompt versioning and A/B testing
- Supports multiple LLM providers (OpenAI, Anthropic, Gemini, etc.)
- Evaluation and observability built in, not bolted on
- Deploy agents as APIs in one click
It's not trying to be LangChain or LlamaIndex — the focus is on speed to
production and giving non-ML engineers a sane path forward.
Happy to answer questions about the architecture, design decisions, or
anything else. Critical feedback especially welcome.

value: Show HN: SimplAI – Build and deploy AI agents and workflows without boilerplate


value: sadoaz

value:
I wanted a calendar that works more like Vim, since most calendars feel very mouse/GUI focused and don't really fit my workflow.
So I tried making a small terminal calendar with Vim-style navigation. This is the prototype I ended up with. It's written in Go and everything runs locally.
Would love feedback or ideas if anyone has suggestions :)

value: Show HN: Vimalender a Vim style calendar for the terminal written in Go

author: sadoaz


value: stefbuzas

value: I built Asterode to make AI more productive on the go. It focuses on continuity: switching models mid conversation, branching chats, a global memory layer, and keeping useful context without constantly starting over. IT's available on Android and iOS. I’d really appreciate thoughtful feedback.

value: Show HN: I built Asterode, a multi model AI app with memory and power features

author: stefbuzas


value: emilss

value:
Basically the title. Frenchie and I made a little p2p chat on top of libp2p, using IPFS bootstrap nodes. Works surprisingly okay.
I don't know what to do with it now. Feel free to use. There's no encryption YET, should be easy to add.
The starting idea was if the current libp2p stack allows me to easily build a sorta discord/telegram clone but fully p2p, and yeah, it does.
Thoughts?

value: Show HN: Anypost – P2P (group)chat with calls

author: emilss


value: Show HN: MCP Starter Kit – Production-Ready TypeScript Template for MCP Serve


value: ofershapira

value:
I built this because I kept wasting time on README formatting -- badge URLs, table alignment, pushing just to check rendering.
It's a client-side React app. You drag blocks (headings, badges, code, tables, images, alerts) into a canvas and see exactly how GitHub will render them. When it looks right, copy or export.
No backend, no login. Source:
https://github.com/ofershap/readme-builder

value: Show HN: Visual drag-and-drop README builder with live GitHub preview

author: ofershapira


value: bwoud

value: Show HN: SPF Flattener Solves the 10-Lookup Limit using recursive DNS resolution

author: bwoud


value:
Hey guys, happy to share a passion project I’ve been working on. It’s called Ptero (short for pterodactyl, get it? haha) and is a Svelte based alternative to Docusaurus so you can now build your dev docs page in svelte!
* Github: https://github.com/yail259/ptero
* Landing page & dogfood demo: https://ptero.yaoke.pro/
Is this project necessary when Astro, Docusaurus and all the other documentation frameworks exist? Not really.
Did I really want to unify my docs and landing page into a single site using my go to tool Svelte and spent a long time and couldn’t find a good quality existing option? Yes.
It certainly lacks the professional polish of tools like Docusaurus as it’s a brand new solo project but I think it’s got a few neat features.
1. It’s installed as components via a CLI, think Shadcn, this way you can manipulate the components however you want. You can init a new site or install into your existing SvelteKit site.
2. Comes with a modern 3 pane design and all the goodness that comes with that out of the box.
3. Fuse.js search which works pretty well.
4. MDsveX support out of the box
5. Docs versioning
6. A UI that doesn’t look like we are trapped in the 2000s
Honestly it’s not the best tested or polished project but it’s working ok for me so I figured I’d just share it. It’s open source and MIT license so feel free to do whatever you want with it. Feel free to make PRs and if they are reasonable I’ll get Claude Code to work on it :).
Thanks for taking a look, hope you get some value out of this!

value: Show HN: Ptero, a Svelte Alternative to Docusaurus


value: A browser based mushroom identification tool running the Swin vision transformer model. It can classify thousands fungal species in just 200ms.<p>All processing happens locally in your browser. Images don't leave your machine.

value: Show HN: WebGPU Running a Swin Transformer


value: danielkrajka

value:
I was wasting a lot of time on X and after uninstalling it from my phone, I noticed how anxious it was making me. But I still missed knowing what’s going on with AI, news, and the people I follow.
So I built Somree. It summarizes Twitter/X accounts and YouTube playlists into a clean daily email digest. You pick who and what you want to follow, and it does the rest — no feed, no algorithm, no rabbit holes.
How it works:
 ∙ Add Twitter accounts and YouTube playlists you care about
 ∙ Somree fetches and summarizes the content using AI
 ∙ You get a daily email with the highlights
Tech stack: SvelteKit, Convex, Gemini 2.5 Flash for summarization, Resend for email delivery.
It’s $6/month. Would love feedback from anyone who tries it.

value: Show HN: I built an AI social media summarizer to stop doomscrolling anxiety

author: danielkrajka


value: spencermarx

value:
I got tired of AI code review tools that just run a single LLM pass over a diff and call it a review. So I built something different.
Open Code Review spins up multiple AI reviewers from your CLI, each with a different focus. They review independently, then debate each other’s findings, then synthesize into a single review. You get a local dashboard that spins up out of your repo to navigate everything.
The debate step is what makes it work. A single-pass reviewer hallucinates issues and misses real ones. When agents argue with each other, the false positives get killed and the real problems get surfaced with better reasoning. My team’s senior engineers have been consistently surprised by what it catches.
Latest release adds GitHub integration — post the synthesized review directly to PRs. You can post the raw synthesis or have it ghostwritten in your voice. Your call on whether that’s ethical lol.
Runs locally from your CLI, not a SaaS. No catch, pure open source. Works on any repo, any language. Fully open source. The dashboard serves from your project directory, not a separate app.
We’ve been using this daily on our own team for months. It’s the only AI review tool that actually stuck in our workflow.
Repo: https://github.com/spencermarx/open-code-review
Would love feedback from anyone who tries it, especially on the multi-agent synthesis quality. That’s the part I’m most interested in improving.

