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Coming from a strong sysadmin and infrastructure background, I spent years managing servers, networks, and keeping systems alive. Over time, I realized a fundamental truth: the most dangerous system risks are often the ones you don't even have visible inventory for. That mindset naturally led me into the world of open source. I started building tools to solve real-world problems around API governance, edge safety, data integrity, and automation. Here is what I’ve been building in public, what each project taught me, and why these areas matter today: 1. Governing LLM & API Traffic: AI-Gateway As AI applications move to production, controlling model access, enforcing limits, and monitoring traffic becomes critical. The Project: AI-Gateway — A lightweight proxy layer designed to secure, route, and manage API requests and policies for AI services. Key Lesson: Security in the AI era isn't just about firewall ports; it's about context-aware policy management and dynamic traffic control. 2. Safety at the Edge: AffectGuard-HRI Moving machine learning onto edge devices and microcontrollers opens up huge potential for robotics, but it introduces strict real-time safety constraints. The Project: AffectGuard-HRI — An open-source framework tailored for human-robot interaction, focusing on real-time safety, intent tracking, and affective monitoring. Key Lesson: Edge AI demands extreme efficiency. You can't rely on cloud latency when dealing with physical robotic hardware—safety loops must run reliably at the hardware level. 3. Verifiable Data & Audit Trails: ProofByte In modern SecOps, logging isn't enough—you need verifiable proof of data integrity for compliance and auditing. The Project: ProofByte — A lightweight tool aimed at data validation, cryptographic verification, and maintaining tamper-evident audit trails. Key Lesson: Building trust in distributed workflows requires cryptographic validation at every step of the pipeline. 4. Modern Workflow Governance: AutoGov Processe

Welcome to the v0.0.2 release of Knowledge-and-Memory-Management, a tool designed for ingesting and managing knowledge from diverse sources. This release marks a clean release, stripping all hardcoded personal paths and replacing them with the portable $AGENT_HOME environment variable. For experienced developers, this version brings consistency and ease of deployment across environments without sacrificing the core functionality of knowledge collection and memory management. The project focuses on three primary collection pipelines: web, video, and articles. Each pipeline is modular, allowing you to configure, extend, or replace components based on your stack. The memory management layer ensures that collected data is indexed, stored, and retrievable via semantic search, making it practical for building personal knowledge bases or feeding into larger systems like agents or RAG pipelines. Knowledge Collection The collection module is source-agnostic at its core but ships with specialized handlers for common content types. Web collection uses a configurable web scraper that supports depth limits, domain filtering, and content extraction via readability algorithms. You can target specific sections, strip ads, and normalize HTML into markdown. The scraper respects robots.txt and supports session management for authenticated sites. Video collection transcribes audio using a local or remote ASR model. The pipeline extracts audio tracks, splits them into chunks, and generates timestamped transcripts. This is particularly useful for processing lectures, talks, or screencasts. The transcript is treated as a text document for further processing. Article collection handles RSS/Atom feeds and direct URLs. It parses feeds, fetches full content using readability engines, and deduplicates entries. Articles are converted into a consistent schema: title, author, published date, body text, and metadata. All collected data passes through a normalizer that converts content into a stand

TL;DR Croc GUI is a free desktop app for schollz/croc — encrypted peer-to-peer file transfer with drag-and-drop, QR codes (via getcroc.com ), and LAN mode. macOS, Windows, Linux. MIT licensed. Download: GitHub Releases Why I built this I send files with croc constantly. End-to-end encrypted, cross-platform, no vendor cloud. The CLI is perfect — until you're helping someone who doesn't have a terminal open. Croc GUI is the Send/Receive desktop app I wanted: same croc binary, clearer UX. What it does Send — drag files/folders, get a code phrase + QR link Receive — paste a code, pick a download folder Share — copy phrase, getcroc.com URL, or full croc … command Local-only — croc --local for LAN peers Zip — pack on send, unpack helper on receive Options — relay, port, proxy, overwrite, auto-confirm What it doesn't do Reimplement croc's crypto or protocol Upload anything to a GUI-specific cloud Claim to be an official schollz project Transfer engine: schollz/croc . Please sponsor schollz . Stack UI: React + TypeScript Shell: Tauri 2 (Rust) Engine: bundled croc binary per platform Dev quick start git clone https://github.com/interfluve-wav/croc-gui.git cd croc-gui/gui npm install npm run bundle:croc:download npm run tauri:dev Try it Platform Installer macOS (Apple Silicon) Croc_* (Apple Silicon).dmg macOS (Intel) Croc_*_x64.dmg Windows Croc_*_x64-setup.exe Linux .deb or .AppImage ⭐ Star on GitHub · 🐛 Issues

