AI Model vs Agentic Harness: What Actually Drives AI
AI models alone aren’t what makes systems powerful. Martin Keen explains the difference between AI models and agentic harness components like tools, memory, and loops.
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I don't prompt agents anymore...
To build reliable AI agents, focus on Control Graphs—structured workflows (SOPs). Implement these using code frameworks like LangGraph or by defining SOPs in text for LLMs to follow. Always include verifiers and use scripts for deterministic steps.
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Lecture 113: Every Microsecond Matters: Achieving Near Speed-of-Light Latency in GPU Collectives
This talk details advanced low-latency collective communication algorithms for GPU clusters, crucial for accelerating LLM inference. By replacing expensive global memory barriers with techniques like Sentinel synchronization and atomic operations, significant speedups are achieved.
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Lecture 113: Every Microsecond Matters: Achieving Near Speed-of-Light Latency in GPU Collectives
Achieving near speed-of-light latency in GPU collectives is vital for modern AI workloads. The talk introduces low-latency techniques like Sentinel synchronization and the LL128 atomic algorithm to eliminate expensive global memory barriers.
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How to trace your vibe-coded agent with W&B Weave
Learn how to add full observability tracing to an AI agent using W&B Weave. This process uses the W&B MCP server and SDK, allowing deep inspection of conversations, turns, LLM calls, and tool usage without modifying existing code.
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AI News & Strategy Daily | Nate B Jones
Stripe Paid $7.5 Billion For OpenRouter. You Are Living In The Age Of Startups.
Stripe paid a reported $7.5 billion for OpenRouter, signaling that intelligence consumption is becoming a core economic flow. The rapid growth of token volume and the rise of agents require new infrastructure for agent-to-agent commerce.
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Anthropic, OpenAI & Thoughtworks on Context Engineering
Context engineering is now the main lever on what a coding agent can actually do. The three remaining constraints are human time, attention, and the context window.
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Intelligence EXPLOSION: Harness Engineering with Pi Agent, Deepseek, and Gemini
The most flexible system wins. To outperform individual LLMs, engineers must combine compute using multi-agent orchestration patterns: Opinion, Debate, and Collaborate.
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How AI Coding Agents Understand Your Codebase & Developer Tools
Good code must fit the system's architecture, not just run. AI coding agents need repo awareness, planning, and respect for established patterns to avoid creating 'fast chaos.'
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AI News & Strategy Daily | Nate B Jones
OpenAI Pays $280,000 For This Job. You Don't Have To Be An Engineer.
The Forward Deployed Engineer (FDE) role is critical for bridging the gap between AI capability and real-world enterprise deployment. Learn the three core skills: domain knowledge, finding leverage points, and owning the system's measurable impact.
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What Is a Digital Librarian AI Agent? Connecting SQL & Vector Database
A Digital Librarian AI Agent solves the 'what vs. why' data problem by connecting SQL databases (for hard numbers) and vector databases (for policy context). This agent uses multi-step workflows to deliver grounded answers, transforming siloed data into a reasoning engine.
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The Agent Behind the Curtain: Building the Oz Cloud Agent Platform — Safia Abdalla, Warp
Warp's cloud agent platform is designed to absorb complexity before it reaches the user. It uses multi-harness support and advanced API primitives to orchestrate sub-agents, automating tasks from issue triage to PR review.
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Inside 847 Production Clinical AI Notes — Sebastian Fox, Composo
Evaluating high-stakes AI requires moving beyond static rubrics. The solution is a continuous loop: Discover failure modes from real outputs, capture expert judgment, and calibrate every output against accumulated context.
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Agent Frameworks Considered Harmful — Rémi Louf, .txt
Building a robust agent runtime requires moving beyond traditional frameworks. By using typed events, content addressing, and an append-only log, developers can achieve unprecedented auditability and reliability in AI orchestration.
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Coding Agents Don't Scale Themselves. Neither Do Your Teams. — Patrick Debois, Tessl
Scaling automation requires shifting focus from fixing agent-produced code to improving the underlying system. Success depends on establishing centralized, reusable platforms and mandates for context authoring.
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Preferences Over Benchmarks: Model Routing — Archana Kamath & Tyler Gillam, DigitalOcean
Model routing argues that there is no single best LLM. By using a purpose-built Mixture of Experts model, developers can dynamically select the optimal AI model per request based on cost, latency, and task type.
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What If Your Chip Design Team Moved Like a Single Body? — Abduallah Mohamed, AIDAChip
In complex engineering domains like chip design, alignment is the biggest bottleneck. The solution requires building a shared nervous system—a living graph that tracks intent and constraints to prevent costly systemic failures.
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FinOps for AI Agents: Who Spent All the Tokens? — Tisha Chawla & Susheem Koul, Microsoft
TokenOps is an out-of-band control plane that manages AI agent costs by moving beyond simple hard caps. It uses a 'boundary annotation' to track attribution and employs 'steer' policies to guide agents toward cost efficiency.
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Give the Agent a Budget, Not a Token — Sachin Malhotra, Anthropic
Instead of giving an agent a token, give it a budget. A budget has four dimensions: how much, how fast, what can be undone, and who notices.
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How Multi-Vector Retrieval Works at Scale
Discover how Multi-Vector Retrieval solves the limitations of single vector embeddings for AI agents. Learn about sparse multi-vector encoding, scaling to billion documents, and achieving sub-50ms P99 latency.
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