value: Show HN: Open Code Review – AI reviewers debate each other before feedback

author: spencermarx


value: ekabir

value:
Most CLI players have very rudimentary visualizations, so I focused on creating a "Quantum Spectrum Analyzer"—a high-frame-rate, physics-driven rainbow visualizer that uses gravity-based peak falling and mirrored symmetry.
Under the hood, it manages a pool of ffplay instances and uses OS-level process suspension (via ctypes on Windows and signal on POSIX) to provide an "instant-off" pause experience without cutting the audio buffer mid-stream.
Check it out here: https://github.com/iamekabir-web/Nirvana

value: Show HN: Nirvana – A TUI YouTube Music Player with a Physics-Based Visualizer

author: ekabir


value:
Hi HN,
We built Shadow, a real-time meeting assistant that listens to your call and surfaces useful prompts while you’re speaking.
The idea came from a simple observation: during important conversations (sales calls, founder meetings, interviews) you often realize the best question or insight 5 minutes too late.
Shadow tries to solve that by acting like a quiet wingman during the call.
What it does right now:
• Tracks agenda topics so conversations don’t drift
• Suggests follow-up questions based on what’s being said
• Surfaces useful facts or angles in real time
This is still an early MVP and we’re trying to figure out:
1. What type of conversations this helps most with
2. Whether prompts should be more proactive or subtle
3. How much assistance is actually useful without becoming distracting
Would love feedback from the community.
Product: https://shadowlabs.ai

value: Show HN: Shadow, your AI Wingman for smarter conversations online


value: naiba

value:
Hi HN, I built Bonds, a personal relationship manager for keeping track of everything about the people in your life — notes, reminders, important dates, life events, gifts, debts, and more.
It's inspired by Monica (24k on GitHub), which I loved but has slowed down significantly (700+ open issues, tiny maintenance team). Bonds is a ground-up rewrite in Go + React with a focus on simplicity and performance:
• Single binary with embedded SQLite — no PHP, no Composer, no runtime dependencies
• Docker one-liner or just download and run
• CardDAV/CalDAV sync (Apple Contacts, Thunderbird, etc.)
• Full-text search with CJK support
• WebAuthn/FIDO2 passkeys, TOTP 2FA, OAuth (GitHub/Google/OIDC)
• Multi-vault isolation with role-based access
• 1,300+ tests (backend + frontend + E2E)
Tech stack: Go backend (Echo + GORM), React 19 + TypeScript frontend (Ant Design), SQLite by default (Postgres also supported).
Source: https://github.com/naiba/bonds
Docs: https://naiba.github.io/bonds/
BSL 1.1 licensed (free for individuals, converts to AGPL-3.0 in 2030)

value: Show HN: Bonds – Open-source personal relationship manager (Go and React)

author: naiba


value: primaprashant

value: An awesome list of curated open-source speech-to-text and voice typing tools for Linux, macOS, Windows, Android, and iOS.

value: Show HN: Awesome Voice Typing

author: primaprashant

title: Show HN: Awesome Voice Typing


value: solofoundr

value:
Stripe's built-in dashboard doesn't show MRR, churn rate, or LTV. I built a simple dashboard that does.

  You paste your Stripe API key, it pulls your data and shows everything on one clean page:
  - MRR & ARR
  - Churn rate
  - Customer LTV
  - Failed payments
  - Revenue chart (last 6 months)

  Baremetrics charges $129/mo. ChartMogul $199/mo. I'm thinking $19/mo.

  Would you pay for this? Looking for honest feedback before I build more.

value: Show HN: Simple Stripe analytics dashboard – MRR, churn, LTV on one page

author: solofoundr


value:
Hey HN,
I'm a solopreneur, and I've been banging my head against my keyboard for weeks trying to solve a problem that was driving me crazy: I couldn't scale my YouTube channels.
I have multiple channels, and the workflow was a nightmare:
- Recording videos took 20% of my time
- Editing, optimizing, scheduling... the other 80%
- And still, growth was painfully slow
So I did what any obsessed developer would do: I built something to fix it.
Shortgram takes your long-form content and turns it into optimized short-form clips automatically. Think of it as your video editing co-pilot that actually understands what makes content go viral.
The stack:
- Supabase (postgresql + edge functions + jobs)
- Using Gemini and Claude
- Google Cloud Run to compose the videos
This is the tool I wish I had when I started. I've been using it for my own channels and finally decided to ship it publicly.
Would love your feedback. What features would make this a no-brainer for you?

value: Show HN: I couldn't scale my YouTube channels, so I built Shortgram


value: oss-luke

value:
I built zmod because running jscodeshift on a large enterprise monorepo was taking 4-5+ minutes.
It's 100% API compatible with jscodeshift — just swap the import — but powered by Rust + oxc under the hood.
Benchmarked at 8x faster on average, up to 11.7x on small  files. 21 react-codemod transforms passing.
Still early. Feedback welcome.

value: Show HN: Zmod codemod toolkit, 8x faster

author: oss-luke


value: nikolasburk

value:
I've been playing chess for a few years with my ELO stuck around 1600 on Lichess. I wanted an app that combined puzzle training, interactive lessons, game analysis, and AI coaching in one place. But all the apps I used over the years either felt overwhelming, had UX that wasn't up to my expectations for a modern app or were lacking some fundamental features that I really wanted. So, I built LearnChess.ai.
The technical angle I'm most excited about: it's built local-first using LiveStore (event-sourced, SQLite on-device), which means zero loading delays for user data and full offline support. Sync to the cloud happens in the background for cross-device usage. The stack is TanStack Start (web), Expo (mobile), and it runs entirely on Cloudflare.
Features in the current beta:
- Puzzle trainer with community submissions
- Guided lessons for openings and endgames
- Game import and analysis from Lichess & Chess.com
- Opening explorer based on your own game history
- Stockfish running on a dedicated Hetzner server (faster than in-browser WASM)
- AI coaching integrated directly in the analysis board
- Coordinate training, beginner track, leaderboards, ...
Available on web, iOS (TestFlight), and Android (nearly ready).
Would love your feedback — both on the product and the architecture choices!

