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LLM Systems
Practical thinking on retrieval, generation, evaluation, prompting, deployment, and reliability.
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The Actuarial Approach to AI Evals: Wilson Confidence Intervals and Statistical Sample Sizing
Why running 20 test cases gives you virtually zero statistical confidence in your agent's reliability, and how to use Wilson score intervals to measure real performance.
BlogHow to Evaluate AI Agents for Production: Moving Beyond LLM-as-a-Judge
Why LLM-as-a-judge fails to protect production agents, the mathematics of compounding multi-turn error, and how to build a state-based evaluation framework that engineering teams can inspect.
BlogEvaluating Agentic Tool Calls and Environment Deltas: The Deterministic Testing Layer
How to test multi-turn AI agents by intercepting tool schemas, snapshotting environment state, and catching parameter hallucination before it touches production infrastructure.
BlogMachine-Speed Attacks Break Human-Speed Defenses: Why Zero Trust Must Be Architectural
CyberStrikeAI used 44 autonomous AI agents to industrialize exploitation across 19 organizations in under 24 hours. When threat actors automate the full kill chain, manual SOC triage collapses. Dr. Chase Cunningham warned us years ago: trust is a vulnerability, and defense must be deterministic.
BlogDevRel for AI Agents: Building Developer Trust When Code Generates Code
How developer relations changes when your product is an AI agent, multi-agent orchestration framework, or agent-native developer tool.
BlogHow to Evaluate an AI Agent (Before It Ships)
Agent evaluation is not LLM evaluation. A practical three-layer framework — component, trajectory, outcome — for evaluating AI agents before they reach production, plus a maturity model to figure out where your team is and what to build next.
BlogAI Agents for Commercial Real Estate: From Deal Flow to Due Diligence
How autonomous AI agents — not chatbots — can compress the slowest parts of a commercial real estate practice: prospecting, underwriting, due diligence, zoning research, and reporting.
BlogFrom Chat Window to Workflow: A Non-Technical Guide to AI Model APIs
A plain-language guide to AI model APIs for people who use chat windows but want to automate. Covers requests, responses, parameters, costs, and where to get hands-on practice.
BlogRAG Is a Product Pattern, Not a Magic Trick
Why retrieval-augmented generation only becomes trustworthy when content quality, retrieval, ranking, UX, evaluation, and feedback are treated as product work.
BlogHow to Know If Generative AI Fits Your Problem
A practical decision tree for choosing between prompting, RAG, fine-tuning, vision-language models, traditional ML, rules, or no AI.
BlogThe Hidden Work Behind Just Add AI
A reality check on the operational work behind AI features: data, evaluation, reliability, UX, safety, compliance, monitoring, and trust.
BlogThe Generative AI Stack, Explained Without Vendor Fog
A plain-English map of the generative AI stack: models, data, retrieval, orchestration, evaluation, deployment, UX, and security.
BlogFrom Prompt to Product
A practical lifecycle for turning a prompt experiment into a reliable AI workflow with evaluation, feedback, production constraints, and launch proof.
BlogFrom Model Selection to First Run
A practical workflow for choosing an initial AI model, running a baseline, inspecting outputs, and deciding what to test next.
BlogThe FRAME Method for Picking AI Projects
A practical framework for deciding which generative AI ideas are worth building before teams waste time on demos that never become products.
BlogWhy Evaluation Is the New Prompt Engineering
Prompting helps you ask better questions, but evaluation is the production discipline that makes AI systems reliable, comparable, and trustworthy.
BlogEnglish Is the New Interface
Natural language is becoming a primary way people operate software, but AI interfaces still need state, constraints, corrections, and product judgment.
BlogEAGLE-3 Speculative Decoding in vLLM for Qwen3-30B-A3B-Instruct-2507
A current guide to EAGLE-3 speculative decoding with vLLM, using Qwen3-30B-A3B-Instruct-2507, a matching Red Hat AI speculator, and a tokenizer sanity check that works in Transformers 5.12.1.
BlogLet AutoScheme Pick the GGUF: Mixed Quantization with AutoRound and Qwen3.6
A clean, current tutorial for exporting mixed-quantized GGUF files from Qwen3.6 with AutoRound, AutoScheme, llama.cpp, and Transformers 5.12.1.
BlogFrom Prompts to Practice: Instruction Tuning Qwen3 with Transformers 5
A modern walkthrough for instruction tuning a small Qwen3 instruct model with Hugging Face Transformers, TRL, PEFT, QLoRA, and chat templates.
BlogCompress Qwen3.5 with AutoRound in Transformers 5.12.1
A current, practical guide to quantizing Qwen3.5 with AutoRound, using the modern Transformers 5.12.1 stack and a clean W4A16 workflow that scales from 0.8B to larger checkpoints.
BlogCoDeC Contamination Detection in Transformers 5.12.1 with Qwen3, Qwen2.5, and Gemma 3
A cleaned-up CoDeC walkthrough with a current Transformers 5.12.1 implementation, model-loading notes for Qwen3, Qwen2.5, and Gemma 3, and a practical scoring script.