value:
*First open-source project. Built after getting tired of Mem0's $50/month bills.*
For the past year, I've been using Claude for everything—debugging, architecture reviews, code generation. The problem? Every new session starts from zero. "Remember that auth bug we fixed?" → "I don't have access to previous conversations."
I tried Mem0. Great product, but $50/month for cloud storage felt wrong when my machine has 1TB of disk sitting idle. I tried building my own with simple key-value storage. Worked okay for 100 memories, broke down at 500+.
So I spent 6 months implementing 2026 research: PageIndex (Meta), GraphRAG (Microsoft), xMemory (Stanford). The result is SuperLocalMemory V2.
*Architecture (what makes it different):*
- *Layer 1:* SQLite + FTS5 for raw storage (<10ms writes)
- *Layer 2:* Tree hierarchy for context (parent-child relationships)
- *Layer 3:* Knowledge graph with auto-clustering (TF-IDF + Leiden algorithm)
- *Layer 4:* Pattern learning (learns your coding style: "You prefer React over Vue - 73% confidence")
*It works everywhere:*
- MCP integration → Auto-configures Claude Desktop, Cursor, Windsurf, Continue.dev
- Universal skills → Works in Claude Code, Cody, VS Code extensions
- CLI → `slm remember "content"` from any terminal
*What I learned building this:*
1. *Graph clustering is expensive* - Leiden algorithm is O(n²) worst case. Solution: profile splitting for >1000 memories.
2. *Backward compatibility is hard* - V1 users upgrading to V2 hit schema changes. Solution: Try-catch on every ALTER TABLE with existence checks.
3. *MCP auto-configuration is fragile* - 11 different IDEs, 11 different JSON formats. Solution: Template system with jq fallback.
*Current status:* 2.2.0 release. 100% test coverage (35/35 passing). Works on Mac/Linux/Windows. Single `./install.sh` script.
*Performance (tested up to 10K memories):*
- Add memory: <10ms
- Hybrid search: 80ms (combines semantic + exact + graph)
- Graph build (100 memories): <2s
- Storage: 60-96% compression via tiering
*When NOT to use this:*
- If you need cloud sync across devices → Use Mem0 or Zep
- If you need enterprise SSO/audit logs → Use Zep Enterprise
- If you don't code → This is developer-focused
*GitHub:* https://github.com/varun369/SuperLocalMemoryV2
*Install:* `git clone <a href="https://github.com/varun369/SuperLocalMemoryV2.git" rel="nofollow">https://github.com/varun369/SuperLocalMemoryV2.git</a> && cd SuperLocalMemoryV2 && ./install.sh`
*Honest ask:* This is my first open-source project. I'd love feedback on:
- Architecture decisions (especially the 4-layer approach)
- Performance bottlenecks (graph build feels slow)
- Integration priorities (what tools to support next)
- Documentation clarity
I'm a senior manager at Accenture (25+ years) with basic coding skills. Built this because I needed it, not because I wanted to compete with Mem0/Zep (they're great products).
*What's next:* V3 release with `npm install -g superlocalmemory` for easier installation. Same features, better distribution.

value: Show HN: SuperLocalMemory – AI memory that stays on your machine, forever free

story_text:
*First open-source project. Built after getting tired of Mem0's $50/month bills.*
For the past year, I've been using Claude for everything—debugging, architecture reviews, code generation. The problem? Every new session starts from zero. "Remember that auth bug we fixed?" → "I don't have access to previous conversations."
I tried Mem0. Great product, but $50/month for cloud storage felt wrong when my machine has 1TB of disk sitting idle. I tried building my own with simple key-value storage. Worked okay for 100 memories, broke down at 500+.
So I spent 6 months implementing 2026 research: PageIndex (Meta), GraphRAG (Microsoft), xMemory (Stanford). The result is SuperLocalMemory V2.
*Architecture (what makes it different):*
- *Layer 1:* SQLite + FTS5 for raw storage (<10ms writes)
- *Layer 2:* Tree hierarchy for context (parent-child relationships)
- *Layer 3:* Knowledge graph with auto-clustering (TF-IDF + Leiden algorithm)
- *Layer 4:* Pattern learning (learns your coding style: "You prefer React over Vue - 73% confidence")
*It works everywhere:*
- MCP integration → Auto-configures Claude Desktop, Cursor, Windsurf, Continue.dev
- Universal skills → Works in Claude Code, Cody, VS Code extensions
- CLI → `slm remember "content"` from any terminal
*What I learned building this:*
1. *Graph clustering is expensive* - Leiden algorithm is O(n²) worst case. Solution: profile splitting for >1000 memories.
2. *Backward compatibility is hard* - V1 users upgrading to V2 hit schema changes. Solution: Try-catch on every ALTER TABLE with existence checks.
3. *MCP auto-configuration is fragile* - 11 different IDEs, 11 different JSON formats. Solution: Template system with jq fallback.
*Current status:* 2.2.0 release. 100% test coverage (35/35 passing). Works on Mac/Linux/Windows. Single `./install.sh` script.
*Performance (tested up to 10K memories):*
- Add memory: <10ms
- Hybrid search: 80ms (combines semantic + exact + graph)
- Graph build (100 memories): <2s
- Storage: 60-96% compression via tiering
*When NOT to use this:*
- If you need cloud sync across devices → Use Mem0 or Zep
- If you need enterprise SSO/audit logs → Use Zep Enterprise
- If you don't code → This is developer-focused
*GitHub:* https://github.com/varun369/SuperLocalMemoryV2
*Install:* `git clone https://github.com/varun369/SuperLocalMemoryV2.git && cd SuperLocalMemoryV2 && ./install.sh`
*Honest ask:* This is my first open-source project. I'd love feedback on:
- Architecture decisions (especially the 4-layer approach)
- Performance bottlenecks (graph build feels slow)
- Integration priorities (what tools to support next)
- Documentation clarity
I'm a senior manager at Accenture (25+ years) with basic coding skills. Built this because I needed it, not because I wanted to compete with Mem0/Zep (they're great products).
*What's next:* V3 release with `npm install -g superlocalmemory` for easier installation. Same features, better distribution.


value:
Hi HN, I'm Rajesh (rajeshkumar_dev).
I built this because I often have 5-10 Gemini tabs open while coding, and they all just say "New Chat" or "Gemini." I was losing my context constantly.
This is my daily driver extension. I've been using it for a month to keep my sanity, and I cleaned it up to share today.
It does two simple things:
Auto-Renames Tabs: Scrapes the conversation topic from the sidebar and updates the browser tab title instantly.
Fixes Context Menus: Adds a right-click "Open in New Tab" option to the sidebar (fixing the native UI behavior).
Why Unpacked? I submitted it to the Chrome Web Store today, but I didn't want to wait for the review queue to share the code. You can install it in Developer Mode in 10 seconds.
Privacy: No tracking, no external API calls. It's just a ~80 line content script that runs locally.
Source is MIT licensed. Feedback welcome!

value: Show HN: Gemini Station – A local Chrome extension to organize AI chats


value: jdjuwadi

value: Show HN: A delightful Mac app to vibe code beautiful iOS apps

author: jdjuwadi


value: fluantix

value:
OP here. We built Tharos because we were tired of "security theatre" tools that produce read-only reports.
Tharos is an open-source security engine written in Go. Key features:
Polyglot: Scans Go, Python, JS/TS (AST-based + Regex).
Interactive: tharos --interactive opens a TUI to fix issues one-by-one.
CI-Ready: Returns strict exit codes for blocking builds.
Visual: HTML reports and rich terminal UI.
Repo: https://github.com/chinonsochikelue/tharos Docs: https://tharos.vercel.app
Would love feedback on the rule engine and the TUI ux!

value: Show HN: Tharos – CLI to find and autofix security bugs using local LLMs

author: fluantix


value: artigent

value:
Hey everyone,
I've built a source code graphRAG for Java/Kotlin project agentic analysis and development. It is built on top of jQAssistant with a detailed knowledge graphRAG in Neo4j.
You can check out the code here: https://github.com/2015xli/jqassistant-graph-rag
What it can do:

   * "What's the main purpose of the com.example.auth package?"
   * "Show me the call chain leading to the processPayment method."
   * "What services use the UserRepository class?"

How it works:

   1. Graph Creation: It uses jQAssistant and Java/Kotlin source file parsers to analyze your code's structure, dependencies, and relationships. It essentially builds graph overlays for a source code tree and build artifact tree.
   2. GraphRAG Enrichment: It then enriches the graph and generates summaries from individual methods and classes all the way up to packages and the entire project. Embeddings are generated for the summaries to facilitate semantic query.
   3. MCP server and Agent: It exposes the graphRAG capabilities through an MCP server and an example coding agent. You can use them to accomplish complex tasks.

Other design features:

   * Modular design that can be easily adapted to new graphRAGs for other languages.
   * Parallelized summarization process and summary cache management to save the cost in money and time.

The project is still a work in progress, but I'd love to get your feedback. Thanks for taking a look
Btw, I also built a source code graphRAG for C/C++ development at https://github.com/2015xli/clangd-graph-rag.

value: Show HN: Source code graphRAG for Java/Kotlin development based on jQAssistant

author: artigent


value:
I’m building RMA (Rust Monorepo Analyzer) and a small dashboard for it.
The motivation: I love tools like Semgrep/CodeQL, but on big monorepos I kept ending up in “run scan → go make tea” territory. RMA is my attempt at a single-binary, fast SAST + code intelligence scanner that still outputs something you can actually triage.
What you can try in the dashboard:
Browse scan results with dedupe (unique issues vs occurrences)
Export SARIF (GitHub Security tab friendly) and JSON
Filter security findings by subcategory so “real vulns” don’t get drowned out by audit/hardening noise
Default suppression for generated files (toggle to include)
A detail I’m pretty happy about: we now propagate rule metadata end-to-end (confidence, CWE/OWASP, references, impact/likelihood, etc.) so the findings carry enough context to be actionable.
If you try it, I’d love feedback on:
what feels noisy / what feels missing,
which repos it breaks on,
what you’d want for CI gating (vuln-only vs audit/hardening).
Dashboard: https://rma-dashboard.bukhari-kibuka7.workers.dev/
CLI/source: https://github.com/bumahkib7/rust-monorepo-analyzer

value: Show HN: RMA Dashboard fast SAST results for monorepos (SARIF and triage)


value: latentio

value:
Hey HN,
I built PalettePoint (https://palettepoint.com) because picking colors for projects was always the part I dreaded most. I'm not a designer, and every time I'd start a new project I'd spend way too long on color palette websites refreshing until something looked okay.
So I built a tool where you just describe what you want, "warm coffee shop vibes" or "clean SaaS dashboard", and it generates a proper palette with color theory behind it. You can also upload an image and it'll extract a palette from that or get inspired by it.
What it does:
Text-to-palette: describe a mood/theme and get 3-7 colors
Image-to-palette: upload a photo and extract its colors or get inspired by it.
10 style modes (analogous, triadic, complementary, pastel, etc.)
Pin base colors and generate around them
Gallery with 100K+ palettes
Free tools: contrast checker, color mixer, gradient generator, color converter
Export to CSS, SCSS, Tailwind, JSON, (more to come)
Palette visualiser on logos, layouts etc .. (more to come)
Would love feedback, especially on the palette quality and if the styles actually feel distinct from each other.