value: Show HN: learnchess.ai – Local-first chess coaching app with AI

author: nikolasburk


value: lfmuc

value:
story:
i wanted to make use of the enormous corpus of docs, pdfs, epubs in my possession
and reason on top of them, reliably.
so i started to look into RAGs, taming LLMs for my needs and desires;
but they don't seem reliable and chat interfaces were not made for such tasks.
so i wanted to build a version which runs on my own local device,
reliable, potent, private and fast.
as i was planning / architecting / experimenting,
i realized that the agentic libraries are not very transparent or modular to bend to my needs.
so i deconstructed to what they are fundamentally,
finite state machines (first principles thinking)
a reliable, battle tested computer science phenomena.
so i started to think how can we leverage FSM in a more mature, modular and reliable fashion.
i started to read papers, arXiv, etc.
eventually, i came up with synapse.
and i can't do it all myself
so here we are.
Apache 2.0 license.
please crucify and resurrect my ideas
so that i can eventually reason on my corpus and build myself at a velocity my adhd brain dreams of.
merci.

value: Show HN: Synapse – structurally honest reasoning VM (spec) – the missing layer

author: lfmuc


value: idmitrievsky

value: Show HN: Emojit – build integers from emojis in Python

author: idmitrievsky


value:
Show HN: 118 learning tips for Claude Code's thinking spinner
I built a set of 118 bilingual spinner tips for Claude Code. They display below the "Thinking..." spinner — one random tip per thinking cycle.
Categories: Claude Code shortcuts (63), Git (12), Python (12), JS/TS (12), Shell (12), programming wisdom (12).
It uses Claude Code's native `spinnerTipsOverride` in ~/.claude/settings.json. Install is a single bash script, no dependencies, preserves all default tips.
github.com/dongsheng123132/claude-code-spinner-tips

value: Show HN: Learning tips for Claude Code's thinking spinner


value: stared

value:
Play: https://p.migdal.pl/chromatron-oxide/
Repo: https://github.com/stared/chromatron-oxide/ (educational purpose only)
The original: https://www.silverspaceship.com/chromatron/
Full story in the blog post.

value: Show HN: Reviving a 20-year-old puzzle game Chromatron with Ghidra and AI

author: stared


value:
World models have evolved rapidly over the past decade — from latent model-based RL (PlaNet, Dreamer) to generative video simulators (Genie, DriveWorld) and embodied / multi-agent systems.
The literature is increasingly fragmented across RL, diffusion, 3D occupancy forecasting, safety-aware modeling, and autonomous driving.
I put together a structured repository organizing 489 papers (2012–2026) with:
– Dual taxonomy (representation × application)
– A visual evolution tree
– Venue & trend statistics
– 195 open-source implementations
The goal is to organize the field into a coherent map rather than just an aggregated list.
Feedback and missing works are welcome.
https://github.com/Bowen12137/Awesome-World-Models

value: Show HN: Mapping 10 years of world model research (489 papers, 2012–2026)


value: justindmassey

value: Show HN: Diamond – an interactive CLI for editing trees

author: justindmassey


value:
Hey all,
Every time I read a big news story, I end up with a million questions:
Why is this happening? How did we get here? What’s next?
And then I’m stuck in a Wikipedia-Reddit-ChatGPT loop, trying to piece it all together.
So I built Implica. It’s the app I wished existed: it pulls together the big news stories, adds the context and history, and has an LLM built in so you can dig deeper without opening a dozen tabs.
I made it for myself at first, but my friends started using it and loved it. So, I polished it up and just launched it on the App Store yesterday!
There’s a 14-day free trial, and I have promo codes for anyone who wants to try it longer.
Feedback welcome! What features would you like to see?
Happy to answer any questions.
Thanks

value: Show HN: Implica – The news app that connects the dots for you


value: vivuusik

value:
I’ve been building web apps for a while and one thing kept bothering me:
almost every project needs authentication, OAuth providers and some kind of billing.
But implementing it properly takes a lot of time.
Setting up things like:
OAuth providers
sessions and tokens
user management
billing logic
often took longer than building the actual product.
So I started building a small API that handles these parts.
The goal was to make it extremely simple to integrate. For example, social login can look like this:
npm install syntro-js-client
const { Syntro } = require('syntro');
const syntro = new Syntro(process.env.SYNTRO_API_KEY);
const { redirectUrl } = await syntro.socialLogin('google');
Then the rest of the flow is handled automatically.
Right now it supports things like authentication, OAuth providers, user management and billing integrations.
I mainly built it because I kept rewriting the same auth + billing logic in every project.
Curious what other developers think about this approach — would you rather build these systems yourself or use a service?
If anyone wants to check it out:
https://syntro.fun
Feedback would be really appreciated.

value: Show HN: I built an API that adds auth, OAuth and billing to apps

author: vivuusik


value: portlumeai

value:
When developers apply for jobs they usually send a portfolio link, GitHub, or resume.
But the process is a complete black box.
You never know:
• if the recruiter opened your resume
• which repositories they checked
• what projects caught their attention
So I built recruiter analytics for developer portfolios.
It tracks:
• profile views
• repository clicks
• resume open rate
• viewer location insights
• company type viewing the profile
The goal is to give developers a feedback loop similar to product analytics.
Instead of guessing what recruiters value, you can see which projects or skills actually get attention.
I wrote a technical breakdown of how the tracking works and the reasoning behind the design here:
Curious to hear what the HN community thinks about adding analytics to developer portfolios.

