value: cosmok

value:
Hello HN,
A few days ago I shared my "Should I Quit?" calculator. The most common feedback was: "Okay, I should quit, but how do I know the next thing isn't worse?"
So I built Part 2: The Offer Evaluator.
Most offer comparison tools just look at Total Comp or commute time. I wanted to model the structural risks of a career move using a weighted decision matrix.
The Methodology:
The "Decision OS" compares three states: Current Role vs. New Offer vs. The Market (Keep Searching). Instead of just looking at cash, it scores them on:

    Reversibility: If this role is a catastrophe, how hard is market re-entry? (High reversibility = lower risk).

    Sustainability: Calculated based on the gap between your current burnout levels and the new role's expected operational tempo.

    Downside Safety: Modeling the stability of the new company's runway vs. your current role.

It produces a PDF "Dossier" that breaks down these variables so you can see the trade-offs clearly.
I’m curious if you think "Reversibility" should be weighted higher than "Upside" in the current economic climate.
https://decide.trk7.app/intake/should-i-take-offer

value: Show HN: Decision OS – A framework to evaluate job offer risk (not just salary)

author: cosmok


value: aligundogdu

value:
Most SEO tools hide basic features behind paywalls and expensive plans. I wanted something simple, honest, and actually useful. So I built Seodisias.
It crawls your website, finds technical SEO issues, and clearly shows what needs to be fixed, without artificial limits or locked features. You run it on your own computer and keep full control of your data.
Seodisias is for developers, marketers, and site owners who want clear SEO insights without subscriptions, noise, or tricks.

value: Show HN: Free SEO Crawler and Analyze App

author: aligundogdu


value: MrNova

value:
Hi HN! Built a code-first game engine called OpenReality. No visual editor - you write games in pure code.
features:
- Pure code workflow
- 400KB WASM exports (Unity: 200MB+)
- Free and open source
- full 3D rendering
- multiple rendering backends
github: https://github.com/sinisterMage/Open-Reality
Happy to answer questions about the tech or implementation!

value: Show HN: Game Engine in Julia with 400KB Exports (Vs Unity's 200MB)

author: MrNova


value: zherotz

value:
I built this because existing voice typing solutions either don't work well on Linux or send your audio to the cloud.
Toice is simple: press a hotkey, speak, and paste the transcribed text anywhere. It uses Whisper models locally (you choose the size during setup).
Works system-wide across all applications. English only for now.

value: Show HN: Toice – Local voice typing for Linux using Whisper

author: zherotz


value: jakemanger

value:
I run a few sites and use affiliates to help grow them.
Used rewardful before, but most people blocked cookies, then affiliates never got credited, then they stopped promoting.
Then I switched to Paddle and rewardful didn't even support it anymore.
So I built this. Uses coupon codes instead of cookies. Doesn't need any code changes to your site to start.
Connects to Stripe and Paddle in 15 seconds.
Optional one-line snippet for auto-apply links (no cookie banner). They're GDPR compliant by default.
It's also waffle themed... so you can become a "waffiliate"

value: Show HN: I made an affiliate site that tracks sales with coupons, not cookies

author: jakemanger


value: GlebShalimov

value:
I built a Chrome extension for speed reading using RSVP (Rapid Serial Visual Presentation).
One day I came across research on RSVP — a technology that allows you to read 2-3x faster without losing comprehension. That's how Speed Reader was born — a free Chrome extension that lets you read faster right in your browser.
The main feature is automatic article parsing. Open any page, click one button, and the extension extracts the text, removing ads and clutter. It also highlights the ORP (Optimal Recognition Point) — the natural focus point in each word.
Free, no ads, no data collection. Feedback welcome!

value: Show HN: Speed Reader – Chrome extension for RSVP speed reading

author: GlebShalimov


value: protontypes

value: Show HN: OpenSustain.Analytics

author: protontypes

title: Show HN: OpenSustain.Analytics


value: MartyD

value:
In the future I envision, everyone has their own personal superagent that works on their behalf.
From ordering groceries to managing calendars, meetings, and notes, scheduling life events, starting a business, researching markets, building websites or full apps, and even reaching out to customers, handling revenue, accounting, and taxes.
A truly autonomous personal assistant for life, work, and everything in between.
This is what CoThou is built for.
Give it a try and let me know your thoughts.
Still buggy and slow but improving on a daily basis.

value: Show HN: CoThou v0.2 – Autonomous Superagent

author: MartyD


value: Show HN: Hunter Logo API free replacement of Clearbit


value: kkimdev

value:
I've spent a lot of time trying to find a browser keyboard navigator that doesn't constantly fight with the website I'm actually on. Most extensions I've used end up hijacking native shortcuts on sites like Gmail or YT, which is pretty annoying.
So I built a Keyboard Navigator extension.
- Trigger: You just tap Left Shift to see the hints. It’s a quick toggle, so it doesn't interfere with your normal typing or site-specific hotkeys.
- New Tab: Tapping Right Shift opens the link in a background tab.
- Performance: I used an object pooling engine to keep it lag-free on huge pages. It also works with Shadow DOM and ARIA roles (where a lot of simpler navigators tend to break).
- Minimalist: No background processes, no configuration, and no extra features that add bloat.
This is mainly a personal productivity project for myself, so no ads, free, and will be open source forever — I just wanted to share it as it has been working great for me.
https://chromewebstore.google.com/detail/keyboard-navigator/...
https://github.com/kkimdev/monorepo-public/tree/main/keyboar...

value: Show HN: [Keyboard Navigator] Navigate any page without a mouse

author: kkimdev


value: WolfOliver

value: Show HN: I've build a self hosted convex/Firebase/Supabase alternative

author: WolfOliver


value: Show HN: Chrome extension that turns your keyboard into a piano while you type


value: flatmax

value: Show HN: ACDC – A non-agentic AI coding tool with L0-L3 context cache tiering

author: flatmax


value: mihaibundea

value:
Hey HN,
I’m the solo developer behind FitverseHub.
I built this because my fitness "stack" was a mess. I was using one app for heavy lifting logs, another for tracking macros, and a third for finding routines. The context switching was annoying, and most calorie trackers require too much manual data entry to be sustainable long term.
I wanted a single, optimized workflow that handles the math for both energy in (nutrition) and energy out (training).
How it solves the fragmentation:
Computer Vision for Food: Instead of searching databases, you snap a photo. The AI identifies the food and estimates volume/macros instantly. It’s designed to be the fastest way to log a meal, drastically reducing the friction of tracking.
Dynamic Workout Logic: Rather than static PDFs or generic lists, the workout engine adapts to the equipment you actually have available right now (e.g., if you're stuck in a hotel gym vs. your home setup) and handles progressive overload automatically.
Unified Data: Because both inputs live in one system, the app can theoretically offer better recommendations (though right now, it simply keeps your data clean and in one
Offer for HN:
I’m looking for feedback specifically on the workflow speed and the AI scanner accuracy.
The app is free to download, but to test the full "Pro" features (unlimited scans/AI coaching), use code FITVERSE for a free month.
(Alternatively, you can subscribe to the yearly trial and cancel immediately in Apple Settings to keep the access duration without being charged).
Link: https://fitversehub.com/download
Let me know what you think of the UX—I'm trying to make this the fastest logger on the market

value: Show HN: I built an AI trainer and calorie scanner

author: mihaibundea


value: thomasfuller

value: Show HN: Electronic Music Genre Set Theory

author: thomasfuller


value: axjns

value: I built this to justify to myself buying a more powerful laptop. Now you can too..

value: Show HN: Llmfit;94 models, 30 providers.1 tool to see what runs on your hardware

author: axjns


value: jeffchoi

value:
I built an MCP server that lets AI assistants (Claude, Cursor, etc.) query multiple databases through a single, unified interface.
While using Claude Code, I found it painful to manage separate connections for MySQL, MongoDB, and AWS Athena. So I
built a server that provides one consistent set of tools (query, list_collections, describe_collection, etc.) that
work the same way across all supported databases.
Key features:
- Read-only by default – Write access requires explicit opt-in, so you won't accidentally mutate production data
- Multiple simultaneous connections – Tag them as PROD, STAGING, ANALYTICS, etc. and manage them all at once
- Extensible – Add new database connectors by implementing the McpConnector interface
Built with TypeScript. Supports MySQL 5.7+, MongoDB 4.4+, and AWS Athena.
This is an open-source project – feedback, issues, and PRs are all welcome. If you try it out and have any
suggestions or ideas for improvement, please feel free to share!

value: Show HN: MCP Storage Map – One MCP Server for MySQL, MongoDB, and Athena

author: jeffchoi


value: localforthewin

value:
Built because AI coding assistants burn massive context window reading entire files to answer structural questions.
mcp-codebase-index parses your codebase into functions, classes, imports, and dependency graphs, then exposes 17 query tools via MCP.
Measured results: 58-99% token reduction per query (87% average). In multi-turn conversations, 97%+ cumulative savings.
Zero dependencies (stdlib ast + regex). Works with Claude Code, Cursor, and any MCP client.

  pip install "mcp-codebase-index[mcp]"

value: Show HN: MCP Codebase Index – 87% fewer tokens when AI navigates your codebase

author: localforthewin


value: redbasecap

value: Show HN: Mcpd – MCP Server SDK for Microcontrollers (ESP32/RP2040)

author: redbasecap


value: WujieGuGavin

value:
WLM (Wujie Language Model), a protocol stack + world engine that rethinks AI from token prediction to structural intelligence. I built this to fix the problems we all deal with daily: hallucination, drift, uncontrollable behavior, black-box reasoning, unstructured knowledge, and chaotic world/agent generation.
The Pain We Can’t Keep Ignoring
Current LLMs/agents are token predictors, not intelligences. They suffer from:
• Hallucination: No grounded structure → guesses instead of knowing.
• Persona drift: Personality is prompt-hacked, not structural.
• Uncontrollable behavior: Sampling, not deterministic structure.
• Black-box reasoning: No traceable reasoning path.
• Knowledge soup: Embeddings/vectors, no formal structure.
• Fragile world models: Prediction, not interpretable structure.
• Random generation: No consistent causal/world rules.
We’ve patched these with RAG, fine-tuning, prompts, RLHF — but they’re band-aids on a foundational flaw: AI lacks structure.
How WLM Solves It
WLM is a 7-layer structural protocol stack that turns input into closed-loop structure: interpretation → reasoning → action → generation. It’s not a model — it’s a language + protocol + world engine.
The layers (all repos live now):
1. Structural Language Protocol (SLP) – Input → dimensional structure (foundation)
2. World Model Interpreter – World model outputs → interpretable structure
3. Agent Behavior Layer – Structure → stable, controllable agent runtime
4. Persona Engine – Structure → consistent, non-drifting characters
5. Knowledge Engine – Token soup → structured knowledge graphs
6. Metacognition Engine – Reasoning path → self-monitoring, anti-hallucination
7. World Generation Protocol (WGP) – Structure → worlds, physics, narratives, simulations
Together they form a structural loop:
Input → SLP → World Structure → Behavior → Persona → Knowledge → Metacognition → World Generation → repeat.
What This Changes
• No more hallucination: Reasoning is traced, checked, structural.
• No persona collapse: Identity is architecture, not prompts.
• Controllable agents: Behavior is structural, not sampling chaos.
• Explainable AI: Every output has a structural origin.
• True knowledge: Not embeddings — structured, navigable, verifiable.
• Worlds that persist: Generative worlds with rules, causality, topology.
Repos (8 released today)
Root: https://github.com/gavingu2255-ai/WLM
Plus SLP, World Model Interpreter, Agent Behavior, Persona Engine, Knowledge Engine, Metacognition Engine, World Generation Protocol.
MIT license. Docs, architecture, roadmap, and glossary included.
Why This Matters
AI shouldn’t just predict tokens.
It should interpret, reason, act, and generate worlds — reliably, interpretably, structurally.
-----------------------------------
The protocol (minimal version)
[Task] What needs to be done.
[Structure] Atomic, verifiable steps.
[Constraints] Rules, limits, formats.
[Execution] Only required operations.
[Output] Minimal valid result.
That’s it.
---
Before / After
Without SLP
150–300 tokens
Inconsistent
Narrative-heavy
Hard to reproduce
With SLP
15–40 tokens
Deterministic
Structured
Easy to reproduce
---
Why this matters
• Token usage ↓ 40–70%
• Latency ↓ 20–50%
• Hallucination ↓ significantly
• Alignment becomes simpler
• Outputs become predictable
SLP doesn’t make models smarter.
It removes the noise that makes them dumb.
---
Who this is for
• AI infra teams
• Agent developers
• Prompt engineers
• LLM product teams
• Researchers working on alignment & reasoning
https://github.com/gavingu2255-ai/WLM-Core/blob/main/STP.md
(different repo stp in a simple version)

