BW / 2026 · Vol. 01 / Issue Nº 03 Filed under Code · Intelligence SF · NYC · Dallas EN

Books and papers I keep coming back to.

Books 07
  • 01
    Infinite Jest David Foster Wallace
  • 02
    Memories, Dreams, Reflections Carl Gustav Jung
  • 03
    The Verificationist Donald Antrim
  • 04
    The Poetics of Space Gaston Bachelard
  • 05
    London Fields Martin Amis
  • 06
    CivilWarLand in Bad Decline George Saunders
  • 07
    At the Mountains of Madness H.P. Lovecraft
Whitepapers 27
  • 01
    The Bitter Lesson Rich Sutton · 2019

    General methods leveraging computation beat hand-crafted structure. Foundational to how I think about AI engineering.

  • 02
    Self-Discover: Large Language Models Self-Compose Reasoning Structures Zhou et al. (DeepMind) · 2024

    LLMs compose reasoning modules at query time. Built a prototype implementing this.

  • 03
    Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs Gandhi et al. (Stanford) · 2025

    Four cognitive traits a model needs for self-improving reasoning: Verification, Backtracking, Subgoal Setting, Backward Chaining.

  • 04
    DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning DeepSeek · 2025

    Landmark paper demonstrating RL-based reasoning emergence in open-weight models.

  • 05
    s1: Simple Test-Time Scaling 2025

    Minimalist approach to test-time compute scaling that matches or exceeds far more complex methods.

  • 06
    Chain of Draft: Thinking Faster by Writing Less 2025

    LLMs reason effectively with much sparser intermediate steps — an efficiency counterpoint to chain-of-thought.

  • 07
    Learning to Reason in 13 Parameters (TinyLoRA) 2026

    8B model to 91% GSM8K accuracy with only 13 trainable parameters via RL. arXiv:2602.04118.

  • 08
    Building Effective AI Agents Anthropic · 2025

    The canonical practical guide to agent construction. Shaped how the field thinks about agent architectures and the workflow-vs-autonomy spectrum.

  • 09
    Why Do Multi-Agent LLM Systems Fail? (MAST) Cemri et al. · 2025

    First failure taxonomy for multi-agent systems: 14 failure modes across 3 categories. A sobering reality check on benchmark gains.

  • 10
    Multi-agentic Software Development is a Distributed Systems Problem 2025

    Reframes multi-agent coordination through distributed systems theory: consensus, partial failure, message passing.

  • 11
    Expensively Quadratic: the LLM Agent Cost Curve 2025

    Rigorous analysis of why agent costs grow quadratically with complexity. Essential for production agent systems.

  • 12
    Agentic Software Engineering: Foundational Pillars and a Research Roadmap 2025

    Comprehensive research agenda at the intersection of agents and software engineering.

  • 13
    AsymFlow: Asymmetric Flow Models Hansheng Chen et al. (Stanford/Princeton) · 2026

    1.57 FID on ImageNet 256x256. Introduces a latent-to-pixel finetuning route.

  • 14
    Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity Zhang et al. · 2025

    Identifies typicality bias as the root cause of mode collapse. Training-free, 1.6-2.1x diversity improvement.

  • 15
    Specifications: The missing link to making the development of LLM systems an engineering discipline 2025

    Argues that formal specifications are the key to transforming LLM development from alchemy into engineering.

  • 16
    Spec-Driven Development with AI Personal synthesis · 2025

    Synthesis of 45+ papers on how specifications, formal methods, and AI intersect to create reliable software engineering workflows.

  • 17
    Augmented Coding: Beyond the Vibes Kent Beck · 2025

    A sober, thoughtful assessment of AI-assisted coding from the creator of Extreme Programming and TDD.

  • 18
    QualityFlow: An Agentic Workflow for Program Synthesis Controlled by LLM Quality Checks Hu et al. · 2025

    SOTA on MBPP and HumanEval. Dynamic quality-check gating for code generation workflows.

  • 19
    Cognitive Load is What Matters 2024

    The fundamental constraint in software is human working memory, not tool speed or language features.

  • 20
    Malleable Software in the Age of LLMs 2025

    How LLMs change the nature of software itself — from fixed artifacts to fluid, recomposable systems.

  • 21
    TextGrad: Automatic Differentiation via Text 2024

    Applies backpropagation-style optimization to text, treating language model outputs as differentiable.

  • 22
    MegaTrain: Full Precision Training of 100B+ Parameter LLMs on a Single GPU 2026

    120B params on a single H200. 1.84x throughput over DeepSpeed ZeRO-3. arXiv:2604.05091.

  • 23
    From Local to Global: A GraphRAG Approach to Query-Focused Summarization Microsoft · 2024

    The paper that launched the GraphRAG paradigm. Directly relevant to the graphrag-claude-code project.

  • 24
    The Illustrated Transformer Jay Alammar · 2018

    The most widely referenced educational resource on transformer architecture. Visual clarity on the mechanism that started it all.

  • 25
    Magic Ink: Information Software and the Graphical Interface Bret Victor · 2006

    A vision for software as a medium for understanding rather than a toolbox for tasks.

  • 26
    The Homogenizing Effect of Large Language Models on Human Expression and Thought Sourati et al. · 2025

    Evidence across linguistics, psychology, and CS that LLMs risk standardizing language and reasoning.

  • 27
    The Space of Minds Andrej Karpathy · 2025

    A philosophical exploration of what kinds of intelligence are possible, grounding AI capabilities in a broader framework.

Last updated May 22, 2026