Channel
Digests from AI Engineer
Learn how Context Engines solve AI agent limitations by ingesting data from Slack, Notion, and GitHub history. Discover how to build truly intelligent, context-aware development tools.
Read article
Multi-GPU AI workloads are bottlenecked by communication links. The speaker introduces ParallelKittens, primitives that simplify high-performance kernel writing. A benchmark tests if LLMs can generate optimized CUDA kernels for complex multi-GPU patterns.
Read article
Mike Krieger discusses how to be 'unreasonable' with AI, detailing workflows like porting Python to TypeScript over a weekend using LLMs. He also shares lessons on scaling infrastructure and structuring research labs.
Read article
Agentic Commerce requires moving beyond brittle browser automation. The solution involves standardized protocols like MCP, UCP, and AP2 to enable direct API calls between customer and merchant agents.
Read article
Learn how KV Cache-Aware Routing and P/D Disaggregation optimize LLM inference for agentic workloads. Discover strategies to reduce P99 latency from 900ms to 100ms using advanced Kubernetes patterns.
Read article
DevRel is shifting its focus from human developers to AI agents as users and recommenders. Success requires Generative Engine Optimization (GEO), treating the agent's experience as a measurable user journey.
Read article
Justin Joyce outlines a three-pillar AI framework to scale GTM teams. By using agentic workspaces (Cloudflare OS) and skill files, non-technical users can query data and build reports automatically.
Read article
Go-to-market must be treated as an AI engineering problem. The core thesis is that GTM is fundamentally a data problem, requiring the creation of a 'live model of your world' that autonomous agents can act upon.
Read article
Optimizing open source libraries for LLMs requires shifting focus from human SEO to agent primitives. Key strategies include manually curating LLMs.txt and bundling markdown into node_modules.
Read article
Scaling an internal GTM AI assistant required prioritizing quality over coverage. The journey moved from simple agent instructions to complex architectures using Semantic Views, Skills, and MCPs.
Read article
A 200 status code does not mean the page is real. The gap was not model quality. It was the layer underneath that lets an agent work on the open web.
Read article
James Zou discusses designing environments, like the Einstein Arena and DSGym, for AI agents. These platforms enable open scientific collaboration and advanced benchmarking by moving beyond restrictive workflows.
Read article
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.
Read article
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.
Read article
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.
Read article
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.
Read article
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.
Read article
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.
Read article
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.
Read article
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.
Read article