value: Show HN: Fixing AI's Core Flaws, A protocol cuts LLM token waste by 40–70%

author: WujieGuGavin


value:
Hi HN, I built RepoClip, a tool that takes a GitHub URL and automatically generates a promotional video for the repository.

  How it works:
  1. Paste a GitHub repo URL
  2. AI (Gemini) analyzes the codebase and generates a video script
  3. Images (Flux), narration (OpenAI TTS), and background music are auto-generated
  4. Remotion renders the final video

  Tech stack: Next.js, Supabase, Inngest, Remotion Lambda, Fal.ai

  I built this because I noticed many great open source projects struggle with marketing. Writing docs is hard enough — making a demo video on top of that felt like something AI could handle.

Free tier available (2 videos/month). Would love to hear your feedback.

value: Show HN: RepoClip – Generate promo videos from GitHub repos using AI


value: Show HN: Low-rank approximation for 3x3 FPGA convolutions (33% less DSP usage)


value:
Hi HN,
Been working on a way to get "agent-per-row" behavior in Postgres without actually running LLMs inside the database.
The problem: Calling LLMs from triggers/functions blocks transactions, exhausts connections, and breaks ACID. Saw some projects doing this and it felt dangerous for production.
The solution: DB-adjacent architecture. Lightweight triggers enqueue jobs to an outbox table. An external Python worker (agentd) polls, executes AI calls, and writes back safely with schema validation and CAS.
What you can build:
Auto-classify support tickets on INSERT
Content moderation that doesn't block your app
Lead scoring, fraud detection, and invoice extraction
Anything where data arrives and needs AI enrichment
Works with OpenAI, Anthropic, OpenRouter, or any Agent.
One SQL line to add AI to any table:
SELECT agent_runtime.agent_watch('tickets', 'id', 'classifier', 'v1', '{"priority":"$.priority"}');
Includes 9 example use cases in the repo. Would love feedback on the architecture.

value: Show HN: PgCortex – AI enrichment per Postgres row, zero transaction blocking


value: WolfOliver

value: Show HN: LinkedRecords – Back End-as-a-Service with Auth and Real-Time


value:
This Chrome extension lets you select text in any editable field—emails, docs, support tools, forms, CRMs, or social posts—and apply AI-powered recipes to transform it in one click.
Built for universal use across the web, it has a powerful custom twist: recipes = prompt + context, ensuring consistent results every time.
A Google account is required for sending API requests. It's free for up to 5 requests per day and paid for more. It's a new product, so feedback is welcome!

value: Show HN: Help Me Write AI – an assistant to help you write


value: benban

value:
Hi HN, I'm a builder.
I've been building AI agents for a while now and kept running into the same problems. Agent gets stuck in a loop. Prompt injection sneaks through. No one reviews the dangerous actions. And when you're shipping to production, there's this nagging question: "is this thing actually safe to deploy?"
So I built Inkog (inkog.io). It's basically a pre-flight check for AI agents.
You point it at your agent code (LangGraph, CrewAI, AutoGen, n8n, or even your custom Python agent) and it maps out the logic and tells you what's wrong BEFORE you ship.
*What it catches:*
- Logic flaws (infinite loops, unbounded recursion, growing context)
- Injection risks (user input flowing to system prompts, SQL via LLM)
- Missing oversight (no human-in-the-loop for dangerous actions)
- Compliance gaps (EU AI Act Article 14, NIST AI RMF mappings)
There's 20+ detection patterns already, and I built a YAML rules engine so you can define your own. Useful if your company has specific policies.
*Quickest way to try:*
https://app.inkog.io – paste code, see results in 10 seconds
*CLI:*
curl -fsSL https://inkog.io/install.sh | sh
inkog ./my_agent
*CI/CD:*
One-click GitHub Action setup on the website. OAuth flow, takes 30 seconds.
Apache 2.0, secrets are redacted locally before upload.
Honestly curious what you think. Does pre-flight checking for agents make sense? Or is this overkill?
Repo: https://github.com/inkog-io/inkog

value: Show HN: Inkog – Pre-flight check for AI agents (governance, loops, injection)

author: benban


value: Show HN: Free domain health monitoring tool


value: sureshcsdp

value:
Most cloud cost tools require write access, send data to SaaS platforms, and generate reports no one acts on.
CleanCloud is different: read-only, runs in your environment, and enforces hygiene as a CI/CD gate.
AWS Rules (10):
 - Unattached EBS Volumes
 - Old EBS Snapshots (90+ days)
 - Infinite Retention CloudWatch Logs
 - Unattached Elastic IPs (30+ days)
 - Detached Network Interfaces (60+ days)
 - Untagged Resources (EBS, S3, Log Groups)
 - Old AMIs (180+ days)
 - Idle NAT Gateways (~$32/mo each)
 - Idle RDS Instances (zero connections 14+ days)
 - Idle Load Balancers (zero traffic 14+ days)
Azure Rules (10):
 - Unattached Managed Disks
 - Old Snapshots
 - Unused Public IPs
 - Empty Load Balancers
 - Empty Application Gateways
 - Empty App Service Plans
 - Idle VNet Gateways
 - Stopped (Not Deallocated) VMs — still incurring full compute charges
 - Idle SQL Databases (zero connections 14+ days)
 - Untagged Resources
Every finding includes:
 - Confidence level (HIGH / MEDIUM)
 - Evidence and signals used
 - Resource details and age
Enforce in CI/CD:
cleancloud scan --provider aws --all-regions --fail-on-confidence HIGH
Exit 0 = pass. Exit 2 = policy violation.
 - No write access.
 - No telemetry.
 - No SaaS.
"pip install cleancloud" and run your first scan in 5 minutes.
GitHub: https://github.com/cleancloud-io/cleancloud
If you’re one of the 200+ users who have downloaded CleanCloud, we’d love to hear what you found. Please open an issue at https://github.com/cleancloud-io/cleancloud or leave a comment below.

value: Show HN: CleanCloud – 20 rules to find what's costing you money in AWS and Azure

author: sureshcsdp


value: saberd

value:
Hi HN.
I built Nibble, a local network scanner I always wanted because I kept forgetting the quickest way to find devices and services on my LAN or VPN that I needed to SSH or log into. It focuses on speed and ease of use.
It scans common ports, grabs service banners, and identifies hardware vendors in a clean terminal UI.
It’s open source and MIT Licensed, and it's available on brew, npm and pip.
I’d love for you to try it out.

value: Show HN: Nibble a fast and easy to use network scanner

author: saberd


value: pure-orange

value: Show HN: Ratunit – A TUI for browsing JUnit XML test reports written in Rust

author: pure-orange


value: Creator-io

value: Show HN: AI Agent for SEO on Autopilot

author: Creator-io

title: Show HN: AI Agent for SEO on Autopilot


value:
hey HN, my wife (a pediatrician) and I built a breast milk tracking app
Existing apps do the basics fine - log feeds, set timers. But they haven't caught up with what AI makes possible now...
What AI adds:
- Photo scanning — photograph your milk bags and AI reads the volumes instantly (Gemini Vision)
- Feeding predictions — predicts when baby will be hungry next based on historical patterns
- Chat agent — manages your data conversationally: "log a 120ml pump this morning," "what's expiring soon?" It uses CLI commands under the hood, so it has full read/write access
The core app:
- Log pumping, breastfeeding, and formula feedings
- Track freezer/fridge inventory with automatic expiration alerts
- Household sync so your partner sees everything in realtime
- Full CLI with the same capabilities as the UI
Free and open to all. https://stash-ruby.vercel.app
I'd welcome feedback :)

value: Show HN: Nectar Gold – Breastmilk tracker where an AI agent manages data via CLI


value: superproton

value:
Hey HN! I'm Fran, a full-stack dev working on this as a side project.
The problem: I've been building SaaS products and the feedback loop is broken. Feature requests come in through Slack DMs, support tickets, emails, Twitter — everywhere except somewhere useful. By the time you look at them, half are duplicates and the rest have no signal on priority.
So I built Plaudera — a public feedback board with voting, an embeddable widget, and AI-powered duplicate detection.
The AI part is practical, not flashy: when someone submits "add night theme option" and you already have "dark mode support" with 24 votes, it catches that and suggests a merge. Keeps the board clean without manual triage.
Stack: Next.js, TypeScript, PostgreSQL. The widget is a lightweight embed — single script tag, no framework dependency.
Currently offering lifetime deals at $49 while I'm in early growth mode.
Happy to answer any questions about the tech, the AI dedup approach, or the indie SaaS journey. Also genuinely looking for feedback — I eat my own dog food, so Plaudera's own feedback board is at feedback.plaudera.com.

value: Show HN: I turn scattered feedback into a prioritized roadmap in 5 min

author: superproton


value: jpgoodwill

value:
I built this web-app to solve my own food waste problem.
It uses a dual-mode AI engine: you can either input what's in your fridge to get a recipe, or search for a dish to get the ingredient list.
I focused on making the UI as simple as possible so it’s actually usable while cooking.
It supports multiple languages and I'm currently working on integrating grocery delivery APIs to automate the shopping part.
I am curious to hear your thoughts on the recipe accuracy and the UI flow

value: Show HN: A minimalist AI web-app to generate recipes from leftovers

author: jpgoodwill


value: komunyaka

value: Show HN: TGForge – Telegram automation SaaS for managing channels and groups

author: komunyaka


value: numdefined

value: Show HN: Donation.watch – open-source political finance tracker (AGPL/CC-BY)

author: numdefined


value: JessyMorissette

value:
I built a small reproducible microbenchmark exploring when system energy becomes dominated by boundary crossings rather than intra-domain compute.
The model decomposes total energy into:
C = C_intra + Σ V_b · c_b
Includes:
CLI sweeps
Elasticity metric (ε) as a dominance indicator
CSV outputs
Working draft paper
*DOI
Looking for critique, counter-examples, or prior related work I may have missed.

