Track Awesome Artificial Intelligence Updates Weekly
A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.
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Aug 03 - Aug 09, 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 20 - Jul 26, 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 25 - May 31, 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.
Dec 01 - Dec 07, 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.