REPO·RANK #293·1/6 firing·Python
Rank#293Python
Cross-signal1.00 / 6.0
Single-channel signal so far. and X posts (5 / 24h). Reddit, Bluesky and Dev.to are still cold — typical for a niche project at this stage.
Cross-signal · 30D+5%
GitHubQUIET+0 stars24h
HackerNewsQUIET0 posts7d
RedditQUIET0 posts7d
BlueskyQUIET0 posts7d
Dev.toQUIET0 posts7d
XFIRING5 posts24h
Stars7.4k+251 7d+0 today
Forks642+0 7d9% fork ratio
Contribs5+0 30dteam-led
Momentum20.43.7stars-velocity score
SurfaceTHIN1 / 6last commit 43m ago
Coverage2 / 13 sources fired (7d)
·60
Added 251 stars over the past week, climbing the Python leaderboard with a steady 7-day curve.
// 01 · GITHUB MOMENTUM
20.4
0 stars 24h | +251 7d
// 02 · MENTIONS
5
0 in 24h | 1 source
// 03 · CROSS-SIGNAL
1.00
1/6 channels firing
// 04 · PACKAGE ADOPTION
-
no linked package yet
// 05 · PROJECT SURFACE
thin
last commit 43m ago
// 01
Breakdown
How is this scored?
- 6.0
- strong signal across ≥5 of 6 channels
- 5.0
- strong signal across ≥4 of 6 channels
- 4.0
- strong signal across ≥3 of 6 channels
- 3.0+
- active on 2+ channels
- <1.0
- low or no cross-channel activity
Each channel contributes 0-1. Per-channel tiers: GitHub (breakout 1.0 / hot 0.7 / rising 0.4), HN (front-page 1.0 / ≥3 mentions 0.7 / 1-2 mentions 0.4), Bluesky (≥5 mentions 1.0 / 2-4 0.7 / 1 0.4), dev.to (≥3 articles 1.0 / 2 0.7 / 1 0.4), Reddit (corpus-normalized 48h velocity), X (≥10 mentions 24h 1.0 / 3-9 0.7 / 1-2 0.4).
- GitHub momentum0.00
- Reddit0.00
- HackerNews0.00
- Bluesky0.00
- dev.to0.00
- X (Twitter)1.00
* Reddit bar shows a per-repo velocity proxy (raw score / 100); the score formula uses the corpus-normalized version so a single repo's bar may not match its contribution to the corpus-wide ranking.
// KNOWN REPO · PACKAGE · LAUNCH · SITE SURFACES
GitHubLearningCircuit/local-deep-research7.4k stars
Websitenot linkedhomepage not tracked yet
Docspendingdocs scanner pending
npmnoneno linked npm package
ProductHuntnoneno launch linked
Paper/modelpendingHF/arXiv resolver not attached yet
// 02
Growth
Today
7.3k
+130 last 24h
7d
+1.4k
25% wow
30d
+1.4k
25% mom
Spike
+5.8k/d
5d ago · peak day
// 03
Mentions
// last scanreddit 26mhn 39mbluesky 1hdevto 3hph 3hnpm 12h
- X→ OPEN
Ya puedes correr “Deep Research” en tu propio laptop. Local Deep Research es un asistente open-source que investiga en la web, papers académicos y tus propios documentos, y luego te entrega un reporte con fuentes. Tiene modos rápidos, análisis más largos y generación de informes completos. La parte fuerte: si lo conectas con Ollama + SearXNG, puedes hacerlo correr localmente sin mandar tus búsquedas a una herramienta cerrada. También usa una base de datos cifrada por usuario y, según el repo, no tiene telemetry. Básicamente: investigación profunda, pero bajo tu control. GitHub: https://t.co/VOH3OacoUE▶ Twitter5d ago - X→ OPEN
What is it? local-deep-research is a powerful tool that brings "Global Web Search" and "Deep Reasoning" directly to your local machine. Previously, generating professional industry reports via AI meant enduring high subscription fees and privacy risks. Now, this project allows you to use local models (like Qwen3 or Llama 3) combined with 10+ professional search engines (like arXiv, PubMed) to peel back layers of internet data and deliver a logically rigorous, long-form report. 