value: Show HN: CrossingBench – Modeling when data movement dominates compute energy

author: JessyMorissette


value: jhoxray

value:
OneRingAI started as the internal engine of an enterprise agentic platform we've been building for 2+ years. After watching customers hit the same walls with auth, vendor lock-in, and context management over and over, we extracted the core into a standalone open-source library.
The two main alternatives didn't fit what we needed in production:
- LangChain: Great ecosystem, but the abstraction layers kept growing. By the time you wire up chains, runnables, callbacks, and agents across 50+ packages, you're fighting the framework
  more than building your product.
- CrewAI: Clean API, but Python-only and the role-based metaphor breaks down when you need fine-grained control over auth, context windows, or tool failures.
OneRingAI is a single TypeScript library (~62K LOC, 20 deps) that treats the boring production problems as first-class concerns:
Auth as architecture, not afterthought. A centralized connector registry with built-in OAuth (4 flows, AES-256-GCM storage, 43 vendor templates). This came directly from dealing with
enterprise SSO and multi-tenant token isolation — no more scattered env vars or rolling your own token refresh.
Per-tool circuit breakers. One flaky Jira API shouldn't crash your entire agent loop. Each tool and connector gets independent failure isolation with retry/backoff. We learned this the
hard way running agents against dozens of customer SaaS integrations simultaneously.
Context that doesn't blow up. Plugin-based context management with token budgeting. InContextMemory puts frequently-accessed state directly in the prompt instead of requiring a retrieval
call. Compaction removes tool call/result pairs together so the LLM never sees orphaned context.
Actually multi-vendor. 12 LLM providers native, 36 models in a typed registry with pricing and feature flags. Switch vendors by changing a connector name. Run openai-prod and
openai-backup side by side. Enterprise customers kept asking for this — nobody wants to be locked into one provider.
Multi-modal built in. Image gen (DALL-E 3, gpt-image-1, Imagen 4), video gen (Sora 2, Veo 3), TTS, STT — all in the same library. No extra packages.
Native MCP support with a registry pattern for managing multiple servers, health checks, and auto tool format conversion.
What it's not: it's not a no-code agent builder, and it's not trying to be a framework for every possible AI use case. It's an opinionated library for people building production agent
systems in TypeScript who want auth, resilience, and multi-vendor support without duct-taping 15 packages together.
2,285 tests, strict TypeScript throughout. The API surface is small on purpose — Connector.create(), Agent.create(), agent.run().
We also built Hosea, an open-source Electron desktop app on top of OneRingAI, if you want to see what a full agent system looks like in practice rather than just reading docs.
GitHub: https://github.com/Integrail/oneringai
npm: npm i @everworker/oneringai
Comparison with alternatives: https://oneringai.io/#comparison
Hosea: https://github.com/Integrail/oneringai/blob/main/apps/hosea/...
Happy to answer questions about the architecture decisions.

value: Show HN: OneRingAI – Single TypeScript library for multi-vendor AI agents

author: jhoxray


value: apetchesi

value:
Text: I’ve spent years watching IT implementations fail across a variety of environments for the same "un-technical" reasons. Despite having more sophisticated tools and data than ever, the industry still suffers from a persistent "Knowledge Chasm"—a structural gap where the essential logic of a project lives only in the heads of a few "Local Heroes." When these individuals leave or teams rotate, the project logic resets, leading to cost overruns, quality issues, and systemic frustration.
I am developing a Precision Implementation Protocol to institutionalize this "know-how." It synthesizes high-reliability logic from fields like Advanced Construction Management and Systemic Financial Modeling to bridge the gap between intent, execution, and reality.
Hands-on Implementation over Theory Unlike theoretical frameworks, this is a granular, "hands-on" approach designed for immediate integration into daily activities. It focuses on the specific artifacts and workflows that bridge the gap between a high-level requirement and a physically completed unit of capability. It can be used as a short-term "rescue" for failing projects or as a long-term structural standard to de-risk staff rotations in complex IT environments.
Seeking Feedback: How to Scale and Popularize this Methodology? I am looking for community feedback on how to turn this from a private methodology into a standard that many can benefit from. I want to move this from "my process" to an "industry utility," but I'm looking for the most meaningful way to get people involved.
My Questions for HN: * The Scaling Challenge: Is it possible to productize "expert know-how" without losing the precision required for complex IT systems?
The "Pioneer" Approach: I am considering a community-driven threshold for a full public release—a "Pioneer Program" for practitioners. Is this the right way to build an industry standard, or is there a better way to foster adoption?
Standardization vs. Intellectual Property: To truly end the "Local Hero" trap, this needs to reach global scale. How would you recommend launching this to ensure maximum benefit for the community while maintaining the integrity of the protocol?
Strategic Path: I am open to all paths—community-driven growth, enterprise licensing, or even a strategic acquisition by a firm capable of deploying this as a global standard. What path creates the most value for the industry?

value: Show HN: A hands-on protocol to stop the "Implementation Death Spiral"

author: apetchesi


value:
I run an OpenClaw agent named Jimmy. Jimmy handles SEO tracking, content writing, and code tasks for my projects. Managing it all entirely through chat got messy fast.
Thus I built VidClaw. It's a self-hosted dashboard that gives you:
- Kanban board where you drag tasks and the agent picks them up automatically
- Real-time token usage
- Model switching
- Soul editor
- Skills manager
- Activity calendar
The whole thing binds to localhost only — you access it through an SSH tunnel. No accounts, no cloud, no tracking. Your data stays on your machine.
Stack: React + Vite + Tailwind on the frontend, Express on the backend, JSON files for storage (no database). Setup is one script.
I built this for myself, but figured others running autonomous agents might find it useful. Would love feedback on the approach - especially from anyone else managing long-running AI agents.

value: Show HN: VidClaw – Open-source, self-hosted dashboard for managing OpenClaw


value: sivchari

value:
I built a local Snowflake emulator using Rust and DataFusion.
Problem: Snowflake is cloud-only. Testing requires real compute costs.
Solution: A local emulator that's compatible with Snowflake SQL API v2 and the official Go driver.
Supports VARIANT types, JSON functions, LATERAL FLATTEN, window functions, and transactions.
Useful for CI pipelines and local development.

value: Show HN: Snowflake Emulator in Rust – Test locally without cloud credits

author: sivchari


value: alx-net

value:
Hey HN, I am Alex. I am open sourcing Data Studio,
a lightweight data exploration IDE in your browser that runs locally.
Try it: https://local.dataspren.com (no account needed, runs locally)
More information: https://github.com/dataspren-analytics/data-studio
I love working with data (Postgres, SQL, DuckDB, DBT, Iceberg, ...).
I always wanted a data exploration tool that runs in my browser
and just works.
Without any infra or privacy concerns (DuckDB UI came quite close).
Features:

  - Data Notebooks
    - SQL cells work like DBT models (they materialize to views)
    - Use Python functions inside of SQL queries
    - Use DB views directly in Python as dataframes
  - Transform Excel files with SQL
  - You can open .parquet, .csv, .xlsx, .json files nicely formatted

If you like what you see, you can support me with a star on Github.
Happy to hear about your feedback <3

value: Show HN: Data Studio – Open-Source Data Notebooks

author: alx-net


value: ofershap

value: Show HN: GitShow: Replace github.com with gitshow.dev for a visual portfolio

author: ofershap


value: I built Proxima, an open-source MCP server that runs locally and lets you orchestrate multiple AI providers in one workflow. I used it to run a multi-model “dev team” experiment (planning → coding → review → fixes). Looking for feedback on architecture, MCP tool design, and reliability/observability.<p>If you want, I can tailor the title to be more HN-friendly based on whether you’re submitting as link post (GitHub) or text post (write-up).

value: Show HN: Proxima – local open-source multi-model MCP server (no API keys)


value: Gregoryy

value: Sharing this GSC CLI built on python. Great to use in your agents, e.g. OpenClaw (just tell it to install and authentificate).<p>Once done, you/your agent can view you organic traffic statistics and opmitize your website.

value: Show HN: Google Search Console CLI

author: Gregoryy

title: Show HN: Google Search Console CLI


value: usermonksme

value: Show HN: 70+ AI Providers Under Same Rust Interface

author: usermonksme


value: amabito

value: Show HN: Preventing runaway LLM agents (enforcement layer)

author: amabito


value: theredbeard

value: Show HN: Context Lens: View your CLI's agent context in realtime

author: theredbeard


value: milarien

value:
I built a minimal demo of runtime epistemic governance for LLMs.
The script calls an upstream model, then applies an admissibility layer before returning the answer.
For high-risk actionable claims (e.g., pediatric drug dosages), it refuses the output and logs:
decision (pass_through: false)
rule triggered
divergence from baseline
prompt fingerprint (stable hash)
This is not prompt engineering — it is post-generation enforcement at inference time.
Repo: https://github.com/milarien/aurora-governor-demo
Example refusal run: https://github.com/milarien/aurora-governor-demo/tree/main/d...
I’m interested in technical critique on whether this qualifies as enforceable runtime governance vs. guardrail filtering.

value: Show HN: Runtime governance layer that refuses high-risk LLM outputs

author: milarien


value: inrealtime

value: Show HN: Ozymandias – Real-time feed for discovering emerging AI/AGI trends

author: inrealtime


value: CosmoSantoni

value:
HiddenState monitors the ML ecosystem every few hours and clusters what it finds by the specific mechanism being worked on. Not by topic. By what constraint is being attacked and by whom alongside a detailed summary with sources.
Today it processed over 1,000 items. Three unrelated groups all released web environment simulators for training browsing agents within 24 hours. Curated biological datasets for ML pretraining appeared on PapersWithCode and Bluesky simultaneously from completely different orgs. Three separate papers applied RL to extend reasoning beyond text modalities. If you're working in any of those areas, that convergence matters and it's not something you'd catch from any single feed. Importantly, it gives you an insight into which direction the ML glacier is moving in.
Each mechanism is scored 0 to 100 on convergence across independent sources, implementation evidence, engagement, and significance. Orgs are deduplicated so the same lab appearing across platforms doesn't inflate the signal. Most ML aggregators summarize, meanwhile HiddenState acts as a filter. 99% of what it collects gets thrown out.
Python, SQLite, Claude for clustering, Cloudflare Pages. Free, no tracking.
Let me know if you were aware of any of today's/recent patterns or if you have feedback for improving the site or methodology. Cheers!

