Reading & watching
Reading & Watching. Curated. No filler.
Papers, books, channels, conferences, communities. Curated. No filler.
Papers.
The minimum reading list. Top ten in read order. Work through them once and the rest of the field reads more clearly. Bonus reading after.
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Attention Is All You Need
The transformer paper. Every model you use is descended from this architecture. Read it slowly. The math is approachable and the diagrams carry the load. If you only ever read one ML paper, this is it.
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
The encoder-only branch of the family tree. Shows why pre-training on raw text then fine-tuning beat the older task-specific approach. Still the mental model behind most embedding and classification work.
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Language Models are Few-Shot Learners (GPT-3)
The paper where in-context learning showed up at scale. Long, but the early sections are the point: you can prompt a model into a new task without training. Everything since has been a reaction to this.
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Training language models to follow instructions with human feedback (InstructGPT)
How GPT-3 became useful. Introduces RLHF as a production technique. Read this before any alignment or fine-tuning paper. The data-collection sections matter as much as the algorithm.
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Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
The shortest paper that changed how people prompt. Show your work, and the model does too. Pair with the Self-Consistency follow-up.
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ReAct: Synergizing Reasoning and Acting in Language Models
The mental model behind every agent framework you will touch. Think, act, observe, repeat. The paper is more honest than most about where the pattern breaks.
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Constitutional AI: Harmlessness from AI Feedback
Anthropic's alternative to pure RLHF. Use a written constitution and AI critique to scale alignment work. Important for understanding how Claude is trained and for thinking about your own policy work.
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Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (RAG)
The original RAG paper. Most production AI work you do will be some variant of this pattern. The implementation has moved on, but the framing still holds.
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Direct Preference Optimization: Your Language Model is Secretly a Reward Model
DPO killed off most RLHF pipelines for fine-tuning. Simpler, more stable, no separate reward model. Read this if you fine-tune at all.
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DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
The 2025 paper that showed reasoning could be trained with pure RL on outcome rewards, no SFT warm-start required. Triggered the reasoning-model wave you see across every lab. Read for the methodology and the honest ablations.
Bonus reading.
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AI Risk Management Framework (AI RMF 1.0)
The framework federal agencies and contractors point at when they say 'AI governance.' Not a paper in the academic sense. Read it if you sell to or work in the government. Pair with the Generative AI Profile (NIST AI 600-1).
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Model Cards for Model Reporting
The case for documenting what a model does, where it fails, and on whom it was evaluated. Short. Sets the bar for honest model release notes.
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OWASP Top 10 for Large Language Model Applications
Community-maintained list of the most common LLM-app security failures: prompt injection, insecure output handling, data leakage. Read before you ship anything user-facing. Updated yearly. Check the link for the current version.
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Sparks of Artificial General Intelligence: Early experiments with GPT-4
Microsoft Research's case that GPT-4 was qualitatively different. Useful as a snapshot of the moment people stopped arguing about whether LLMs were a big deal. Cherry-picked examples and a contested framing. Read it with skepticism, not as gospel.
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Mixtral of Experts
The clearest write-up of a working sparse mixture-of-experts model at the time. Read this to understand why open-weights MoE took off and how the routing actually works. Pair with the Switch Transformer paper for the lineage.
Books.
A working library. We tell you who each book is for and where it falls down.
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Who for: Engineers moving from notebooks to production. The clearest single book on ML systems.
Dense but practical. The chapters on data distribution shifts and monitoring are the reason to buy it. Skip the intro if you already know the ML basics.
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Who for: Anyone shipping LLM-powered apps. The follow-up to Designing ML Systems.
Best single source on RAG, evals, fine-tuning trade-offs, and inference cost. Some sections will age fast. Read it now.
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Who for: Engineers who learn by doing. Pairs well with Alammar's Illustrated Transformer posts.
Visual, code-forward, and current. Good bridge from foundations to applied work. Light on production concerns, so pair it with Huyen.