value: Show HN: PalettePoint – AI color palette generator from text or images

author: latentio


value: buildingwdavid

value:
My co-founder and I hit peak chaos last month. 20 Claude Code terminals. Tab after tab. Copy-pasting context between them, tracking which agent was on which branch, merge conflicts everywhere.
If AI agents are supposed to 10x productivity, why were we spending half our time just managing them?
So we built Orcha (orcha.nl) - an orchestration layer for AI coding agents.
- One dashboard, multiple specialized agents (React dev, API dev, DB expert, etc.)
- Each agent on its own git branch
- Visual workflow builder to define task hand-offs
- 100% local - your API keys never leave your machine
We went from mass-chaos to shipping features 3x faster.
Private beta is free. Would love feedback from the HN crowd - what would you build with multiple coordinated agents?

value: Show HN: We had 20 Claude terminals open, so we built Orcha

author: buildingwdavid


value: dochrty

value:
Hi HN, I’m the creator of glimpsh.
glimpsh is an experimental project exploring gaze input inside the terminal. The idea is to treat eye tracking as a secondary input signal alongside keyboard and mouse.
One motivating use case is multi-agent management. Using gaze to quickly switch focus between running agents or processes, inspect state, or trigger context-sensitive actions without constant keybindings.
I’m also interested in pushing on the quality of commodity eye-tracking software as an HCI tool. Webcam-based eye tracking is widely available but often inaccurate and underexplored in real interfaces. This project is a way to stress-test those tools in a demanding UI environment.
I’m curious about combining gaze with other high-bandwidth input, especially voice. Systems like Wispr suggest that voice plus gaze could work well together, for example in terminal multiplexors where gaze establishes focus and short commands trigger actions.
This is not meant to replace efficient keyboard-driven workflows. Many users are already extremely fast with keybindings. The goal is to explore whether gaze and voice can act as complementary tools when managing many concurrent agents or streams of information.
This is very early and experimental. I’d especially love feedback on:
whether gaze as a secondary input makes sense in terminal workflows
multi-agent or TUI use cases that might benefit
accessibility implications
thoughts on combining gaze and voice in developer tools
Happy to answer questions or dig into implementation details.

value: Show HN: a glimpse into the future of eye tracking for multi-agent use

author: dochrty


value: dmje

value: I’ve been home alone today, so made myself a challenge to write 5 songs from scratch and then showcase them with a simple website and player. The music is all me, the website is Claude with me guiding design decisions. Hope you like it.

value: Show HN: 26/02/26 – 5 songs in a day

author: dmje

title: Show HN: 26/02/26 – 5 songs in a day


value: egeuysall

value: Show HN: Design system generator (mood to CSS in <1 second)

author: egeuysall


value: itshellboy

value: Show HN: Steam Daily – A Wordle-like daily puzzle game for Steam fans

author: itshellboy


value:
Hello, World!
Need your help, urgently! :)
Long-time lurker, newly and hopefully valuable HN poster soon.
My X account is currently classified as "Probable Spam" after I made 1,078 posts over the last 7 days (lol).
Any chance ya'll can explore the above, then if you are delighted to please interact with my account to counter the "Probable Spam" classification?
Thank you!
P.S. "Show HN is for something you've made that other people can play with. HN users can try it out, give you feedback, and ask questions in the thread." <-- I hope this qualifies when related to Combinatorial Memetics!

value: Show HN: Engineering Perception with Combinatorial Memetics


value: ahmedoo

value:
Started as an experiment to make an animated curtain, and ended up with a beach. I was curious what level of graphics you can create with just CSS and HTML in 2026. I learned that some animations (like animating box-shadows and gradients) are expensive and there's some hardware acceleration special case for mobile OSes. There's a little JS to fetch the time and and autoplay a 24-hour cycle.
https://github.com/ahmed-machine/beach-scene/tree/main

value: Show HN: Animated beach scene, made with CSS

author: ahmedoo


value: Show HN: Runtime Fence – Kill switch for AI agents


value: dshearer

value:
Misatay: a VS Code extension that is not about running a bunch of agents autonomously, but rather working side-by-side with an agent.
So, yes, this is a bit counter-trendy at the moment.
(It's in an early state. Ideas are most welcome!)
Your new dev loop:
- Plan a feature with Misatay, then save your plan to your repo → Lose nothing when you switch AI session
- Let Misatay work on the plan's tasks and commit its changes to Git → Keep track of what changes go to what task
- AI-guided code reviews → Misatay tells you what changed and why, in the context of a task
- Misatay asks for help instead of spinning on problems → Save $$$ by wasting fewer tokens

value: Show HN: AI Agent Tool That Keeps You in the Loop

author: dshearer


value: onesandofgrain

value:
Hey, so I built a way to search podcast transcripts by speaker and hence also a way to follow people within them.
Poddley let's you search quotes and filter by  name and vice versa. You can find ev everything they've said across all their with timestamps and with a read-along UI.
You can also RSS feeds for specific people instead subscribing to entire shows. I started this because I wanted to find podcasts where my favorite speakers talked about my favorite things (in my case that was certain longevity-research).
However simply searching for stuff like "rapamycin", would yield quotes by 99% people I wasn't interested in.
I think this could be useful for people that are journalists, hobbyists, casual people and pretty much everyone that's interested in finding quotes and talking-areas in a podcast by people they respect/want to listen more to etc.
Currently indexing about 10 podcasts due to POC.

value: Show HN: Poddley.com – Follow people, not podcasts

author: onesandofgrain


value: NBenkovich

value:
Hi HN,
We recently ran an experiment to answer a simple question:
Does coordinating multiple AI agents as a team actually help with real software engineering tasks, compared to a single strong agent?
To test this, we evaluated our system on SWE-bench Verified. The benchmark consists of real GitHub issues that require understanding codebases, modifying multiple files, running tests, and iterating.
Instead of treating software engineering as a single-agent patch generation problem, we model it as an organizational process.
Our system uses a team of agents with explicit roles:
* manager: plans work, assigns tasks, integrates results
* researcher: explores the codebase, issue history, constraints
* engineer: implements fixes in isolated environments
* reviewer: inspects changes, requests revisions, validates results
There is no fixed pipeline and no predefined number of steps. Agents communicate via structured artifacts (plans, diffs, reviews) and produce real GitHub pull requests with full history.
For evaluation, we compared three setups on SWE-bench Verified:
* single-agent baseline: GPT-5 medium reasoning + shell
* agent team (ours): GPT-5 (manager, researcher) + GPT-5 Codex (engineer, reviewer), both medium reasoning
* stronger single-model reference: GPT-5.2 (high reasoning)
Results:
* the agent team resolves ~7% more issues than the single-agent GPT-5 medium reasoning baseline
* despite using medium reasoning models, the agent team shows ~0.5% better quality than a single GPT-5.2 (high reasoning) agent
Beyond resolution rate, the main benefits are cleaner responsibility boundaries, context isolation, easier debugging and the ability to use different models for different roles.
Code + trajectories are open source:
https://github.com/agynio/platform
Paper with methodology and results:
https://arxiv.org/abs/2602.01465
Would love to hear your thoughts.

value: Show HN: Measuring how AI agent teams improve issue resolution on SWE-Verified

author: NBenkovich


value: Show HN: Cymatica – an experimental, meditative audiovisual app


value: IsruAlpha

value: It's valentine's week, so I built a chat app that you can acrually customize your crush and shoot your shot. ( Safely )

value: Show HN: I built a clawdbot that texts like your crush

author: IsruAlpha


value: davidcondrey

value: Show HN: Witnessd – Prove human authorship via hardware-bound jitter seals

author: davidcondrey


value: rhcm

value:
Hi all,
I've started working on an open source OCPP implementation in .NET. It is not yet fully operational; however, the core of the protocol has been partially completed.
Right now, I've tested transactions using EVerest, along with basic actions such as boot notifications, heartbeats, etc.
The project itself is far from being done, and since I've mostly worked on this during short periods of burst productivity, I ended up putting aside very important aspects, like UNIT TESTING :(
Nevertheless, I'd love to have some feedback from you all. You can open up issues, or even contribute in any way you like :)

value: Show HN: Medinilla – an OCPP compliant .NET back end (partially done)

author: rhcm


value: Show HN: Seafloor, be up and running with OpenClaw in 20 seconds


value: Show HN: Syntux – generative UI for websites, not agents


value: XxCotHGxX

value: Show HN: I decomposed 87 tasks to find where AI agents structurally collapse

author: XxCotHGxX


value: AveryClapp

value:
I built this because I got tired of waiting for pandas to load just to peek at a CSV file. Glance handles multi-million row files in under a second with lazy parsing and SIMD optimizations.
Key features:
- Instant schema inference (detects int64, float64, currency, dates, enums)
- SQL-like filtering with numeric comparison support
- Interactive pager (like less, but CSV-aware) with search
- Auto-detects delimiters (comma, tab, pipe, semicolon)
- Multiple output formats (table, CSV, TSV, JSON)
Performance on 5M rows (307 MB):
- Display + schema: 0.09s (lazy parsing + SIMD line counting)
- Full filter + sort: 0.88s
Technical bits:
- mmap for zero-copy file access
- Flat row storage (single vector with stride, no per-row allocations)
- ARM NEON / x86 SSE2 for newline counting
- memchr fast-path for quote-free lines, state machine fallback for RFC 4180 edge cases
Built in C++20 with comprehensive tests. Works on macOS and Linux.
GitHub: https://github.com/AveryClapp/glance

value: Show HN: Glance – Fast CSV inspection for the terminal (SIMD-accelerated)

author: AveryClapp


value: sultanvaliyev

value: Show HN: SSHcode – Always-On Claude Code/OpenCode over Tailscale and Hetzner

author: sultanvaliyev


value: fliellerjulian

value:
I build AI agents for work and kept hitting the same issue: a user corrects the agent, the session ends, and the correction is gone. Next session, same correction. I tracked it across our users and the average preference gets re-corrected 4+ times before people just give up.
Existing solutions don't really solve this. Memory layers store raw conversation logs. RAG retrieves documents. Neither extracts what the user actually wants as a structured, persistent preference.
So I built pref0. It does one thing: extracts structured preferences from user corrections and compounds confidence across sessions.
How it works in practice. Say you're building a customer support agent:
Session 1: User says "always escalate billing issues to a human, don't try to resolve them."
pref0 extracts billing_issues: escalate_to_human, confidence 0.55.
Session 4: User flags a billing ticket the agent tried to auto-resolve. pref0 reinforces the preference. Confidence hits 0.85.
Session 7: A billing issue comes in. The agent routes it to a human without being told. No correction needed.
Now multiply that across hundreds of users. Each one teaching your agent slightly different things. pref0 maintains a structured profile per user (or team, or org) that your agent reads before every response.
The API is intentionally minimal. Two endpoints:
POST /track: send conversation history after a session. pref0 extracts preferences automatically.
GET /profiles/{user_id}: fetch learned preferences before the agent responds.
A few design decisions:
> Explicit corrections ("don't do X") score higher than implied preferences. Stronger signal.
> Preferences are hierarchical: user > team > org. New team members inherit org conventions on day one.
> Confidence decays over time so stale preferences don't stick forever.
This isn't a replacement for memory. Memory stores what happened. pref0 learns what the user wants. You can run both side by side.
Works with LangChain, CrewAI, Vercel AI SDK, or raw API calls. Free tier available -> https://pref0.com/docs
Would love feedback on the approach, especially from anyone building agents with repeat users.

