Ranked by daily star velocity (stars gained today) over the last 24 hours (UTC 2026-08-06 21:07 to 2026-08-07 21:07), using Trendshift’s daily view as the primary signal and each repo’s README for details; cumulative stars are a reference only. Repos featured yesterday that have since decelerated — prime-agent (+1.8k→+753), cloudflare-os (+1.6k→+640), cloudflare/computer (off the daily board), and open-kimi-ppt-skill (+456→+277) — were dropped. Only anydoc, whose gain grew, returns.
firecrawl/anydoc — +1.6k ⭐ today (Daily #1 · 3rd consecutive day)
Cumulative stars: ~8k+ (Trendshift weekly +7.4k) · https://github.com/firecrawl/anydoc
anydoc is an open-source tool that converts a wide range of office and document formats — Word, PowerPoint, Excel, OpenDocument, RTF, EPUB, CSV, and PDF — into clean Markdown. Written in Rust for speed, it ships Node.js and Python bindings so it drops easily into data pipelines or agent workflows. It is especially useful as a preprocessing step in LLM/RAG pipelines, normalizing heterogeneous documents into consistent Markdown. Together with sibling Firecrawl projects such as pdf-inspector (which detects scanned vs. text-based PDFs), it forms a “documents-to-Markdown” stack. It debuted at daily #1 on Aug 6, slipped to #4 on Aug 7, then reclaimed #1 on Aug 8 with an even larger +1.6k gain — a third straight day of acceleration.
- Inputs: Word, PowerPoint, Excel, OpenDocument, RTF, EPUB, CSV, PDF
- Output: clean Markdown
- Rust core with Node.js and Python bindings
- Fits LLM/RAG preprocessing and document-normalization pipelines
Practical use: deploy as a batch Markdown-normalizer before loading internal documents into a RAG system.
Tags: #DocumentConversion #RustLang #MarkdownExport #PythonBindings #NodeBindings #OfficeFormats
Panniantong/Agent-Reach — +454 ⭐ today (Daily #12 · New)
Cumulative stars: new, fast-rising · https://github.com/Panniantong/Agent-Reach
Agent-Reach is a Python CLI and library that gives AI agents “eyes on the internet,” letting them read and search 13 platforms including Twitter/X, Reddit, YouTube, GitHub, Bilibili, and XiaoHongShu. Rather than a wrapper, it positions itself as an installer / doctor / config tool: after setup, the agent calls upstream tools directly — the bird CLI for Twitter, yt-dlp for YouTube subtitles, and Jina Reader for web pages. Twitter search runs at zero API cost via cookie authentication on the bird CLI, and it also supports Bilibili search, RSS/Atom feeds, and web-page reading. The agent-reach doctor command shows the status of every available platform at a glance. Its “zero API fees” pitch for reaching social and niche platforms is the driver behind the recent surge.
- Read/search 13 platforms: X, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu, and more
- Twitter via bird CLI with cookie auth — zero API cost
- YouTube subtitles via yt-dlp; web pages via Jina Reader; RSS/Atom feeds
agent-reach doctorreports per-platform status
Practical use: add multi-platform collection to research or social-monitoring agents without paid APIs.
Tags: #AgentWebAccess #SocialMediaScraping #CLITool #ZeroAPICost #TwitterSearch #YouTubeSubtitles
MiniMax-AI/MiniMax-H3 — +313 ⭐ today (Daily #6 · New)
Cumulative stars: new (surging right after the open-weight release) · https://github.com/MiniMax-AI/MiniMax-H3
This is MiniMax’s official repo for open-sourcing (open-weighting) its next-generation general-purpose video model, H3. It understands multimodal context spanning text, images, video, and audio, and generates video with native stereo audio at up to 2K resolution and up to 15 seconds long. It ships as two task-specific checkpoints — FL2VA (first-and-last-frame mode) and Ref2VA (omni-reference mode) — each pairing an Omni Transformer with a processor, tokenizer, text encoder, Visual VAE, and a standalone Audio VAE. Being an open-weight model that generates video and audio together is its key differentiator, and community tooling (ComfyUI workflows, INT8 quantization, VRAM optimizers) is forming quickly around it. Demand for open video models is fueling the climb.
- Up to 2K resolution, up to 15-second clips
- Native stereo audio generated with the video
- Two checkpoints: FL2VA (first/last frame) and Ref2VA (omni-reference)
- Omni Transformer plus Visual VAE and standalone Audio VAE
- Multimodal context: text, image, video, audio
Practical use: a candidate for low-cost local/on-prem experiments with audio-inclusive short-form video generation.
Tags: #VideoGeneration #OpenWeights #NativeAudio #MultimodalModel #OmniTransformer #TextToVideo
Pan-Chera/Multi-Agent-CAD — +177 ⭐ today (Daily #18 · New)
Cumulative stars: new (research project) · https://github.com/Pan-Chera/Multi-Agent-CAD
MAC (Multi-Agent CAD) is a decoupled multi-agent framework that turns natural-language text into parametric CAD, with the core idea of using constrained test-time compute to cut compute cost sharply. It runs from the CLI or via a browser interface with a 3D preview, and connects to various LLMs through an OpenAI-compatible interface. The authors report roughly a 13.0× reduction in per-prompt cost versus baseline approaches, and the project is released under the MIT license. It is a “text-to-CAD” approach that generates printable 3D/CAD models with far less computation, landing amid growing research interest in the area.
- Natural-language text to parametric CAD
- Decoupled multi-agent design with constrained test-time compute
- ~13.0× lower per-prompt cost vs. baselines
- CLI or browser interface with 3D preview
- OpenAI-compatible LLM connectivity; MIT license
Practical use: for teams validating natural-language-to-CAD drafting cheaply during design prototyping.
Tags: #TextToCAD #MultiAgentFramework #TestTimeCompute #3DModeling #ParametricCAD #ResearchTool
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