Tags › #LLM 11 posts
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Picking the Wrong Agent Topology Is Your Most Expensive Mistake
Five multi-agent design patterns — with a decision guide for when each one earns its complexity and when it will burn you.
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From Quadratic to Linear: A Survey of Subquadratic Sparse Attention
Why standard attention breaks at 128K tokens, how four families of efficient attention tried and partially failed to fix it, and how content-dependent sparse routing achieves linear scaling without sacrificing retrieval accuracy.
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The Agent Harness Pattern: What Poker Taught Me About Multi-Agent Systems
How a Texas Hold'em simulator became a blueprint for any domain where autonomous agents compete, negotiate, and adapt — turn by turn.
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Building a Self-Improving Personal Knowledge Base Powered by LLM
Inspired by Andrej Karpathy's post on LLM knowledge bases, I built a system where Claude Code skills manage a personal wiki end-to-end — ingesting raw content, compiling concept articles, synthesizing connections, and answering questions. You never touch the wiki. The LLM owns it.
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Gemma 4 Explained: How One Model Family Spans Phones and Frontier-Class Reasoning
A technical deep-dive into Gemma 4's four core ideas — MatFormer elastic inference, hybrid attention with p-RoPE, parallel dense+MoE FFN, and native agentic tooling — with the Gemma 1–3 lineage as context.
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The Tool Use Loop: How Claude Code Executes Code, Edits Files, and Talks Back
A tool call is a structured JSON request from the LLM to run a named function. Here's exactly how Claude Code handles the full lifecycle — from API call to file edit to loop continuation.
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Two Bets on Generative Recommendation: Semantic IDs vs. Fine-Tuned LLMs
A head-to-head comparison of the two paradigms remaking recommendation — semantic ID autoregressive models and fine-tuned LLMs — with trade-off analysis and a look at how they're converging.
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The Attention Bottleneck: How Modern LLMs Solved a Problem That Nearly Broke the Transformer
From vanilla multi-head attention to Flash Attention 3 — the engineering bottlenecks that drove every major attention variant and the math behind each fix.
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The Harness Is the Moat: Why Autonomous AI Agents Live or Die by Their Architecture
Model quality is commoditising. The durable competitive advantage in 2026 is harness architecture — the deterministic enclosures that make probabilistic agents reliable. A deep analysis of the four architectural primitives every production harness must implement, and how Autoresearch, Ralph Loop, Superpowers, and GSD each solve them differently.
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From Vibe Coding to Harness Engineering: How to Actually Ship AI-Assisted Software
Vibe coding gets you a working prototype in 10 minutes. Harness engineering is how you ship it to production. Here's the difference, why it matters, and how to make the transition.
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Why LLM Inference Costs Will Keep Falling
An analysis of hardware trends, algorithmic improvements, and market forces driving down the cost of running large language models.