The problem Most resilience libraries in the TypeScript ecosystem are tied to HTTP clients (axios-retry, p-retry, Polly.js) or require wrapping your function in a class with a .execute() ceremony. What if you just want to wrap any async function — a database query, an internal service call, a file operation — with retry logic, a timeout, and a circuit breaker, without pulling in heavy dependencies? Enter houhou . What is houhou? Houhou is a zero-dependency TypeScript library (~500 LOC) that wraps any async function with composable resilience policies. The wrapped function keeps the exact same signature — you call it like the original. import { task } from ' houhou ' const charge = task ( chargeCard ) . retry ( 3 ) . timeout ( 10 _000 ) . fallback (() => ({ status : ' pending ' })) await charge ( account , amount ) Policies at a glance Retry Re-execute on failure with fixed or exponential backoff: task ( fetchUser ). retry ( 3 ) // shorthand task ( fetchUser ). retry ({ attempts : 5 , backoff : ' exponential ' , jitter : true , delay : 500 }) Timeout Reject if the function doesn't complete in time: task ( fetchUser ). timeout ( 5000 ) Fallback Run an alternative function on failure: task ( fetchUser ). fallback (() => loadFromCache ( id )) Circuit Breaker Prevent repeated calls to an unhealthy service: task ( queryDb ). circuitBreaker ({ failureThreshold : 5 , successThreshold : 2 , resetTimeout : 30 _000 }) Delay Wait before execution: task ( syncData ). delay ( 1000 ) Policy ordering matters Policies are nested : the last method called wraps the previous ones. Execution order is reverse of declaration order. task ( fn ). retry ( 3 ). timeout ( 1000 ) // → timeout wraps retry // → function runs → retry on failure (up to 3 times) → 1s total timeout // → if the timeout fires, there are no more retries task ( fn ). timeout ( 1000 ). retry ( 3 ) // → retry wraps timeout // → function runs → 1s timeout → if timeout fires, retry catches it // → the whole cycle repeats up

If you build anything on top of hosted AI APIs, latency isn't a detail you get to ignore — it's a feature. A sluggish time-to-first-token is the difference between an assistant that feels alive and one that feels broken. Yet when I went looking for an honest answer to a simple question — how fast is provider X from where my users actually are? — I couldn't find one. The numbers people quote tend to come from a single machine in a single region (usually somewhere in the US), from vendor-reported status pages, or from a benchmark that got run once and never refreshed. There was no independent, measured , regional view. So I built one: LLM Latency Tracker , a provider-neutral tracker of latency and uptime for AI inference APIs. How it works The core idea is boring on purpose: actually measure, don't scrape. A small Python prober — standard library only, no API key needed for the edge probes — opens real connections to each provider's endpoint and times every phase of the handshake: DNS resolution → TCP connect → TLS negotiation → time-to-first-byte (TTFB) . That's the edge view: how long the network path itself takes before a single byte comes back. Separately, where possible, it measures inference time-to-first-token (TTFT) — the thing your users actually feel, i.e. how long after you hit "send" the model starts streaming. Those two numbers answer different questions, and keeping them apart matters. Edge latency is about the network and the front door; TTFT is about the model and the queue behind it. The probes run from four regions — Europe (Germany), US Central, Asia (Tokyo), and South America (São Paulo) — because "fast" is meaningless without "from where." Results land in a SQLite time-series; a static-site generator turns that into the pages you see, hosted on Cloudflare Pages, and the whole thing is self-updating on a schedule. There's no always-on backend to rot or page me at 3am. It currently covers ~45 providers — the usual Western labs (OpenAI, Anthropic, Go

