Anemll on Twitter / X
Up to 3.5x faster LLM inference on Apple Neural Engine:ANE is a Tensor Processing Unit, unlike GPU, it requires fixed-shape tensors. The KV cache size is set at compile time. A fixed 4096 context always runs at the slowest speed, even for short replies.Variable Context: start… pic.twitter.com/a1mLlmMYq7— Anemll (@anemll) February 16, 2026
LukeW | The Evolution of AI Products
At this point, the use of artificial intelligence and machine learning models in software has a long history. But the past three years really accelerated the ev...
Personalized Machine Learning
This page contains collects information and supplementary material for my textbook Personalized Machine Learning:
ggml
AI inference at the edge. ggml has 22 repositories available. Follow their code on GitHub.
generative-ai/gemini/agents/always-on-memory-agent at main · GoogleCloudPlatform/generative-ai
Sample code and notebooks for Generative AI on Google Cloud, with Gemini Enterprise Agent Platform - GoogleCloudPlatform/generative-ai
transformers/src/transformers/models/auto/tokenization_auto.py at 1d5296f2de31ecee4fdd789dc2b2fe068b320284 · huggingface/transformers
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. - huggingface/transformers
Heaps do lie: debugging a memory leak in vLLM. | Mistral AI
The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models.
Library: Faculty Guide to Generative AI: Detecting AI
pguso/ai-agents-from-scratch
Demystify AI agents by building them yourself. Local LLMs, no black boxes, real understanding of function calling, memory, and ReAct patterns.
The Hitchhiker's Guide to Agentic AI: From Foundations to Systems
The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems. The book covers the full stack from first principles to production deployment, organized around a central thesis: building great agentic systems requires understanding every layer of the pipeline, not just one. The book opens with the LLM substrate -- transformer architecture, GPU systems, training and fine-tuning (SFT,LoRA, MoE), model compression, and inference optimization -- treated as essential foundations rather than the primary focus. It then develops the alignment and reasoning layer: reinforcement learning from human feedback (RLHF), PPO, DPO and its variants, GRPO, reward modeling, and RL for large reasoning models including chain-of-thought and test-time scaling. The second half is devoted to agentic AI proper. Topics include agentic training and trajectory-based RL, retrieval-augmented generation (RAG and Agentic RAG), memory systems (in-context, external, episodic, and semantic), agent harness design and context management, and a taxonomy of agent design patterns. Inter-agent coordination is covered in depth: the Model Context Protocol (MCP), agent skills and tool use, the Agent-to-Agent (A2A) communication protocol, and multi-agent architectures spanning centralized, decentralized, and hierarchical topologies. The book concludes with agent development frameworks, agentic UI design, evaluation methodology for agentic tasks, and production deployment. Each chapter pairs rigorous theoretical foundations with implementation guidance, code examples, and references to the primary literature.
Library: Faculty Guide to Generative AI: Welcome
Library: Faculty Guide to Generative AI: Welcome
Training AI Agents with RL | Unsloth Documentation
Learn how to train AI agents for real-world tasks using Reinforcement Learning (RL).
Introducing dtoolAI — dtoolAI 0.1.0 documentation
dtoolAI is a Python library to make reproducible AI model training and use easier. The dtoolAI package provides:
Artificial Neural Engine Machine Learning Library. Contribute to Anemll/Anemll development by creating an account on GitHub.
