Review pull-requests on https://github.com/NixOS/nixpkgs
A GitHub :octocat: app to automatically review Python code style over Pull Requests
A Tree Search Library with Flexible API for LLM Inference-Time Scaling
FreeProxy: Collecting free proxies from internet. (全球海量高质量免费代理,支持爬取数十个免费代理分享源,支持自定义规则代理筛选,爬虫与数据分析必备,每日更新海量免费代理。)
Summarize and query from a lot of heterogeneous documents. Any LLM provider, any filetype, advanced RAG, advanced summaries, scriptable, etc
The official bitbake Git is at https://git.openembedded.org/bitbake/. Do not open issues or file pull requests here.
⚔️Python Rate-Limiter using Leaky-Bucket Algorithm Family
The httplib2 caching algorithms packaged up for use with requests.
Built on **Agentic RAG** (Agent-driven Retrieval-Augmented Generation) technology, it not only accurately answers pre-sales and after-sales questions but also generates personalized usage reports and optimization suggestions by deeply analyzing device data.
You plan the release, the factory builds it, you test it in staging. GitHub issues in, independently reviewed and merged pull requests out, all on Archon workflows.
Interactive 3D map of the human tech tree, from the first stone tools to 2024: technologies stacked as a tower of eras in three.js, with prerequisite and relation edges, hover cards and a guided camera tour. All data lives in plain JSON, so adding a technology, era or domain is one pull request away.
From ticket to reviewed pull request. Free and open-source, on your machine.
Turn your documents into a Markdown wiki you can read, that answers with citations. Works with any LLM provider, Ollama included. CROW classifier routing: 89% right note vs 62%, 2.1× faster ingest, 68% fewer tokens.
RAG ReAct Agent - A Retrieval-Augmented Generation system with ReAct (Reasoning+Acting) agent loop for intelligent question answering with multi-hop reasoning
Ultrafast Qwen3-TTS: sub-50 ms time-to-first-audio at 10 requests per second.
🚀🚀 A multimodal System One decision model that gives calibrated answers to typed questions about screens, photos, video and text in one forward pass.
A small open decision model: state + typed questions -> calibrated probabilities. A Jev / System One re-creation on Qwen3.5.
Terminal-first, knowledge-grounded multi-agent software delivery pipeline: scope requirements, implement changes, run tests, and gate pull requests with deterministic QA and ensemble code review.
Claude Opus 5 API (claude-opus-5): model id, per-million-token pricing, cache write and cache read rates, streaming and Anthropic-style requests through an OpenAI-compatible gateway.
Agent evals and guardrails as Jev decisions: one request per trace, a fraction of a cent, fast enough for the agent loop. Runs locally with Kev or Laya.
Qpro Companion: facial tracking and experimental controller tools for rooted Quest Pro. USB/wireless ADB, Virtual Desktop and Steam Link.
A research workspace where an agent works beside you: chat, PDFs, notebooks and files in one dock, plus custom panes the agent builds on request.
Ask your house a question, get a number back. Home Assistant integration for TypeSafe Jev: typed answers as sensors, four actions for automations, and a conversation agent for Assist.
An open source, self-hosted, privacy-focused AI workbench
Enables tongue tracking and eye convergence on a rooted Quest Pro
백엔드 기술 면접 질문과 답변. 단순한 개념 설명이 아니라 '뭐라고 답해야 하나'와 '이렇게 답하면 틀린다'를 함께 알 수 있도록 합니다
用苏格拉底式逐问学习把教材、截图、PDF、笔记、题目或一个主题变成主动推导对话。Use when the learner明确要求“苏格拉底式学习”“逐问带我学”“一次只问一个问题”“先别告诉我答案”“边问边引导我推出来”、interactive quiz、active recall or question-led tutoring。每个活跃回合只问一个简短、可回答的问题,并根据学习者上一步的推理调整难度;遇到陌生抽象概念时可使用具名人物的小故事,空间、因果、流程、几何或图形关系确有帮助时可调用 Codex 内置生图并提供文字替代。不用于只要直接解释、摘要、翻译、代做/解题、批量出题、评分、写苏
OpenJev: open-source, local alternative to Jev. System-One decision models: typed questions in, calibrated probabilities out; general decisions and browser-agent steps.
