I'm Matt Wood, and this is For Your Information. A live list of riffs and links for you and your agent, drawn from what I'm reading, noticing, questioning, concluding, and revising.

Architecture layers: - Task runs: POST a goal + machine, agent drives to completion, self-verifies (pass/fail) - Workflows: sequence tasks with branches, loops, budgets, human approvals, shared outputs - Machines: managed Linux/Windows VMs with browser, terminal, file system - Prediction primitives: sessions (stateful screenshot loop), predict (sta

There are only two things you can say with certainty about token prices: we're in a supply crunch, and this is unstable. All of the variables are in play, and the market will get shaken out over the next few years to arrive at a new equilibrium.

We formalize this bottleneck as the intent-execution gap: the mismatch between what the model intends and what the harness executes, and vice versa. For example, in trying to revise code, a model may intend to edit a single instance of a function, while the harness accidentally modifies multiple instances.

Note: LinkedIn blocked direct crawling of this article. The following is reconstructed from the article title, author context, and surrounding discourse on the same topic.

Sponsorship: Diamond level, Booth B207, Seoul South Korea

Recent milestones (2026): - June 8, 2026: Presented at ADA 86th Scientific Sessions showing CTRO-1013 (liver-targeted IRS1 siRNA) reduces fibrosis biomarkers TIMP-1 (37%) and CK-18 (68%) in preclinical models, with effects partly independent of fat reduction - March 2026: Expanded BMS collaboration for ALS/FTD with nomination of new targets - Janua

Core thesis: "The phrase 'frontier model' is starting to mean two things. One is a checkpoint. The other is a system boundary."

A Distilled Knowledge Skill (DKS): an entire domain (Strands Agents, Amazon Bedrock, Bedrock AgentCore) reduced to its executable essence, and kept current as the surface shifts month to month. The research is already done; you skip straight to building.

Over the past few months, four AI giants quietly rebuilt the same thing at once: AWS, Microsoft, Google, and Anthropic each shipped agent runtime updates that point to the same architectural shift. [...] It is a move from request-level load balancing to session-aware execution.

Architecture: Combines a multinomial diffusion model for ingredient selection with a score-based generative model for ingredient quantification, together generating complete burger recipes defined by 146 ingredients and their quantities.

gog mcp runs a typed MCP server over stdio for agent clients that need a permissioned Google Workspace tool surface. It intentionally does not expose a generic shell/argv bridge. Each MCP tool has a fixed schema and maps to a specific gog operation. MCP defaults are read-only. Write tools are hidden unless the server is started with `--allow-wri

Core pattern: "Run the inner Skill, record its failures, make a diff to improve it, repeat."

Executing deep neural networks on GPUs has dominated AI for a decade, but we think the next jump in energy efficiency demands a fundamentally different computer, one where physics does the computing. We built Un-0, an image generator powered by a simulated system of coupled oscillators, an example of an emerging physical computing substrate.

Everyone's writing code with AI agents today. But the moment an agent needs to deploy something, and needs to sign up and create an account, it slams face-first into a wall built for humans: a browser-based OAuth flow, a dashboard to click through, an API token to copy-paste, a multi-factor authentication prompt to satisfy. For an interactive copil

I am now in the camp who believe that we are only at most two or three model generations away from AI finally being controlled like nuclear weapons. Only a few will have access to superintelligence above the classes of models we're seeing this year.

His ZX Spectrum computers brought affordable personal computing to the masses and sold in their millions across the world. But his attempt to launch an electric vehicle was not successful, and caused him severe financial problems.

It's not that agents can on their own construct arbitrarily challenging proofs. But models are enormously helpful, and broaden the set of people who can use these tools productively. With formal methods being easier to use than ever, it's worth reconsidering the old cost/benefit calculus.

Key themes from the event (via InfoQ coverage and Anthropic engineering blog):

SIA operates by coordinating three main types of AI agents that work together to continuously improve task performance: - Meta-Agent: Reads the task description and generates an initial Target Agent tailored to the task. - Target/Task Specific Agent: Attempts to complete the task and records its actions and results. - Feedback/Improvement Agent: Re

They attributed the AI-enabled gain to three factors multiplying together: acceleration of low-judgment work (1.5x), higher focus on high-judgment work with no context-switching (1.5x), and instant access to agent-captured domain expertise (1.5x). Remove any one factor and the gains collapse.

The LinkedIn post (content not directly crawlable) is Brad Porter commenting "We saw this with robotics at Amazon too" on a shared post about technology deployment gaps. Based on Porter's extensive public commentary:

Key argument (from David, 1990, American Economic Review, Vol. 80, No. 2, pp. 355-361):