🚀 Key Features ✅ Consumer-Grade "Nuclear Power": Achieves nearly 95% accuracy on SimpleQA benchmarks using just a standard RTX 3090 GPU. ✅ Full-Spectrum Search: Native support for arXiv (Academic), PubMed (Medical), and the private documents sitting on your hard drive. ✅ End-to-End Encryption: Your search footprint and analysis are entirely localized—no more worrying about IP theft. ✅ Universal Local LLM Support: Seamlessly integrates with Ollama and llama.cpp; you choose the brain. 💡 Tech Highlights The secret to its success is solving the "Hallucination Problem in AI Search."It implements "Multi-Source Cross-Verification." Instead of trusting the first result, it prompts multiple sub-agents to challenge and verify data across different sources. This "adversarial" search logic ensures that the output maintains high academic rigor. 🛠️ Who is it for? Students/Researchers: When writing literature reviews, let it scan all relevant papers and summarize key findings for you. Geeks/Developers: Hardcore users who want high-quality search services without paying AI "tributes" to big tech.▶ Twitter5d ago - X→ OPEN
ローカルLLMでDeep Researchを完結させる時代。GitHubで話題のlocal-deep-research、SimpleQA正答率95%は驚異的。 実務で使うならVRAM 24GBのRTX 3090/4090かMac 64GB以上が必須。8GB/12GB環境で妥協すると推論ミスで時間を溶かします。 プライバシー死守しつつarXivやPubMedをAIに深掘りさせるなら、今こそGPU投資の時。 https://t.co/KunXDNi4fk #ローカルLLM #個人開発▶ Twitter6d ago - X→ OPEN
【最強リサーチAI】RTX 3090で正解率95.7%を叩き出すLDRが革命的！🚀 RTX 3090一枚でOpenAIのベンチマーク正解率95.7%を記録した「Local Deep Research (LDR)」が凄すぎます！ ・LangGraphによるタスクの並列処理 ・最大50回の反復思考による圧倒的な精度 ・SQLCipher採用でプライバシーも完全保護 Perplexity超えの調査能力を自宅のPCで実現。 100ページ規模の報告書も自動生成できる、まさに自分専用の最強リサーチャーです！✨ #AI #ローカルLLM▶ Twitter6d ago - X→ OPEN
【完全ローカル】Qwen 3.6 × Agentic Searchでリサーチ革命！ 最新のQwen 3.6-27Bと「Local Deep Research」の組み合わせが凄すぎます！ 事実確認の難関テスト「SimpleQA」で95.7%という驚異的な正答率をマーク。 しかも、RTX 3090 1枚の完全ローカル環境で動作します。 🔹Agentic Search AIが自律的に「次は何を調べるべきか」を判断して深掘り。 人間が数時間かける市場・文献調査を自動で完結させます。 🔹圧倒的なプライバシーと低コスト 入力情報が外部に漏れないノー・テレメトリ仕様で機密情報も安心。 Ollama等で動かせるため、高額なAPI料金は一切かかりません。 リサーチをAIに任せて、高付加価値な戦略立案に集中できる最強の環境を構築しましょう！🚀 #AI #業務効率化▶ Twitter6d ago
// 04
Context
- 9 public repos
- 116 followers
- raullenchai/Rapid-MLXPython · 2.2k stars
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.
- OpenCoworkAI/open-codesignTypeScript · 5.7k stars
Open-source Claude Design alternative. One-click import your Claude Code / Codex API key. Prompt → prototype / slides / PDF. Multi-model (Claude, GPT, Gemini, Kimi, GLM, Ollama). BYOK, local-first, MIT.
- sooryathejas/METATRONPython · 8 stars
AI-powered penetration testing assistant using local LLM on linux (Parrot OS)
- ENTERPILOT/GoModelGo · 865 stars
AI gateway written in Go. Lightweight unified OpenAI-compatible API for OpenAI, Anthropic, Gemini, Groq, xAI & Ollama. LiteLLM alternative with observability, guardrails, streaming, costs and usage tracking.
- AlexsJones/llmfitRust · 25.9k stars
Hundreds of models & providers. One command to find what runs on your hardware.
- Zackriya-Solutions/meetilyRust · 11.9k stars
Privacy first, AI meeting assistant with 4x faster Parakeet/Whisper live transcription, speaker diarization, and Ollama summarization built on Rust. 100% local processing. no cloud required. Meetily (Meetly Ai - https://meetily.ai) is the #1 Self-hosted, Open-source Ai meeting note taker for macOS & Windows.
// 05Deep dive
// STARS7.4k+251/ 7dJust updated
// FORKS642—/ 7dJust updated
// CONTRIBUTORS5—/ 30dJust updated
Open Issues78
Last Commit43m ago
Latest Release—
// TREND24h07d+25130d+341
X
Ranked confirmation layer for repo-specific X buzz in the last 24h.
Mentions 24h
5
Authors 24h
4
Engagement
139
Peak hour
09:14
Score
56.1
Meaningful X activity confirmed. 5 matched posts across 4 authors.
