This issue reviews public repositories created during September 21–27, 2026 that had attracted significant attention by collection time. Ranking is based on Stars at collection time, not GitHub's official Trending list; Stars measure attention, not code quality, security, or long-term maintenance.
The projects cluster around a few practical themes: local AI configuration and deployment, structured decisions without text generation, desktop disk cleanup, and two different code-generated video workflows. The Top 10 had about 19.5K Stars in total, with jev-chat/jev-chat-jarvis leading at 6,807. The feature, usage, and boundary notes below are based on each project's README.
Top 10 projects
1. jev-chat/jev-chat-jarvis
Core functionality: Jev for Android is a chat copilot. It reads supported chat screens through Android accessibility services, falls back to on-device ML Kit OCR when needed, asks a user-configured model to assess intent, risk level, and whether an immediate reply is appropriate, then produces three candidates. It supports QQ, X direct messages, Feishu OCR fallback, and one-shot full-screen OCR for unadapted apps; a local knowledge base, contacts, and optional chat history can inform the decision.
Usage and boundaries: Android 11+ ARM64 users can install the repository's v1.4 APK, configure separate judgment, reply, and vision model endpoints, and enable accessibility, overlay, autostart, and unrestricted-battery permissions. Separate Windows 10 1903+/11 and macOS 13+ Apple Silicon projects are linked. The app only fills the input box and never sends automatically or touches transfers, red packets, or payments. Chat text and enabled context are sent to the model provider chosen by the user; screenshots are OCR'd locally, while third-party processing follows that provider's policy. Android WeChat collection and analysis are explicitly discontinued.
Attention snapshot: 6,807 Stars · 1,171 forks · Kotlin · MIT
2. unreallabsai/unreal-agent
Core functionality: An async-first agent harness. It separates input events, sessions, LLM turns, tool calls, and asynchronously executable operations: the inbox provides session-level idempotency, the coordinator persists inputs and dispatches turns, the session store keeps recoverable and forkable history plus operation state, the context builder records omitted/truncated/compacted context, and tool translators validate calls into operation descriptions.
Usage and boundaries: The README currently organizes the project as a harness/ library, cmd/ executables, and benchmarks/ runners, with replaceable interfaces for adapters, stores, and operation managers. A tool translator runs synchronously on the coordinator event loop and must not perform I/O or suspend it. Session-store items and operations must be serializable and versioned; resuming an unsupported session version fails explicitly. The README does not provide an end-user install command or a verified platform matrix, so the current build entry points should be checked before adoption.
Attention snapshot: 1,996 Stars · 108 forks · Go · MIT
3. Contrastive-LM/CLM
Core functionality: CLM-8B is a System One model that connects states and actions with contrastive learning and exposes a TypeSafe-compatible API. It supports structured noul, choice, and score questions, direct candidate ranking, and use as a verifier on agentic coding benchmarks. The repository includes the clm-serve HTTP service, a browser playground, hot-reloadable heads, and fine-tuning scripts for custom data.
Usage and boundaries: Install with pip install contrastive-lm, start a Qwen3-8B pooling encoder with vLLM on port 8090, then run clm-serve on port 8700 and call /v1/systemone or /v1/rank with CLMClient. States longer than 2,048 tokens are truncated by default; raising both CLM and vLLM limits needs more GPU memory. CPU mode is available, but the README's examples and measurements are primarily GPU-based. CLM_API_KEY enables Bearer authentication, and CORS is off by default. The code and CLM-8B weights are released under Apache 2.0.
Attention snapshot: 1,990 Stars · 179 forks · Python · Apache-2.0
4. tobi/disktree
Core functionality: A disk treemap for Omarchy. It scans the home directory by default, draws nested directories sized by their actual disk cost, supports keyboard or mouse navigation, lets users mark cleanup candidates, keeps free space visible, and requires review before committing; permanent paths always ask again.
Usage and boundaries: On Linux, download a release, run the included install.sh, or use Rust 1.97+ with make install. macOS 11+ can use the signed and notarized app; Windows 10/11 has x86_64 and ARM archives. Linux needs Wayland or X11 plus a Vulkan-capable GPU; macOS source builds need Xcode or its command-line tools, and reading other applications' data requires granting the terminal Full Disk Access. The project says selecting a checkout never runs a program named inside it and uses the trash when available, but scan and removal scope still depends on the current user's permissions.
Attention snapshot: 1,672 Stars · 109 forks · Rust · MIT
5. mexicat/pdoom-video
Core functionality: A generative, code-rendered music-video project with word-synced karaoke typography. The browser preview and offline export share the same time-driven scenes and renderer; it supports 1080p60, true 4K60, adaptive motion-blur sampling, stills/contact sheets, and performance modes.
Usage and boundaries: In app/, install with Bun and run Vite for the preview; offline export uses headless Chrome, Playwright Core, and ffmpeg/libx264 via bun scripts/render.ts video --samples auto .... 4K rendering is GPU-heavy: the README reports frames taking over 10 seconds and a full song reaching about 13 GB at CRF 16. Rebuilding lyric and audio-analysis data additionally needs uv, Demucs, Whisper/CTC tooling, and roughly 4 GB of model weights. The code is MIT, but the song, lyrics, and fonts retain their own licenses, so the media bundle is not automatically MIT-licensed.