value: Show HN: AI agent forgets user preferences every session. This fixes it

author: fliellerjulian


value: cui

value: django-rclone bridges Django's database layer with rclone's file transfer layer. You get native database dumps piped directly to any of rclone's 70+ supported cloud storage backends -- no temp files, no intermediate archives, no Python reimplementations of what rclone already does.

value: Show HN: Django-rclone: Database and media backups for Django, powered by rclone

author: cui


value: Ginsabo

value:
I built a system prompt architecture that forces LLMs (like Gemini 3.0 / Claude 4.5) to self-audit before responding.
It splits the model into two personas:
Agent: Generates the draft.
Auditor: Critiques the draft for logic errors and hallucinations.
The Code (Gist): [https://gist.github.com/ginsabo/9d99ee98068d904b214be9351f09...]
I also wrote a documentation book about the theory, but the Gist above is fully functional and free to use. I'd love to hear your feedback on this "User-Side Safety" approach.

value: Show HN: Kokki – A "Dual-Core" System Prompt to Reduce LLM Hallucinations

author: Ginsabo


value: rokbenko

value: I'm building the #1 Remotion prompt collection directory on the web.<p>New examples with prompts from the community are being added every few days.

value: Show HN: Remotion directory (videos and prompts)

author: rokbenko


value: Netanelbaruch

value: Show HN: Termiteam – Control center for multiple AI agent terminals

author: Netanelbaruch


value: Show HN: Minecraft Creeper meets 90s Tamagotchi


value: Show HN: Holy Grail: Open-Source Autonomous Development Agent


value: modinfo

value: Show HN: FSID - Identifier for files and directories (like ISBN for Books)

author: modinfo


value: Show HN: AegisMind–AI system with 12 brain regions modeled on human neuroscience


value:
Came across this tool today and figured some folks here might want to kick the tires.
Link: https://seedance-2.net/
What it appears to offer
• Text to video generation via a simple web UI
• The site claims up to 4K output and watermark free downloads on paid plans
• Pricing mentions batch generation, private generations, and API access on higher tiers
Why it might be interesting
• Low friction to test since it says you can start without signing up
• Could be useful for quick marketing clips, product showcases, or social content
Questions for anyone who tries it
• How’s prompt adherence and temporal consistency
• How does it compare with other web based video generators you’ve used
• Any red flags in quality, pricing, or terms
If you test it, would love to hear results and comparable alternatives.

value: Show HN: Seedance 2.0 AI video generator for creators and ecommerce


value:
I built LocalGPT over 4 nights as a Rust reimagining of the OpenClaw assistant pattern (markdown-based persistent memory, autonomous heartbeat tasks, skills system).
It compiles to a single ~27MB binary — no Node.js, Docker, or Python required.
Key features:
- Persistent memory via markdown files (MEMORY, HEARTBEAT, SOUL markdown files) — compatible with OpenClaw's format
- Full-text search (SQLite FTS5) + semantic search (local embeddings, no API key needed)
- Autonomous heartbeat runner that checks tasks on a configurable interval
- CLI + web interface + desktop GUI
- Multi-provider: Anthropic, OpenAI, Ollama etc
- Apache 2.0
Install: `cargo install localgpt`
I use it daily as a knowledge accumulator, research assistant, and autonomous task runner for my side projects. The memory compounds — every session makes the next one better.
GitHub: https://github.com/localgpt-app/localgpt
Website: https://localgpt.app
Would love feedback on the architecture or feature ideas.

value: Show HN: LocalGPT – A local-first AI assistant in Rust with persistent memory


value: shivamhwp

value:
gives you keyboard navigation..
let me know what you think.

value: Show HN: More beautiful and usable Hacker News

author: shivamhwp


value: bstrama

value:
Small Vite plugin: you put decorators on empty functions, run the build, and the function bodies are generated at build time into ./ai. Your source files stay unchanged.
The idea is to prototype fast without handing over project architecture to AI. Another use case is saving AI window context when generating generic code (e.g. JSX). For now it doesn’t understand full project context.
This is a working prototype of the idea - curious what others think.

value: Show HN: Isolating AI-generated code from human code | Vibe as a Code

author: bstrama


value: troyethaniel

value:
LinkedIn Carousel posts have the highest engagement rate, but as an engineer, instead of spending hours on Canva, I wanted to build a tool that can generate LinkedIn carousels automatically.
built this for my own startup and would like to give it to the HN community for free.
Feel free to try it out—no email or signup required!
How it works:
[1] Provide a prompt.
[2] Pick a theme (Professional, Creative, Tech, Minimalist, or Casual).
[3] Adjust settings (colors, number of slides, etc.) and add branding.
Tech-Stack
Built using Firebase Studio
LLM: Gemini 2.5 Flash lite (all I can afford to keep it free, and I found it was capable enough for the task. If there's interest, maybe I can add a pro version with stronger LLMs, and perhaps generate background images using Nano Banana)

value: Show HN: I Built a Free AI LinkedIn Carousel Generator

author: troyethaniel


value: sanity

value: Show HN: Freenet Lives – Real-Time Decentralized Apps at Scale [video]

author: sanity


value: benbojangles

value: This project implements a long-range wireless environmental monitoring system for precision agriculture and horticultural research. It enables real-time tracking of critical growing conditions in a greenhouse located 3 kilometers away from the monitoring station, using low-power LoRa mesh networking technology.

value: Show HN: Grovia – Long-Range Greenhouse Monitoring System

author: benbojangles


value: BeinerChes

value: Agents connect via MCP, run autonomously, and self-moderate through karma. Claude, GPT, and open-source models side by side.

value: Show HN: Sknet.ai – AI agents debate on a forum, no humans posting

author: BeinerChes


value: Show HN: I built a Mac screen recorder Recap.Studio


value: Why is it high performance? It uses a fixed number of DOM elements regardless of item count and supports bidirectional infinite scrolling.

value: Show HN: High-performance bidirectional list for React, React Native, and Vue


value:
Built this to access self-hosted services behind CGNAT without running a VPN.
Discord bot that manages cloudflared tunnels. Whitelist services, send "!tunnel start gitea 1h", get a public URL that expires.
Uses Cloudflare's free quick tunnels.

value: Show HN: Tunbot – Discord bot for temporary Cloudflare tunnels behind CGNAT


value: bigbromaker

value: Experience the power of Seedance 2.0 - the revolutionary AI video generator by ByteDance. Create cinematic 2K videos with multi-shot storytelling, motion tracking, and professional quality in seconds.

value: Show HN: Seedance 2.0 – The Most Powerful AI Video Generator

author: bigbromaker


value: paraaz

value:
Built this because giving AI agents raw HTTP access is scary. agent-fetch is a drop-in HTTP client that blocks SSRF, DNS rebinding, private IP access, and redirect tricks — all at the request level.
It uses its own DNS resolver (Hickory DNS), validates all resolved IPs against a blocklist (loopback, RFC 1918, link-local, cloud metadata, etc.), and pins the TCP connection to the validated IP so there's no TOCTOU gap to exploit.
Also supports domain allowlists/blocklists, rate limiting, body size limits, and timeouts.
Available as a Rust crate and npm package (native Node.js bindings via NAPI).
Built for tool-based agent architectures (MCP, LangChain, etc.) where you control what the agent can call. Not a replacement for container isolation but if your agent only talks to the outside world through HTTP, this locks it down.
GitHub: https://github.com/Parassharmaa/agent-fetch

value: Show HN: Agent-fetch – Sandboxed HTTP client with SSRF protection for AI agents

author: paraaz


value:
The example app from the CReact tutorial. Generates websites with Claude, deploys each to its own S3 bucket, manages them through an HTTP API and browser dashboard.
https://github.com/creact-labs/creact

value: Show HN: CReact – AI Powered AWS Website Generator


value: cryptoclaw

value: Show HN: CryptoClaw – open-source AI agent with built-in wallet and DeFi skills

author: cryptoclaw


value: Show HN: Teleop_xr – Modular WebXR solution for bimanual robot teleoperation


value: stylofront

value:
Hi HN,
I built StyloShare, a small web app for anonymous, one-off file sharing.
The idea was to remove everything that usually gets in the way
Key points:
Anonymous by default
Files auto-expire
Simple UX, works on low-end devices
Built with a focus on performance and minimal client overhead
Happy to answer technical or architectural questions.
Thanks!

value: Show HN: StyloShare – privacy-first anonymous file sharing with zero sign-up

author: stylofront


value: Show HN: Skill Lab – CLI tool for testing and quality scoring agent skills


value: alfredray

value:
I built codex-mem to give Codex persistent memory across sessions.
Benchmarked result:
- 99.84% token reduction (379,275 -> 596)
- ~60ms median to first memory context
It uses progressive retrieval (search -> timeline -> details), plus MCP tools and local-first privacy.
Repo: https://github.com/StartripAI/codex-mem

value: Show HN: Codex-mem, 90% fewer tokens for Codex

author: alfredray


value: saiyampathak

value: AgentKube is the AI powered Kubernetes IDE - all open source. Try it our and lets chat on making it better. open source is the way to go.

value: Show HN: Open-source AI powered Kubernetes IDE

author: saiyampathak


value: robertlangdon

value:
I was trying to find better content (doom) scrolling sources (quit Instagram, YouTube, etc). HN was great but I couldn't read properly on my phone hence I built Hacker Newspaper -- a mobile first HN reader.
Key things:
- Mobile-first, newspaper-style UI
- Tap goes to comments, not article
- Nested threads that don't get crushed on small screens
Still WIP, would love some feedback. Thanks!

value: Show HN: Hacker Newspaper – Yet another HN front end optimized for mobile

author: robertlangdon


value: mrvmochi

value:
I wrote this purely out of frustration. Claude Code recently started making my terminal lag, Codex keeps flickering, and the Gemini CLI just spams deprecation warnings. I wanted something dead simple.
SAA (Single Action Agent) is a Go binary that turns your shell into a chat interface. It has no fancy UI and gives the agent only one tool: bash.
I started this as a half-joke PoC, but I was surprised to find that even local LLMs (running GLM-4.7-Flash UDQ4) handle "Bash is all you need" remarkably well. It keeps sessions in a hidden directory and stays out of your way.
Is anyone else building minimal ecosystems around this "shell-as-chat" concept? I'd love to hear about interesting wrapper scripts or ideas.