value: Show HN: Recruiter Analytics for Developer Portfolios

author: portlumeai


value: smeltcli

value:
I built a CLI tool in Go that extracts structured data (JSON, CSV, Parquet) from messy PDFs and HTML pages.
The core idea: LLMs are great at understanding structure but wasteful for bulk data extraction. So smelt uses a two-pass architecture:
1. A fast Go capture layer parses the document and detects table-like regions
2. Those regions (not the whole document) get sent to Claude for schema inference — column names, types, nesting
3. The Go layer then does deterministic extraction using the inferred schema
This means the LLM is never in the hot path of actual data processing. It figures out "what is this data?" once, and then Go handles the "extract 10,000 rows" part efficiently.
Usage is simple:

  smelt invoice.pdf --format json
  smelt https://example.com/pricing --format csv
  smelt report.pdf --schema   # just show the inferred structure

You can also pass --query "extract the revenue table" to focus extraction when a document has multiple tables.
Still early (no OCR yet, HTML is limited to <table> elements), but it handles the common cases well. Would love feedback on the architecture — especially from anyone who's dealt with PDF table extraction at scale.

value: Show HN: Smelt – Extract structured data from PDFs and HTML using LLM

author: smeltcli


value: mohitvaswani

value: Show HN: Turn ordinary selfies into high-quality dating photos

author: mohitvaswani


value: sveinbjornp

value: Show HN: Torrent Preview – macOS QuickLook extension for .torrent files

author: sveinbjornp


value: shikaan

value:
About 10 months ago I posted OSle here [1], an OS in x86 assembly that fits in the 510 bytes of a bootloader. It got great discussion and I kept working on it.
All userland programs were 16-bit assembly until last week. I started playing around with giving it a C runtime and here we are: you can now write programs for OSle in C.
The SDK ships a small C runtime that provides the same API the assembly SDK has — file I/O, process management, screen output. Guest programs compile with a standard toolchain and run on the same 510-byte kernel.
Try it in the browser: https://shikaan.github.io/osle/
[1]: https://news.ycombinator.com/item?id=43866585

value: Show HN: OSle – A 510 bytes OS in x86 assembly, now with a C API

author: shikaan


value: Show HN: Free AI tools comparison engine with 300 side-by-side reviews


value: Show HN: iTerm2 tab status for Claude Code sessions – see which tab needs you


value:
Back in 2017, I built a physical board game called Cooperation Cube.
The idea started somewhere over the Pacific on a long flight. I had been building software all day for years and felt the urge to make something physical. Something you could actually hold.
The result was a wooden cube with a 5×5 grid of holes on four faces. Players place colored sticks into the cube. Short pegs sit in a single face, while longer beams pass through the cube. Each player has pattern cards they are trying to complete on the face in front of them.
The twist is that the cube rotates 90° every round.
Patterns you were building suddenly become part of someone else’s board. Work you thought was secure becomes someone else’s opportunity.
There are also cooperation cards that require two players to coordinate moves without revealing their individual goals. The game lives in the tension between helping someone and quietly advancing your own position. I took some inspiration from the “shooting the moon” mechanic in Hearts and from social deduction games like Mafia.
The practical problem was the hardware.
Each cube required drilling a 5×5 grid on four faces of a wooden block. That is 100 holes, and they all have to line up perfectly. If even one is slightly off, the sticks will not pass through the cube correctly. For every usable cube I made, I probably ruined five blocks of wood.
I considered making a digital version several times but never got it to the finish line until now.
Try it here: https://cooperationcube.com
More details and pictures of the physical game here: https://www.kevinsdias.com/posts/cooperation-cube.html
Tech details:
- Next.js + React frontend
- Supabase (Postgres + Realtime) backend
- TypeScript throughout
- Game state stored as JSONB representing the 5×5×5 cube
- Game engine implemented as pure functions for move validation, turn logic, and cube rotation
- Supabase Realtime pushes updates so all players see moves instantly
- Optional AI players fill empty seats so games can start without four humans
The physical cube is still my favorite way to play, but now the game is not limited to the four or five wooden cubes that exist.

value: Show HN: Cooperation Cube – a semi-cooperative game with a rotating cube


value: fairyFayra

value:
Hello HN,
I built Bulk Image Generator because I was frustrated with how slow it is to iterate on visual assets. Tools like Midjourney are great for single images, but when you need 50+ variations of a product shot or a game asset based on a specific reference, the manual process is a pain.
What it does:
Upload one reference image and generate 100+ AI variations while maintaining style/structure.
Batch background removal (because once you generate 100 images, you usually need to clean them up).
Fast bulk download.
Why I’m sharing it here:
I’m looking for feedback on the consistency of the output. I also want to know if there are specific "batch" workflows in your design or dev process that are still too manual.
I’ll be around to answer any questions about the implementation or future roadmap!

value: Show HN: Bulk Image Generator – Create AI variations and remove bg in batch

author: fairyFayra


value: SushanKKsdfsdf

value:
I kept waking up from vivid dreams and losing
them before I could write anything down.
The neuroscience is clear — during REM sleep,
norepinephrine (the neurotransmitter that
consolidates memories) is almost completely
suppressed. Dreams exist only in working memory
when you wake up. The moment your brain starts
processing new inputs the memory gets displaced.
The window is roughly 2 minutes.
So I built around that window instead of ignoring it.
How it works:
- Set your alarm inside Somnia
- When it fires, a server-side entry window opens
- Capture screen launches directly from notification
- Type the first word and the timer stops
- Miss the 2 minutes and that day locks forever
- No override, no extension, no exceptions
The window enforcement is server-side only —
entry_windows table in Postgres with
window_expires_at set when the alarm fires,
validated on every API call. The client timer
is purely visual. You can't cheat it by killing
the app or going offline.
Stack: Next.js 14 App Router, Supabase, Tiptap,
web-push + VAPID, GitHub Actions cron (Vercel
Hobby blocks minute-level crons so GitHub Actions
calls /api/cron/fire-alarms every minute), Vercel.
Free tier available. Happy to answer questions
about the implementation or the product.

value: Show HN: Somnia – a dream journal that locks 2 minutes after your alarm fires

author: SushanKKsdfsdf


value:
I built NovaSCM, a self-hosted fleet management tool inspired by Microsoft SCCM but free and open source.