Attention snapshot: 1,323 Stars · 135 forks · TypeScript · MIT
6. yetone/magpie
Core functionality: A menu-bar/tray utility for managing models across local AI coding agents. It edits only the selected configuration key, saves and switches profiles, and exposes a local gateway at 127.0.0.1:3425/v1 compatible with OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages. Different vendors appear in one model catalog, with streaming and tool calls included.
Usage and boundaries: Download macOS, Windows, or Linux builds from usemagpie.ai, run the Linux installer, use go install github.com/yetone/magpie@latest, or build locally. Add vendors with magpie provider add, then point agents at the loopback gateway; magpie fetches vendor model lists but never reads keys from shell environment variables. macOS releases are signed and notarized, while Windows and Linux builds are not signed. Provider keys are kept in local providers.json with mode 0600, subscriptions must be signed in again on each machine, and running agents need a restart to read changed model configuration. MIT licensed.
Attention snapshot: 1,321 Stars · 76 forks · Go · MIT
7. JohnHeibel/PDoomVideo
Core functionality: A second generated project for the same P(doom) music video. It contains nine chapters, shared characters and timeline code, a p5.js/p5.brush drawing page, and scripts that render frames in headless Chrome before encoding an MP4 with ffmpeg. The README says the repository was generated by Claude Opus 5.5 and keeps the first generation under legacy/.
Usage and boundaries: It needs Node.js, Google Chrome, and ffmpeg. Run npm install, then node render.mjs --frames=0:156.6 --workers=4 to render frames and node render.mjs --encode --out=out/pdoom.mp4 to encode. If Chrome is not at the default Windows path, pass --chrome=<path>. The README declares no standard open-source license, and the song and lyrics belong to other authors, so reuse of code or media should be cleared with the relevant rights holders first.
Attention snapshot: 1,296 Stars · 141 forks · JavaScript · not declared
8. deepopen-com/deepopen
Core functionality: A multilingual, non-autoregressive structured decision engine. It evaluates choice, score, and noul questions over text, email, ticket, or JSON state in one forward pass. Three checkpoints are selected per request by Router; the repository also includes model routing, prompt-protection, content-safety, ticket-triage presets, and fine-tuning/calibration notebooks.
Usage and boundaries: Install with pip install deepopen, choose or preload English and multilingual checkpoints through Router, and run on CPU or CUDA; models and a demo are published on Hugging Face. The README explicitly says the base checkpoints are near chance on zero-shot typed decisions, while the 0.766 result comes from a checkpoint fine-tuned on that benchmark. The models are overconfident by default, and the multilingual checkpoint needs temperature fitting before its probabilities are trusted. Large option sets hit the head_max_len budget, score is the weakest primitive, and the English checkpoint collapses outside English. Apache 2.0 licensed.
Attention snapshot: 1,044 Stars · 112 forks · Python · Apache-2.0
9. mikehasa/golive-skill
Core functionality: GoLive is an Agent Skill that helps a coding agent take an app live across hosting, databases, auth, domains, email, and payments. It detects requirements, produces a plan, waits for approval, uses the user's accounts, verifies observable results, records created resources, checks drift, and can plan teardown based on ownership evidence.
Usage and boundaries: The README requires Node.js 20+, npm/npx, Git, and a coding agent that can load skills; Codex and Claude Code are verified. Install with npx skills add ... --global or the npx golive@alpha install package flow. The 0.1.0-alpha.5 built-in scope includes Vercel/Netlify, Supabase/Neon, Porkbun/GoDaddy DNS, Resend, Stripe test payments, and Supabase Auth. Real writes need a plan ID and --yes; DNS, deletion, and live-mode steps have additional confirmations. Rollback is opt-in and only targets deployments GoLive recorded itself, and Vercel promotion/rollback is not live-validated. Credentials are not printed but are stored locally in a 0600 plaintext file. MIT licensed.
Attention snapshot: 1,017 Stars · 75 forks · TypeScript · MIT
10. kryvora-network/kryvora-node
Core functionality: The Kryvora Network node daemon connects worker hardware to the Kryvora Hub, runs telemetry probes, verifies peer state, and reports proof of availability. It includes a worker engine, telemetry daemon, local state/peer-nonce/telemetry storage, and mutual-TLS gRPC/HTTP communication with a 30-second heartbeat.
Usage and boundaries: A standard verification worker needs at least 2 CPU cores, 2 GB RAM, 20 GB SSD, stable broadband, and local telemetry port 4177 open. The README provides Linux systemd, Docker Compose, and Go 1.22+ source-build paths. Before launch, connect a wallet to obtain the Arbitrum One Genesis Node Key bootstrap token; unauthenticated nodes are rejected by the Hub. Port 4177 exposes /health, /status, and /metrics; security reports go to the project's stated email rather than public issues. The README declares Apache 2.0.
Attention snapshot: 984 Stars · 26 forks · Go · Apache-2.0
Data limitations
This issue uses a collection-time snapshot from GitHub Search API, so Star counts can change after publication. No listed project was installed, executed, or security-audited here. Before using anything involving API keys, chat content, model weights, third-party accounts, wallets, or production resources, review the README, license, dependencies, and provider policies.
Source: GitHub Search API · Collected 2026-09-28 (Asia/Singapore)