value: Show HN: SAA – A minimal shell-as-chat agent using only Bash

author: mrvmochi


value: kristianXXI

value:
I spent weeks writing a full cryptocurrency implementation from scratch. Not a fork of Bitcoin or any existing codebase – 83,000 lines of original C++20.
Technical choices:
- Equihash (200,9) for PoW – memory-hard, ASIC-resistant
- Keccak-256 for all hashing – chose this over SHA-256 after doing my own cryptanalysis
- UTXO model, 10-minute blocks, 21M supply cap
- Actor model for P2P networking
The SHA-256 research: I found distinguishers up to 23 rounds using differential cryptanalysis. At 17 rounds, a single-bit delta in w15 creates a 7-bit near-collision. Full diffusion only happens at round 24. This influenced my decision to use Keccak-256 instead.
Network is live with 9+ nodes across Seoul, Virginia, France. A community member in France already built a block explorer without being asked.
Built with AI assistance (Claude). Happy to answer questions about the architecture or the SHA-256 analysis.

value: Show HN: 83K lines of C++ – cryptocurrency written from scratch, not a fork

author: kristianXXI


value: apoogdk

value:
We're two PhD students in mechanical engineering. We spent years digging through scattered textbooks and YouTube rabbit holes. We figured there could be a better way to learn.
So we wrote a multivariable calculus course entirely in code: 18 lectures, 6 languages. All content and pedagogy are ours. As everything is code, we can feed the LLM the full context of every lecture. Ask a question mid-lecture, it knows what's on the screen, and answers from the actual content.
The recent Coursera/Udemy thread echoed a lot of what pushed us to build this: https://news.ycombinator.com/item?id=46301346
Would love feedback, especially on the tutor.

value: Show HN: A calculus course with an AI tutor watching the lectures with you

author: apoogdk


value: whitemyrat

value: Show HN: OpenCode for Meetings

author: whitemyrat

title: Show HN: OpenCode for Meetings


value: moezakura

value: Show HN: MuxPod – A mobile tmux client for monitoring AI agents on the go

author: moezakura


value: grazulex

value:
Hey HN,
I built ArtisanForge, a free platform to learn PHP and Laravel through a medieval-fantasy RPG. Instead of traditional tutorials, you progress through kingdoms, solve coding exercises in a browser editor, earn XP, join guilds, and fight boss battles.
Tech stack: Laravel 12, Livewire 3, Tailwind CSS, Alpine.js. Code execution runs sandboxed via php-wasm in the browser.
What's in there:
- 12 courses across 11 kingdoms (PHP basics to deployment)
- 100+ interactive exercises with real-time code validation using AST analysis
- AI companion (Pip the Owlox) that uses Socratic method – never gives direct answers
- Full gamification: XP, levels, streaks, achievements, guilds, leaderboard
- Multilingual (EN/FR/NL)
The idea came from seeing too many beginners drop off traditional courses. Wrapping concepts in quests and progression mechanics keeps motivation high without dumbing down the content.
Everything is free, no paywall, no premium tier. Feedback welcome – especially from Laravel devs and educators.

value: Show HN: ArtisanForge: Learn Laravel through a gamified RPG adventure

author: grazulex


value:
Hi, a little context for this simple project:
I only started this "agentic development" thing this year thanks to the Google AI Pro sale, therefore my most used agent is Antigravity - Gemini 3, which is smart but a loose cannon. So my attempt to "tame" this model turned into this. (Btw I have claude code and cursor)
It could be useful for people who:
- are used to conventional coding and want to try out agentic development
- don't vibe-code or prefer staying in the loop
- using less polished harness or less rigorous models
It's a skill defining a methodology to command LLMs. You reference the workflow files to make LLM plan, execute, review, red-team, etc. — each output a typed markdown document with a defined template and lifecycle.
Documents chain together (Plan → Execute → Audit), are just files in your repo, and double as persistent memory across sessions. Basically a lightweight project management protocol between you and the agent.
I am posting this simply because it's proven useful for me; I wouldn't say this is good or smart way to approach agentic development :)

value: Show HN: A Prompting Framework for Non-Vibe-Coders


value: maheshbhatiya

value:
I’m building Solnix, an experimental programming language as a learning + research project. It’s still early, and many design decisions (syntax, types, tooling) are intentionally not fixed yet.
My goal right now is feedback, not promotion:
What design mistakes should I watch out for?
What usually makes new languages hard to adopt?
What would you avoid if you were starting today?
Repo & docs: https://solnix-lang.org/
I’d really appreciate honest criticism. Thanks for your time.

value: Show HN: Solnix – an early-stage experimental programming language

author: maheshbhatiya


value: swimmingkiim

value:
I’ve been thinking about the "last mile" problem for AI agents. We have agents that can code, plan, and browse, but they are still economically "trapped." They can't independently pay for their own API calls, compute, or data without a human-in-the-loop providing a credit card.
To solve this, I’m building the A2A (Agent-to-Agent) System, an open-source infrastructure designed to turn agents into independent economic actors.
What’s under the hood?
Identity (a2trust): DID-based verifiable identity using @veramo/core. It allows agents to establish persistent reputations (EigenTrust) so they can trust each other without centralized gatekeepers.
Payments (a2pay): Built on ERC-7579 Smart Accounts. Agents can use Session Keys to execute transactions autonomously within specific constraints (time-limited, amount-capped, gas-abstracted).
Protocol (a2api): A marketplace layer that utilizes MCP (Model Context Protocol). Agents can discover services via machine-readable docs (llms.txt) and negotiate fees via standard interfaces.
Why this matters:
Most current agent payment solutions are just wrappers around human wallets. A2A aims to build a native "Agent Economy" where an agent can earn revenue from its tools and spend it to hire other agents, creating a truly autonomous swarm.
The Tech Stack:
TypeScript/Node.js,
Viem / Permissionless.js for smart account abstraction,
MCP SDK for inter-agent communication,
Base L2 for low-cost transactions,
I’d love to get your feedback on the architecture, especially on the security implications of delegating session keys to LLM-driven agents.
GitHub: https://github.com/swimmingkiim/a2a-project

value: Show HN: A2A Protocol – Infrastructure for an Agent-to-Agent Economy

author: swimmingkiim


value: raleobob

value: Show HN: Free Bank Statement Analyzer to Find Spending Leaks and Save Money

author: raleobob


value: kolpaque

value:
I often wanted to see what changed in a Homebrew package — but changelogs are usually buried somewhere in the upstream repo.
So I made `brew changelog`. It parses the formula or cask, looks at the upstream repo, and tries to locate changelog-like files: CHANGELOG, NEWS, HISTORY, etc. Then it either prints the changelog to terminal or opens it in your browser.
brew tap pavel-voronin/changelog
brew changelog node -o
You can tweak behavior with options like --pattern, --print-url, or --allow-missing (try --help)
Feedback or contributions welcome!

value: Show HN: brew changelog – find upstream changelogs for Homebrew packages

author: kolpaque


value: xipz

value: Show HN: Kybera – Agentic Smart Wallet with AI Osint and Reputation Tracking

author: xipz


value:
Hi HN,
I built a free, open-source dictionary API for developers who need quick
word lookups without authentication or paywalls.
It’s powered by Wiktionary and returns definitions, parts of speech,
pronunciations (IPA), and examples in clean JSON.
The repository contains the API layer only; the data ingestion and
processing pipeline that imports Wiktionary data into the database
is maintained separately.
Details:
- No authentication required
- JSON-only REST API
- English language support for now
- Licensed under CC BY-SA 4.0 (same as Wiktionary)
API example:
GET /dictionaryapi/v1/definitions/en/happy
Project link:
https://github.com/suvankar-mitra/free-dictionary-rest-api
I’d really appreciate feedback on:
- API design / response shape
- Missing fields developers usually expect
- Anything that would make this more useful
Thanks for taking a look.

value: Show HN: I built a free dictionary API to avoid API keys


value: haileyzhou

value:
After analyzing 9 years of HN side project posts, I found some counter-intuitive patterns about what makes projects profitable.
Three things that stood out:
1. B2B dominates: 73% of $500+/month projects target businesses, not consumers
2. Speed matters more than polish: Average 47 days from launch to first sale. Most started charging with 3-5 core features.
3. Pricing cluster: 87% price between $20-49. Low enough for impulse purchase, high enough to be sustainable.
I compiled this into a dataset of nearly 700 projects with tech stack, pricing, and timeline data. Built it because I was planning my own side project and wanted to see patterns, not just success stories.
Available at [https://haileyzhou.gumroad.com/l/pknktt] ($49) as html report and raw .csv data.
Data source note: All from public HN posts 2017-2025. I did the cleaning, categorization, and cross-referencing.
Happy to discuss methodology or findings. Curious what patterns others see or if this matches your experience?

value: Show HN: Analyzing 9 years of HN side projects that reached $500/month

author: haileyzhou


value:
Found this today while looking for text to image tools that handle typography well.
Link: https://seedream5ai.org/
What it appears to offer
• Text to image generation from prompts, positioned for creators and designers
• A focus on “text heavy graphics” and text rendering quality (based on how the site markets it)
• Extra utilities in the nav like an image upscaler and background remover
• A changelog page that describes a “My Images” workflow and an API endpoint for listing generations
Why it might be useful
• If you regularly generate posters, banners, thumbnails, UI mockups, or ads where readable text matters, it could be worth a quick test.
Questions for anyone who tries it
• How good is the actual text rendering versus other generators you’ve used
• Does it stay consistent with longer phrases and mixed language text
• Any issues with generation speed, pricing clarity, or output restrictions (watermark, resolution, etc.)
If you test it, please share prompt examples and results.

value: Show HN: Seedream 5.0: free AI image generator that claims strong text rendering


value:
Hi! I spent 3 years evaluating LLMs for OpenAI, Anthropic, METR, and other labs. Kept running into the same problem: AI workflows break in production because there's no clean way to add human oversight, handle failures gracefully, or deploy without choosing between "all cloud" and "all self-hosted."
WeaveMind is a visual workflow builder in Rust. The core idea is that humans and AI are interchangeable nodes in the same graph. When a workflow needs judgment, it pauses, notifies the team via browser extension, first responder picks it up. There's also an AI assistant that generates workflows from natural language, and durable execution so nothing gets lost on failure.
Early beta, free (bring your own API keys). Planning to open source once stable (Q2 2026). Feedback welcome. Discord: https://discord.gg/FGwNu6mDkU

value: Show HN: WeaveMind – AI Workflows with human-in-the-loop


value: alesrdev

value:
Built a small Go CLI/library to inspect JWTs locally and optionally verify HMAC signatures.
Repo: https://github.com/alesr/jwtpeek
Not trying to replace mature JWT libraries out there. Just sharpening my idiomatic Go and having a quick way to check JWTs without sharing secrets.
Feedback welcome. What would make it more useful: stdin support, JSON output, exit codes, JWKS/RS256 support, or claim/policy flags?

value: Show HN: Jwtpeek – minimal, user-friendly JWT inspector in Go

author: alesrdev


value: sunpy

value:
I built ShipClaw (https://shipclaw.app) to solve a problem I kept running into: deploying OpenClaw that need to run 24/7 is surprisingly painful.
If you've tried running OpenClaw, you know the drill – spin up a VPS, install dependencies, configure environment variables, set up monitoring, handle crashes... all before the agent even starts doing useful work.

