Tag · 10 posts
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.
A malicious LiteLLM 1.107.0 was published to PyPI and Docker on October 21, 2026 and stole credentials from every machine that installed it. How the attack worked, how to check if you're affected, what to rotate, and the habits that stop the next one.
Chatbots answer, automations follow fixed rules, and AI agents decide and act. A clear explanation with everyday examples, a side-by-side table, how they combine, costs and risks, and a simple way to choose the right one for a task.
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.
The demo worked, then real users arrived. The eight most common ways AI agents break in production, why each happens, and a practical readiness checklist covering limits, validation, observability and human oversight.
How AI agents remember things between steps and sessions: the four kinds of memory, how to write and retrieve memories, the summarise-vs-retrieve trade-off, stale memories, privacy, and a simple design you can build this week.
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.
How to estimate what an AI feature will cost before you build it: token maths with worked examples, the hidden costs teams miss, caching and model routing, and a clear framework for when self-hosting an open model beats paying per token.
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