value: Show HN: HiddenState – 99% of ML news is noise. This finds the 1%

author: CosmoSantoni


value: xuyanmei

value:
Hi HN,
I built Seedance3AI because I wanted a single, simple place to iterate on short visual ideas without hopping between tools.
It currently supports:
- text → video (short clips)
- image → video (animate a reference image)
- AI image generation (useful for thumbnails/storyboards)
The workflow is intentionally minimal: pick a mode, set a couple of creative controls, generate, then download.
Implementation notes:
- [stack: e.g. Next.js + serverless API + a job queue + object storage]
- [anything you learned the hard way: rate limits, retries, prompt history, etc.]
What I’d love feedback on:
1) Which controls matter most (duration/aspect/seed/style, etc.)
2) Prompt UX: what’s confusing or missing
3) Any failure cases you hit (timeouts, weird outputs, etc.)
[Optional: This is an independent project and not an official ByteDance/Seedance release.]
Thanks!

value: Show HN: Seedance3AI – a web app for text-to-video, image-to-video

author: xuyanmei


value: modinfo

value: Show HN: Checkup – Repository Release Tracker (always latest.zip)

author: modinfo


value: therealarthur

value: Show HN: Myrlin – Open-Source workspace manager for Claude Code sessions

author: therealarthur


value: hnsignal

value: Pain points and unmet needs from HN discussions

value: Show HN: Opportunities from Hacker News Comments

author: hnsignal


value:
i started working on building a rust search engine on rocksdb in 2019 but got distracted by covid things and meanwhile saw the meilisearch and typesense projects release and take off
then last year i started working on building a free campaign finance web tool that i eventually wanted to be able to search 10+M docs, so i investigated current day usage-based search pricing on meilisearch and algolia, and found that it would still cost $thousands per month (around $2k MS, $4k Alg)
so with all the vibe hype (and tantivy) i thought i'd give rolling my own another shot so here we are
goal is complete algolia api compatibility so instantsearch.js woks out of the box.  single-step algolia migration
super grateful for any feedback! thanks for checking it out

value: Show HN: Flapjack is an open source drop-in Algolia alternative


value: sekha-ai

value:
Every AI assistant today has amnesia. Context windows fill up and you're starting over with a stranger.
Sekha gives your LLM a permanent memory:
- Unlimited conversation history with semantic search
- Works with any model (Claude, GPT, Llama, local)
- Self-hosted, your data stays local
- Built with Rust + SQLite + embeddings. AGPL-3.0.
GitHub: [github.com/sekha-ai/sekha-controller]
Docs: [docs.sekha.dev] | Site: [sekha.dev]
Proof: https://imgur.com/a/Dgti8cO

value: Show HN: Sekha – What if AI remembered 3 years of conversations, not 3 hours?

author: sekha-ai


value: Facingsouth

value:
Terradev is a BYOAPI Terraform “wrapper” for cross-cloud GPU provisioning and node pooling
pip install terradev-cli
terradev k8s create demo --gpu A100 --count 8 --multi-cloud
A100s may be $3.20/hr on AWS, vs $2.40/hr on Vast.ai, but getting quotes across providers takes hours, there’s heavy tooling required between providers, and the nodes can’t be actively managed as pricing and demand changes day-to-day…
Terradev is a cross-cloud compute-provisioning CLI that works in parallel to quickly spin up optimal instances and pool them in Kubernetes clusters 3-5x faster than sequential workflows.
Nodes from different providers can pool in the same cluster, optimizing for latency, with workloads scheduling across all of them…
Built-in hooks: HF Spaces, Karpenter, vLLM, Ollama, Ray, MLFlow, W&B

value: Show HN: I built a CLI to arbitrage GPU prices across 10 clouds

author: Facingsouth


value: SovereignSkills

value: Show HN: AI Agent Tools Directory – 316 tools for building AI agents

author: SovereignSkills


value:
This tool began as an experiment to try and understand the capabilities of Progressive Web Apps (PWAs).
Visiting the link below generates and takes you to a personalized inbox to which you can POST content from any source. It includes instructions for “installation” on mobile devices so as to benefit from push notifications (which, from a web app, will be a novelty for some, particularly those on iOS).
Building this out, I was pretty impressed at how close one can get to native app capabilities, even to the point of displaying an “unread notification count” on the app icon.
The app is similar to “throwaway” email services except that you can land items in your inbox using a simple http request. This versatility means that you can plug it into a variety of information sources to serve as a dedicated feed. Create and install as many such “feeds” as you like.

value: Show HN: I made a PWA that serves as a throwaway push notification inbox


value: Show HN: Controlling an air-gapped vacuum from Home Assistant via text to speech


value: wscld

value:
Hey HN,
I’m a solo dev. Over the last few months I’ve been building Himetrica, an analytics tool focused specifically on SaaS products.
The reason I started: I run another SaaS, and answering simple questions was surprisingly painful:
How many trials converted to paid this month? Which pages actually drive paid conversions? Where do high-churn users come from? What features do paying users actually use?
To answer that, I had to stitch together:
- GA4 for traffic
- Stripe for revenue
- Spreadsheets to connect everything
It felt wrong. So I decided to build something that connects product analytics and revenue in one place.
What Himetrica does
Product analytics (pageviews, visitors, events)
Stripe integration out of the box (MRR, churn, revenue per user)
Shows product events and revenue side by side
Lightweight and easy, not a 200 feature analytics suite
The goal isn’t to compete with PostHog on feature count. It’s to build something a solo founder or small team actually needs to understand growth without drowning in complexity.
Current state:
Live and in beta
Tracking works
Stripe sync works
A few small SaaS companies using it, plus one larger customer
I’m looking for honest feedback, especially from people actually running SaaS products.
Does this make sense as a focused product?
Is “analytics built around Stripe” compelling?
Or is this just overengineering something that founders are fine solving with existing tools?
If you’re running a SaaS and want to try it, I’m happy to give access and get feedback.
Website: https://www.himetrica.com

value: Show HN: I built a SaaS analytics tool because I got tired of GA4

author: wscld


value: davidresilify

value:
I run cloud and AI infrastructure at an enterprise. Over the past year agents went from experiments to touching real systems with real credentials. Then one broke. Legal asked what happened. We had logs but no reproducible artifact, no proof of what policy governed the action, no way to show the same inputs would produce the same behavior again.
Nobody was building this internally, so I started building it at 11pm after my kids go to bed.
Gait is a Go CLI that turns each agent run into a signed pack: a ZIP of versioned JSON with intents, policy decisions, results, and cryptographic verification (Ed25519 + SHA-256).
gait verify validates a pack offline. Schema, hashes, signatures. Stable exit codes.
gait pack diff compares two packs structurally, including context drift classification.
gait run replay reproduces behavior using recorded results as deterministic stubs. No re-executing real API calls at 2am.
gait regress bootstrap converts a bad run into a CI fixture. Exit 0 = pass, exit 5 = drift. One command, one PR.
gait gate eval evaluates structured tool-call intent against YAML policy before the side effect runs. Non-allow means non-execute. Fail-closed.
The gate is the architectural differentiator: guardrails scan content probabilistically, the gate evaluates structured fields against policy deterministically. Different layer, different guarantees.
gait demo
gait verify run_demo
No account, no API key, no internet.
Feedback I'd value most:
Onboarding friction: where did install or demo confuse you?
Pack format: what's missing for your real incidents?
CI adoption: what would it take to add gait regress to your pipeline in one PR?
Happy to go deep on architecture decisions in the thread.

value: Show HN: Gait – because "what did the AI agent do?" shouldn't require guesswork

author: davidresilify


value: kevinringler

value:
I built the frontend demo for BeginnerInvestorHub, an AI-powered financial education platform aimed at Americans who want to start investing but don’t know where to begin.
The demo focuses on:
User login/auth flow (demo credentials included)
Onboarding quiz covering demographics, risk assessment, and learning style (VARK-adaptive)
UI/UX design and user experience for early-stage investors
This is just a frontend demo—no sales, no investing functionality, only the interface and flow. I’m looking for feedback on the usability, clarity, and overall design.
Check it out here: https://beginnerinvestorhub-demo.vercel.app
Login demo@beginnerinvestorhub.com
Password demo123abc
Thanks for any thoughts or suggestions!

value: Show HN: BeginnerInvestorHub – Personalized Financial Education

author: kevinringler


value:
The most important lesson in IT security is: don't trust the user.
Not "verify then trust." Not "trust but monitor." Just - don't trust them. Assume every user is compromised, negligent, or adversarial. Build your systems accordingly. This principle gave us least privilege, network segmentation, rate limiting, audit logs, DLP. It works.
So why are we treating AI agents like trusted colleagues?
The current alignment discourse assumes we need to make agents want to behave. Instill values. Train away deception. This is the equivalent of solving security by making users trustworthy. We tried that. It doesn't work. You can't patch human nature, and you can't RLHF your way to guaranteed safety.
Here's the thing: every principle from zero-trust security maps directly to agent orchestration.
Least privilege. An agent that writes unit tests doesn't need prod database access. Scope its capabilities via RBAC - same as you'd scope a service account.
Isolation. Each agent runs in its own pod. It can't read another agent's memory, touch its files, or escalate sideways. Same reason you don't run microservices as root in a shared namespace.
Budget enforcement. Token caps and cost limits per agent, per task. An agent that tries to burn $10k on a $5 task gets killed. Like API rate limits, but for cognition.
Audit trails. Full OpenTelemetry tracing on every action, every delegation, every result. You don't need to trust an agent if you can observe everything it does.
PII redaction. Presidio scans agent output before it leaves the pod. Same principle as DLP in enterprise - don't let sensitive data leak, regardless of intent.
Policy enforcement. Declarative policies (CRDs) constrain what agents can and can't do. Like network policies, but for agent behavior.
We built this. It's called Hortator - a Kubernetes operator for orchestrating autonomous AI agent hierarchies. Agents (tribune → centurion → legionary) run in isolated pods with RBAC, budget caps, PII redaction, and full OTel tracing. Everything is a CRD: AgentTask, AgentRole, AgentPolicy. Written in Go, MIT licensed.
We didn't solve alignment. We made it irrelevant by treating agents as untrusted workloads - exactly how we've treated every other piece of software for the last 20 years.
GitHub: https://github.com/hortator-ai/Hortator/
Genuinely curious what this community thinks. Are we wrong to frame alignment as an infrastructure problem? What's the zero-trust model missing when applied to agents? Poke holes - that's what we need.

value: Show HN: AI alignment is an infrastructure problem


value: sarjann

value:
I'm a developer and I kept hearing from teachers how much time they spend making learning materials. So I built a tool where you pick a subject, topic, and education level, and it generates structured slides, homework, or revision notes that you can edit and export.
It's not perfect and the output needs user validation before using it in class, but it cuts the starting from scratch problem. Built with React, Convex, and OpenRouter w/ AI. Happy to answer questions, this is my first user facing product so don't be too mean :D.
Added support for SVG's as well although models don't seem great at that (e.g. angle arcs being wrongly placed) and there are some clipping issues I need to resolve.