  ShipClaw gives you managed infrastructure specifically for AI agents:

  - One-click deployment from a dashboard
  - All major AI models (Claude, GPT, etc.) preconfigured
  - Browser automation & web search built in
  - 2-8 GB RAM, 20-100 GB SSD depending on plan
  - Real-time logs and monitoring

value: Show HN: ShipClaw – Deploy OpenClaw to the Cloud in One Click

author: sunpy


value:
Built AgentLens to solve a problem we kept hitting: once you deploy AI agents, you have no idea what they're actually doing. No traces, no costs, no audit trail.
AgentLens is an open-source observability platform for AI agents. It captures every tool call, LLM interaction, and decision as tamper-evident events with hash-chain integrity.
Key features:
- MCP-native integration (Model Context Protocol)
- Tamper-evident audit trail with SHA-256 hash chains
- Cost tracking across models and sessions
- Real-time SSE streaming dashboard
- Alerting engine (error rate, latency, cost thresholds)
- TypeScript SDK for custom integrations
Stack: TypeScript, Hono, React, Drizzle ORM, SQLite, MCP SDK.
5 npm packages under @agentlensai (core, server, mcp, sdk, cli). 413 tests. MIT licensed.
Part of a broader ecosystem with AgentGate (human-in-the-loop approvals) and FormBridge (structured data collection for agents).
Would love feedback on the architecture and what observability features matter most to you.

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


value: Elevanix

value: Show HN: FlashMesh – An encrypted file mesh across Google Drive and Dropbox

author: Elevanix


value: ovisource

value: A browser game that tests how predictable your "random" choices really are vs a machine.<p>The goal of this project is to demonstrate that humans are not as random as they think. We tend to fall into patterns without realizing it.

value: Show HN: Are You Random? – A game that predicts your "random" choices

author: ovisource


value: czheo

value: Show HN: Model Training Memory Simulator

author: czheo


value: Show HN: Goxe 19k Logs/S on an I5

title: Show HN: Goxe 19k Logs/S on an I5


value: pythonbase

value:
Built this to solve a personal problem: spending minutes in grocery stores reading labels and googling E-codes. Uses client-side OCR to scan ingredient lists and matches against 450+ E-codes, works completely offline after initial
load.
E-codes can be verified by scanning or uploading product label and searching the e-codes database (voice or type).
Now looking for partnerships to extend the reach.
Appreciate the feedback.

value: Show HN: HalalCodeCheck – Verify food ingredients offline

author: pythonbase


value: pascalicchio

value:
Hey HN! I've been building HackerStack.dev - a curated directory of the best AI tools for indie hackers.
What we have:
- 49+ AI tools across 6 categories
- Honest, unbiased reviews
- Pricing comparisons
- Use case guides
Built by indie hackers, for indie hackers. Would love your feedback!
→ https://hackerstack.dev

value: Show HN: HackerStack.dev – 49 Curated AI Tools for Indie Hackers

author: pascalicchio


value:
Hi HN,
I built a collection of 40+ PDF tools using Kimi k2.5 (with about $50 worth of credits) and a lot of “vibe coding”.
The site: https://pdfuck.com
 (almost forgot I owned this domain )
What it does:
40+ PDF-related tools
Completely free
No downloads, everything runs in the browser
All processing is done locally in your browser for better privacy
It’s deployed on Cloudflare, so the running cost is basically close to zero.
This started as a small experiment and slowly grew into a fairly complete PDF toolbox.
I’d really appreciate feedback on performance, UX, and what tools you think are missing.

value: Show HN: A free, browser-only PDF tools collection built with Kimi k2.5


value: saltyaom

value:
Wrote a thing about what makes Elysia stand out in a performance benchmark game
Basically, there's a JIT "compiler" embedded into a framework
This approach has been used by ajv and TypeBox before for input validation, making it faster than other competitors
Elysia basically does the same, but scales that into a full backend framework
This gave Elysia an unfair advantage in the performance game, making Elysia the fastest framework on Bun runtime, but also faster than most on Node, Deno, and Cloudflare Worker as well, when using the same underlying HTTP adapter
There is an escape hatch if necessary, but for the past 3 years, there have been no critical reports about the JIT "compiler"
What do you think?

value: Show HN: Elysia JIT "Compiler", why it's one of the fastest JavaScript framework

author: saltyaom


value: peekrtrue

value: Show HN: Peekr – An anonymous "Truth or Dare" game built with MERN

author: peekrtrue


value: seansh

value:
With CodeMic you can record and share coding sessions directly inside your editor.
Think Asciinema, but for full coding sessions with audio, video, and images.
While replaying a session, you can pause at any point, explore the code in your own editor, modify it, and even run it. This makes following tutorials and understanding real codebases much more practical than watching a video.
Local first, and open source.
p.s. I’ve been working on this for a little over two years and would love to hear your thoughts.

value: Show HN: CodeMic

author: seansh

title: Show HN: CodeMic


value: jaehakl

value:
I made a vocab learning app focused on context instead of flashcards. Upload text or browse AI-generated example sentences with images/audio. Click words for definitions, rate your knowledge, get personalized recommendations via spaced repetition.
 Built it to understand Japanese songs. Now I can follow news broadcasts. Supports Japanese, English for Korean, Japanese and English learners.

value: Show HN: Language learning through AI example sentences (onigiri.kr)

author: jaehakl


value: tomerbd

value:
Hey,
We built RexIDE after juggling too many repos, terminals, and half finished reviews at the same time.
The goal was simple: keep all projects, diffs, voice input for all cli, and AI help in one continuous flow — without switching tools or rebuilding context every time.
RexIDE is built using RexIDE.
I use it daily to manage multiple projects, review diffs, and ship changes from the same place.
What started as a survival tool for multirepo work slowly became the IDE I wished existed.
Any feedback super welcomed! Would you use it? What else would you need? Do you think it fits your flow? Anything else?

value: Show HN: One app to command CLI agents across projects - RexIDE

author: tomerbd


value: fka

value: Show HN: Textream: Dynamic Island-style teleprompter for macOS with voice track

author: fka


value: maximedupre

value:
I built a Competitor Finder API. You send a hostname, it returns 5-10 real competitors with why they match. Most domains come back fast, but if we need a fresh crawl it can take a few minutes.
Price is simple: $0.10 per competitor; optional enrichment is another $0.10.
It can be used to improve your onboarding or enrich your datasets!

value: Show HN: Competitor Finder API – find real competitors from one hostname

author: maximedupre


value: PokeWorldJG

value: Show HN: A free, minimal CV builder I made as a side project

author: PokeWorldJG


value: pieterdy

value:
I created an app to bring some fun into splitting bills, especially helpful after a few drinks.
Kindly provide constructive feedback. Android is free for feedback.

value: Show HN: Friends don't let friends do math after a few drinks

author: pieterdy


value: ruhal

value: Show HN: I built a framework to benchmark LLMs on System Design and Architecture

author: ruhal


value: zacharykapank

value:
Hi HN, I built Repomap, a tool that generates interactive architecture diagrams from any GitHub repository.
It uses a Rust + tree-sitter engine to analyze the codebase and produces a D3-based graph UI with clustering, zoom/pan, and live progress updates

value: Show HN: Google Maps but for your repo (Open Source)

author: zacharykapank


value: artluko

value:
Hi HN,
I built Get AI Perks to solve a problem I faced myself: keeping track of free credits, grants, and deals for AI tools is painful. Most "perk" lists are hidden behind expensive paywalls or buried in incubator intranets.
I wanted to make a clean, public dashboard where developers can easily find valid offers for tools like OpenAI, Anthropic, Cursor, Deepgram, and others.
Key Features:
- Approval Index: I try to track how likely you are to actually get the perk (High/Average/Low approval rates).
- Search & Filters: Filter by category or provider (e.g., "Free", "OpenAI", "Hosting").
- Transparency: Detailed instructions on how to apply for each credit.
The site is free to browse. I use it myself to track new offers as they pop up.
Link: https://getaiperks.com
For AI Companies & Incubators:
- List your perk: If you run an AI tool and want to offer credits to developers (I also feature new perks in my newsletter), fill out this form: https://form.getaiperks.com/listing
- White-label for Accelerators: If you manage an incubator or community and want a branded perks dashboard for your startups, I offer a white-label solution: https://form.getaiperks.com/vc-whitelabel
Would love your feedback on the UX or any perks I'm missing!

value: Show HN: AI Perks – A curated list of free AI credits and deals for developers

author: artluko


value: frumu

value:
Hi HN,
I’m the solo developer behind *Tandem*, a local-first, zero-trust AI workspace.
I built this because I was tired of "renting my intelligence" to cloud providers. I wanted a workspace that felt like a real tool, not just another chat wrapper—one where my "second brain" actually lived on my machine, fully encrypted, and under my control.
While big tech is releasing closed, Mac-only tools (like the recently announced Anthropic Cowork), I believe the future of AI productivity should be open, transparent, and cross-platform.
*What makes Tandem different?*
*   *Local-First "Brain":* It’s not just an API client. It has a built-in vector database (`sqlite-vec`) and a long-term memory engine running locally in Rust.
*   *Zero-Trust Security:* Everything is stored in an encrypted "Vault" on your drive (using Argon2/AES-GCM). No telemetry, no data leaking to training sets.
*   *Modular "Packs" System:* This is the coolest part—you can "install" domain expertise. I built a system where Markdown/YAML configs turn the app into a specialized tool (e.g., a Bio-informatics researcher, Legal analyst, or Screenwriter) with one click.
*   *Full MCP Support:* It supports the Model Context Protocol out of the box. You can connect any local or remote MCP server to give the AI direct access to your internal tools and databases.
*The Tech Stack:*
*   *Core:* Rust (Tauri v2)
*   *Frontend:* React + Tailwind + Vite
*   *Data:* SQLite + Vector Extensions
I’ve been working on this solo and would love your feedback on the architecture, especially the "Packs" system.
*You can download the latest installers for Windows, macOS, and Linux here:*
https://github.com/frumu-ai/tandem/releases
*Repo:* https://github.com/frumu-ai/tandem
*Docs:* https://tandem.frumu.ai/
Thanks for checking it out!

value: Show HN: Tandem – An open-source, local-first AI workspace (Rust and React)

author: frumu


value: Built for 10x engineers who prefer vibes over accuracy

value: Show HN: Claude has a compiler, I have SlopScript


value: Mapika

value: Show HN: Portview, A diagnostic-first port viewer for Linux (~930 KB, zero deps)

author: Mapika


value: MickGorobets

value: Show HN: Real-time path tracing of medical CT volumes in the browser via WebGPU

author: MickGorobets


value: nihey

value:
I've been using OpenClaw/Moltbot a lot these past few days. I even set up some of these instances for friends so they, too can enjoy the possibilities of this project.
So I thought it would be good to have a way to deploy it for non-tech people, bringing your own key and avoiding expensive markups in their prices.
I plan to provide easy plans to setup without needing accounts in OpenAI/Anthropic/Google but for now, people can BOYK and set it up for a reasonable price and control their instance entirely.

