Track Awesome Artificial Intelligence Updates Daily

A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.

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Jul 27, 2026

Learn / Books

• Machine Learning Bookcamp: A project-based introduction to building and deploying machine learning systems by Alexey Grigorev.

Build AI systems / Speech and audio

• FunASR (⭐19k): Speech recognition toolkit with streaming ASR, VAD, punctuation, speaker diarization, and OpenAI-compatible serving for voice AI applications.

Jul 18, 2026

Learn / Books

• Reinforcement Learning: An Introduction: Sutton and Barto's foundational treatment of reinforcement learning concepts and algorithms.

• Speech and Language Processing: The continuously updated NLP reference by Dan Jurafsky and James Martin.

Learn / Courses

• Fast.ai Practical Deep Learning: A code-first introduction to deep learning.

• Karpathy's Neural Networks: Zero to Hero: Build neural networks and language models from first principles.

• Stanford CS336: Language Modeling from Scratch: Build language models from data preparation through evaluation and deployment.

• MIT 6.S191: Introduction to Deep Learning: Deep learning foundations and applications.

• Made With ML: An open course on designing, developing, deploying, and iterating on production ML systems.

Learn / Foundational papers

• Training Compute-Optimal Large Language Models: Showed how model size and training data should scale together under a compute budget.

• Retrieval-Augmented Generation: Combined parametric language models with external retrieval for knowledge-intensive tasks.

• LoRA: Introduced low-rank adaptation for parameter-efficient model fine-tuning.

• Training Language Models to Follow Instructions with Human Feedback: Established the instruction tuning and RLHF recipe used by InstructGPT.

• ReAct: Combined reasoning traces with actions for tool-using language-model agents.

• Direct Preference Optimization: Reframed preference alignment as a simple classification objective without explicit reward modelling.

Build AI systems / Guides and playbooks

• Building Effective Agents: Anthropic's practical patterns and tradeoffs for agentic systems.

• A Practical Guide to Building Agents: OpenAI's guide to models, tools, instructions, orchestration, and guardrails.

Build AI systems / LLM application engineering

• Anthropic Prompt Engineering: Techniques for defining success criteria, testing prompts, and improving model behaviour.

• Effective Context Engineering for AI Agents: How to select, structure, and manage the context available to long-running agents.

• 12-Factor Agents (⭐24k): Practical principles for building controllable LLM applications around deterministic software.

• OWASP Top 10 for LLM Applications: Risks and mitigations for developing and deploying generative AI applications.

Build AI systems / Agent frameworks

• Pydantic AI: Typed agent development built around Pydantic.

• LangGraph: Low-level orchestration for long-running, stateful agents.

• OpenAI Agents SDK: A small SDK for tools, handoffs, guardrails, tracing, and agent orchestration.

• Google Agent Development Kit: Google's framework for developing and evaluating agents.

• Microsoft Agent Framework: Microsoft's successor to AutoGen and Semantic Kernel for agents and graph-based workflows.

Build AI systems / Durable and asynchronous agents

• Effective Harnesses for Long-Running Agents: Patterns for agents that make progress across multiple context windows and recover from failure.

• Running Agents: Lifecycle, session, exception, and durable-execution patterns in the OpenAI Agents SDK.

• Human-in-the-Loop: Pause, inspect, approve, reject, and resume tool calls without losing agent state.

• Gemini and Temporal Durable Agent: A concrete implementation of durable execution, retries, and human approval for an agent workflow.

Build AI systems / Retrieval and data

• LlamaIndex: Data ingestion, indexing, retrieval, and agent workflows.

• Haystack: Modular pipelines for retrieval and generative AI applications.

Build AI systems / Evals and reliability

• Demystifying Evals for AI Agents: A practical method for building task suites, graders, transcripts, and evaluation harnesses.

• Promptfoo: Test cases, assertions, model comparisons, and red-team checks for LLM applications.

• Ragas: Evaluation and experimentation for retrieval and generative AI applications.