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Who for: The fastest honest path through classical ML. Read this before anything thicker.
Closer to 150 pages now. Math is present but not punishing. Burkov's follow-up Machine Learning Engineering is also good.
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Who for: Engineers who want to understand generative models from the ground up: GANs, VAEs, diffusion, transformers.
Code is in Keras/TensorFlow, which dates it slightly. The conceptual chapters hold up. Good for the why behind the how.
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Who for: Practitioners who want a survey of RAG, agents, and deployment patterns in one volume.
Uneven. Some chapters are excellent, others read like blog posts stitched together. Skim the table of contents and read what you need.
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Who for: Every engineer. Not an AI book, but you will not build serious AI systems without this knowledge.
Still the best book on distributed systems for working engineers. A second edition has been in progress; the first edition is fine.
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The Phoenix Project
2013Who for: Anyone who has watched a deploy go sideways and wondered why nobody learned. Translates directly to MLOps.
It is a novel. Some find that cringey. The lessons are real. Pair with The DevOps Handbook if you want the practitioner manual.
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Who for: Anyone running A/B tests or evals on AI systems. The bar for honest measurement.
Written by the people who built experimentation at Microsoft, Google, and LinkedIn. Dense. Worth it.
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Streaming Systems
2018Who for: Engineers building real-time pipelines: feature stores, online inference, event-driven ML.
Beam-flavored but the concepts generalize. The watermarks and windowing chapters are the payoff.
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Who for: The operator mindset book. Read it once a year.
Lectures from a man who watched computing get built. Chapter on 'You and Your Research' is famous for a reason. Stripe Press reprint is the nice edition.
Channels.
YouTube, blogs, podcasts. Grouped by tier: foundational first, frontier next, federal last.
Foundational
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Andrej Karpathy
YouTubeThe Zero to Hero series and the LLM lectures are the single best free curriculum on how these models actually work. Watch with a code editor open.
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3Blue1Brown
YouTubeFor the math. The neural networks and attention episodes give you the visual intuition the papers assume you already have.
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AssemblyAI
YouTubePractical engineering content on speech, transcription, and applied LLM patterns. Less hype than most channels.
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Sean Carroll's Mindscape
PodcastNot an AI podcast. A clear-thinking podcast. The AI episodes are good; the rest sharpens how you reason about claims.
Frontier
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Yannic Kilcher
YouTubePaper walkthroughs by someone who reads them critically. Cadence has slowed; check recent uploads. _status as of 2026-06_
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AI Engineer (Summit talks)
YouTubeConference talks from people shipping production AI. The eval and RAG talks are where the field is actually learning.
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Simon Willison's Weblog
Blog/websiteThe most useful single blog in AI right now. Daily TILs, honest model reviews, and llm CLI tutorials. Read the archive.
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Anthropic Research
Blog/websitePrimary source for Constitutional AI, interpretability, and Claude's capability work. Read the originals, not the recaps.
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Latent Space
PodcastSwyx and Alessio interview the people building the stack. Long-form, technical, low fluff. Best AI engineering podcast.
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The Cognitive Revolution
PodcastNathan Labenz goes deep with researchers and founders. Episodes are long. Worth it for the frontier-lab interviews.
Federal-relevant
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The federal AI beat. Procurement, policy, agency rollouts. Necessary if you sell to or work for the government. _status as of 2026-06_
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GovExec Daily
PodcastBroader federal management, not AI-specific. Useful background for how agencies actually buy and deploy technology. _status as of 2026-06_
Newsletters.
Email and RSS. Subscribe to two or three, not ten. Volume beats signal otherwise.
Conferences.
The ones worth a plane ticket. Audience chip tells you who shows up.
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AFCEA TechNet Cyber
Federal/clearedAnnual, typically May, Baltimore/DC area
DoD cyber and AI procurement crowd. Vendor-heavy floor, but the cleared sessions and side meetings are the value. _status as of 2026-06_
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AFCEA WEST
Federal/clearedAnnual, February, San Diego
Sea services focus: Navy, Marines, Coast Guard. AI and unmanned systems tracks have grown. Good for west-coast cleared work.
