Dev
GitHub repos gaining traction - what high-signal users are starring and what's climbing the board, captured daily and enriched from GitHub. Raw material for spotting new tech and patterns worth building on.
1,506
repos tracked
254
surfaced this week
209
created < 30d
Python
top language
17 repos
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Small Python client for Model Context Protocol (MCP) servers.
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Ask the oracle when you're stuck. Invoke GPT-5 Pro with a custom context and files.
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High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 158 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.
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Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms, Tasks, Search & Drive with AI - Comprehensive Google Workspace / G Suite MCP Server & CLI Tool
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Connectome agent host runtime — runs Connectome agents, MCPLs, and the context-manager stack
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🐎 Ryan Lopopolo’s anthology, field guide, and agent context bundle for harness engineering
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Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
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Ask the oracle when you're stuck. Invoke GPT-5 Pro with a custom context and files.
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A data-structure parameterization system written for embedding context in JSON objects
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Doing simple retrieval from LLM models at various context lengths to measure accuracy
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[ICML'26] Scaling Long-Horizon LLM Agent via Context-Folding
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Official Implementation of SCAIL-2: Unifying Controlled Character Animation with End-to-end In-Context Conditioning
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Ghostty Blackhole puts a real, ray-traced black hole inside your terminal. It grows as Claude Code's context window fills up, live. A fresh session is a quiet hole in the corner. A full one swallows half your screen. You'll always see /compact coming.
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Ask the oracle when you're stuck. Invoke GPT-5 Pro with a custom context and files.
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[CVPR 2026 Oral] "INSID3: Training-Free In-Context Segmentation with DINOv3"
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KVarN is a native vLLM KV-cache quantization backend for your agents: 3-5x more context, throughput above FP16, and FP16-level accuracy. Calibration-free, one flag.
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High-quality search for AI-native applications.