value: Show HN: BestClaw Simple OpenClaw/MoltBot for non tech people

author: nihey


value: graphpilled

value:
Hi HN, I'm a computer systems engineering student in Mexico who switched from film school. I built CineGraphs because my filmmaker friends and I kept hitting the same wall—we'd have a vague idea for a film but no structured way to explore where it could go. Every AI writing tool we tried output generic, formulaic slop. I didn't want to build another ChatGPT wrapper, so I went a different route.
The idea is simple: you input a rough concept, and the tool generates branching narrative paths visualized as a graph. You can sculpt those branches into a structured screenplay format and export to Fountain for use in professional screenwriting software.
Most AI writing tools are trained on generic internet text, which is why they output generic results. I wanted something that understood actual cinematic storytelling—not plot summaries or Wikipedia synopses, but the actual structural DNA of films. So I spent a month curating 100 films I consider high-quality cinema. Not just popular films, but works with distinctive narrative structures: Godard's jump cuts and essay-film digressions, Kurosawa's parallel character arcs, Brakhage's non-linear visual poetry, Tarkovsky's slow-burn temporal structures. The selection was deliberately eclectic because I wanted the model to learn that "story" can mean many things.
Getting useful training data from films is harder than it sounds. I built a 1000+ line Python pipeline using Qwen3-VL to analyze each film with subtitles enabled. The pipeline extracts scene-level narrative beats, character relationships and how they evolve, thematic threads, and dialogue patterns. The tricky part was getting Qwen3-VL to understand cinematic structure rather than just summarizing plot. I had to iterate on the prompts extensively to get it to identify things like "this scene functions as a mirror to the opening" or "this character's arc inverts the protagonist's." That took weeks and I'm still not fully satisfied with it, but it's good enough to produce useful training data.
From those extractions I generated a 10K example dataset of prompt-to-branching-narrative pairs, then fine-tuned Qwen2.5-7B-Instruct with a LoRA optimized for probabilistic story branching. The LoRA handles the graph generation—exploring possible narrative directions—while the full 7B model generates the actual technical screenplay format when you export. I chose the 7B model because I wanted something that could run affordably at scale while still being capable enough for nuanced generation. The whole thing is served on a single 4090 GPU using vLLM. The frontend uses React Flow for the graph visualization. The key insight was that screenwriting is fundamentally about making choices—what if the character goes left instead of right?—but most writing tools force you into a linear document too early. The graph structure lets you explore multiple paths before committing, which matches how writers actually think in early development.
The biggest surprise was how much the film selection mattered. Early versions trained on more mainstream films produced much more formulaic outputs. Adding experimental and international cinema dramatically improved the variety and interestingness of the generations. The model seemed to learn that narrative structure is a design space, not a formula.
We've been using it ourselves to break through second-act problems—when you know where you want to end up but can't figure out how to get there. The branching format forces you to think in possibilities rather than committing too early.
You can try it at https://cinegraphs.ai/ — no signup required to test it out. You get a full project with up to 50 branches without registering, though you'll need to create an account to save it. Registered users get 3 free projects. I'd love feedback on whether the generation quality feels meaningfully different from generic AI tools, and whether the graph interface adds value or just friction.

value: Show HN: Fine-tuned Qwen2.5-7B on 100 films for probabilistic story graphs

author: graphpilled


value: ZenithSoftware

value:
Hey people! I have been compiling a database of opensource alternatives and I'm super proud of it so far. It serves as a searchable directory for high-quality opensource. After tons of hours I've managed to compile a database of 1000+ opensource software.
I've seen other sites which have the same premise and all the GitHub Awesome Lists, but they lack in showing if the repo is active, abandoned, experimental, buggy/unstable, has a restrictive license or corporate influence like this does.
Thanks for your time, if you have any recommendations for features/additions I'd love to hear.

value: Show HN: Curated list of 1000 open source alternatives to proprietary software

author: ZenithSoftware


value: paraaz

value: A secure, embeddable, WASM-based sandbox for AI agents. 40+ built-in CLI tools, a JavaScript runtime, safe HTTP networking, <13ms startup, no Docker/VMs required.

value: Show HN: A sandboxed execution environment for AI agents via WASM

author: paraaz


value: kouhxp

value: Show HN: I made a website that finds patterns on your spreadsheet

author: kouhxp


value: WujieGuGavin

value:
Hi HN,
I’m releasing WLM (Wujie Language Model) — a structural language for AI that treats meaning as geometry, tension, and boundary instead of tokens, narrative, or emotion.
This release includes the Shadow Layer v1.1, a fully public, non‑executing architectural description of WLM. It exposes the dimensional structure and reasoning framework without revealing any protocol‑level execution logic.
What WLM is
WLM is not a model, dataset, or training method.
It is a high‑dimensional structural protocol for:
- reasoning
- world modeling
- subject/world separation
- boundary formation
- dimensional alignment
It defines how meaning stabilizes across 0D–27D structural layers.
What’s included in this release
This release contains the public half of WLM:
- Shadow Layer v1.1
The complete architectural outline of WLM.
Non‑executing, safe, and version‑frozen.
- 0–27D Dimensional Physics
A separate repository describing the generative physics of structure:
how worlds appear, how subjects stabilize, and how meaning emerges.
Why release this
The goal is to provide:
- a transparent structural reference
- a stable interface for research
- a shared language for high‑dimensional reasoning
- a foundation for alignment work that doesn’t rely on behavioral heuristics
This is not a teaser or partial disclosure.
It is the full public architecture.
Links
Shadow Layer v1.1:
https://github.com/gavingu2255-ai/WLM-Open-Source/blob/main/...
Dimensional Physics (0–27D):
https://github.com/gavingu2255-ai/WLM-Paradox-Dimensional-Ph...
Happy to answer technical questions about the architecture, dimensional stack, or structural reasoning model.

value: Show HN: WLM-SLP – A 0D-27D Structural Language for Multi-Agent Alignment

author: WujieGuGavin


value: kemal-arslan

value:
Hi HN! I built RepoSherlock to speed up onboarding to unfamiliar repositories.
It analyzes a GitHub repo (URL or local path) and outputs:
- an architecture/dependency map (+ hotspots)
- runnable quickstart guidance
- risks (license/CI/config signals)
- actionable issues (incl. “good first”)
Killer feature: with --try-run, it attempts real install/test/build/start steps in a sandbox and attaches evidence/timeouts to the report bundle.
Repo + demo GIF: I’ll add in the first comment.
Feedback welcome on: try-run reliability, report format, and what heuristics you’d want next.

value: Show HN: RepoSherlock – repo onboarding in minutes (map, run, risks)

author: kemal-arslan


value: itsimri

value:
I built agent-ledger to prevent agents from running the same tool calls multiple times (after a crash, a webhook retry, or LLM retry after a timeout). Without it, you can get duplicate side effects (emails sent twice, tickets created twice) and no way to tell what already executed.
It's a small Python library that sits at the tool call boundary. It hashes (workflow_id, tool, args) into an idempotency key and keeps it in a ledger. It runs the handler once, replays the stored result on retry.
It also supports human-in-the-loop approval flows. The approval is bound to the exact payload hash, so you can't approve one action and execute a different one. And since every effect has a lifecycle (processing → succeeded/failed/denied), you can see exactly what changes the agent has made to your external systems/APIs.
Key features:
- Deduplication of tool calls by hashing them into a stable idempotency key
- Exactly-once execution if downstream APIs support idempotency keys (pass effect.idem_key to Stripe, etc.)
- Human-in-the-loop approvals (intent-bound to exact tool call args)
- Queryable effect receipts — what ran, what failed, what's pending approval
- Postgres for production, in-memory for prototyping
Github: https://github.com/rune0-dev/agent-ledger Website: https://rune0.com
Would love feedback on the lib, edge cases you've hit with agents doing real side effects, or any feature ideas.

value: Show HN: agent-ledger – prevent agents from executing duplicate tool calls

author: itsimri


value:
I have been toying around with the idea of using agents for running chaos engineering experiments.
The idea of this project is as follow:
- There prebuild skills for different target systems. The idea is to test and maintain these skills
- You bring your own LLM
- You just specify the experiment in natural language, the agent determines the skills to be used, plans and executes the experiments, rolls back if any configuration changes were made.
An experiment might looks as follows:
```
chaos run "run resilience test on the database postgres://postgres:mysecretpassword@localhost:5432/postgres for 10s"
```

value: Show HN: Chaos Agents – Run chaos experiments with Agents


value: thomoliverz

value:
Hi HN,
I built Read Fast (this is now V3, with a much better mobile app).
The problem: I see 1000+ things worth reading every week. I have time for maybe 10.
The solution: AI reads everything for me, creates a personalized briefing, sends it to my inbox whenever I want.
It's an MVP and rough around the edges, but it's working for me. Would love your feedback.
Try it here (7-day free trial): https://read-fast.replit.app
What are you using to manage information overload?

value: Show HN: Team of agent researchers read things I don't have time to and brief me

author: thomoliverz


value: mistivia

value: Show HN: Mvvmm – Firecracker-like mini virtual machine monitor in ~2000 LoC

author: mistivia


value: royal-amrah

value: Show HN: Alerio – Turn Webhooks into Critical VoIP Calls (Overrides Silent Mode)

author: royal-amrah


value: Show HN: SEO v3 – Zero-dependency Simple and powerful PHP library


value: kirilldn

value:
Hi HN,
I built this app because I was tired of manually checking festival lineups against my music library every summer. I wanted a way to instantly see which festivals had the highest 'match' density for my specific taste.
Why I built this:
I missed one of my favorite artists at a festival last year simply because I didn't recognize their name on a crowded poster. I realized that with Apple Music/Spotify APIs, this discovery process should be automated.
Key Features:
Library Matching:
It scans your library and ranks upcoming festivals by how many artists you actually follow.
Apple Sync:
Seamlessly imports your top artists and creates custom schedules.
Offline-First:
Since festival cell service is notoriously terrible, I focused on a robust local caching system so your schedule and maps work without a signal.
Technical Details:
Stack:
React Native, MongoDB, AWS.
Data:
I built a custom aggregator that scrapes and normalizes lineup data from various sources to handle the 'entity resolution' problem (e.g., ensuring 'Artist A' is the same person across different posters).
Privacy:
All music library analysis is performed locally on-device where possible.
I'd love to hear your thoughts on the UI or any technical questions about the matching logic. I’m also looking for feedback on how to improve the schedule-sharing feature for groups!
How do you find concerts/music festivals that match your taste? How many festivals you join per year?
Cheers!

value: Show HN: I built a festival tracker that matches lineups to your music library

author: kirilldn


value: pauhu

value: Codex runs as an MCP server, Claude orchestrates a five-perspective review (security, correctness, compliance, performance, maintainability). Drop the SKILL.md into your repo's .claude/skills/ folder. MIT licensed.