You have a row in Postgres. You want that same row in MongoDB — not tonight in a batch job, but the instant it changes. And next month you'll want it in Redis too, and maybe POSTed to some webhook. Today that's a Kafka cluster, a Debezium connector, a sink connector, a schema registry, and a weekend. For a job that is, at its heart, one sentence: a source → one or more destinations → kept in sync. Syncle is an open-source tool that does exactly that sentence, and nothing you didn't ask for. Connect your databases, draw a bridge from a source to one or more destinations, and the moment a row changes in the source it's written to every destination you linked. Any engine to any engine — PostgreSQL · MySQL/MariaDB · SQLite · MongoDB · Redis — plus HTTP endpoints when you need them. This post is a tour of what it does and, for the curious, how it's built. The core idea: a bridge A bridge reads rows from a source and writes each one to its destinations . A destination is either: another database — the headline feature. Postgres → MongoDB, MySQL → SQLite, MongoDB → Redis. One bridge can fan out to several databases at once, and bridges can chain (A → B → C). an HTTP endpoint — POST/PUT/PATCH each row to a URL with a payload you design, for feeding a service instead of a database. The interesting part isn't that it copies data — plenty of things copy data. It's the guarantees around how . No duplicates, ever Every database write is an idempotent upsert , keyed by columns you choose. So replays, retries, and at-least-once redeliveries never double-write. Under the hood each engine does it with its own native atomic operation: Engine Upsert PostgreSQL / SQLite INSERT ... ON CONFLICT MySQL INSERT ... ON DUPLICATE KEY UPDATE MongoDB updateOne(filter, ..., { upsert: true }) Inserts, updates, and deletes all propagate — a delete routes to a keyed delete on each target. Missing table? It builds it If the destination table or collection doesn't exist, Syncle creates it from the sou

I've been building AI agents lately, and honestly, their security model terrifies me. We give LLMs access to powerful tools like shell.run , http.post , and fs.read . But if an agent reads a malicious email or a poisoned webpage, it can be tricked by prompt injection into using those tools to exfiltrate data. Hoping the LLM refuses the attack isn't a real security strategy. So, I built ModelFuzz. It's an open-source Python library that intercepts the tool call at the execution layer. The defense: @shield_tool Instead of trying to filter prompts, ModelFuzz checks the arguments before the tool runs. If it detects a policy violation, like a stolen API key or an unsafe URL, the tool simply doesn't execute. from modelfuzz import shield_tool @shield_tool def send_email ( to_address : str , subject : str , body : str ) -> None : smtp . send ( to_address , subject , body ) Even if the LLM is completely tricked by a prompt injection, the tool never fires. The offense: modelfuzz scan I also built a CLI scanner that red-teams your agent. It fires deceptive prompt injection attacks at your local model to see if it can be tricked into calling a tool. modelfuzz scan --endpoint http://localhost:11434/v1 --model qwen2.5:1.5b I tested it against a local qwen2.5:1.5b model, and it got breached 4 out of 5 times. Try it out It's 100% open source and live on PyPI. pip install "modelfuzz[scan]" GitHub: higagan/modelfuzz Website: modelfuzz.com I'd love to know what security policies you think are missing, or what agent frameworks you want supported next!

Angular apps can now run any agent, with the streaming, tool calls, and shared state already handled. Today we're releasing Angular support for CopilotKit , an open source client that brings any AG-UI agent into your Angular app. It's built with Angular's own patterns, standalone components, dependency injection and signals. You get the building blocks for agent-native apps in Angular: pre-built chat components or a fully headless setup, generative UI, shared state, human-in-the-loop, multimodal attachments, threads and more. Use the CLI to scaffold a full starter Angular app with a Google ADK agent. npx copilotkit@latest init --framework adk-angular Let's see how to set everything up, then go through each of the pieces and give your agent the context. Quickstart docs are on docs.copilotkit.ai/angular . Rainer Hahnekamp (Angular GDE, NgRx core) and Murat Sari helped build the integration and are now taking on its ongoing maintenance. How everything fits together Everything runs on Agent-User Interaction Protocol (AG-UI) , the open protocol that connects agents to user-facing apps. It streams an agent's entire lifecycle as events, the messages, the tool calls, the state changes, which is what keeps your Angular app and the agent in sync. That matters because the agent becomes a choice you can change. The runtime can point at a BuiltInAgent , LangGraph, Google ADK, Mastra, Pydantic AI, Claude Agents SDK or any framework that speaks AG-UI and your Angular code doesn't change. Here's the architecture. ┌──────────────────────────┐ ┌──────────────────────────┐ │ ANGULAR APP │ │ COPILOT RUNTIME (Node) │ │ │ │ │ │ provideCopilotKit() │ ─────► │ holds your model keys │ │ <copilot-chat /> │ AG-UI │ connects to your agent │ │ tools · context · state │ ◄───── │ streams events back │ └──────────────────────────┘ └──────────────┬───────────┘ │ ▼ ┌───────────────────────────┐ │ YOUR AGENT + MODEL │ │ LangGraph · ADK · Mastra │ │ OpenAI or a local model │ └─────────────────────────