Build AI systems / Deployment and observability

• Langfuse: Tracing, evaluation, prompt management, and metrics for LLM applications.

• vLLM: An inference and serving engine for language models.

• LiteLLM: A model gateway and unified interface for multiple model providers.

Agentic software engineering / Coding agents

• GitHub Copilot coding agent: An asynchronous agent that works from GitHub issues and opens pull requests.

Agentic software engineering / Software factories and agent orchestration

• Harness Engineering: OpenAI's field report on building software with coding agents, repository constraints, automated checks, and human steering.

• Codex Orchestration with Symphony: A reference architecture that turns project work into isolated, observable coding-agent runs.

• How We Built Our Multi-Agent Research System: Production lessons on orchestrator-worker agents, parallel search, evaluation, and operational reliability.

Contributing / Software factories and agent orchestration

• serve software developers learning or practising AI engineering;

• provide technical or practical value beyond a product homepage;

• be current and maintained, unless its value is foundational and durable;

• add something meaningfully different from the existing entries;

• use a factual description supported by a primary source.

May 18, 2026

Learn / Books

• Build a Large Language Model from Scratch: Implement transformers in PyTorch with Sebastian Raschka.

• Hands-On Large Language Models: A visual and practical guide by Jay Alammar and Maarten Grootendorst.

• LLM Engineer's Handbook: LLMOps, fine-tuning, serving, and production workflows.

• The 100-Page Language Models Book: A concise, technical introduction by Andriy Burkov.

• Deep Learning: Foundations and Concepts: A probability-grounded treatment by Christopher and Hugh Bishop.

• Understanding Deep Learning: Theory, intuition, and practical notebooks by Simon Prince.

Agentic software engineering / Coding agents

• Claude Code: A terminal agent with hooks, subagents, skills, and repository-level instructions.

• Codex CLI (⭐99k): An open-source terminal agent with sandbox and approval controls.

• Gemini CLI (⭐106k): An open-source terminal agent built around Gemini and extensible tools.

• Cursor CLI: A terminal agent connected to Cursor's editor and cloud workflows.

• Aider: An open-source pair programmer with Git integration and broad model support.

• OpenCode: An open-source, provider-independent terminal agent with a client-server architecture.

• OpenHands: An open-source platform for running software development agents locally or in the cloud.

• Cline (⭐65k): An open-source coding agent available as an editor extension, CLI, and SDK.

• Continue: Open-source coding agents for IDE and CI workflows with source-controlled configuration.

Nov 24, 2025

Learn / Books

• Artificial Intelligence: A Modern Approach: The broad reference for classical AI, including search, reasoning, planning, learning, and robotics.

• Designing Machine Learning Systems: Scalable, maintainable machine learning systems by Chip Huyen.

• AI Engineering: Building applications with foundation models by Chip Huyen.

• Deep Learning: Mathematical foundations by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.

Learn / Courses

• Hugging Face LLM Course: Transformers, fine-tuning, datasets, and modern NLP tooling.

• Full Stack Deep Learning: The full lifecycle of building and shipping AI products.

• Google Generative AI Learning Path: An introductory path through generative AI concepts and Google Cloud tooling.

• DeepLearning.AI Short Courses: Focused courses on current generative AI engineering techniques.

Learn / Foundational papers

• Attention Is All You Need: Introduced the Transformer architecture.

• Scaling Laws for Neural Language Models: Explored relationships between model performance, data, and compute.

• Language Models are Few-Shot Learners: Demonstrated in-context learning at scale.

• Constitutional AI: A method for training helpful and harmless AI assistants using written principles.

Build AI systems / LLM application engineering

• OpenAI Cookbook: Code examples for structured outputs, tool use, retrieval, evals, and other LLM application patterns.

Build AI systems / Retrieval and data

• Docling (⭐63k): Document parsing and conversion for AI applications.

Build AI systems / Evals and reliability

• OpenAI Evals (⭐19k): An open-source framework and registry for evaluating language models and systems.