burkan2/hsum
hSUM: Stateful Understanding & Memory — local evidence for agents. Keep the source. Return the passage.
01 / MACHINE INTELLIGENCE
Models you can run. Agents you can inspect. Tools you can build on.
← All topicshSUM: Stateful Understanding & Memory — local evidence for agents. Keep the source. Return the passage.
Selfhost modern LLM stacks. Run the whole fleet from your terminal
Turn any website into a compact CLI tailored for AI agents. Browse the web in hundreds of tokens, not tens of thousands.
Cayu is the runtime for long-running agents that need explicit environments, durable sessions, controlled tools, secrets boundaries, evals, and replay
Real-time multimodal AI pipelines on Apple Silicon
Postgres intelligence for ai agents & apps.
11 installable editorial systems for AI agents, based on leading public style guides.
GitX is a portable AI-agent skill for creating clean Git commits, tagged branches, and safe pushes across Codex, Claude Code, Cursor, and other skills-compatible agents.
Ultrafast Qwen3-TTS: sub-50 ms time-to-first-audio at 10 requests per second. - GitHub - nari-labs/nari-qwen3-tts: Ultrafast Qwen3-TTS: sub-50 ms time-to-first-audio at 10 requests per second.
Open recipes, engine patches, and benchmark harnesses for LLM inference on Intel Arc Pro B60/B70 (Battlemage, Xe2). MoE 35B at 160 t/s decode / 7.5K t/s prefill single-stream, 27B at 50~ t/s decode / 1.7K t/s prefill single stream. vLLM XPU MTP unlocked. Muse Glimmer recipe added!!
open source agent engineering platform: traces, evals, and metrics to debug and improve your AI agents. Integrates with LangGraph, CrewAI, Claude Agent SDK, and more. - GitHub - chirpz-ai/pandaprobe: open source agent engineering platform: traces, evals, and metrics to debug and improve your AI agents. Integrates with LangGraph, CrewAI, Claude Agent SDK, and more.
TSP自托管、零运维的 A 股「选股 + 监控 + 回测」量化工作台 | 基于 TickFlow 数据源 | LLM能力驱使策略定制+个股分析+复盘 | 自由接入第三方数据源与个性化扩展数据 | 个人开源 ,非TickFlow官方项目
GGUF Quantization support for native ComfyUI models
Hardware-in-the-loop benchmark for agentic embedded AI deployment. Agents iteratively edit model or firmware artifacts, the framework compiles and flashes them, and the score comes from real hardware measurements such as deployability, current, energy, and temperature.
Open-source AI-augmented offensive security harness. 13+ autonomous agents, 150+ LLM providers, 5,300+ models, 7,600+ Ed25519-signed attack skills, 56+ built-in tools, 176+ MCP tools. MITRE ATT&CK, OWASP WSTG, CIS Benchmarks. Post-exploit: Linux/Windows/macOS/AWS/Azure/K8s/CI-CD. Web UI + Cloudflare Tunnel. Your AI red team.
oh-my-zsh for Claude Code — 16 agents, 35 commands, 32 skills, 21 safety hooks in one install. v4.0 adds an adversarial review loop: a second agent that never sees the first one's reasoning. MIT.
Open-source, self-hostable markdown team wiki with a built-in MCP server — so AI agents read, write, and search your docs as first-class teammates. Live multiplayer, semantic + full-text search. Go + PostgreSQL + React/Milkdown.
Emotional long-term memory framework for AI agents and companions
Anti-laziness skill for AI agents. Core: the Depth Tree method, which splits a task N layers deep and gives every leaf the full time budget of the whole task, so effort multiplies with depth. Grounded in 2025-2026 research on model laziness, underthinking and premature completion.
Code, datasets, and results for "The Embedder's Dilemma: LLMs Are Better, but at What Cost?" (COLM 2026). MTEB(LLM) benchmarks 10 LLMs and 26 embedding models on 37 tasks with full cost and throughput accounting.
Open the project to explore the idea and the code.
A seed agent: the smallest starting point from which an agent can grow
Run Kimi K3 locally on CPU with ~55GB measured runtime RAM. A single-file Linux inference server powered by cPilot Runtime.
The agent skills I actually use to build software with coding agents. The PIV loop, planning, worktrees, and the meta-skills for building your own AI Layer.