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AUSA Annual Meeting
Federal/clearedAnnual, October, Washington DC
Army's big show. Massive vendor floor and warfighting function panels. AI sessions are growing but still secondary to platforms.
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USENIX Security Symposium
Federal/clearedAnnual, August, varies
Academic security with strong federal attendance. The ML security and adversarial AI papers land here. Not federal-only; co-located with academic crowd.
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AI Engineer Summit / World's Fair
IndustryMultiple per year, San Francisco and NYC
The conference for people shipping AI products. Talks are on YouTube after; go in person for the hallway track. _status as of 2026-06_
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NeurIPS
Academic/researchAnnual, December, varies (often Vancouver or New Orleans)
The biggest ML research conference. Most engineers will get more from the papers than the in-person event. Workshops are the best part.
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DEF CON AI Village
IndustryAnnual, August, Las Vegas
Inside DEF CON. The AI red-teaming and jailbreak research lives here. Pair with the main DEF CON if you do security work.
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Anthropic and OpenAI dev events
IndustryVaries. Anthropic Code with Claude and OpenAI DevDay run roughly annually.
Vendor events. The product announcements and SDK changes are the substance. Recordings are usually posted within a week. _status as of 2026-06_. Check the lab's events page for the next one.
Communities.
Remote first, then in-person by city. Pick one or two. Community is a small habit, not a feed to scroll.
Remote
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Latent Space Discord
DiscordWho for: AI engineers shipping production systems. High signal, paper club, hiring channel.
Invite linked from the Latent Space site. One of the few AI Discords worth real attention.
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Anthropic Discord
DiscordWho for: Claude builders. Good for SDK questions, MCP discussion, and Anthropic team presence.
Official. Quieter than HuggingFace but higher quality for Claude-specific work.
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Hugging Face Forums
RedditWho for: Anyone debugging transformers, datasets, or training runs. Search-first community.
Treat it like Stack Overflow for ML. The forums are more useful than the Discord for technical depth.
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r/MachineLearning
RedditWho for: Paper announcements and occasional good discussion threads.
Signal-to-noise has dropped since 2023. Filter to [R] and [D] flairs. Comments are uneven.
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r/LocalLLaMA
RedditWho for: Anyone running models on their own hardware. Fast-moving, practical.
Best place to track open-weights releases and quantization tricks. Some hype, mostly substance.
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DEF CON AI Village
DiscordWho for: AI security, red-teaming, and adversarial ML. Active around DEF CON, quieter off-season.
Discord invite from the site. The CTFs and writeups are the draw. _status as of 2026-06_
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VetSec
SlackWho for: Veterans in cybersecurity. AI/ML channel exists and is active.
Free to join with proof of service. The mentorship and job channels are the real value.
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Vets Who Code
SlackWho for: Veterans learning software engineering, including ML tracks.
Curriculum-first community. AI work is growing but not the primary focus.
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Operation Code
SlackWho for: Veterans transitioning into tech. General-purpose, not AI-specific.
Older community, large network. Use it for mentorship and connections more than AI-specific help.
DMV
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DC AI / Data Science Meetups
In-person meetupWho for: DMV-area engineers wanting in-person events. Mix of federal and commercial crowd.
Multiple groups exist (Data Science DC, DC AI, NoVA Data Science). Cadence varies by group. _status as of 2026-06_. Check each group's recent events before committing.
San Diego
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San Diego AI / ML Meetups
In-person meetupWho for: San Diego engineers. Navy and biotech crowd.
No single dominant group. Search Meetup for current active ones. _unverified_ which specific groups are most active right now.
Huntsville
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Huntsville AI
In-person meetupWho for: Huntsville/Redstone engineers. Strong defense and aerospace overlap.
Active meetup with regular talks. Good entry point if you work on or near the arsenal. _status as of 2026-06_