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Dear Reader,

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Welcome to the 300th issue of our newsletter and the first one of 2026! Our warmest wishes for a New Year that brings everything you hope for.

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We kick off the year by tracing a path from fundamentals to flywheels: on the systems side, there’s a practical case for pushing vector graphics rendering off the CPU and onto the GPU. TRMs BigQuery notes read like a reminder that “performance” is mostly about habits (filter early, scan less, join smarter, and instrument what you spend).

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On the model side, one explainer demystifies attention by walking through how inputs get linearly projected into the Q/K/V streams and why that separation matters, while a broader “physics” framing argues we should stop worshipping leaderboard deltas and instead run controlled, synthetic experiments to uncover repeatable laws about what models can (and can’t) learn. Then we zoom out: a concise agentic-AI primer maps the modern stack, such as tools, planning, memory, multi-agent patterns, and when to use which.

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Meanwhile, the business press is starting to ask the awkward question that will hang over the next wave of AI adoption: Can the biggest labs turn staggering infrastructure spend into durable profits before investors get impatient? Further, a set of career lessons ties it together with the human layer: obsess over user problems, bias toward shipping, and treat clarity as a senior skill because the best ideas still die in ambiguity.

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On the academic front, the theme running through this week’s papers is that we’re building faster than we’re understanding and the bill is quickly coming due.

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One study examines how LLMs are reshaping scientific output, raising the uncomfortable possibility that “more papers” can coexist with weaker signals of novelty, credit, and real progress. In parallel, a survey on graph-based RAG makes the case that retrieval won’t stay a flat list of documents for long: as tasks get messier, we’ll increasingly lean on structured relationships (entities, links, provenance, constraints) to ground generation in something closer to a working memory than a search result.

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A pointed critique shows how easy it is to “find” meaning in models ​through sloppy statistical rituals, echoing broader reproducibility concerns familiar to anyone who’s shipped ML into production, where interviews with practitioners underscore that most failures are organizational before they’re algorithmic. Security-wise, new work on adversarial generalization and inductive backdoors suggests we should treat capabilities as an attack surface: models can learn hidden behaviors that survive fine-tuning and appear innocuous under typical evaluations. That helps explain why “just add agents” is not a free win. Scaling agent systems requires a rigorous science of coordination, feedback loops, and failure modes, especially as recursive language models promise longer-horizon computation by repeatedly reusing internal reasoning steps.

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Finally, the causality angle argues for elevating LLMs from text predictors to scaffolds for causal structure, while keeping our feet on the ground about what evidence is actually warranted and designing systems that fail loudly when the world disagrees.

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Our current book recommendation is "Building AI Agents with LLMs, RAG, and Knowledge Graphs" by S. Raieli and G. Iuculano. You can find all the previous book reviews on our website. This week's video is a longer lecuture on the Physics of Language Models.

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Semper discentes,

The D4S Team

"Building AI Agents with LLMs, RAG, and Knowledge Graphs" by S. Raieli and G. Iuculano is a clear-headed guide for anyone trying to turn “cool LLM demo” into an agent that can retrieve facts, use tools, and stay anchored to real information. Raieli and Iuculano keep the focus on what matters in practice. How RAG and knowledge graphs change the reliability profile of an agent, and when you need more structure than “just prompt it better.”

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For data scientists and ML engineers, the best part is the build-oriented progression. It connects core concepts to concrete patterns—single-agent tool use, retrieval pipelines, and multi-agent coordination—without drowning you in theory. The examples feel like things you’d actually adapt into a prototype at work, and the overall framing consistently nudges you toward grounded, auditable behavior instead of vibes-based generation.

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The tradeoff is breadth: if you already know transformers cold, some early sections may read like a warm-up, and the “production” angle is more of a practical starting line than a full MLOps reliability handbook. Still, as a one-stop map of modern agent building—especially where RAG and knowledge graphs stop being buzzwords and start being design choices—it’s an intense, usable read that tends to leave you with a short list of things you want to try next.

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1. ​Vector graphics on GPU [gasiulis.name]
2. ​21 Lessons From 14 Years at Google [addyosmani.com]
3. ​Physics of Language Models [physics.allen-zhu.com]
4. ​OpenAI’s cash burn will be one of the big bubble questions of 2026 [economist.com]
5. ​Agentic AI Crash Course [github.com/aishwaryanr]
6. ​Performance Hints for BigQuery [trmlabs.com]
7. ​The Q, K, V Matrices​ [arpitbhayani.me]

• ​Scientific production in the era of large language models (K. Kusumegi, X. Yang, P. Ginsparg, M. de Vaan, T. Stuart, Y. Yin)
• ​Graph Retrieval-Augmented Generation: A Survey (B. Peng, Y. Zhu, Y. Liu, X. Bo, H. Shi, C. Hong, Y. Zhang, S. Tang)
• ​Now What? ​(J. J. Hopfield)
• ​The Dead Salmons of AI Interpretability (M. Méloux, G. Dirupo, F. Portet, M. Peyrard)
• ​Weird Generalization and Inductive Backdoors: New Ways to Corrupt LLMs (J. Betley, J. Cocola, D. Feng, J. Chua, A. Arditi, A. Sztyber-Betley, O. Evans)
• ​Operationalizing Machine Learning: An Interview Study (S. Shankar, R. Garcia, J. M. Hellerstein, A. G. Parameswaran)
• ​Recursive Language Models (A. L. Zhang, T. Kraska, O. Khattab)
• ​Towards a Science of Scaling Agent Systems (Y. Kim, K. Gu, C. Park, C. Park, S. Schmidgall, A. Ali Heydari, Y. Yan, Z. Zhang, Y. Zhuang, M. Malhotra, P. P. Liang, H. W. Park, Y. Yang, X. Xu, Y. Du, S. Patel, T Althoff, D. McDuff, X. Liu)
• ​Large Causal Models from Large Language Models (S. Mahadevan)

Physics of Language Models

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All the videos of the week are now available in our YouTube playlist.

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