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The Honesty Check

DSpark's second machine, on its own: the AI's confidence scores are overconfident, like a forecaster who says 90% but is right 70% of the time, so DSpark corrects them to match reality.

Part four of the speculative decoding series, and the second of three that open up DSpark one machine at a time. This one is the honesty check. DSpark puts a confidence number on every guessed word, but those numbers run high, the same way a weather forecaster who says 90% is often right only 70% of the time. Calibration shaves them down until they match reality, without changing their order, which is what lets the next machine trust them. Plain words, a worked example, a diagram, and an auto-playing visualizer. Short, one idea only.

• Inference

The Cheat Sheet

DSpark's first fix, on its own: how a tiny cheat sheet, the same trick your phone uses, makes each guessed word fit the one before it.

Part three of the speculative decoding series, and the first of three that open up DSpark one machine at a time. This one is the cheat sheet, the paper's Markov head: the same trick your phone's predictive text uses, where the word you just typed nudges the next one. It explains what a score even is, why the fast guesser is blind, and how a small nudge breaks the tie, with a worked example, a diagram, and an auto-playing visualizer. Short, one idea only.

• Inference

Draft Better, Verify Smarter

A fast drafter gets sloppy toward the end, and checking its whole guess wastes the big model's time. DSpark makes two small changes that fix both, and the output never changes.

Part two of the speculative decoding series, in plain language. Part one left a dilemma: a draft model is either accurate but slow or fast but sloppy, and checking a long shaky draft wastes the big model's time. This part walks through DSpark, an open method from DeepSeek, one small change at a time. First, let each drafted word see the word before it, so the fast drafter stops contradicting itself. Second, score each word's chance of surviving, multiply those scores into a running number that only falls, and stop checking the moment it stops being worth it. Two interactive visualizers, one of them auto-playing through the whole cycle, and the production numbers, with no change to the output.

• Inference

Vectorless RAG

When a document has a structure, stop shredding it. Navigate it.

Part six of the RAG series. The default RAG recipe chops a document into chunks, embeds them, and retrieves by vector similarity. For a long, structured document that throws away the one thing that made it readable: its structure. Vectorless RAG keeps the document whole, builds a table-of-contents tree, and has a model reason its way to the right section, the way a human expert uses a table of contents. This covers why similarity is not relevance, how tree-navigation retrieval works, where it fails, when to reach for it, and when plain vector search is still the right tool. With an interactive navigator.

• RAG

Draft, Then Verify

Writing one token at a time makes a large model slow. Speculative decoding speeds it up by letting a small model guess ahead.

Part one of a series on speculative decoding, the trick that lets a large language model generate text faster with no change to the output. This part covers why one-token-at-a-time is slow, the lossless draft-and-verify rule that fixes it, and the real difficulty: the draft model. It walks through the two families of draft models and the flaw in each, autoregressive drafters that are accurate but slow, and parallel drafters that are fast but decay down the block. With two interactive visualizers. Part two covers how DeepSeek's DSpark gets the best of both.

• Inference

The Harness Is the Agent

Why agent reliability lives in the scaffolding, not the model.

When an agent works in production, we credit the model. When it fails, the cause is almost always in the harness around it: the loop, the tools, the context, the guardrails, the checks. The harness is the part you actually own.

• AI Agent Harness

Speaking at AI Engineer World's Fair

AI Engineer World's Fair 2026 talk: agents need feature flags. We ship the most behavior-changing systems we have ever built, agents that send email, move money, and modify databases, with none of the canary, segment-targeting, and kill-switch discipline web teams settled a decade ago. The talk lays out six flag types for the six behavior surfaces an agent has, a rollout playbook with the numbers to track, and why a stop button is now required by law.

• LLM Ops & Eval

Speaking at AI Engineer World's Fair

AI Engineer World's Fair 2026 talk: your coding agent is creating review debt, the accumulating gap between the code an agent produces and the code humans have actually reviewed, trusted, and understood. Five signal families and ten deterministic checks turn that gap into a single defensible score, validated across 524 real pull requests.

• LLM Ops & Eval

How MCP Grew

Four spec revisions, an official registry, the Linux Foundation, and the ecosystem that adopted it.

The closing piece of the MCP series: the four revisions that shaped the protocol through its first year and a half, the official registry and MCP Apps, the move to the Linux Foundation and open governance, how it went from one release to an industry standard, and where the 2026 roadmap points.

• MCP

A Coding Agent Is a Loop Around a Compiler

The model writes the code; the harness makes it correct by running it.

A coding agent that only writes code is a fancy autocomplete. The thing that turns a plausible diff into a change you can merge is the code harness: the sandbox, the tools, and the loop that runs the code and reads the real errors.

• Code Harness