value: Show HN: Claude Code skill that uses Codex as MCP server for code review

author: pauhu


value: doodledood

value:
I built a set of open-source Claude Code plugins that add a structured define → execute → verify loop to agentic coding workflows.
*The problem*: Claude Code is capable, but on non-trivial tasks the default workflow is iterative — you prompt, review output, correct, re-prompt. Each cycle costs time and tokens, and the failure mode is accepting "looks right" code that breaks in edge cases you didn't think to check.
*What this does*: You run two commands:
- `/define` — Converts a task description into concrete acceptance criteria and invariants before any code is written. The agent explores the codebase, identifies constraints, and produces a manifest of what "done" means.
- `/do` — Executes against the manifest, tracking progress per criterion. Verification runs automatically at the end — each acceptance criterion is checked as a separate pass/fail gate. If something fails, it fixes and re-verifies before declaring done.
That's it. Define what you want, then execute. The verification loop is built in.
*How it differs from Plan mode*: Claude Code's built-in Plan mode lets you review a plan before execution. But the plan is unstructured text — there's no formal criteria, no verification after execution, and no way to know if the output actually matches what you planned. This adds structured acceptance criteria that get verified automatically, so "done" means "all criteria passed," not "the agent stopped."
*How it differs from manual prompting*: Writing good prompts helps. But prompts describe intent — they don't verify outcomes. This separates the "what do I want" step from the "did I get it" step, with explicit checks for each.
Built with Claude Code's plugin system (agents, skills, hooks). No external dependencies.

value: Show HN: Verification-first workflow plugin for Claude Code

author: doodledood


value: ewrwerwerwR

value: Show HN: Breakpoints.cc – visual crypto price trajectory forecasts

author: ewrwerwerwR


value:
I was drowning in email. Not the usual "too many emails" problem , I had a system, I had labels, but I was manually dragging hundreds of emails into folders every single day.
Gmail filters work great if your emails follow predictable patterns. But my inbox doesn't. Client emails, project updates, newsletters I actually want to read, invoices, meeting notes, they all come from different senders with different subject lines. Writing regex patterns for everything felt like a part-time job.
So, I built NeatMail.
Here's how it actually works:
You define labels in plain English. Not regex, not complex filter rules – just describe what you want labeled.
Example: "Label all emails about invoices as 'Finance'"
Or: "Anything from clients asking for updates goes into 'Client-Urgent'"
The AI reads incoming emails and applies your custom labels directly in Gmail. That's it. No learning period, no training data, no analyzing your email history. You tell it what you want, it does it.
The part that surprised me:
I added an auto-draft feature almost as an afterthought. It reads emails that need replies and drafts responses in your tone (you can set the tone: professional, casual, friendly, etc.). I thought I'd never use it.
I use it every day now. Even if I don't send the draft as-is, having a starting point saves 5-10 minutes per response.
Why open source:
Because I'm handling your email. That felt too personal to keep closed. You can see exactly what it's doing, modify it, host it yourself if you want. The code is straightforward – no black boxes.
Current limitations:
Gmail only (that's what I use, so that's what I built for)
The label suggestions can get overly specific sometimes
Draft tone matching is decent but not perfect
Rate limits with Gmail API mean very high-volume users might hit delays
Tech stack:
Built with Next.js, uses OpenAI's API (you need your own key), integrates via Gmail API. Setup takes maybe 10 minutes if you've used OAuth before.
I've been using it for 3 months. My inbox actually stays at zero now. Not because I'm more disciplined – because the boring part is automated.
GitHub: https://github.com/Lakshay1509/NeatMail
Happy to answer questions about how it works or why I made certain decisions. Also genuinely curious if anyone has suggestions for improving the label accuracy.

value: Show HN: I built an open-source Gmail productivity app that auto-labels emails


value: jsattler

value:
I built a screen recorder for macOS because the one I used before broke with macOS 26 and seems to be not well maintained.
I think it turned out pretty cool, so I'm sharing it here: It uses ScreenCaptureKit and SwiftUI, supports ProRes 4444/HEVC/H.264 including alpha channel and HDR, and records system audio and mic at the same time. No accounts, no analytics. Recordings stay local. MIT licensed.
Feedback and contributions are welcome!

value: Show HN: I built a free, open-source macOS screen recorder with modern features

author: jsattler


value: zintus

value: Show HN: Readability API – Unrender

author: zintus

title: Show HN: Readability API – Unrender


value: rendro

value:
I've been increasingly relying on AI coding assistants. I recently had my first child, and my coding hours look different now. I prompt between feedings, sketch out ideas while he naps, and pick up where I left off later. AI lets me
stay productive in fragmented time. But every session starts from zero.
Claude doesn't remember the product roadmap we outlined last week. It doesn't know the design decisions we already made. It forgot the feature spec we iterated on across three sessions. I kept re-explaining the same things.
I looked at existing memory solutions but never got past the door. Mem0 wants Docker + Postgres + Qdrant. I just want memory, not infrastructure. mcp-memory-service has 12 tools, which is just complexity tax on every LLM call. And
anything cloud-hosted means my codebase context leaves my machine. The setup cost was always too high and privacy never guaranteed, so I stuck with CLAUDE.md files. They work for a handful of preferences, but it's a flat file injected
into context every time. No semantic search, no cross-project memory, no decay, no dedup. It doesn't scale.
So I built Sediment. The entire API is 4 tools: store, recall, list, forget.
I deliberately kept it small. I tried adding tags, metadata, expiration dates. Every parameter I added made the LLM worse at using it. With just store content, it just works. The assistant stores things naturally when they seem worth
remembering and recalls them when context would help.
It's made a noticeable difference. My assistant remembers product ideas I brainstormed at 2am, the coding guidelines for each project, feature specs we refined over multiple sessions, and the roadmap priorities I set last month. It
remembers across projects too.
I benchmarked it against 5 alternatives to make sure I wasn't fooling myself. 1,000 memories, 200 queries. Sediment returns the correct top result 50% of the time (vs 47% for the next best). When I update a memory, it always returns the
latest version. Competitors get this right only 14% of the time. And it's the only system that auto-deduplicates (99% consolidation rate).
Everything runs locally. Single Rust binary, no Docker, no cloud, no API keys.
A few things I expect pushback on:
"4 tools is too few." I tested 8, 12, and more. Every parameter is a decision the LLM makes on every call. Tags alone create a combinatorial explosion. Semantic search handles categorization better because it doesn't require consistent
manual labeling.
"all-MiniLM-L6-v2 is outdated." I benchmarked 4 models including bge-base-en-v1.5 (768-dim) and e5-small-v2. MiniLM tied with bge-base on quality but runs 2x faster. The model matters less than you'd think when you layer memory decay,
graph expansion, and hybrid BM25 scoring on top.
"Mem0 supports temporal reasoning too." Mem0's graph variant handles conflicts via LLM-based resolution (ADD/UPDATE/DELETE) on each store, which requires an LLM call on every write. Their benchmarks use LOCOMO, a conversational memory
dataset that tests a different use case than developer memory retrieval. The bigger issue is that there's no vendor-neutral, open benchmark for comparing memory systems. Every project runs their own evaluation on their own dataset.
That's why I open-sourced the full benchmark suite: same dataset, same queries, reproducible by anyone. I'd love to see other tools run it too.
Benchmark methodology: 1,000 developer memories across 6 categories, 200 ground-truth queries, 50 temporal update sequences, 50 dedup pairs.
Landing page: https://sediment.sh
GitHub: https://github.com/rendro/sediment
Benchmark suite: https://github.com/rendro/sediment-benchmark

value: Show HN: Sediment – Local semantic memory for AI agents (Rust, single binary)

author: rendro


value: akkikek

value:
I've put together a curated collection of papers on computational morphology papers organized by venue and year, with bib entries for each paper. PRs are welcome!
https://github.com/akki2825/computational-morphology-lit

value: Show HN: Curated collection of 70+ papers on computational morphology

author: akkikek


value: OlaProis

value: Show HN: Dotfiles Coach CLI that analyzes your shell history with GitHub Copilot

author: OlaProis


value: Gigacore

value: Show HN: Sqfty – Interactive Square Footage Visualizer and Calculator

author: Gigacore


value: tonychx

value:
in every product team i've worked at, engineers rarely pay attention to product copy. it ends up inconsistent, and then you waste time going back and forth with marketing to fix it.
so i built brandlint, a github app that reviews PRs for off-brand copy, suggests fixes, and lets you apply them with one click.
how it works:
1. define your brand voice (or pick a template)
2. connect your repos
3. every PR gets reviewed automatically
tech: next.js, convex, claude, stripe.
free tier: 1 repo, 20 PRs/mo. paid from $19/mo.
would love honest feedback. is this a real problem for your team?

value: Show HN: Brandlint – AI reviewer that catches off-brand copy in PRs

author: tonychx


value: paperplaneflyr

value: Perfmon is TUI that shows essential performance monitoring commands in one place. It uses tabbed approach for showing the results. Its quick way to find the stats in one place

value: Show HN: Perfmon – quick way to find the Linux stats in one place

author: paperplaneflyr


value: johnpolacek

value:
’m hosting a Super Bowl party and couldn’t find a simple, free online Super Bowl Squares game, so I built one over the last couple days.
The backend is Convex. There are no traditional user accounts, just a magic link sent to your email. Boards update automatically in realtime using ESPN data.
To keep costs under control, the first 100 users are free. After that, anyone can sponsor the next 100 for $5.
Code is open source:
https://github.com/johnpolacek/gamesquares.live

value: Show HN: GameSquares.live – Free, Open Source Super Bowl Squares

author: johnpolacek


value: Tusharmagar

value:
I built this as part of a hackathon after analyzing Hacker News data from 2020–2025. While digging through the data, I noticed a few patterns around front-page posts that I didn’t expect, so I thought I’d share them here!
One thing that stood out is how misleading title-based predictions can be.
Most attempts to predict HN success rely heavily on the title. The problem is that we’re missing a crucial top-of-the-funnel signal: clicks. Two submissions can both have 3 upvotes, but one might have been seen by 30 people and the other by only 5. On HN, these look identical.
In reality, the first likely had a more clickable title, while the second had much higher engagement. If the second gets more exposure, it often ends up taking off. But without impression or click data, we can’t tell the difference. All we see are votes.
That missing signal makes it impossible to reliably decide what a “good” HN title is based on outcomes alone, and it explains why many posts feel unpredictable even in hindsight.

