204 tested guides on AI agents, n8n, RAG, and automation — newest first.
OCR-then-parse converts a page to text outside the model, so a language model only ever sees what the OCR engine decided to keep. A vision-language model reads the page itself, pixels and text together, which is why it can answer a question about a specific box on the page while
Constrained decoding is the mechanism behind OpenAI's Structured Outputs and Anthropic's strict tool use: it compiles your JSON Schema into a grammar and masks invalid tokens before they can ever be sampled, so shape failures stop happening. What it cannot do is check that the
Apple and Google both ship a language model built into the phone itself, callable through a platform SDK at zero marginal cost per call, fully offline, with data that never leaves the device. Here is what Apple's Foundation Models framework and Google's Gemini Nano actually do,
Model distillation trains a brand-new small model to imitate a larger teacher model's outputs on one narrow task, so production runs on the cheap model instead of paying frontier prices for every call. Here is the actual mechanism, what OpenAI's Model Distillation feature and
An LLM gateway is a proxy layer that gives your app one stable endpoint across several model providers, then decides which one answers each request. Most of what ships as "smart routing" is really 2 jobs, automatic fallback and cost-based rules, not model choice driven by
Installing a community MCP server means running someone else's code with your credentials, and nothing resembling an app-store review checks it first. Here is what the research and the official spec actually document about tool poisoning, supply-chain risk, confused-deputy
When an agent calls a tool on a user's behalf, most systems today can only see the agent, not the person. Here is what RFC 8693 token exchange and the newly shipped ID-JAG pattern actually do about that, and why a single shared credential stops working the moment more than one
A long-running agent that crashes at hour 6 either starts over from zero or resumes from its last checkpoint, depending on whether you built it with durable execution. Here is what checkpointing and replay actually do, how Temporal, Inngest, DBOS, and Restate differ, and what it
Realtime voice AI agents split into 2 real architectures: a cascaded pipeline of 3 chained models, or a single native speech-to-speech model. This piece grounds the tradeoff in OpenAI, Google, and Amazon's own documented specifics, not vendor marketing.
Graph retrieval builds an entity-and-relationship graph out of your documents and walks it at query time instead of ranking chunks by similarity. On a published benchmark it beat reranked vector search by 10.5 points on complex reasoning and 13.1 on contextual summarization, and
An AI agent with real tool access is only as safe as its weakest credential and its execution boundary. This post covers the containment half of agent security: scoping every tool to its own narrow credential instead of one shared key, and running anything that executes code
Browser agents and computer-use agents both drive real interfaces, but they read the page in completely different ways: one asks the browser for a structured list of elements, the other looks at a screenshot and guesses. That difference is why a DOM-aware browser agent is
