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

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.

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):

"There are three phases to AI coding. The first is autocomplete. The second is AI-assisted chat panels in IDEs. We are now entering the Agent-First era." — Zach Lloyd

Issue 1: Pod stuck in Pending state - Agent correctly identified missing Fargate profile for the test namespace.

Key details from coverage (NYT article paywalled; sourced from aggregated reporting):

AI Doomerism = a Secular Apocalypse Narrative. Practically, the "AI doom gospel" translates traditional religious forms into technical language. In the rationalist/effective altruist discourse, God becomes a superintelligence, prophecy becomes timelines and probability estimates, and hell becomes human extinction from AI.

Problem: Adapting foundation models to a morphologically rich language (Azerbaijani) with limited training data, no existing blueprint for efficient LLM training in that language.

"Learning can only take place through the attempt to solve a problem and therefore only takes place during activity."

The 1997 analogy: We are in an era of radical uncertainty where the technology is transformative but most high-value use cases haven't been built yet and the "winners" are not yet clear. Just as it was impossible in 1997 to predict that a search engine with a quirky logo would reshape the world, we cannot yet see the final shape of the AI-driven ec

Authors: Tianyi Zhou, Dongrui Liu, Leitao Yuan, Jing Shao, Xia Hu

Mollick's post (May 30, 2026): "It does seem like meaningfully better AI releases are accelerating, especially from OpenAI & Anthropic. To illustrate, I caused this timeline to be created. It only lists new models that scored 3 points or higher over previous models in the Artificial Analysis index."

The problem: At Meta, significant lines of code per human-landed diff grew by 105.9% year over year and per-developer diff volume rose 51%, with agentic AI responsible for over 80% of that growth. Meanwhile, the share of diffs receiving timely review has declined, exposing a widening gap between code supply and reviewer bandwidth.

Thinking Display: See how the agent works through a problem as it happens. Thinking display streams the model's reasoning in real time, so you can follow its logic, catch a wrong turn early, and understand why it chose an approach. Enabled by default; toggle from /settings > Display > Show thinking.

Architecture: Built with Electron, TypeScript, and AWS SDK. Uses Kiro CLI's Agent Client Protocol for conversational AI. Three agents collaborate to build features and improve code/UX quality.

Architecture: - Multi-agent pipeline with orchestrator, workers, and reviewers operating in a DAG workflow - MCP tool servers: filesystem, git, bash, Lean REPL/LSP, mathlib - Supports multi-node execution via SLURM - Configurable LLM backends (Claude Opus 4.6, GPT, Gemini)

(LinkedIn source blocked; synthesized from Columbia Business School event, Fortune reporting, and Forbes coverage of Chatterji's research)

Key features: - Live presence for agents and humans in the same document - Comments, suggestions, and provenance tracking - Integrates via MCP/skill install with Claude Code, Codex, OpenClaw - Designed for pre-implementation work: scoping, planning, spec review - Free, no login required - Uses X-Agent-Id headers for agent identity in presence

Core idea: Recursive Reasoning Models (RRMs) use repeated computation to refine a persistent latent state rather than append new elements to an output sequence. This decouples reasoning depth from both parameter scale and output length: a compact model can perform many steps of internal computation by repeatedly applying shared transition functions