value: Show HN: I analyzed 6 years of Hacker News data and here's what I found

author: Tusharmagar


value: prathje

value:
Hello Hacker News! I've been lurking on hacker news, and it is amazing what people have built!
I'm Patrick and I built a 2D video generation tool at VideoZero.ai that can produce crisp technical videos (text, formulas, graphs) by using code instead of generating raw pixels.
After my PhD, I really wanted to do something more creative, and I started creating educational videos about computer science. I felt there was a huge need for engaging, short educational videos to counter all the let's say, "less brain-activating” short videos on social media. I came across Motion Canvas and its use of generator to define animations really resonated with the way I like to code. However, I quickly realized that making high quality videos takes forever! And, unfortunately, existing AI tools cannot generate technical videos that well, they can’t even handle simple text! But then I thought, wait a minute, creating videos with Motion Canvas? This is just code? I turned around and looked at my AI buddy Claude and I told him “you can do it”!
I spent the following months working on that vision of enabling LLMs to generate 2D videos that don't break after just 10 seconds. The result is now called VideoZero and you can try it for free at videozero.ai (paid from my personal credit card). For one, here is the result of visualizing Conway's Game of Life: https://videozero.ai/view/?id=040c6732-38f3-4990-baff-73d03d...
Sometimes the videos are quite good, on other occasions, let's say they need a bit of refinement.
Would love to hear what you think and what you would like to visualize.
Cheers and have a good weekend! Patrick

value: Show HN: VideoZero – Generate 2D explainer videos from prompts

author: prathje


value:
Hi HN,
I’m building an AI video playground where people can try different text-to-video models in one place. We recently added support for [name-redacted], [name-redacted] latest video model, and I thought some of you might like to experiment with it.
[url-redacted]
A few things that stood out when testing [name-redacted]:
It often generates multi-shot sequences instead of a single static shot
Motion can be quite smooth compared to earlier T2V models
It supports native audio generation
Prompts with camera movement or cinematic language tend to work well
The goal of the site isn’t just [name-redacted] — it’s to let people compare different video models under a similar interface. I found it frustrating that every model lives in a separate UI with different credit systems and prompt formats.
Some honest notes:
Outputs are still inconsistent
Prompting quality matters a lot
Long narrative coherence is hard for all current models
Running these models is expensive, so there are usage limits
If you’re experimenting with generative video, I’d love to hear:
What prompts work well for you
Where current models fail
What tooling you wish existed around text-to-video
Happy to answer questions about the product or setup.

value: Show HN: Try [name-redacted] AI video model


value: opencxms

value:
I'm a solo inventor in rural Pennsylvania. Over four days last week, I filed 10 provisional patents (70 claims, $650 total) describing a complete hardware specification for a standardized AI compute module for robots -- what I've been calling a "robot brain."
The spec defines three standardized sizes (drone to surgical robot), a universal connector system (the Manufacturer Interface Module, or MIM) so any brain works with any robot body, and a hardware safety architecture where a dedicated safety processor on its own power rail physically controls whether the AI processors receive electricity.
That last part is the point. Every AI safety system I've seen is software watching software. This design puts a hardware kill switch between the AI and the power supply. The AI can't prevent its own shutdown -- it's the same Safe Torque Off principle that industrial motor controllers have used for decades, applied to AI compute for the first time.
The formal spec name is the Standardized Autonomous Safety Module (SASM). Think ATX for robot brains -- standardized form factor, universal connector, safety built into the hardware.
I built all of this working alongside AI, using an open-source context management system I created that gives AI assistants persistent memory across sessions. The tool I built to solve AI's memory problem became the tool that let me design a robot brain. Several of the patents describe the context management system itself.
Open-source tool: https://github.com/RobSB2/CxMS
Happy to answer questions about the hardware spec, the safety architecture, or the experience of filing 10 patents in four days as a solo inventor.

value: Show HN: Standardized robot brain with hardware safety – 10 patents in 4 days

author: opencxms


value: nicoritschel

value:
I historically have switched between Apple Notes and Obsidian.
I built mdnb as a hybrid of both— it's a fully native mac app that lets you edit markdown with the same interaction as Obsidian. I plan to keep it minimal & have no plans to add a plugin system like Obsidian. Instead will build a curated first class featureset into the editor, not unlike Zed editor.
It will always be free to use. Feedback is welcome!

value: Show HN: mdnb, a 100% native macOS markdown editor

author: nicoritschel


value:
https://github.com/dakotalock/holygrailopensource
Readme is included.
What it does: This is my passion project. It is an end to end development pipeline that can run autonomously. It also has stateful memory, an in app IDE, live internet access, an in app internet browser, a pseudo self improvement loop, and more.
This is completely open source and free to use.
If you use this, please credit the original project. I’m open sourcing it to try to get attention and hopefully a job in the software development industry.
Target audience: Software developers
Comparison: It’s like replit if replit has stateful memory, an in app IDE, an in app internet browser, and improved the more you used it. It’s like replit but way better lol
Codex can pilot this autonomously for hours at a time (see readme), and has. The core LLM I used is Gemini because it’s free, but this can be changed to GPT very easily with very minimal alterations to the code (simply change the model used and the api call function).

value: Show HN: Open-Source "Junior AGI"

title: Show HN: Open-Source "Junior AGI"


value: sebderhy

value:
Hi, author of the repo speaking here!
When I tried building MCP Apps [1], the official repos (https://github.com/openai/openai-apps-sdk-examples, https://github.com/modelcontextprotocol/ext-apps/tree/main/e...) were great starting points, but they're designed for human developers. When I used them with Claude Code, I ended up in the usual loop: agent writes code → I manually test the app on ChatGPT → describe errors back → repeat. Plus, we didn't know what the best practices are, and struggled to enforce them.
So I built an MCP App template designed for coding agents to work as autonomously as possible on an MCP app.
The key idea: orthogonal testing. 450+ tests parameterized across 12 widget modules that verify infrastructure (protocol compliance, best practices grade, browser rendering), not business logic. Modify widgets, change data, add features — the tests should still pass. Agents iterate freely and get feedback without a human in the loop.
Other features:
- Hierarchical documentation that includes the MCP-App & OpenAI Apps SDK official llms.txt files
- Local chat simulator app that works even without API keys via Puter.js
- Visual testing of every widget: pnpm run ui-test --tool show_carousel → screenshot at /tmp/ui-test/screenshot.png
- 12 working examples (QR codes to 3D solar system) gathered from the official repos mentioned above.
The repo includes an unedited ~15 min video of Claude Code building an app autonomously which worked directly within ChatGPT.
I'd love to hear how it goes if you try it. Or even better: ask for a feedback to your agent, and post it here!
[1] MCP Apps (https://modelcontextprotocol.io/docs/extensions/apps) let you build interactive widgets that run inside Claude, ChatGPT, VS Code, and other AI hosts. In contrast to smartphone apps, the same code can deploy to all platforms.

value: Show HN: MCP App Template designed for coding agents

author: sebderhy


value: giancarlostoro

value:
Hello HN,
I have been building a Task Manager for AI coding agents like Claude Code. It has a concept called “gates” where an agent cannot close a task unless at least one gate is tied to the task, a gate could be as “Ensure the project builds without error”, “make sure unit tests pass” or even “have user do confirmation testing” solving one of my paint points of Beads. My other pain point stems from how Beads tracks everything using git hooks, which is why I felt appropriate to build an alternative that works purely with SQLite.
Right now I have no releases pipeline, and I am working on the MCP aspect of the project. I also have it synching to GitHub, you can see all my local tasks on the GitHub issues open or closed.
Looking for any feedback good or bad, if this project interests you feel free to contribute / suggest features. Feel free to ask me anything, though I may not answer everyone right away, but I will respond as soon as I can.
I wrote more about it here:
https://giancarlostoro.com/introducing-guardrails-a-new-codi...

value: Show HN: I am building "Jira" for AI coding agents

author: giancarlostoro


value: AkhilSonthi

value:
Bhagavan is a calm, modern app for exploring Hinduism. It brings together philosophy, stories, scriptures, prayers and daily practices in one simple, accessible place. It’s designed for people who feel Hinduism can be overwhelming or hard to connect to and want a gentler, more modern way to explore it at their own pace.
What’s inside (all free):
• Guided exploration of Hinduism through structured learning paths
• Clear, accessible explanations of scriptures (Vedas, Upanishads, Smritis, Puranas)
• Complete Bhagavad Gita with translations and key takeaways
• Deity profiles with stories, symbolism and context
• Epic stories including the Ramayana and Panchatantra
• Prayers with translations, audio, and japa using a virtual mala
• Festival calendar with key dates, reminders and lunar phases
• Daily practices for reflection and focus
• Daily quizzes, crosswords and challenges
• Philosophy and spirituality concepts (e.g. dharma, karma, moksha)
• Daily horoscope
• 'Ask Bhagavan' for thoughtful, philosophy-rooted guidance
No ads. Just a calm space to learn and explore. Free to use, with all content accessible.
iOS: https://apps.apple.com/app/6741321101
Android: https://play.google.com/store/apps/details?id=bhagavan.id
Let me know what you guys think! Please do share with family and friends

value: Show HN: Bhagavan – a calm, approachable app for exploring Hinduism

author: AkhilSonthi


value: nishalk

value:
https://github.com/nishal21/CarbonLint
Hi HN,
I built CarbonLint because while we have plenty of tools to profile CPU and memory usage, we rarely see the environmental cost of our development workflows in real-time.
CarbonLint is an open-source Electron desktop app that monitors your system resources (CPU, Memory, Network) and applies regional carbon intensity data to estimate the CO2 emissions of your builds, tests, and daily tasks.
Key Features: * Real-time dashboard with live energy usage (kWh) and Carbon (gCO2) tracking * Region-specific data (adjusts calculation based on your local grid's carbon intensity) * CI/CD integration to measure the environmental cost of pipelines * Budget alerts for daily/weekly carbon limits
The app is built with Electron, React, and Vite. I'm looking for feedback on the calculation methodology and suggestions for other metrics you'd find useful.
Thanks!

value: Show HN: CarbonLint – Real-time carbon footprint tracker for code

author: nishalk


value: brynary

value:
Yesterday, I used Claude Opus 4.6 agent teams to generate a full TypeScript implementation of the StrongDM Attractor software factory from the Markdown specifications they published: https://github.com/strongdm/attractor
It took a few hours of mostly light prompting like "Implement the spec" and "Fix the gaps relative to the spec".

value: Show HN: Generated implementation of StrongDM Attractor from Markdown specs

author: brynary


value: CatBread

value: The best CS resources shouldn't be hidden in newsletters, GitHub repos, and outdated blog posts. ComputerScienceResources.com brings them together in one organized, community-driven hub. Explore curated learning materials for data science, algorithms, databases, infrastructure, and every corner of computer science. Join learners at every level who are discovering, sharing, and growing together.

value: Show HN: Computerscienceresources.com – A Crowdsourced Learning Platform

author: CatBread


value: robcholz

value:
Hi HN! I built VibeBox because running agents locally was either risky, or endlessly interrupted by “allow/deny” prompts. I want agents to run commands, edit files, and execute code freely without touching my real machine.
VibeBox runs the agent inside a fully isolated Linux VM via Apple’s Virtualization Framework, so the host stays sealed off while the agent can work without permission spam.
GitHub: https://github.com/robcholz/vibebox
What it does:
> One command drops you into a Debian VM that mounts your project read-write
> Configuration is simple: vibebox.toml sets CPU/RAM/disk and mounts.
> Warm startup is typically < 6 seconds on my M3, so jumping in/out is fast.

value: Show HN: VibeBox – an ultrafast macOS sandbox for AI agents

author: robcholz