Top matched posts
- @ai_hakase_128 eng
【完全ローカル】Qwen 3.6 × Agentic Searchでリサーチ革命！ 最新のQwen 3.6-27Bと「Local Deep Research」の組み合わせが凄すぎます！ 事実確認の難関テスト「SimpleQA」で95.7%という驚異的な正答率をマーク。 しかも、RTX 3090 1枚の完全ローカル環境で動作します。 🔹Agentic Search AIが自律的に「次は何を調べるべきか」を判断して深掘り。 人間が数時間かける市場・文献調査を自動で完結させます。 🔹圧倒的なプライバシーと低コスト 入力情報が外部に漏れないノー・テレメトリ仕様で機密情報も安心。 Ollama等で動かせるため、高額なAPI料金は一切かかりません。 リサーチをAIに任せて、高付加価値な戦略立案に集中できる最強の環境を構築しましょう！🚀 #AI #業務効率化
mediumphrase6d agoopenMatched through a repo-specific project phrase query.
- @ai_hakase_8 eng
【最強リサーチAI】RTX 3090で正解率95.7%を叩き出すLDRが革命的！🚀 RTX 3090一枚でOpenAIのベンチマーク正解率95.7%を記録した「Local Deep Research (LDR)」が凄すぎます！ ・LangGraphによるタスクの並列処理 ・最大50回の反復思考による圧倒的な精度 ・SQLCipher採用でプライバシーも完全保護 Perplexity超えの調査能力を自宅のPCで実現。 100ページ規模の報告書も自動生成できる、まさに自分専用の最強リサーチャーです！✨ #AI #ローカルLLM
mediumphrase6d agoopenMatched through a repo-specific project phrase query.
- @ai_negi_lab_com2 eng
ローカルLLMでDeep Researchを完結させる時代。GitHubで話題のlocal-deep-research、SimpleQA正答率95%は驚異的。 実務で使うならVRAM 24GBのRTX 3090/4090かMac 64GB以上が必須。8GB/12GB環境で妥協すると推論ミスで時間を溶かします。 プライバシー死守しつつarXivやPubMedをAIに深掘りさせるなら、今こそGPU投資の時。 https://t.co/KunXDNi4fk #ローカルLLM #個人開発
mediumphrase6d agoopenMatched through a repo-specific project phrase query.
- @MEMEMAX00111 eng
What is it? local-deep-research is a powerful tool that brings "Global Web Search" and "Deep Reasoning" directly to your local machine. Previously, generating professional industry reports via AI meant enduring high subscription fees and privacy risks. Now, this project allows you to use local models (like Qwen3 or Llama 3) combined with 10+ professional search engines (like arXiv, PubMed) to peel back layers of internet data and deliver a logically rigorous, long-form report. 🚀 Key Features ✅ Consumer-Grade "Nuclear Power": Achieves nearly 95% accuracy on SimpleQA benchmarks using just a standard RTX 3090 GPU. ✅ Full-Spectrum Search: Native support for arXiv (Academic), PubMed (Medical), and the private documents sitting on your hard drive. ✅ End-to-End Encryption: Your search footprint and analysis are entirely localized—no more worrying about IP theft. ✅ Universal Local LLM Support: Seamlessly integrates with Ollama and llama.cpp; you choose the brain. 💡 Tech Highlights The secret to its success is solving the "Hallucination Problem in AI Search."It implements "Multi-Source Cross-Verification." Instead of trusting the first result, it prompts multiple sub-agents to challenge and verify data across different sources. This "adversarial" search logic ensures that the output maintains high academic rigor. 🛠️ Who is it for? Students/Researchers: When writing literature reviews, let it scan all relevant papers and summarize key findings for you. Geeks/Developers: Hardcore users who want high-quality search services without paying AI "tributes" to big tech.
mediumphrase5d agoopenMatched through a repo-specific project phrase query.
- @filicroval0 eng
Ya puedes correr “Deep Research” en tu propio laptop. Local Deep Research es un asistente open-source que investiga en la web, papers académicos y tus propios documentos, y luego te entrega un reporte con fuentes. Tiene modos rápidos, análisis más largos y generación de informes completos. La parte fuerte: si lo conectas con Ollama + SearXNG, puedes hacerlo correr localmente sin mandar tus búsquedas a una herramienta cerrada. También usa una base de datos cifrada por usuario y, según el repo, no tiene telemetry. Básicamente: investigación profunda, pero bajo tu control. GitHub: https://t.co/VOH3OacoUE
mediumphrase5d agoopenMatched through a repo-specific project phrase query.