Why CPUs matter for agentic AI
LLM-as-a-Judge Simply Explained: The Complete Guide to Run LLM Evals at Scale - Confident AI
In this article, I'll debunk what LLM judges are and go through why they are the best for LLM evaluation.
Agents 2.0: From Shallow Loops to Deep Agents
An overview of the architectural shift from Shallow Agents (Agent 1.0) to Deep Agents (Agent 2.0) and how to build complex AI agents that can handle multi-step tasks over extended periods.
YouMind-OpenLab/awesome-gpt-image-2: 🚀 Curated GPT Image 2 prompt library — OpenAI's next-gen image model with pixel-perfect text rendering, cross-image consistency, and commercial-grade illustration. 16 languages. Free & open source.
🚀 Curated GPT Image 2 prompt library — OpenAI's next-gen image model with pixel-perfect text rendering, cross-image consistency, and commercial-grade illustration. 16 languages. Free & open...
How to grep video
Notes on context engineering and agent harnesses for video libraries: designing the structured representations to make media legible to LLMs.
Since we started joining meetings from our computers, video has become the default way that organizations capture what happens at work. We’re at the point now where recording things
Tolaria — A second brain for the AI era
Organize your notes as Markdown files. With native relationships, Git, and Claude Code integration. Free forever.
Multi-Agents: What's Actually Working
Atomic — Everything You Know, Connected
A personal knowledge base that turns freeform notes into a semantically-connected, AI-augmented knowledge graph.
GPT Images 2.0 For Game Sprites? Here's how.
New blog: Building agents that reach production systems with MCP.
When should agents use direct APIs vs CLIs vs MCP? Plus patterns for building MCP servers, context-efficient clients and pairing MCP with skills.
https://t.co/JEogw5vWly
The Definitive Guide to Harness Engineering
Compound Engineering v3
A new way to think about composing skills to increase leverage: Skill Graphs 2.0
The Agent Governance Stack: Treat Your AI Agent Fleet Like Your Engineering Org
12 Agentic Harness Patterns from Claude Code
The leaked source revealed how a production coding agent is actually assembled. Here are the design patterns behind it.
How Anthropic’s Claude Thinks
In this article, we will look at what the Claude researchers found.
Knowledge Graphs for AI
Bridging the gap between organisational data, implicit organisational reasoning, and AI reasoning with Knowledge Graphs purpose-built for AI.
Structured Test-Time Scaling: From Multi-Agent Systems to General Inference Architectures
A unified theoretical framework for structured test-time scaling, showing how topology compression, scope isolation, and decoupled verification—a three-layer structural decoupling—bypass the linear collapse of long-horizon reasoning across multi-agent systems, recursive architectures, and coding agents.
What goes into the system prompt vs. what goes into an Agent Skill?
Agent’s system prompt is for identity, constraints, and persistent context.
• What the agent is and what it should always or never do
• How it should generally approach problems (reasoning style)
• Persistent
RLMs are the new reasoning models
The runtime behind production deep agents
Dive into Claude Code: The Design Space of Today's and Future...
Claude Code is an agentic coding tool that can run shell commands, edit files, and call external services on behalf of the user. This study describes its comprehensive architecture by analyzing...
Using Claude Code: session management and 1M context | Claude
Learn how to manage context in Claude Code—when to continue, rewind, compact, or clear a session, and how subagents keep parent context clean.
Using Claude Code: Session Management & 1M Context
Harness, Memory, Context Fragments, & the Bitter Lesson
this is a work in progress mental dump on interesting intersections between how we use and design a harness, implications for memory being accumulated over long timescales, and the search bitter lesson we can’t escape
this
Your harness, your memory
Latent Briefing: Efficient Memory Sharing for Multi-Agent Systems via KV Cache Compaction
The New Software: CLI, Skills & Vertical Models
Anthropic sees the moat. Do you?
You Don't Know LLM Training: Principles, Pipelines, and New Practices