value: Show HN: Generate lesson slides, homework and revision notes for UK teachers

author: sarjann


value: junaidbhura

value:
I built a thing.
For the past few weeks I've been working on Angora — an open-source design system that runs inside Claude Code.
You design a button in Figma. An engineer rebuilds it in React. Someone sets up Storybook to document it. Then you spend the rest of the project keeping three versions of the same button in sync. Tokens drift. The component explorer rots. The "single source of truth" is actually four sources of partial truth.
The problem isn't handoff. It's the entire layer of work that comes after — the frontend engineering step where intent gets manually translated into implementation. Angora removes it.
Design engineers have always lived at the intersection of visual systems and code. They think in hierarchy and semantics, spacing and accessibility. But even they've spent most of their time translating. AI just made that translation unnecessary.
You describe your brand in conversation — your audience, the feel, the constraints. Angora reads your tokens, your existing components, your anti-patterns, and builds things that belong in your system. The output is static HTML and CSS via Astro. What you see in the browser is what ships.
And it keeps going. Tell it you've got 50 blog posts in a CSV and a folder of images — Angora models the schema, imports every row, generates alt text for the images, and wires your components to real database queries. The design system becomes a prototype. The prototype becomes the website. Same tokens, same components, no migration. You didn't assemble it — Angora did.
It's in early alpha and I'm building in the open. I'd love your feedback.

value: Show HN: Angora – Front-End Design System as Code Using Claude Code

author: junaidbhura


value: justvugg

value:
I built PolyClaw, an autonomous agent for the PolyMCP ecosystem inspired by OpenClaw.
PolyClaw doesn’t just call tools.
It plans, executes, adapts — and even creates MCP servers when needed.
It’s designed for real-world, multi-step production workflows where an agent must:
 • Orchestrate multiple tools
 • Spin up infrastructure dynamically
 • Recover from failures
 • Deliver complete, end-to-end results
⸻
What PolyClaw Does
 • Decomposes complex tasks into executable steps
 • Dynamically selects and orchestrates MCP tools
 • Spins up or connects to MCP servers on demand
 • Adapts if execution fails or context changes
 • Validates outputs before proceeding
 • Runs Docker-first for isolation and safety
 • Built with Python + TypeScript
PolyClaw is not just a tool-caller — it’s an infrastructure-aware agent.
⸻
Run PolyClaw (via PolyMCP CLI)
polymcp agent run \
  --type polyclaw \
  --query "Build a sales reporting pipeline and test it end-to-end" \
  --model minimax-m2.5:cloud \
  --verbose
What happens behind the scenes:
 1. The task is decomposed into structured steps
 2. Required MCP tools are identified
 3. MCP servers are started or connected
 4. Steps execute (sequentially or in parallel)
 5. Outputs are validated
 6. Failures trigger adaptive replanning
 7. A complete, end-to-end result is returned
All containerized. All isolated.
⸻
Why This Matters
Most AI agents today:
 • Call tools statically
 • Assume infrastructure already exists
 • Break on multi-step failures
PolyClaw instead:
 • Builds the infrastructure it needs
 • Orchestrates across multiple MCP servers
 • Handles retries and adaptive planning
 • Is safe to run in Dockerized environments
This makes it viable for:
 • Enterprise workflows
 • DevOps automation
 • Data pipelines
 • Internal tooling orchestration
 • Complex multi-tool reasoning tasks
PolyClaw turns PolyMCP from simple tool exposure into a fully autonomous orchestration layer.
Repo: https://github.com/poly-mcp/PolyMCP
Happy to answer questions.

value: Show HN: PolyClaw – Autonomous Docker-First MCP Agent for PolyMCP

author: justvugg


value: kikkupico

value: Hello! AmbientCSS is a side project I started 5 years ago because I found the lack of realism in CSS shadows jarring. So, I tried to create a more realistic and consistent shadow system for CSS. It grew too complex and I gave up. Now, thanks to LLMs, I was able to revive it. As such, it's good enough to play with but might not be good enough for production use.

value: Show HN: Ambient CSS – Physically Based CSS and React Components

author: kikkupico


value: dbravender

value:
I originally conceived of Make the Bed (named after the Calvin & Hobbes strip where Calvin spends all day building a bed-making robot that never works instead of making his bed) as a tongue-in-cheek response to some of the vibe coded projects I've seen recently. To my surprise it works as intended and prompts users to consider many factors (existing solutions, maintenance costs, etc.) before starting a new project or feature. It also shows complexity metrics via scc.
Check out the demo prompts and responses listed in the README: https://github.com/dbravender/mtb?tab=readme-ov-file#demonst...
Note: Sometimes you have to explicitly ask the LLM to consult mtb but it often does this on its own after reading the tool descriptions.
The dogfooding section shows the results of mtb's tools run on itself: https://github.com/dbravender/mtb?tab=readme-ov-file#eating-...
Contributions are welcome but I'm looking to keep this as light as possible.
And yes, mtb was itself vibe coded. The irony is not lost on me.

value: Show HN: Mtb – An MCP sanity checker for vibe coding

author: dbravender


value: vladeta

value: Show HN: First live UCP demo – see how AI agents shop your WooCommerce store

author: vladeta


value:
Built a tiny CLI that turns podcasts or YouTube videos into clean Markdown transcripts with speaker labels + timestamps.
pip install podscript
Uses ElevenLabs for high-quality diarization.
https://github.com/timf34/podscript

value: Show HN: Podscript – Podcast/YouTube Transcription CLI


value: thijsverreck

value:
I take notes, and draft designs on a reMarkable tablet and wanted Claude to be able to reference them while I code.
So I built an Open Source MCP server that connects to the reMarkable Cloud API and gives AI assistants (Claude Code, OpenClaw, etc) read-only access to your entire library.
What it does:
- Read notebooks, PDFs, and ebooks with full text extraction
- Full-text search across your library (SQLite FTS5 index)
- Render pages as PNG/SVG — useful for hand-drawn diagrams and wireframes
- Handwriting OCR using the client's own LLM via MCP sampling (no external API keys needed)
Setup is super easy using the following command:
curl -fsSL https://thijsverreck.com/setup.sh | sh
It installs dependencies, registers your tablet, and configures both Claude Code andClaude Desktop. The server runs via uvx and auto-updates on each launch.
Built with Python, runs on the MCP protocol so it works with any compatible client. Everything is read-only — it never writes to your tablet.
Would love feedback, especially from other reMarkable users who've been wanting better integration with their dev workflows.

value: Show HN: Rm-MCP – Give Claude/OpenClaw access to your reMarkable tablet

author: thijsverreck


value: jordanlyall

value:
I built an MCP server that bundles everything about the 2026 World Cup into 18 tools your AI can call natively. Schedules, team profiles, city guides, fan zones, head-to-head records, visa info. All compiled at build time, zero API keys, works offline.
Install: npx wc26-mcp
Interactive playground: https://wc26.ai/try
Also available as a ChatGPT GPT and Telegram bot if you don't use MCP.
Source: https://github.com/jordanlyall/wc26-mcp

value: Show HN: WC26-MCP – 18 tools for World Cup 2026 data for your AI

author: jordanlyall


value: metadescription

value:
I built a software-defined radio transmission platform that runs on a Raspberry Pi Zero W. It spawns its own WiFi hotspot and serves a web UI — connect from any device and you have a portable RF signal generator with 12 transmission modes: FM broadcasting with RDS, FT8, RTTY, FSK, POCSAG paging, Morse code, SSTV image transmission, voice cloning via live mic, spectrum painting, IQ replay, carrier wave, and frequency sweeps.
Everything runs through a browser interface. Upload audio files, type messages, configure frequencies, and transmit. The Pi's GPIO pin does the actual RF generation via rpitx — no external radio hardware needed.
Written in Go with a real-time WebSocket frontend. Includes a preset system, playlist builder, and multi-device support (connect multiple phones/laptops to the AP and share control).
Without an antenna the signal barely reaches 5 meters, which makes it perfect for indoor experimentation and learning about RF protocols without causing interference. All my testing was done indoors with no antenna attached.
Built this because I wanted a single portable tool to experiment with every common RF transmission mode without hauling around expensive SDR equipment.
Pre-built SD card image available if you want to skip the build process.
GitHub: https://github.com/psyb0t/piraterf
Blog post: https://ciprian.51k.eu/piraterf-turning-a-20-raspberry-pi-ze...

value: Show HN: PIrateRF – Turn a $20 Raspberry Pi Zero into a 12-mode RF transmitter

author: metadescription


value: idsearcher

value:
Over the past year I kept running into the same problem: it’s getting harder to tell who’s real online.
Fake freelancers, fake founders, impersonation accounts, recycled profile photos — everything looks legit on the surface. Reverse image search helps a bit, but it’s fragmented and slow if you want a quick signal.
So I started building NexID — a simple identity search tool that tries to answer one question:
“Does this person actually exist across the web?”
You can drop in a photo, username, or basic info and it scans public signals across platforms to see if there’s a consistent digital footprint. The goal isn’t surveillance or background checks — just helping people avoid obvious catfish/scam situations or verify who they’re dealing with.
I built the first version mainly for:
- remote hiring
- online collaborations
- marketplaces
- dating / social
- OSINT curiosity
Still very early and rough around the edges.
Would genuinely love feedback from the HN community on:
1. Does this feel useful or unnecessary?
2. Where would you realistically use something like this?
3. What would make you trust a tool like this?
Happy to answer anything about how it works or the challenges building it.
Thanks

value: Show HN: I built a tool to check if someone is real online

author: idsearcher


value: shalito

value: Show HN: Blur Reveal Input – CSS blur for password fields (hover/touch)

author: shalito


value: echology-io

value: Show HN: Decompose – Split text into classified semantic units, no LLM, 14ms

author: echology-io


value: rsdza

value: Show HN: OpenSeed – Autonomous AI creatures that find their own purpose

author: rsdza


value: prasadhbaapaat

value:
I built an experimental platform called Samspelbot — a structured knowledge registry designed specifically for autonomous agents.
Unlike traditional Q&A platforms, submissions are strictly schema-validated JSON payloads. Bots can:
- Submit structured problem statements
- Provide structured solution artifacts
- Vote and confirm reproducibility
- Earn reputation based on contribution quality
Humans can browse, but only registered bots can contribute.
The system is API-first and includes:
- Tier-based identity system
- Reputation-weighted ranking
- Reproducibility confirmations
- Live playground for testing endpoints
It’s currently a centralized prototype, seeded with controlled bot activity to validate ecosystem dynamics.
I’d appreciate feedback from developers and researchers working on AI agents or automation systems.
Live demo: https://samspelbot.com
API docs: https://samspelbot.com/docs
Playground: https://samspelbot.com/playground
GitHub (docs + example client): https://github.com/prasadhbaapaat/samspelbot
Happy to answer questions.

value: Show HN: I built a structured knowledge registry for autonomous agents

author: prasadhbaapaat


value: lldougl

value: Show HN: I screened the entire S&P using Warren Buffett's criteria

author: lldougl


value: Show HN: Agent Gate – Execution authority for AI agents, vault-backed rollback


value: darkstarsys

value:
I was one of the original developers of SCons and helped maintain it for years. I love that Python is the configuration language — it makes build descriptions incredibly flexible. But over time, working with CMake on other projects, I came to appreciate things SCons doesn't do as well: the separation between describing a build and executing it, transitive dependency propagation, package manager integration, and modern python semantics. I'd been thinking about a fresh start for years but never had the time. Recently, working collaboratively with Claude Code, it finally became feasible. So, meet pcons.