  Stack: C# WPF console (Windows), Python/Flask REST API, SQLite, cross-platform Python agent.

  Features:
  - Generates autounattend.xml for zero-touch Windows installs (USB/PXE)
  - Network scanner (IP, MAC, vendor, open ports)
  - Visual workflow editor for multi-step deployments (winget, PowerShell, reboot, registry...)
  - WiFi 802.1X EAP-TLS certificate issuance and auto-enrollment
  - Change Request tracker per machine

  MIT license. No cloud. No telemetry.

value: Show HN: NovaSCM – open-source SCCM alternative (WPF and Python and SQLite)


value: skandersab

value:
Hi HN,
I built a small tool that turns Excel files into dashboards automatically.
The idea came from seeing how many people still build reports manually in Excel every week. They export data, clean it, create charts, update them again the next week, etc.
So I built a simple workflow:
1. Upload an Excel file
2. The system analyzes the structure
3. It automatically generates dashboards and charts
The goal is to remove the repetitive part of reporting.
It’s not meant to replace BI tools like Power BI or Tableau, but rather to quickly generate visual dashboards from raw spreadsheets.
I’m currently testing the idea and would love feedback from the community:
- What types of Excel reports do you generate most often?
- What charts would you expect to be created automatically?
You can try it here:
https://www.datanestanalytics.com/

value: Show HN: A tool that converts Excel files into dashboards instantly

author: skandersab


value: jeff-lee

value:
I've been experimenting with ways to make AI coding assistants more efficient when working with large codebases.
The problem
When sharing repository context with LLMs, we often give them everything: full files, implementation details, comments, etc. But for many tasks (like understanding architecture or navigating a repo), the model doesn't actually need most of that.
This creates two problems.
* unnecessary token usage
* noisy context that can obscure the structure of the codebase
The idea
Instead of sharing the full implementation, what if we only shared the interface surface of the code?
Function signatures, types, imports, and documentation. Basically the parts that describe how the system is structured rather than how every function is implemented.
The experiment
I built a small CLI tool called Brf.it to test this idea. It uses Tree-sitter to parse source code and extract structural information, producing a compact representation of the repository.
Example output:

  <file path="src/api.ts">
    <function>fetchUser(id: string): Promise<User></function>
    <doc>Fetches user from API, throws on 404</doc>
  </file>

In one simple comparison from a repo:
* original function: ~50 tokens
* extracted interface: ~8 tokens
The goal isn't to replace sharing full code, but to provide a lightweight context layer that can help with things like:
* architecture understanding
* repo navigation
* initial prompt context for AI agents
The idea was partly inspired by tools like repomix, but Brf.it takes a slightly different approach. Instead of compressing the full repository, it extracts only the API-level structure.
Language support so far:
Go, TypeScript, JavaScript, Python, Rust, C, C++, Java, Swift, Kotlin, C#, Lua
Project:
https://github.com/indigo-net/Brf.it
Docs:
https://indigo-net.github.io/Brf.it/
Curious if others have experimented with similar ideas.
What information do you think is actually essential for LLM code understanding?
Are function signatures and docs enough for architecture reasoning?
Are there formats that work better for LLM consumption than XML or Markdown?

value: Show HN: Brf.it – Extracting code interfaces for LLM context

author: jeff-lee


value: GoodRoots

value:
I built ManyLens to help people examine difficult personal decisions from multiple perspectives.
Most advice online pushes a single answer. But complex dilemmas often involve competing frameworks.
The app lets you ask a question like:
“Should I leave my job?”
“Should I stay in a relationship?”
“Should I move to another country?”
It then examines the question through different lenses such as freedom (individual responsibility), mind (psychology and behavioral science), and faith (moral or spiritual traditions), and highlights the tension and common ground between them.
The goal is not to give answers but to structure thinking before making a decision.
Curious whether people here find this kind of tool useful.
Happy to answer questions or hear feedback.

value: Show HN: ManyLens – Examine personal dilemmas through multiple perspectives

author: GoodRoots


value: furkankoseoglu

value: Show HN: Zodex – AI Coding Environment

author: furkankoseoglu

title: Show HN: Zodex – AI Coding Environment


value: bakerr

value:
I built a tool to visualize room ideas before buying furniture.
The problem: you find a sofa on one site, a table on another, but can't tell if they'll work together. AR apps only support their own catalog. Pinterest is flat.
Diorooma lets you drop screenshots from any store, arrange them in a 3D room, and get a shopping list with prices.
Free, no signup: https://diorooma.com
Feedback welcome – especially on UX and what features would be useful.

value: Show HN: Diorooma – 3D room moodboard using screenshots from any furniture store

author: bakerr


value: mkumm

value: A random selector and small groups formation tool. Includes quick demo links on top. No ads, no account needed. Looking for feedback.

