A curated directory of production-ready open-source AI tools and libraries organized by category (core frameworks, models, inference, agents, RAG, training, deployment, benchmarks, safety). Highlights practical CLI tools like PR-Agent, Gemini CLI, LLM, and Repomix that directly integrate AI into developer workflows.

Comprehensive reference guide organizing 45+ LLM architectures with visual model cards and detailed explanations of attention variants (MHA, GQA, sliding window, etc.) used in modern models. Includes both a web gallery and printable poster, serving as a practical learning resource for understanding contemporary transformer architectures.

A curated resource listing LLM APIs with permanent free tiers for text inference, including first-party APIs from model trainers and third-party platforms hosting open-weight models. Covers rate limits, available regions, and notable models—useful reference for engineers exploring cost-free inference options during development and experimentation.

A comprehensive AI engineering curriculum spanning 260+ lessons across 20 phases (~290 hours) covering fundamentals from linear algebra to autonomous agent swarms in Python, TypeScript, Rust, and Julia. Each lesson produces reusable artifacts (prompts, skills, agents, MCP servers) that can be immediately integrated into AI coding workflows, with personalized learning paths based on existing ML/DL knowledge.

Google DeepMind released a cognitive taxonomy framework for measuring AGI progress, grounded in psychology and neuroscience, identifying 10 key cognitive abilities. They're launching a $200K Kaggle hackathon where engineers can design evaluations for five priority abilities (learning, metacognition, attention, executive functions, social cognition) using their new Community Benchmarks platform to test against frontier models.

Google released Gemini 3.1 Flash-Lite, a new lightweight model optimized for high-volume production workloads at $0.25/1M input tokens and $1.50/1M output tokens. It delivers 2.5X faster time-to-first-token and 45% faster output speeds than 2.5 Flash while maintaining quality, making it ideal for real-time applications like translation, content moderation, UI generation, and agentic workflows at scale.

Google DeepMind released Nano Banana 2 (Gemini 3.1 Flash Image), a new image generation model combining advanced reasoning and world knowledge with Flash-speed inference. The model is now available across Google products (Gemini app, Search) and offers improved subject consistency, photorealism, and instruction-following capabilities with reduced latency compared to the Pro version.

Comprehensive technical comparison of 10+ major open-weight LLM releases from January-March 2026, analyzing architectural innovations like mixture-of-experts, sliding window attention, QK-norm, and gating mechanisms across models from Arcee, Moonshot, Qwen, and others. Serves as a practical reference for understanding current design patterns and trade-offs in large model architecture.

Google released Gemini 3.1 Pro, an upgraded core model with significantly improved reasoning capabilities (77.1% on ARC-AGI-2, more than 2x better than 3 Pro). Available through Gemini API, Vertex AI, and consumer products, it excels at complex problem-solving tasks including code generation, system synthesis, and advanced reasoning workflows that engineers building with AI will find immediately applicable.

Google DeepMind released Lyria 3, an advanced music generation model integrated into the Gemini app, allowing users to create 30-second tracks from text descriptions or images with SynthID watermarking for AI-generated content detection. The model improves on previous versions with better audio quality and customization, and is also rolling out to YouTube creators for Dream Track.

Comprehensive overview of inference-time scaling techniques for LLMs, covering methods like chain-of-thought prompting, self-consistency, best-of-N ranking, and rejection sampling with verifiers. The author shares practical experimentation results (achieving 15% to 52% accuracy improvement) and categorizes approaches from both academic literature and proprietary LLM implementations, making it directly applicable to deployed systems.

A comprehensive retrospective on 2025's major LLM developments, starting with DeepSeek R1's January release showing that reinforcement learning (specifically RLVR/GRPO) can enable reasoning-like behavior in LLMs, and revealing that state-of-the-art model training may cost an order of magnitude less than previously estimated. The article examines how post-training scaling through verifiable rewards represents a significant algorithmic shift from SFT/RLHF approaches, opening new possibilities for capability unlocking.

DeepSeek V3.2 is a new open-weight flagship model achieving GPT-5/Gemini 3.0 Pro-level performance with a custom sparse attention mechanism requiring specialized inference infrastructure. The article provides technical deep-dive into the model's architecture, training pipeline, and what's changed since V3/R1, making it essential for engineers working with state-of-the-art open-source models.

Comprehensive overview of alternative LLM architectures beyond standard transformers, including diffusion models, linear attention hybrids, state space models (SSMs), and specialized architectures like code world models. The article surveys emerging approaches aimed at improving efficiency and modeling performance, with comparisons to current SOTA transformer-based models like DeepSeek R1, Llama 4, and Qwen3.

Practical guide covering four main LLM evaluation methods: multiple-choice benchmarks, verifiers, leaderboards, and LLM judges, with code examples and analysis of their strengths/weaknesses. Essential reading for engineers comparing models, interpreting benchmarks, and measuring progress on their own projects.

Deep dive into Qwen3 architecture implementation from scratch in PyTorch, covering the open-weight model family's design choices and building blocks. Provides practical code examples and architectural patterns directly applicable to understanding modern LLM internals and building custom variations.