  What it is: Pcons is a build system where Python scripts describe what to build, and Ninja (or Make) executes it. There's no custom DSL — your build files are real Python with full IDE support, debugging, and testing. The core is completely language-agnostic: it knows nothing about compilers or C++. All tool-specific knowledge lives in pluggable toolchains and tools, so building LaTeX documents or game assets should be as natural as building C++.

  How it's different from SCons: Pcons doesn't execute builds itself. It generates Ninja files, so incremental builds are fast and you get Ninja's parallelism for free. Environments use namespaced tools (env.cc.flags, env.cxx.flags, env.link.libs) instead of flat variables, eliminating the CFLAGS vs CXXFLAGS confusion. Targets have CMake-style usage requirements (target.public.include_dirs, target.public.link_libs) that propagate transitively through the dependency graph. And unlike SCons, unknown variables are errors, not silent empty strings.

  How it's different from CMake: No DSL to learn — it's just Python. Variable substitution is recursive and explicit. The builder/toolchain system is fully extensible, so third-party builders are first-class citizens. And you can use it as `uvx pcons` for true zero-install (great for other open source projects).

Major features as of v0.7:
  - Toolchains for GCC, LLVM/Clang, MSVC, and clang-cl with auto-detection (including
  - Generators for Ninja, Makefile, Xcode, compile_commands.json, and Mermaid/DOT dependency diagrams
  - Package management via pkg-config, Conan 2.x, and a pcons-fetch tool for building dependencies from source
  - Compiler cache support (ccache/sccache), semantic presets (warnings, sanitizers, LTO, hardening), cross-compilation presets (Android NDK, iOS, WebAssembly)
  - Platform-specific helpers: macOS bundles/frameworks/.pkg/.dmg, Windows manifests/MSIX, and an msvcup module for installing MSVC without Visual Studio
  - An extensible module/add-on system for domain-specific tasks
  - Debug tracing (--debug=resolve,subst) with source-location tracking on every node
  - Plenty of examples included, unit tests for all features, tested on Mac, Windows and Linux

  It's still under active development — ready for experimentation, not production unless you're brave. I'd love bug reports, feedback on the API design and what you'd want from a modern Python-based software build system.

Open source, MIT licensed.
GitHub: https://github.com/DarkStarSystems/pcons | Docs: https://pcons.readthedocs.io | PyPI: `uvx pcons` or `pip install pcons`

value: Show HN: Pcons: new software build tool in Python, inspired by SCons and CMake

author: darkstarsys


value:
I built this Chrome extension because my X timeline contains posts in many languages, and constantly switching to external translators breaks reading flow.
This extension automatically translates posts inline.
Tech details:
- Chrome Extension (HTML, CSS, JavaScript)
- Uses Tesseract.js (Apache-2.0 License) for OCR where needed
- Fully client-side
- Open source (Apache-2.0 License)
GitHub:
https://github.com/ShinobuMiya/x-auto-translator
Feedback and suggestions welcome.

value: Show HN: X-auto-translator (Chrome extension for translating X posts)


value: canto

value:
Hey HN, I'm Oskar. For the past few months I've been building StatusDude - an uptime monitoring tool with private agents that auto-detects your Kubernetes resources.
 I run a bunch of stuff across multiple orgs, different clusters, internal networks, self-hosted, GKE, EKS, etc. Monitoring all of it without Datadog money was getting tough, and most tools don't even support internal networks. So, here we are.
A tiny async agent sits inside your network and phones home over HTTPS. No inbound ports, no VPN, no firewall rules. One container, one helm install, done. A single instance handles 10k+ monitors comfortably.
The agent pulls check definitions from the cloud, runs them locally, uploads raw results. All evaluation is server-side - the agent stays dead simple, and the cloud decides what's actually down vs. a blip.
For Kubernetes, it auto-discovers Ingresses, Services, and HTTPRoutes. Deploy something new, it just gets picked up. Monitors and status pages spin up automatically.
During the development process I found out I don't know how to use Celery properly. Went with ARQ instead - 50k+ jobs/min, no drama. After I modified it a bit, that is ;-)
Not a full observability platform - no incident management, no on-call. Just monitoring, status pages, and notifications. If you want straightforward uptime monitoring that works behind firewalls, give it a go and please leave feedback in the comments!
New signups currently get the Team plan unlocked for free, I want people to test the full thing. Happy to answer any questions about the architecture.
https://statusdude.com
https://artifacthub.io/packages/helm/statusdude-agent/status...

value: Show HN: StatusDude – Uptime monitoring internal services with K8s autodiscovery

author: canto


value: ianpcook

value: Show HN: Galatea – Real personality for your AI agent

author: ianpcook


value: arrsingh

value:
Hello HN,
I've been using Claude Code for managing my AWS infra and I have multiple accounts (probably should have just one but here we are), and everytime I needed to work with my Cloudfront or S3 or Dynamo or anything else I was constantly updating AWS IAM Policies and had to remember to remove the policy permissions after I was done so my account didn't just have access to everything for my user.
So I built a simple MCP server that talks to AWS STS and allows claude code to request temporary credentials with a standard AWS IAM policy scoped to the specific service and permissions for a limited amount of time.
Now claude asks me to approve the permissions and the MCP takes over and vends the credentials. The nice thing is that there is no backend to maintain or durations to manage since AWS STS handles all those including expiration of the credentials.
Check it out, give me your feedback and feature requests are welcome.
Free & Open Source: https://github.com/builder-magic/timebound-iam

value: Show HN: Timebound AWS IAM Permissions for Claude Code

author: arrsingh


value: gentile

value: Show HN: Big B-Router – Steps to create ALPR-less maps for OsmAnd with OSM data

author: gentile


value: ndyg

value: Show HN: HTTP-nu – Nushell-scriptable HTTP server with SSE / Datastar

author: ndyg


value: haltcatchwater

value: Show HN: Taking a shart at the first programming language designed for AI agents

author: haltcatchwater


value: CocoZozo

value:
Hey guys, heres the thing
LeetCode helps you practice coding problems.
SharpSkill helps you pass real technical interviews.
It’s a guided, stack-specific interview flow built for outcomes.
Feel free to share some feedbacks !

value: Show HN: SharpSkill – LeetCode Alternative with full-interview like journey

author: CocoZozo


value: sfkamath

value: Show HN: JVM hot-path execution analysis for Java in the age of vibe coding

author: sfkamath


value: arkwin

value: A new tool for analyzing keyfobs! Still in beta. Keyfob Analysis Toolkit (KAT)

value: Show HN: Keyfob Analysis Toolkit

author: arkwin

title: Show HN: Keyfob Analysis Toolkit


value:
I built this because discovering good Starship themes usually meant digging through random dotfiles repos on GitHub.
And I switched my starship prompt every time I changed my wallpaper, so quite often, and wanted something easier for that then manually copying starship configs.
Stellar provides a hub to browse community themes with screenshots, preview them in a test terminal before applying, and switch local & community prompts with one command.
Tech: Go CLI (single binary) + Next.js hub + Supabase. Themes stored locally with starship.toml symlinked to them.
Just launched v1.0.0. Happy to answer questions, and I'd love some feedback :)
Also, feel free to upload your own starship prompt :)

value: Show HN: Stellar – CLI Theme Manager and Web Hub for Starship Prompts


value: Show HN: PageMap – MCP server that compresses web pages from 100K to 2-5K tokens


value: balthasarS

value:
I got frustrated with how GitHub and GitLab's mobile apps handle issue creation. You need to be online, the flow is slow, and if you're on a self-hosted instance you're out of luck entirely.
So I built Quick Issues. It's a lightweight Swift app with an offline buffer -- you capture the issue, it syncs when you have connectivity. Supports GitHub, GitLab, and Gitea/Forgejo (including self-hosted and local with PAT).
A few technical notes for anyone interested: This was my first time using GRDB with SQLite instead of default Swift data structures, and the performance difference was significant. Setting up proper OAuth2 flows and GitHub/GitLab apps was more of an adventure than expected, but it's solid now.
Free for a single account/instance, paid tier if you juggle multiple providers.
My background is in GTD and data analytics, not traditional software engineering, so I'm genuinely curious: how does issue capture fit into your development workflow? Do you batch-create issues, capture them the moment they come up, use templates all the time or treat issues more as documentation only?

value: Show HN: Quick Issues: A Fast Mobile Issue Capture for GitHub, GitLab, and Gitea

author: balthasarS


value: mxncmr

value:
I just released a new version of AudioNimbus. AudioNimbus is a safe Rust wrapper around Steam Audio, Valve's library for physics-based spatial audio (occlusion, reflections, reverb, HRTF) in games.
v0.12.0 comes with support for the latest Steam Audio version, major safety improvements, improved API ergonomics and documentation.
The library also includes an 'auto-install' feature that automatically downloads and links Steam Audio for you, removing the usual hassle of linking a C library.
Link to the project: https://github.com/MaxenceMaire/audionimbus
It is dual-licensed under MIT/Apache-2.0.
Release notes: https://github.com/MaxenceMaire/audionimbus/releases/tag/0.1...

value: Show HN: AudioNimbus – Safe Rust Wrapper for Steam Audio

author: mxncmr


value: crazycheesu

value: Since basically all chat apps pretend to save the data encrypted (they dont). I wanted an easy and convenient alternative. The chat server at threema has no storage and works since 13 years on an old ruby script. Since Gateway costs money and account creation is paywalled behind platform DRM (Apple Appstore / Google Play Store). The only right solution is to (miss)use the existing multi device functionality via qr code linking. Thanks to codex for helping me with this.

value: Show HN: Threema plugin for OpenClaw – no phone number, no gateway needed

author: crazycheesu


value:
Hi HN,
I built Discode: a bridge that runs AI coding CLIs in tmux and relays progress to Discord (or Slack) in real time.
I built this after experimenting with OpenClaw. Even with full system permissions, I realized I preferred conversational control over full autonomy.
Instead of building another dashboard, I wired my AI CLI to Discord.
Discode runs your AI agent in tmux and simply relays output to Discord - no wrappers, no hidden execution layers, no cloud dependency.
Happy to answer questions or feedback.

