Category · 6 posts
Models, retrieval, evals and the practical side of building with LLMs.
An honest comparison of ChatGPT, Claude and Gemini: what each does best, free vs paid plans, writing, coding, research, images and voice, privacy settings, and a simple way to pick the right assistant for your work.
Which AI model is best in late October 2026? We compare the top models from Anthropic, Google, OpenAI, Meta, DeepSeek and others across four trusted sources: LMArena, the Artificial Analysis Intelligence Index, SWE-bench Verified and real usage on OpenRouter, plus prices and how to choose.
MCP is the open standard that lets AI apps connect to tools and data. A beginner-friendly explanation of hosts, clients and servers, tools, resources and prompts, how it compares with APIs and function calling, a 20-line server example and the security risks to know.
Why the biggest lever in LLM apps is no longer the wording of your prompt but what the model sees. A practical guide to selecting, ordering, compressing and budgeting context, with patterns you can use today.
Stop shipping on vibes. How to build a golden dataset, combine code checks, LLM-as-judge and human review, measure retrieval quality, catch regressions on every change, and keep evals cheap enough to run all the time.
Prompt injection is the top security risk for LLM apps. How direct and indirect injection work, why the 'lethal trifecta' turns them into data theft, real attack patterns, and the layered defenses that actually reduce risk.
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