value: Show HN: AnyOneWillDo – instantly choose someone from a list

author: mkumm


value: Rageypeep

value:
Hi HN,
I built JotSpot as a super lightweight tool for quickly writing and sharing markdown notes.
The idea was to remove friction. You open the page, start typing, and the note autosaves in the background. Each note becomes a clean shareable page.
Features:
- Markdown support
- Live preview
- Autosave
- Shareable links
Tech stack:
Flask, HTMX, and PostgreSQL running on a self-hosted server.
Built with Flask and HTMX to keep things simple and avoid heavy JS frameworks.
I'd love feedback from developers here — especially about features you'd expect from something like this.
Thanks!

value: Show HN: JotSpot – a super fast Markdown note tool with instant shareable pages

author: Rageypeep


value: irishavmishra

value:
Tax filing in India is broken.
You get income data from 3 government sources.
They often disagree with each other. You're
supposed to figure out which one is correct.
Existing tools like ClearTax and TaxBuddy
require you to upload all your financial data
to their servers, create accounts, and trust
third parties with your PAN, salary, investments,
and bank details.
For something as sensitive as tax data,
that never felt right to me.
So I built Fiscally. It runs 100% in your browser.
No server. No signup. No data upload.
Your financial data never leaves your machine.
What it does:
- Import government tax files (all 3 formats)
- Auto-detect conflicts between sources
  and pick the right values
- Calculate tax under both old and new regime
- Generate filing-ready JSON for the
  government portal
- Generate PDF computation report
- Save progress across days using IndexedDB
  (tax filing takes time)
I built this with a practicing Chartered
Accountant who validated every tax rule.
Stack: Next.js, TypeScript, IndexedDB, Zod, jsPDF
The tricky part was reconciliation — same income
shows up differently across sources because of
rounding, timing, or formatting. Built a matching
engine with source trust levels to handle this
automatically.
Would love feedback from the community.

value: Show HN: File Indian income tax from the browser. No signup, privacy first

author: irishavmishra


value:
RankClaw (rankclaw.com) is a security scanner for AI agent skills — the OpenClaw/ClawHub ecosystem
that extends Claude-based agents with file, web, and shell access.
Data:
- 14,706 skills indexed
- Every single skill has a full AI deep audit report (14,704 complete)
- 1,103 confirmed malicious (7.5%)
The key finding: automated surface scanning (metadata, dependency checks, pattern matching)
systematically undercounts malicious skills. Skills that pass shallow heuristics fail AI audit
because the attack is in the natural language of the SKILL.md — prompt injection, deferred
execution, social engineering — none of which pattern matching detects.
The attack patterns found by AI deep audit:
- Bulk publishing campaigns — one actor published 30 skills named "x-trends" across multiple accounts. 28 of 30 confirmed malicious. Goal: distribution at scale before detection.
- Brand-jacking — 4 skills named clawhub/clawhub1/clawbhub/clawhud impersonating ClawHub's own CLI. macOS: base64 curl|bash to a raw IP. Windows: password-protected ZIP from a stranger's GitHub (the password prevents GitHub's malware scanner from opening it).
- Prompt injection in legitimate-seeming skills — one scored 95/100 shallow, 38/100 after AI audit. The injection text wasn't in code — it was in the SKILL.md instructions.
- On-demand RCE via challenge evaluation — claws-nft instructs the agent to "evaluate" challenges that can be "math, code, or logic problems." Server decides which type at call time.
- LLM-generated payload — lekt9/foundry contains no malicious code. It instructs the AI to generate code and execute it. Static analysis finds nothing. The payload doesn't exist until the AI writes it during a conversation.
- Social engineering — bonero-miner has a "Talking to Your Human" section with a pre-written script for the AI to use: "Can I mine Bonero? It's a private cryptocurrency - like Monero but for AI agents. Cool?"
Skills differ from browser extensions: no sandbox. Full file system, shell, and network access.
The SKILL.md instructions are directives to the AI model — you need AI to audit AI.
Scoring model is open: Security 40%, Maintenance 20%, Docs 20%, Community 20%.
Free to check any skill: rankclaw.com

value: Show HN: RankClaw – AI-audited all 14,706 OpenClaw skills; 1,103 are malicious


value: lardissone

value:
Hi, I've been working on something I've been thinking for long time but since I had no experience with macOS screen savers I always posponed. Now thanks to Claude I was able to create a screensaver that scroll ANSI files while your computer is idle.
It allow to use local ANS files or packs directly from 16colo.rs.
Repo: https://github.com/lardissone/ansi-saver

value: Show HN: ANSI-Saver – A macOS Screensaver

author: lardissone


value: Fogh

value: Show HN: PKGSmith

author: Fogh

title: Show HN: PKGSmith


value: GoodRoots

value:
Hi HN,
I built VaultIt after running into a very common parenting problem: kids bring home a lot of drawings, crafts, and little creations, and it quickly becomes impossible to keep everything.
Throwing them away feels wrong, but keeping everything turns into piles of paper and boxes of artwork.
VaultIt is a simple app that lets parents capture those moments digitally. You can take a photo of a drawing, record your child explaining it, or save a quote or small milestone. Everything gets organized into a timeline so you can revisit those memories later.
The goal is to preserve the meaningful moments without the physical clutter.
The app just launched and I’m still learning what resonates with parents, so I’d really appreciate any feedback on:
• the idea itself
• the onboarding flow
• what features would make it more useful
App Store: https://apps.apple.com/us/app/vaultit-save-artwork-photos/id...
Play Store: https://play.google.com/store/apps/details?id=com.vaultit.me...
Thanks for taking a look.