A pure-Python reachability and skeletonization layer that decides what a coding agent actually sees before a request reaches the API.
Qwen3.8-27B on SGLang for NVIDIA RTX PRO 6000 (96GB, SM120): DSpark speculative decoding, full 256K context, 8 concurrent requests
AI Agent 面试真题库:300 + 道大厂真实面试题(附答题框架)+ Java/Python/Go 语言八股文 + 2 个持续迭代 Agent 项目档案。AI Agent 面试准备、大模型应用开发面试、后端面试八股
Runs opencode through rotating Tor exit lanes so free-tier rate limits and stalls stay invisible -- with live per-request proof of the lane and exit IP behind every call.
Play owned Meta Rift and Oculus PC VR games on Linux and Windows.
Your app is guilty until proven secure: an agent skill whose /hack-me breaks into your own running app, proves each bug with a real request, patches it, and re-verifies. Claude Code · Codex · Cursor.
Open-source Quest 3 PCVR streamer (ALVR fork) with PyroWave wavelet video decoded in Vulkan on the Quest's GPU. Quality 120 Hz over USB out of the box; tested profiles picked in the headset.
A launcher to manage FreeCAD versions (stable & dev) and test FreeCAD Pull Requests.
A full-screen terminal status monitor for home servers and NAS boxes. One screen answers "is everything OK?" and shows what's going on: CPU, memory, storage and RAID, disk SMART, UPS, Docker services, cameras, backups, upcoming timers, and a live list of the web requests hitting your reverse proxy.
Seedance 2.5 API quickstart for JavaScript and Python with runnable requests, async polling, limits, errors, and Argolink contract mapping.
Argolink video API documentation patterns for capability discovery, normalized request fields, async jobs, and provider-agnostic examples.
Argolink-focused AI workbench request patterns for GPT-5.4.
An agent skill to discover TypeSafe Jev opportunities, design typed questions, and learn from recent community experiments.
Seedance 2.0 API reference with route mapping, request fields, async jobs, polling, limits, and Argolink contract examples.
Optimized local serving engine for Kimi-Linear-48B: INT4 quantizer, fused decode kernels for a measured 3.18x, and an OpenAI-compatible server. Ships with k3, a bridge that detects the client per request so Claude Code, Codex, Cline, Aider and opencode all work unchanged.
500+ DevOps, SRE, security, cloud, and platform engineering interview questions across 40 topics - short answer, detail, worked example, and interview tips for each.
Open visual probability research: encode an image once, answer multiple structured questions. Public data, trained weights, reproducible baselines and failure cases.
Argolink-focused multi-model gateway routing and request contracts for GPT-6 Sol.
Unofficial open-source study kits for Anthropic certifications: a linked wiki, original practice-question banks, and Claude Code skills that teach, quiz, and track exam readiness.
A focused Argolink guide for GLM 5.3: the exact model ID, chat-compatible request, reasoning controls, long-context limits, and production error handling.
MiniMax H3 API guide for asynchronous text-to-video generation, model limits, request fields, polling, and Argolink integration.
A 4B decision model: calibrated probabilities for yes/no, choice and score questions over long inputs.
Argolink Seedance 2.0 API integration guide with async video generation, request fields, polling, and production-ready REST examples.
Argolink-focused coding-agent request and tool workflow for Claude Opus 5.
n8n HTTP Request workflow guide for Argolink APIs: submit, poll, download, branch on job status, and automate Seedance 2.5 video generation.
[ACM MM 2026] Beyond Visual Similarity: Entity-Aligned Retrieval for Knowledge-Based Visual Question Answering
Local bilingual probability decisions from context, questions, and candidate answers. Independent research preview inspired by TypeSafe Jev.
Argolink-focused image generation skills and safe request patterns for GPT Image 2.
Community app gallery for BUSY Bar. Share your 72x16 LED apps via pull request.
Web application for comparing static highlighting and interactive AI-assisted support in reading comprehension tasks. The system analyzes user-provided text, identifies key passages, and provides highlighting, summaries, explanations, and guiding questions
Claude skill that turns a single request into a narrated mythic short film in a layered cut-paper diorama style — three-colour cardstock worlds, paper wipes, Unsora MCP + local ffmpeg, one assembled MP4.
Ask an open vision-language model typed questions about an image and get probabilities back, on your own machine.