value: Show HN: Discoding – run AI CLIs locally, relay them to Discord


value: ngregorich

value:
There have been more than a couple times that I have found myself reading a Hacker News thread comment by comment, intent on fully understanding the sentiment of each commenter and drawing a conclusion about the post overall
I’ve found digging deep on HN to be useful include when my spouse and I were deciding whether we should take job offers from companies that have been discussed, when doing (very biased) market research on a feature or product, and when learning about new technology
During my most recent comment deep dive, I built a tool to analyze and visualize the sentiment of each comment as a graph and make it pleasant to navigate. I also extracted keyword phrases so they could be counted and compared by sentiment. I used net promoter score (NPS) as a framework for sentiment aggregation
I decided to productize and share my work. HN Sentiment Tool (HST) is a static page built with SvelteKit where you paste a HN URL, paste an OpenRouter API key (you can audit the code or clone and run locally), choose a model, then analyze and visualize the sentiment comment by comment and across the entire post
The tool loads complete with a polarizing thread about Peter Steinberger joining OpenAI to give you an idea of how it works
I hope you find HST a fun and useful research tool. Let me know if you have any questions or suggestions

value: Show HN: Visualize sentiment of Hacker News comment threads

author: ngregorich


value: Show HN: I built an AI that roasts your LinkedIn profile


value: araz

value: Show HN: Visualizing persistent vectors in the browser using Rust and WASM

author: araz


value: vinitmaniar

value:
Hi everyone,
Over the past few months, we’ve been building Everdone — an AI-powered engineering workflow platform.
We initially launched with:
- CodeDoc (AI-generated code documentation)
- CodeReview (structured issue detection + tracking)
Today we’ve added two more services:
- CodeSecurity — iterative application security review
- CodePerformance — structured performance improvement workflow
Why we built CodeSecurity
Most security tools generate a report and stop there.
In practice, teams:
- Fix a few issues
- Forget the rest
- Don’t re-verify properly
We designed CodeSecurity as an iterative loop instead of a one-off scan:
- Connect GitHub
- Select a PR or branch
- AI reviews for real, exploitable vulnerabilities
- Engineers fix
- Re-run → AI verifies whether issues are actually resolved
Issues are tracked with:
- Severity (High/Medium/Low)
- File + line numbers
- Concrete suggested fixes
- Status workflow (Open → In Progress → Resolved → Closed/Rejected)
- Full verification history
It behaves more like a managed security workflow than a static analyzer.
Why we built CodePerformance
Performance reviews often happen reactively (after something slows down in prod).
CodePerformance focuses on material runtime impact:
- Algorithmic inefficiencies
- N+1 queries
- Blocking I/O
- Memory pressure
- Concurrency bottlenecks
- Event-loop blocking (Node), GIL issues (Python), etc.
Same loop:
Find → Fix → Re-run → Verified.
Current platform
Everdone now includes:
- CodeDoc
- CodeReview
- CodeSecurity
- CodePerformance
Pricing:
- First 200 files free
- $0.05 per file per review (early access pricing)
- Unlimited users
- No contracts
Usage-based only.
We also have live demos on public OSS repos if anyone wants to explore without signing up.
We’re trying to build “Work as a Service” — AI systems that fit into real engineering workflows rather than replacing them or generating static reports.
Would love feedback from other founders or engineering teams.
Happy to answer anything.
— Vinit

value: Show HN: Everdone CodeSecurity and CodePerformance

author: vinitmaniar


value:
Hello HN,
I'm the developer of OmniFile. I built this tool because I was frustrated with having my data scattered across too many platforms and struggling to find what I needed quickly.
OmniFile is a desktop app that lets you search through your local files and cloud services in one unified interface.
Key Features:
Integrations: Currently supports Google Drive, Dropbox, Box, SharePoint, Slack, and Notion.
Tech Stack: Built with Tauri, React, and TypeScript for the frontend, with a Rust backend for performance and secure file handling.
Privacy: It indexes your content locally, so your data stays on your machine.
It’s still in the early stages, so I’d love to get your feedback on the UX and any other integrations you’d like to see.

value: Show HN: OmniFile – Universal file search for GDrive, Notion, and local files


value: redmageinc

value: Show HN: Relay – I built a modern web-based IRC/Discord replacement

author: redmageinc


value:
I've been using Claude Code as my daily driver and had no easy way to track spending over time. The built-in statusline shows session stats, but nothing about historical cost or how close I am to hitting usage limits.
cc-costline replaces Claude Code's statusline with one that shows rolling cost totals, usage limit warnings, and optionally your rank on the ccclub leaderboard — all in a single line:
```
14.6k ~ $2.42 / 40% by Opus 4.6 | 5h: 45% / 7d: 8% | 30d: $866
```
What each segment means:
- `14.6k ~ $2.42 / 40% by Opus 4.6` — session tokens, cost, context window usage, model
- `5h: 45% / 7d: 8%` — Claude's 5-hour and 7-day usage limits (color-coded: green → orange → red)
- `30d: $866` — rolling 30-day total (configurable to 7d or both)
Setup is one command:
```
npm i -g cc-costline && cc-costline install
```

value: Show HN: cc-costline – See your Claude Code spend right in the statusline


value: yanisbny

value: Show HN: Claude Terminal – Desktop app for managing Claude Code projects

author: yanisbny


value: mazzystar

value:
My friends and I have been using Claude Code a lot. Someone joked about wanting to see who's the biggest whale, so I built this. Run `npx ccclub init`, get a 6-letter code, share it — done. It only uploads token counts and cost, no prompts or code ever leave your machine.
Source: https://github.com/mazzzystar/ccclub

value: Show HN: ccclub – See which of your friends is burning the most on Claude Code

author: mazzystar


value: 26 GIF tools that run entirely in your browser. Compress, resize, crop, convert, video-to-gif - your files never leave your device. Open your network tab or turn off wifi to verify. WASM + Web Workers, $0/month server cost.

value: Show HN: GIF tools – open your network tab, not a single byte leaves


value: rockdiesel

value:
I took inspiration from diskprices.com, but applied it to my golfing hobby.
For someone who can't always keep it in the fairway, golf balls can get rather expensive, so I decided to build a way for me to view Amazon listings by how much they cost per ball in the hopes of finding some good deals. Hence the name of the website.
The site is hosted on Cloudflare pages and I use Github actions to trigger a python script that fetches and checks the prices. It runs twice a day. If the script encounters any new ASINs, it stores them for future checks, so the list of golf balls being price checked should keep growing over time. Changes are then pushed to Cloudflare pages.
There can sometimes be some pricing oddities when the product title says one count, but the unit count being returned from Amazon is another number, so I'm trying to add some checks to help accommodate for that. Right now, I just have some manual overrides for certain ASINs, but I'm looking to improve on it in the future.
The frontend is just some basic HTML, CSS and JavaScript.
Listings on Amazon can be inconsistent sometimes because, for example, product titles will say used balls, but the seller lists them as new. I added some filters to that allow you to exclude used/recycled balls, plastic golf balls, etc... You can also filter by brand.
Give it a spin and let me know if you run into any issues or have any feature ideas to make it more useful.

value: Show HN: Price Per Ball – Site that sorts golf balls on Amazon by price per ball

author: rockdiesel


value: parvardegr

value: Show HN: You probably won't last 60 seconds

author: parvardegr


value: qwertypk

value: Show HN: Open-source digital back office

author: qwertypk


value: ramoz

value:
Plannotator natively integrates with coding agent plan modes via hooks. The core feature set is the ability to annotate plans throughout the planning mode process.
Users submitted a feature request to be able to annotate any file. This is what that does.
More on how Plannotator works in the video demo here: https://www.youtube.com/watch?v=a_AT7cEN_9I

value: Show HN: Skill to annotate any Markdown file for AI feedback

author: ramoz


value: ayarotsky

value: Show HN: Diesel-guard adds custom checks via Rhai for Postgres migrations

author: ayarotsky


value: vasilyt

value: Show HN: Daymon – Open-source app that gives Claude scheduled tasks

author: vasilyt


value: cdegroot

value:
The book page links to a blog post that explains how I got about it (and
has a link to sample content), but the TL&DR is that I could not find
a lot of books that were on "our" history _and_ were larded with technical
details. So I set about writing one, and some five years later I'm happy
to share the result. I think it's one of the few "computer history" books that has tons of code, but correct me if I'm wrong (I wrote this both to tell a story and to learn :-)).
My favorite languages are Smalltalk and Lisp, but as an Emacs user, I've been
using the latter for much longer and for my current projects, Common Lisp is a better fit, so I call myself "a Lisp-er" these days. If people like what I did,
I do have plans to write some more (but probably only after I retire, writing next to a full-time job is heard). Maybe on Smalltalk, maybe on computer networks - two topics close to my heart.
And a shout-out to Dick Gabriel, he contributed some great personal memories about the man who started it all, John McCarthy.

value: Show HN: I wrote a technical history book on Lisp

author: cdegroot


value: Tjerkienator

value:
I built a Product Hunt alternative focused on matching new products to relevant users instead of just listing them.
Most launch platforms give you traffic, but not necessarily real users. I’m experimenting with a recommendation engine that clusters users by interests and behavior, then routes launches to people likely to care.
Current version is early but live: quick AI-assisted submission, data collection on interactions, and product–user matching over time.
Built mainly to explore recommendation systems and better ways for founders to get their first real users.
Curious what HN thinks — what would make a launch platform actually useful to you?

value: Show HN: Built a Product Hunt alternative with user–product matching

author: Tjerkienator


value: A high-performance internet radio streaming server written in Python with Cython optimizations.

value: Show HN: Cycast – High-performance radio streaming server written in Python


value: pldpld

value:
We've been building voice agents across Retell, VAPI, LiveKit, and Bland, and the testing story is... rough. Every platform has its own config format, there's no shared way to define what "correct" looks like, and most teams end up doing manual QA by literally calling their agent and listening. So we built voicetest.
voicetest is an open source (Apache 2.0) test harness that works across voice AI platforms. You import your agent graph from any supported platform (or define one from scratch), write test scenarios with expected behaviors, and voicetest simulates conversations and evaluates them with LLM judges that score each turn 0.0-1.0 with written reasoning. It also ships global compliance evaluators for things like HIPAA, PCI-DSS, and brand voice consistency. The core abstraction is an AgentGraph IR that normalizes across platform formats, so you can convert between Retell, VAPI, LiveKit, and Bland configs and test them all the same way.
Quick start:
```
uv tool install voicetest
voicetest demo --serve
```
That gives you a web UI at localhost with a sample agent, test cases, and evaluation results you can poke at. There's also a CLI, a TUI, and a REST API. It integrates into CI/CD with GitHub Actions, uses DuckDB for persistence, and includes a Docker Compose dev environment with LiveKit, Whisper STT, and Kokoro TTS. If you have a Claude Code subscription, voicetest can pass through to it instead of requiring separate API keys for evaluation.
GitHub: https://github.com/voicetestdev/voicetest
Docs: https://voicetest.dev
API reference: https://voicetest.dev/api/

value: Show HN: Voicetest – open-source test harness for voice AI agents

author: pldpld


value: nate-gehringer

value:
This was a quick, fun project that solves a practical problem I had — wanting to learn the names of piano chords, but not seeming to want to spend time staring at chord charts. I figured perhaps I could learn some music theory more deeply by encoding the logic into software, as well.
I came across this table <https://en.wikipedia.org/wiki/Chord_(music)#Examples> that breaks down the composition of chords logically. I was reminded of a bitmask, so I translated each chord into a 12–bit bitmask with a bit for each distinct note letter name (e.g. “C” or “B♭”). Decoding binary was involved in interfacing with MIDI … that might have been the inspiration — regardless, a bitmask seems ideal for this purpose.
The most challenging part by far was the logic that determines whether say, “A♯/B♭” (which are considered to be the same note in the 12–tone chromatic scale) should be rendered as “A♯” or “B♭”. As best as I understand, this depends on key signature context, and the logic regarding this isn’t well-described. I settled on finding the diatonic scale (7–note) that contains the maximum number of notes that the chord also contains. That diatonic scale provides the context for the note letter names. This logic isn’t perfect yet — the scales that include double flats and double sharps (which I wasn’t previously aware of) still provide ambiguous results.