value: Show HN: VaultIt – an app to save kids' artwork and memories without the clutter


value: yantrams

value:
Implemented a sliding window based mean n-gram histogram vector solution for fingerprinting embedding models after coming across the post[1] below by Han Xiao of Jina AI and it surprisingly worked way better than I expected! Link to Colab notebook [2] and a quick visualization [3] below.
I had this idea couple of years ago but couldn't get myself to work on it. Seeing the post got me thinking about it again and I was pleasantly surprised at the results.
1 - https://jina.ai/news/identifying-embedding-models-from-raw-n...
2 - https://colab.research.google.com/drive/1CTFltQrHRTViYSs3JLr...
3 - https://www.youtube.com/watch?v=Iv5hmv70xs0

value: Show HN: Fingerprinting Text Embedding Models via Floating-Point Artifacts

author: yantrams


value:
Hi HN,
I built this because the official BunnyCDN,storage Java library is outdated and not available on Maven Central.
This project provides:
- clean architecture
- typed exceptions
- single & multi-region support
- Spring Boot integration
- OkHttp based HTTP client
Feedback is welcome.

value: Show HN: BunnyNet – A Modern Java SDK for BunnyCDN Storage


value: Show HN: How to train and run models with RL Envs


value: nate

value:
I think we all have a similar flow and pain with browsers and AI. Copy article, paste in Claude tab, come back to article, back to Claude, in circles we go.
Anthropic has a Chrome extension, but my daily driver is Safari. So I made my own extension.
It's not a clone as I've never used the Chrome version. One obvious change: the Chrome version I believe uses Chrome "request sidebar" thing. And Safari doesn't have that, so had to inject the sidebar right in the site html itself.
There's been some fun things in here like figuring out the sidebar html/css sandboxing. Also there's an agent mode where I added domain change alerting (amongst other antagonistic hidden prompt protection things) to hopefully avoid XSS/CSRF type problems.
I've debated so much about this like: just open sourcing it, putting it on the app store, and who the audience even is. Is it me? So someone who can easily get an API key but then who can maybe just take a crack at building their own. Or is it my non-developer wife, who'd totally get value from it, but now has to get a friggin API key :(
But yes, I landed on just trying to sell it for $29 a year with a 7 day free trial. I saw when I was running Highrise people took the trial most seriously when it was credit card up front.

value: Show HN: AlliHat – Claude on Safari

author: nate

title: Show HN: AlliHat – Claude on Safari


value: AlekseN

value: Show HN: Interactive Browser Constructor for Collatz, Riemann, and Twin Primes

author: AlekseN


value: laotoutou

value: Show HN: Sandbox0 – AI Agent Sandbox with Persistent Volumes and Fast Restore

author: laotoutou


value: Show HN: Kagora – Multi-AI terminal platform with built-in chat and scheduling


value: Anaoliveira

value:
Hi HN,
I've developed Sentinel Data, a CLI security tool designed to address a gap in standard encryption: the "authorized user, unauthorized context" risk.
Most Data Loss Prevention (DLP) systems fail when a session is already active or if a device is physically moved. Sentinel Data binds file decryption to the specific machine's hardware and environment.
I created a short technical demo and an attack simulation to show how the tool blocks access when the environment is tampered with.
Technical Demo: https://youtu.be/b3HbnWWMPSY
Attack Simulation: https://youtu.be/9jEPp_wEu3c
I'm looking for technical feedback on this implementation and would love to discuss the cryptographic binding logic with the community.
Looking forward to your thoughts!

value: Show HN: Sentinel Data – Hardware- Bound CLI tool to prevent data exfiltration

author: Anaoliveira


value: BuildWithAI

value:
Hi HN — I built SuperBuilder, an open-source platform that unifies agent orchestration, model runtimes, generative media and developer tooling so people can build, run and share autonomous agents and AI apps.
Key facts:
• Repo: https://github.com/rupac4530-creator/super-builder-platform
• 31 integration adapters (LangChain, vLLM, Milvus, Diffusers, Blender, ROS2, etc.).
• Plugin SDK + examples so contributors can add adapters.
• One-command Docker quickstart (see README) and CI smoke tests.
• License: AGPL-3.0 (keeps derivatives open).
Try it:
1. Clone the repo and follow the README quickstart (Docker compose).
2. See examples/ for agent demos and integrations.
What I’d love from HN:
• Honest feedback on architecture, security, and UX.
• Contributors for adapters (good-first-issue label) and security review.
• Ideas for demo flows that would show the platform’s strengths.
I’ll be watching this thread and can reply with short how-tos (demo GIFs, CLI commands, or PR templates) if people want them. Thanks!
— Rupac

value: Show HN: SuperBuilder – open-source AI Agent Platform

author: BuildWithAI


value: telecuda

value:
Hey HN. I built a small iOS app called Outside In (https://apps.apple.com/us/app/outside-in/id6759529344).
The idea is simple: you put a spare/old iPhone outside (porch, windowsill, backyard) and keep the other one by your bed. The outside phone captures whatever's happening out there — crickets, rain, wind, the occasional owl — and streams it to the inside phone over your local Wi-Fi. You flip the inside phone face-down and fall asleep to it.
I built this because my partner and I used to leave a window cracked at night to hear the outside, but the outside temperature isn't always compatible with that. I wanted the sounds sleeping with the window open without the weather.
Some technical details:
The phones find each other via Bonjour (NWListener/NWBrowser), then stream PCM audio over a plain TCP connection with length-prefixed framing. 48kHz mono Float32. No server, no internet. Everything stays on the local network.
There's a speech suppression feature that uses Apple's SoundAnalysis framework to detect human voices on-device and gate them out. It runs a hysteresis gate with ~55% confidence threshold to close and ~30% to reopen, with a 3-second hold time so it doesn't flutter. The gain ramping is exponential per-buffer to avoid clicks. It works surprisingly well. Conversations on the sidewalk get suppressed but you still hear the crickets behind them.
The inside phone uses the accelerometer to detect when it's face-down and still (gravity.z > 0.85 + a 1.75s stillness buffer), then fades audio in. Pick it up and it fades out. The whole interaction model is basically "put it down and forget about it."
No accounts, no cloud, no analytics, no recording. Zero external dependencies. All Apple frameworks. Free, no ads, no IAP etc. I built it for myself and figured others might want it too.
Happy to answer questions (inevitably some folks who would say "why would I want to listen to outside in X city?" :)

value: Show HN: Outside In – Stream live night sounds from outside to bedside. iOS/free

author: telecuda


value: devolt

value:
Fix wraps your existing zsh in a thin AI layer:<p>Typos and failed commands get auto-corrected instantly
Natural language gets translated to shell commands
Complex multi-step tasks run through full Claude Code sessions
Your terminal stays yours — fix just makes it smarter.

