08 - Reading papers without drowning¶

What this session is¶

How to engage with AI research without burning out. The discipline most engineers don't have but should.

The trap¶

There are ~10,000 AI papers per month on arXiv. People who try to "stay current" by reading all of them burn out by month 3. People who don't read any never level up past tutorial-follower.

The right answer is curated, slow, deliberate. Maybe one paper a week, read deeply. Plus a fast skim queue for awareness.

The two reading modes¶

Skim mode (5 minutes per paper)¶

For breadth. ~10 papers per week. Goal: know it exists, vaguely what it does, whether it's worth a deep read later.

Read in order: 1. Title. 2. Abstract. 3. The headline figure (usually Figure 1 or 2). 4. First sentence of each section. 5. Final paragraph of conclusion.

That's it. 5 minutes. Move on.

Tag papers worth a deep read.

Deep mode (2-3 hours per paper)¶

For depth. 1 paper per week, sometimes 2. The paper you tagged from skim. Goal: understand it well enough to explain to a colleague.

Read in order: 1. Abstract + intro. Get the problem. 2. Related work. Skim, skip if you know it. 3. Method. Slow. Re-read until you can explain the diagram out loud. 4. Experiments. Look at the tables. What did they ablate? What's the baseline? 5. Limitations / discussion. Honest? Hand-wavy? 6. Look at the code. Most influential papers release code. Read the actual implementation. The paper and the code disagree, and the code is what's true. 7. Find a blog post / video / podcast about it. Cross-reference your understanding.

Take notes. Specifically: write 5 sentences answering "what's the new idea, why does it matter, what was the baseline, what does it cost, what would I do with it."

What to read¶

The 10 papers every AI engineer should have skimmed¶

(2026 edition; the list updates)

Attention Is All You Need (Vaswani et al, 2017) - the transformer paper.
BERT (Devlin et al, 2018) - bidirectional transformer for understanding.
GPT-3 paper (Brown et al, 2020) - scaling laws + few-shot.
Chinchilla (Hoffmann et al, 2022) - the compute/data tradeoff.
LoRA (Hu et al, 2021) - low-rank adaptation.
InstructGPT / RLHF (Ouyang et al, 2022) - how we made GPT helpful.
Constitutional AI (Bai et al, 2022) - Anthropic's alignment approach.
Mixture of Experts (Switch Transformer) (Fedus et al, 2021) - sparsity for scale.
vLLM (PagedAttention) (Kwon et al, 2023) - serving with KV cache management.
DPO (Rafailov et al, 2023) - preference fine-tuning without RL.

Most are findable on arXiv. Many have annotated walkthroughs (The Illustrated Transformer, etc.).

Where to find what's worth reading¶

HuggingFace Daily Papers (huggingface.co/papers) - curated daily list, voted by community. Use this instead of raw arXiv.
arXiv-sanity-lite (arxiv-sanity-lite.com) - Karpathy's filter. Filter by tags.
Papers With Code (paperswithcode.com) - sorted by benchmarks. Useful for finding state-of-the-art in your specialization.
Newsletters: Sebastian Raschka's "Ahead of AI", Jack Clark's "Import AI". One per week, no more.
Twitter/X: follow 10-20 researchers in your specialization. Their retweets surface things.

Where NOT to look for papers¶

Twitter timeline directly. Will eat your week.
arXiv firehose. Drowns you.
LinkedIn AI influencers. Mostly recycled content.

Reading specific specializations¶

By specialization, here's what's worth tracking:

Applied LLM: new prompting techniques (rare these days), agent frameworks, eval papers, retrieval improvements. ~5 papers per month.
Inference / serving: vLLM team output, FlashAttention variants, kernel-level optimization papers, MoE serving. ~3 papers per month.
Fine-tuning: new PEFT methods, preference algorithm variants, synthetic data papers. ~5 papers per month.
MLOps: less paper-driven. Read blog posts (Anyscale, Databricks, Modal). Conference talks (Ray Summit, MLOps World).
Research engineer: depends on subfield. Talk to your lab's mentors.
Safety: Anthropic / OpenAI / DeepMind safety blogs. METR. Apollo Research. AI safety reading lists curated by AGI Safety Fundamentals.

How to know if a paper is solid¶

Honest tells:

Code released? Major positive signal.
Multiple seeds in experiments? Hides noise; good papers report it.
Honest comparison to strong baselines? Suspicious if their baseline is weirdly weak.
Limitations section that admits real things? Good papers do this.
Reproduced by others? Look for follow-up papers citing it. Did others get similar results?
Author track record? Researchers at top labs aren't always right, but they're usually rigorous.

Red flags: dataset-cherry-picking, only one seed, no released code, breathless claims, no comparison to obvious baselines.

What to do with what you read¶

Add the 5-sentence summary to a notes file.
If you can apply the idea to your project, try a minimal experiment.
Talk about it (blog, lunch chat, Slack). You learn by explaining.

Reading without writing or applying decays in days. Reading + applying makes it stick.

What you might wonder¶

"Do I need to read papers to be an applied AI engineer?" A few, yes. Enough to converse with researchers and not look ignorant in interviews. You don't need to be at the frontier.

"What if the math defeats me?" Try anyway. Get what you can. Look up specific equations. Often the math is more intimidating than the actual idea. Karpathy's videos and "The Annotated X" series rescue many readers.

"Where do PhD-level researchers learn to read this fast?" Reading 10x more papers over 6 years. There's no shortcut. You don't need to match them. You need a working pace, sustained.

Done¶

Have two-mode reading discipline.
Have a curated source list.
Know the 10 papers to skim first.

Next: Building in public →