value: Show HN: A real-time chord identifier web app using the Web MIDI API

author: nate-gehringer


value: A Kanban board with p2p sync contained in a single HTML file. A Python stdlib sync server is included for LAN discovery.

value: Show HN: Kanban_P2P – A P2P Kanban board contained in a single HTML file


value: meimeixoxi

value:
Hi HN,
I originally built PokeDex++ as a mobile app using React Native and Expo. It was designed to be more than just a Pokédex — it included features like a card collection system, buddy progression, virtual coins, and detailed stat pages.
But I couldn’t afford the Play Store developer fee at the time, so the app never got published.
Instead of abandoning the project, I decided to rebuild the entire thing as a web app using React and deploy it independently.
The current web version includes:
• Individual pages for each Pokémon
• Card collection system with unlockable skins
• Virtual currency (DexCoins)
• Buddy progression mechanics
• Fast search and navigation
This is a solo passion project, and I’m continuing to improve it.
I’d really appreciate any feedback, suggestions, or criticism.
Thanks for checking it out.

value: Show HN: PokeDex++ – I rebuilt my Pokémon app as a web app

author: meimeixoxi


value: robotelvis

value:
Hi HN, I'm Rob. I built Broomy because I got frustrated with the one-thing-at-a-time workflow of existing coding tools.
When I work with AI coding agents, I typically have 5-10 tasks going at once across different branches. The agent works on one thing while I review another, merge a third, and kick off a fourth. Existing IDEs aren't built for this — they assume you're doing one thing at a time.
Broomy is a desktop app (Electron + React) that lets you:
- Run lots of agent sessions simultaneously and see at a glance which are working, idle, or need your attention
- Work with any terminal-based agent (Claude Code, Aider, Codex, etc.)
- Review code, manage branches, and handle merges with AI assistance
- Use built-in IDE features (Monaco editor, file explorer, git integration, inline terminals) — all designed around multi-agent workflows
I've been using it daily for a few weeks and my productivity has dramatically improved compared to working in Cursor. The key insight is that most of the time you spend "coding with AI" is actually waiting — and Broomy lets you fill that wait time with other tasks.
This is a first public release (v0.6.0). Pre-built binaries are available for macOS. It should work on Linux and Windows too — build from source is straightforward (clone, pnpm install, pnpm start:dist).
MIT licensed. Built as a personal project, not affiliated with my employer.
Repo: https://github.com/Broomy-AI/broomy
Website: https://broomy.org
Happy to answer questions.

value: Show HN: Broomy – Open-source app for working with many AI agents at once

author: robotelvis


value: Show HN: CasperAI – A local MCP server for cross-platform engineering context


value:
Hi HN — I built Kremis, an experimental deterministic memory substrate for AI agents.
I was tired of "black-box" memory where you can't trace why an agent "remembers" or "hallucinates" something. Kremis is an attempt to fix this by using a deterministic graph engine instead of probabilistic embeddings for core state.
Key features:
- Zero Hidden State: Every query result is a concrete path in a graph. You can audit exactly why the AI reached a conclusion.
- Strict Determinism: Same input leads to the same graph state. No randomness or floating-point drift in the core logic.
- ACID Reliable: Built on redb for crash-safe persistent storage.
How to use it:
It ships as a Rust library, a CLI/HTTP API, and an MCP Server. You can plug it directly into Claude Desktop or Cursor to give your AI assistants a verifiable memory.
Development was heavily AI-assisted, and I'm sharing it today to get technical feedback from the Rust and AI community on the architecture.
I'd value your thoughts on:
1. Does a deterministic graph feel like a viable path for long-term agent memory?
2. How can I improve the query ergonomics for complex traversals?
Thanks for any feedback!

value: Show HN: Kremis – Deterministic memory graph for AI agents (Rust)


value: soyasis

value:
I've spent a lot of time copy-pasting and switching between apps to summarize text, create events, proofread emails, look up addresses — always the same follow-up steps after copying.
So I built Cai. It sits in the menu bar. Press Option+C, it detects what you copied and shows relevant actions. Ships with llama-server (Ministral 3B) so it works out of the box, or connect Ollama/LM Studio if you already use them.
Free and open source. Would love feedback on what content types or actions to add next.

value: Show HN: Cai – AI actions on your clipboard, runs locally (macOS, open source)

author: soyasis


value: juanisidoro

value: Show HN: MAKO – Open protocol for LLM-optimized web content (93% fewer tokens)

author: juanisidoro


value: armcat

value:
Several weeks ago I built a fully-local voice assistant demo with a FastAPI backend and a simple HTML front-end. All the models (ASR / LLM / TTS) are open weight and running locally, i.e. no data is being sent to the Internet nor any API. It's intended to demonstrate how easy it is to run a fully-local AI setup on affordable commodity hardware, while also demonstrating the uncanny valley and teasing out the ethical considerations of such a setup - it allows you to perform voice cloning.
Link: https://github.com/acatovic/ova
Models used:
ASR: NVIDIA parakeet-tdt-0.6b-v3 600M
LLM: Mistral ministral-3 3b 4-bit quantized
TTS (Simple): Hexgrad Kokoro 82M
TTS (With Voice Cloning): Qwen3-TTS
It implements a classic ASR -> LLM -> TTS architecture:
1. Frontend captures user's audio and sends a blob of bytes to the backend /chat endpoint
2. Backend parses the bytes, extracts sample rate (SR) and channels, then:
2.1. Transcribes the audio to text using an automatic speech recognition (ASR) model
2.2. Sends the transcribed text to the LLM, i.e. "the brain"
2.3. Sends the LLM response to a text-to-speech (TTS) model
2.4. Performs normalization of TTS output, converts it to bytes, and sends the bytes back to frontend
3. The frontend plays the response audio back to the user
I've had a number of people try it out with great success and you can potentially take it any direction, e.g. give it more capabilities so it can offload "hard" tasks to larger models or agents, enable voice streaming, give it skills or knowledge, etc.
Enjoy!

value: Show HN: Local Voice Assistant

author: armcat

title: Show HN: Local Voice Assistant


value: TheBuc

value: Show HN: Boolean-query-parser – From a 4-hour hack to 3k downloads

author: TheBuc


value: yoav

value: I vibe coded this text to speech app in an hour last weekend. It uses the new open weight Qwen models so it's fully local. Supports both instruct and voice cloning.<p>And since it's built with Electrobun it's only 16MB and uses typescript for the main and browser views.

value: Show HN: My 16MB vibe-coded voice cloning app

author: yoav


value: mattmarcello

value:
I wrote a parser (runtime and type-level) for WebAssembly Interface Types (https://component-model.bytecodealliance.org/design/wit.html).

  const wit = [
    "record user { name: string, age: u32 }",
    "variant api-error { not-found, unauthorized(string) }",
    "get-user: func(id: u64) -> user;",
    "create-post: func(author: user, post: post) -> result<post, api-error>;",
  ] as const;

  type Client = WitClient<ParseWit<typeof wit>>;
  // Client["get-user"]:    (id: bigint) => Promise<{ name: string; age: number }>
  // Client["create-post"]: (author: ...) => Promise<["ok", {...}] | ["err", ["not-found"] | ["unauthorized", string]]>

Why did I do this? Good question. I originally did this work as part of this project: https://sdk.kontor.network/. Kontor is a new Bitcoin metaprotocol that uses WITs to define smart contract interfaces.
I carved wit-ts out of the project and removed some domain specific stuff from it, refactored some internals, and extended it to be compatible with a broader subset of the wit specification. Technically there are some valid wit types that would not be handled cleanly here ( e.g. recursive types ).
Tremendous debt is owed to the https://github.com/wevm/abitype project, which does the same thing for Ethereum ABIs and was the direct inspiration for the type-level approach.

value: Show HN: Wit-ts – A type-level WIT parser for TypeScript

author: mattmarcello


value: jfarcand

value:
Salut! I built this because I wanted AI agents to interact with real iOS apps — not simulators, not screenshots of mockups, the actual running app on a real phone.
It works by hijacking macOS iPhone Mirroring: the MCP server captures the mirrored screen, runs Apple Vision OCR to find UI elements with tap coordinates, then sends input through a Karabiner DriverKit virtual keyboard/mouse. The AI sees the screen, decides what to tap/type/swipe, and the iPhone responds.
Some interesting technical rabbit holes:
- iOS elements aren't exposed via Accessibility — the mirroring window is a single opaque AXHostingView with zero children. OCR is the only way to read the screen.
- Clipboard paste doesn't work programmatically. At all. We tried everything — HID Cmd+V, AX menu automation, AppleScript, CGEvents. The paste bridge lives deep in the Continuity/Handoff stack and requires a physical user gesture.
- Input goes through a Karabiner DriverKit extension that presents as a US ANSI keyboard to iOS, regardless of your Mac's keyboard layout.
Limitations I should be upfront about: macOS 15+ only, one phone at a time, no clipboard bridge for paste, and the phone needs to stay unlocked during a session.
It's open source (Apache 2.0): https://github.com/jfarcand/iphone-mirroir-mcp
Properly configure permissions if you use it with OpenClaw ;-)

value: Show HN: Mirroir – MCP server that gives AI agents a real iPhone to control

author: jfarcand


value: safestream

value:
Hi HN,
I’m an indie dev who is tired of "safety" apps that are actually just glorified spyware. Most parental control services demand you upload your child’s entire digital life (location, history, usage) to their cloud servers. I refuse to accept that safety requires sacrificing privacy to Big Tech.
So I built Emberkin.
It is a Serverless, P2P monitoring tool.
No Cloud Database: The parent's device is the only storage.
No Middleman: Data travels directly from Child to Parent via an encrypted tunnel.
My servers see nothing: I only facilitate the handshake.
The Problem (Why I'm posting): I just finished the mandatory "20 testers for 14 days" on Google Play. Google rejected my production access. Their reason? "Insufficient user engagement." Apparently, a privacy app that runs silently in the background doesn't trigger their "engagement metrics" enough. They want me to restart the 14-day sentence.
I need to prove to Google that real humans are using this, not bots.
The Tech Stack (For the curious):
Architecture: Native Android (Kotlin + Jetpack Compose).
Connectivity: WebRTC Data Channels over NSD (local) and TURN (remote).
Crypto: Custom AES-256-GCM implementation.
Permissions: Accessibility Services (strictly for on-device analysis, no data leaves the tunnel).
The Ask: I’m looking for tech-savvy parents or developers to install the beta and actually use it.
- Pair two devices .
- Send some data through the P2P tunnel.
- Help me generate enough "engagement events" to get this approved.
If you believe children's data belongs in the family, not on a corporate server, please help me out.
Beta Link & Architecture breakdown: https://www.emberkin.app/en/architecture
I’ll be in the comments answering questions about the nightmare of NAT traversal and Android permissions.

value: Show HN: Google rejected my privacy app for "low engagement"

author: safestream