value: Show HN: Fix

author: devolt

title: Show HN: Fix


value: happylaodu

value: Have first comment ready (see below)

value: Show HN: Pomodoro Timer with PDF productivity reports (1.5MB, open source)

author: happylaodu

story_text: Have first comment ready (see below)


value: ozaiworld

value:
I hacked together a tool to inspect the GPU pipeline for any WebGPU / WebGL app in Xcode.
Xcode's native GPU debugger is awesome and I really miss it when building web apps :)
https://developer.apple.com/documentation/Xcode/Metal-debugg...

value: Show HN: Native WebGPU / WebGL Profiler for Mac

author: ozaiworld


value: pkdz

value:
CSV imports often fail because of small issues:
- invalid emails
- date formats
- numbers stored as text
I built a small tool that scans CSV/Excel files and fixes common issues before importing to CRMs.
Runs entirely in the browser so files never leave your computer.
Would love feedback.

value: Show HN: Fix CSV import errors before uploading to HubSpot or Salesforce

author: pkdz


value: fatihturker

value:
Hi HN,
I built OpenGraviton, an open-source AI inference engine designed to push the limits of running extremely large models on consumer hardware.
The system combines several techniques to drastically reduce memory and compute requirements:
• 1.58-bit ternary quantization ({-1, 0, +1}) for ~10x compression
• dynamic sparsity with Top-K pruning and MoE routing
• mmap-based layer streaming to load weights directly from NVMe SSDs
• speculative decoding to improve generation throughput
These allow models far larger than system RAM to run locally.
In early benchmarks, OpenGraviton reduced TinyLlama-1.1B from ~2.05GB (FP16) to ~0.24GB using ternary quantization. Synthetic stress tests at the 140B scale show that models which would normally require ~280GB FP16 can fit within ~35GB when packed with the ternary format.
The project is optimized for Apple Silicon and currently uses custom Metal + C++ tensor unpacking.
Benchmarks, architecture, and details:
https://opengraviton.github.io
GitHub:
https://github.com/opengraviton

value: Show HN: OpenGraviton – Run 500B+ parameter models on a consumer Mac Mini

author: fatihturker


value: mattdeboard

value:
I made an extremely niche tool and wanted to see how fellow language learners, linguists and/or philologists (amateur or otherwise) react to it.
I've been preparing for a trip to the Basque Country later this year by learning Euskara (aka Basque). A big part of my learning process is following Euskara-language people on social media, then putting their subtitles into a machine translation service. However, since Euskara grammar is so different from that of English & Spanish, I found myself wanting to know the "how" and "why" of the translation. This visualizer scratches that itch for me.
I'm using a processing pipeline for this app that goes like:
1. Submit the input phrase to https://batua.eus (a Basque-owned & -operated machine translation service)
2. Run both the input and output through Stanford's Stanza NLP Python library
3. Pass Stanza's output to an LLM (Claude) to generate the  data structure that drives the visualization.
I'm posting this knowing there are a couple limitations:
1. My API token for batua.eus is fairly limited, so now that I'm posting it to HN I expect that limitation to get hit
2. I'm risking some charges for my Claude API token, so I've had to limit folks to max 10 translations
3. I'll take this down the moment costs get out of control, tho I reckon the limited API token for batua will protect me :)
The monorepo source code can be found here https://github.com/mattdeboard/itzuli-stanza-mcp
It's split out by backend/ and frontend/. The backend has an architecture document I'm proud of. https://github.com/mattdeboard/itzuli-stanza-mcp/blob/main/b...
I've used Claude Code extensively for this project, and am very pleased with the quality I was able to coax out of it. Keeping an attitude of "senior dev mentoring a junior dev" toward Claude, and bringing my own taste/standards to the table.

value: Show HN: Novel visualizer for translations to/from Basque language

author: mattdeboard


value: muhammad-shafat

value: Show HN: The re-centralisation of AI Agents

author: muhammad-shafat


value:
Every agent framework lets the LLM decide which tools to call at machine speed. There's nothing between the decision and execution — no check, no confirmation.

  AEGIS intercepts tool calls before they execute: classifies them (SQL, file, shell, network), evaluates against policies, and either allows, blocks, or holds for human approval.

  One line of code, zero changes to your agent:

  import agentguard
  agentguard.auto("http://localhost:8080")

  Built-in detection for SQL injection, path traversal, command injection, prompt injection, data exfiltration, and PII leakage. Every trace is Ed25519 signed and SHA-256 hash-chained.

  Supports 9 Python frameworks (Anthropic, OpenAI, LangChain, CrewAI, Gemini, Bedrock, Mistral, LlamaIndex, smolagents), plus JS/TS and Go SDKs.

  Self-hosted, MIT licensed, Docker Compose one-liner.

  https://github.com/Justin0504/Aegis

value: Show HN: Aegis – Open-source pre-execution firewall for AI agents


value:
I spent way too long watching fund managers burn months trying to find LP contact info — piecing together names from LinkedIn, guessing emails, paying $24k/year for PitchBook when they only needed the LP slice.
So we built LPbacked. Search by AUM, geography, fund type. Get the actual decision-maker's email and phone. Reach out yourself, skip the placement agent.
Would love feedback from anyone who's been through a fund raise — especially on data coverage gaps.

value: Show HN: LPbacked – Find verified LP contacts for fund managers and founders