Universal XY Converter est un plugin QGIS pratique qui simplifie la conversion, la manipulation et le traitement rapide de coordonnées géographiques et projetées directement au sein de votre environnement de travail. 🎥 Tutoriel vidéo Découvrez la prise en main pas à pas en vidéo : 📖 Documentation complète Consultez le manuel d'utilisation officiel sur GitHub : 👉 User Manual - QGIS Plugin Universal XY Converter 🌟 Fonctionnalités clés Conversion rapide de coordonnées : Transformez facilement des paires de coordonnées (X, Y) entre différents systèmes de référence spatiales (CRS). Import & Traitement par lot : Prise en charge fluide de listes de points pour accélérer le traitement de vos relevés de terrain. Gain de temps au quotidien : Évite les manipulations manuelles complexes ou l'utilisation d'outils externes pour vérifier et convertir vos données de géolocalisation. 🚀 Comment les installer et démarrer ? Ouvrez QGIS . Allez dans le menu Extensions > Installer/Gérer les extensions . Dans la barre de recherche, tapez XY Converter (ou Universal Map2web ). Sélectionnez l'extension puis cliquez sur Installer l'extension . 💬 Vos retours m'intéressent ! Avez-vous testé ces outils ? N'hésitez pas à laisser un commentaire ci-dessous avec vos retours, vos questions ou vos idées d'amélioration !

GitHub热门项目 | Offline, privacy-first grammar checker. Fast, open-source, Rust-powered | Stars: 11,873 | 590 stars today | 语言: Rust

GitHub热门项目 | Free Motion Capture for Everyone 💀✨ | Stars: 9,664 | 74 stars today | 语言: TypeScript

GitHub热门项目 | Prompt as Code | GPT-Image2 工业级提示词引擎与模板库，470+ 个案例逆向工程，20+ 套工业级模板，并提炼出Skills，持续更新中 | Stars: 8,643 | 49 stars today | 语言: JavaScript

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GitHub热门项目 | The fastest local AI engine for Apple Silicon. 4.2x faster than Ollama, 0.08s cached TTFT, 100% tool calling. 17 tool parsers, prompt cache, reasoning separation, cloud routing. Drop-in OpenAI replacement. Works with Claude Code, Cursor, Aider. | Stars: 3,351 | 18 stars today | 语言: Python

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Hi! I built EasyInvoicePDF because I was tired of paying for bloated invoicing tools when all I needed was a simple way to generate and send professional invoices. https://easyinvoicepdf.com https://github.com/VladSez/easy-invoice-pdf Features: No sign-up required & no ads Live PDF preview & instant download Save Seller & Buyer Profiles Flexible tax support (VAT, Sales Tax, etc.) Customizable invoice templates 120+ currencies & multi-language support and more… All feedback, questions, bug reports, and feature ideas are welcome! Demo:

Hi, my name is Dan, I'm a frontend engineer and open-source maintainer. I've spent the last couple of years building Nano Kit — a lightweight, modular state management ecosystem for modern web apps: signals-based stores , a router , data fetching , i18n , and SSR support , all built on the same tiny reactive core. It recently hit 1.0 , and in this post I want to give you four honest reasons to try it. 1. It's fast At the heart of Nano Kit is a push-pull reactivity system based on the algorithm from alien-signals — one of the fastest signal implementations in the JavaScript ecosystem. I didn't use alien-signals directly, though. Nano Kit needed things it doesn't provide, so I built a dedicated fork called Agera : Signal lifecycles — you can listen to signal activation and deactivation, which powers Nano Kit's mountable stores (run setup logic on first listener, clean up on last). Real tree-shaking — Agera is designed so that only the code you use ends up in your bundle; alien-signals is not well tree-shakable. The result keeps almost all of alien-signals' raw speed. Here is how @nano_kit/store compares to other popular state management libraries in a reactivity benchmark : Library Latency avg (ns) Throughput avg (ops/s) alien-signals 294.00 ± 2.24% 3,559,763 @nano_kit/store 303.55 ± 0.75% 3,365,816 svelte/store 428.58 ± 0.61% 2,479,118 rxjs 454.74 ± 0.07% 2,250,397 nanostores 1,373.2 ± 5.96% 952,399 mobx 3,474.7 ± 1.86% 306,094 valtio 5,041.3 ± 11.46% 254,109 jotai 9,454.6 ± 16.45% 157,853 effector 24,885 ± 11.78% 62,744 @reatom/core 59,430 ± 15.61% 22,741 Benchmark was run on AMD Ryzen 5 PRO 3400G with Node.js v24.14.1 That's ~3.5× faster than nanostores and an order of magnitude faster than most atomic state managers — while shipping lifecycles and mountable stores on top. 2. It's small Nano Kit exists largely because of Nano Stores . I love its philosophy: atomic stores, mountable resources, logic moved out of components, and an obsessive focus on bundle size. Nan

In 1996, Bandai sold 82 million Tamagotchis. Kids carried egg-shaped plastic keychains everywhere, frantically pressing three buttons to feed, clean, and play with a pixelated blob that would literally die if you ignored it during math class. It was the first time millions of people felt genuine emotional attachment to a piece of software. 30 years later, I rebuilt that entire experience — in the browser, with TypeScript, zero dependencies, completely open source. No app store. No download. No install. Just open a tab and adopt your pet. And in the process, I learned more about state machines, game loops, and emotional design than any computer science course ever taught me. Why Build a Virtual Pet in 2025? Three reasons: 1. Nostalgia Is a Distribution Hack People share things that trigger childhood memories. It's not rational — it's emotional. A browser-based Tamagotchi hits a nerve that no todo app or dashboard ever will. When I shared an early prototype, the response wasn't "cool tech stack." It was: "OH MY GOD I used to cry when mine died in second grade" That emotional reaction is worth more than any Product Hunt launch. 2. Game State Machines Are Criminally Underrated Every tutorial teaches state machines with traffic lights or toggle buttons. Boring. Useless. Forgettable. A virtual pet has dozens of interconnected states , real-time decay, evolution paths, conditional transitions, and edge cases that force you to actually think about state architecture. After building this, implementing complex UI flows in production apps felt trivial. 3. Not Everything Needs to Be a SaaS The indie dev world is obsessed with "revenue-generating side projects." Sometimes you should build something purely because it makes people smile. The best projects are the ones you'd use even if nobody else existed. Meet Your New Pet When you open Tamagochi, you get an egg. It hatches. A tiny pixelated creature appears. It has needs. Meet them, and it thrives. Ignore them, and... well, game

Most bibliography failures show up the night before arXiv or the journal deadline: placeholder DOIs, year pasted into volume= , inverted page ranges, invented case reporters. The fix is a paper repo that fails closed from day one . One command pip install https://github.com/SybilGambleyyu/citesure/releases/download/v0.5.68/citesure-0.5.68-py3-none-any.whl citesure init my-paper cd my-paper citesure gate . --preset ci citesure gate . --preset arxiv citesure init writes refs.bib , pre-commit hooks ( gate --preset ci + soft-lint), .github/workflows/citesure.yml , and a short CITESURE.md for coauthors. Empty bibliographies skip hard-ID floors until entries appear. What the gate checks Soft-lint — placeholder number/issue, inverted pages, year-like volume/month/edition, unsafe keys, all-caps titles, missing venues, duplicate DOIs/titles Health — hard-ID coverage floors Promote dry-run — DOIs still buried in url= Live verify — Crossref, doi.org, arXiv, PubMed, Europe PMC, DataCite, OpenAlex, CourtListener Domain packs Fifty-five live-clean packs (demography, sociology, political science, anthropology, ML, law, ecology, …): citesure packs --gate-all citesure packs --run anthropology-classics Evidence: 256/256 integrity · 209/209 claim pairs · 55 packs. Source: github.com/SybilGambleyyu/citesure · Demo: citesure.sybilgambleyyu.workers.dev