AI Chronicles

Learn it. Build it. Ship it for others.

Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.

✅（已完结）超级全面的 深度学习 笔记【土堆 Pytorch】【李沐 动手学深度学习】【吴恩达 深度学习】【大飞 大模型Agent】

YC (S26) | AI that knows what you've seen, said, or heard. Records everything you do, say, hear 24/7, local, private, secure. Connect to OpenClaw, Hermes agent and 100+ apps

Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.

50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems.

Build, Manage and Deploy AI/ML Systems

ZenML 🙏: One AI Platform from Pipelines to Agents. https://zenml.io.

Curated list of the best truly open-source AI projects, models, tools, and infrastructure. Daily updated.

🧠 Make your agents learn from experience. Now available as a hosted solution at kayba.ai

Synthetic data curation for post-training and structured data extraction

A set of tools that gives agents powerful capabilities.

Agent samples built using the Strands Agents SDK.

Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools, with 10x faster training through evolutionary hyperparameter optimization.

Agent工程师最全学习路径 · 从零精通 AI 工程 · 20 阶段 503 课 · 中文全量翻译 + 配套站点 + 动画讲解视频 · 如何成为 AI Agent 工程师的修成指南

End-to-end Generative Optimization for AI Agents

The missing bridge between your ML models and your AI agents.

An intelligence orchestra

A model-driven approach to building AI agents in just a few lines of code.

This MCP server provides documentation about Strands Agents to your GenAI tools, so you can use your favorite AI coding assistant to vibe-code Strands Agents.

A fast and minimal framework for building agentic systems

The Ruby framework for programming—rather than prompting—language models.

Chat to Compose Video

Documentation for the Strands Agents SDK. A model-driven approach to building AI agents in just a few lines of code.

Cognitive Memory OS for AI Agents — persistent, self-improving, multi-agent memory

300+ visual cards covering almost all large language model(LLMs) concepts and architectures. Best for revising LLM concepts before any big AI/ML technical interview rounds.

Make your agents learn from experience. One protocol for weights, harness and routing.

A fine-tuning SDK — any open model, any harness, any method. 12 training methods behind one argument; pure-stdlib core.

Various LLM resources and experiments

A curated list of AI agent frameworks, tools, platforms, research papers, and resources

The Full-Stack LLM Engineering Playbook. Architectural patterns for Agents (MCP) & RAG, coupled with advanced Post-Training recipes (SFT, DPO, QLoRA) for domain adaptation. Covers Data Pipelines, Evaluation Frameworks, and System Design.

Full-stack LLM Engineering Lab. Features: Autonomous Agents (ReAct/AutoGPT) | Fine-Tuning Llama/Mistral (SFT/DPO) | Large Model Deployment (DeepSeek 671B / 2.5-bit) | Advanced RAG (Hybrid Search) | Function Calling (Stream/Text-to-SQL/External APIs) | Frameworks (LangChain, Semantic Kernel, OpenAI) | Daily SOTA Paper Tracking. From theory to 0-to-1

Independent framework to test, benchmark, and evaluate LLMs & AI agents locally.

The greatest AI Framework ever made

Curated list of 50+ AI agent frameworks, multi-agent systems, LLM integration tools, and autonomous agent resources for developers

Unified execution runtime for LLM and ML programs.

A curated list of 400+ AI APIs, tools, frameworks, and platforms across 40+ categories

📊 Git-style diff and version control for LLM prompts

🔄 Record, replay, and debug AI agent execution traces

🧠 Manage and optimize AI assistant memories with a three-layer system for automated logging, analysis, and proactive learning.

Integrate the Assinafy API into your Python applications to manage electronic signatures, documents, and webhooks with a synchronous client.

The context engineering platform for AI applications — compile prompts, memory, retrieval, tools, schemas & policies into optimized, validated, observable context packets.

A research-oriented roadmap for learning artificial intelligence, grounded in foundational concepts and modern AI systems.

A learned continuous slow→fast latent channel for real-time game agents: frozen MiniCPM-o 4.5 (fast) + Qwen3-VL-8B-Thinking (slow), 33M-param bridge. The latent helps iff slow reasoning helps (T>F). Paper: arXiv:2606.24470

Unified Python starter for AI projects: classical ML, deep learning, LLM apps, RAG, and agents. uv + Ruff + pytest.

Open-source AI learning platform featuring structured learning paths, projects, roadmaps, AI tools, challenges, exams, and community-driven learning.

Production-grade multi-agent RAG workflow + an ML-infrastructure platform (PyTorch/DDP, RAG, evals, guardrails, MCP). Keyless-by-default; ships a real, reproducible Banking77 retrieval benchmark.

Monitor and control multiple Cursor agents in one terminal to track status, errors, and progress without switching browser tabs.

AI Engineering Roadmap 2026 🚀 | Build & Deploy AI Apps from Scratch

Deploy a local multi-model LLM fleet (llama.cpp) with an agent harness, hermes memory, and a one-command CLI

Agentic synthetic-data generation framework inspired by Meta FAIR's Autodata / Agentic Self-Instruct.

🧠 Hands-on AI Engineering roadmap: LLM Foundations, Prompt Patterns, RAG, LangChain & Multi-Agent Systems — built for production.

Model-agnostic, portable long-term memory framework for AI agents (MCP-native)

Become An AI Engineer in 2026 , Pure Skills NO CS degree. NO bootcamp. Complete 17-week roadmap.

🔥 Curated list of 200+ AI tools for developers — code generation, agents, RAG, CLI, DevOps, and more. Updated weekly.

A unified interface for Large Language Models with memory, reasoning, and tool capabilities. Supports OpenAI, Anthropic, Ollama, HuggingFace, and MLX with hierarchical memory system and agent development features.