omp's runLoopBody uses a double while loop: the inner loop drives the core model call → tool execution rhythm; the outer loop, when the agent would stop, drains queued steering / follow-up / asides to decide whether to run another turn. This design solves delivery timing for 'user typing while model streams' and 'background tasks quietly queueing messages'.
The Cloudflare AI Stack series covers the infrastructure around AI apps: where models run, how gateway control works, how RAG is built, how agents keep running, how memory is governed, and how browser, sandbox, secrets, data, and observability fit into a product.
The Cloudflare Edge Platform series answers one product question: how do Workers, D1, KV, R2, Durable Objects, Queues, Workflows, Cache, Images, Email, Turnstile, Observability, Browser Run, and Containers help you run a website or app cheaply and reliably?
screenshot-to-code is not a one-shot screenshot-to-HTML tool. Its core is a 30-step Agent Loop with 7 tools — extracting real assets from screenshots, self-verifying with Playwright, and running 4 models in parallel so users pick the best output. 74,500+ GitHub stars, MIT License.
Nearly every frontier open-source model in 2026 is MoE: Ornith 35B activates only 3B to beat 31B dense models, MiniMax M3 uses 456B total but 45.9B active to hit SWE-bench Pro 59%, DeepSeek V4 runs 1.6T total with 49B active. This post explains why MoE dominates coding and agentic benchmarks using four case studies.
The core of the Transformer is self-attention: for each token, the model computes how relevant every other token is, then takes a weighted sum. This lets the model reach across distance to figure out that 'it' refers to 'cat' not 'mat' — and is the foundation for how it handles long documents.
Agent Platform turns AI agents from a blank chat window into a structured workflow platform whose behavior can be defined, versioned, observed, verified, and improved. Its built-in Deep Research seed flow demonstrates the complete feedback loop.
Five analysts fan out in parallel within one superstep, so latency is max rather than sum; backtesting and reflection stand before synthesis, restricting the LLM to explaining evidence that already exists.
Lecture 3 does not turn its survey of modern LLMs into a single best recipe. It finds a conservative consensus—pre-norm, RMSNorm, no biases, SwiGLU, and RoPE—plus a small set of deviations justified by inference cost or stability.
Technical PM interviews don't require you to write production code, but you need to be able to read trade-offs. Core skills: API design fundamentals (RESTful, versioning, error handling), high-level system architecture understanding (microservices, database selection, caching), collaboration patterns with engineers (RFC process, technical spec review), and making product decisions under technical constraints.
CCAR-P is the most expensive and most senior of Anthropic's four exams ($175, 63 items, 120 minutes). Integration is the heaviest domain at 19%, but what really separates it from everything else in this series is the other two: Governance, Safety & Risk Management at 14% and Stakeholder Communication & Lifecycle Management at 14% — 28% combined on compliance, risk, discovery interviews, and delivery lifecycle rather than code. The guide names GDPR, HIPAA, and FedRAMP, and its Intended Audience explicitly excludes entry-level developers and anyone doing 'prompt writing without broader system design responsibility.'
AB-100 is the architect tier of Microsoft's agent line, weighted 25-30 / 25-30 / 40-45 with deployment and governance heaviest. Its outline runs on verbs like design, recommend, and propose — it tests judgment, not configuration. Three things to know first: the scope blurb on the official exam page is wrong (it is information-protection and DLP boilerplate, which I verified verbatim), so prepare from the study guide instead; it has a free practice assessment, the only one of Microsoft's three agent credentials that does; and the 15 associate certifications it lists are described as usable, not required. Official specs: $165, English only, pass at 700, one-year validity.
Most teams don't need five databases. PostgreSQL's extension ecosystem covers caching, queues, full-text search, and vector search — but the real decision isn't 'can it do it' but 'where does ops cost cross performance needs.'
"OpenClaw is a Gateway shell around Pi" is obsolete. The docs now say the built-in runtime id is openclaw, that pi is a legacy alias which normalizes to it, and that no external agent framework packages remain. The only Pi-related third-party dependency left is a terminal component toolkit.
AI Agent is not a single technology -- it is an entire architecture system. This article is a systematic navigation: starting from the Agent Three Pillars (Context/Cognition/Action), through the three-stage evolution of AI engineering (Prompt -> Context -> Harness), to eight Multi-Agent design patterns and production-grade Harness infrastructure. Each topic links to a dedicated deep-dive article.
RAG doesn't have to be a rigid three-step process. It's a set of steps that can be dynamically enabled, skipped, or reordered. Pipeline as Code lets the system adapt its behavior without redeployment.
Naive RAG works but has real problems. Advanced RAG patches those problems. Modular RAG rearchitects the whole system to be composable and configurable. Understanding all three generations is the key to understanding why modern RAG systems look the way they do.
RAG and Fine-tuning solve different problems. RAG gives the model new knowledge; Fine-tuning changes the model's behavior and style. In most cases you use both, not pick one.