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Introduction Most “run this model locally” tutorials stop the moment the model produces any output at all. They download a file, start a server, send one test prompt, and call it done. They rarely cover what happens when that output is technically present but practically useless, because the model spent its entire response budget thinking instead of answering. In this tutorial we build a local writing assistant on top of Bonsai, PrismML’s 1-bit quantized language model, serve

Introduction Most “build an AI agent” tutorials show the happy path: write a skill, register it, call it, done. What they skip is the part where the agent confidently does the wrong thing anyway, in a different way every single time you try again, and you have to figure out why. This tutorial is the version that doesn’t skip that part. We build a customer feedback analyzer using OpenClaw, an orchestration layer that dispatches commands to registered skills, paired with OpenAI

Introduction Most LLM evaluation tutorials check whether a generated answer “sounds right” by asking another LLM to grade it. That works for tone and style, but it falls apart for tasks with an objectively correct answer. SQL generation is exactly that kind of task: a query either returns the right rows or it does not, and no amount of LLM-rubric grading can substitute for actually running the query. In this tutorial we build a promptfoo evaluation for a natural language to S

Business teams sit on huge tables of orders, sales, and profit, yet answering a simple question like “which market is most profitable?” usually means waiting on an analyst or building another pivot table. The gap between having the data and understanding it is where decisions slow down. Chat With Your Data closes that gap. It is a conversational analytics dashboard that pairs interactive charts with an AI assistant, so anyone can explore the numbers by clicking or simply by a

Sales reps lose deals in the moments they can't see themselves — talking over the prospect, missing a buying signal, going silent when objections hit. A coach whispering in their ear would fix most of that. Most companies can't afford one per rep. An AI agent listening to the live call can be that coach. Here's how to build one using the OpenAI Realtime API for low-latency audio understanding and LangGraph to orchestrate the coaching logic. Why This Stack OpenAI Realtime API

Use Case We Solve: Private Multimodal Search System for Book & Document Libraries The Problem Collectors, archives, law firms, and publishers often hold large scanned libraries (page-by-page JPGs/PDFs) and need to search them like Google — by keyword, concept, or even a photo — without any of that content touching the public web or a third-party index. Typical Requirements We Address Keyword and exact-text search across scanned pages Semantic search for similar quotes / relat

For most of the last decade, the most prestigious job in tech sat inside the research lab. The engineers building the model — the ones pushing benchmark scores, designing new architectures, training larger systems — held the highest status and, often, the highest pay. That has quietly flipped. In 2026, the fastest-growing, highest-leverage role in AI isn't the one building the model. It's the one getting the model to actually work inside a real company, with real data, real l

What Is Loop Engineering? Loop engineering is the discipline of building small automated control systems — loops — that drive AI coding agents on your behalf, instead of you prompting the agent manually, turn by turn. For roughly two years, working with a coding agent meant: write a prompt, read the output, write the next prompt, repeat. You held the steering wheel the entire time. Loop engineering replaces that. You design a system once — a loop with a defined goal, a way to

What Is Loop Engineering? Loop engineering is the practice of building a small automated system — a loop — that drives an AI coding agent for you, instead of you manually prompting it turn by turn. The old way: You write a prompt → Agent responds → You read it → You write the next prompt → repeat The loop engineering way: You write a loop → Loop prompts the agent → Loop reads the output → Loop decides "done or not done?" → Loop re-prompts automatically → repeat until done You

Introduction Content moderation at scale is one of the most operationally demanding problems in AI applications. Rule-based filters miss context and produce too many false positives. Fully manual review does not scale. A large language model can read text the way a human moderator would, understanding tone, context, and intent, and produce structured output that downstream systems can act on automatically. In this tutorial we build a FastAPI content moderation API that passes