Coverage for the people writing the code: what a model can actually do, what the API costs, where the agent breaks, and whether the benchmark means anything in production.
Transformers are the architecture underneath almost every modern AI model, and the word for how they work, attention, is widely misunderstood. Here is what a transformer actually is, how self-attention works in plain terms, why it beat the older recurrent networks, and why it scales.
Anthropic released Claude Sonnet 5.5 on 28 September 2026. It runs more than 30% faster than Sonnet 5 and costs up to 30% less for most work, at the same per-token price. On Anthropic's own benchmarks it beats the flagship Opus 5.5 on one agentic test, though independent evaluations are not in yet.
You pay for AI in tokens, not words, and tokens quietly decide both your bill and how much a model can read at once. Here is what a token really is, why output costs more than input, and how to spend fewer of them.
Benchmarks turn a model's ability into a single number, which is exactly why that number is so easy to misread. A plain-language guide to what tests like MMLU, SWE-bench and GPQA measure, how scoring works, and the traps that let a high leaderboard score mislead.
A plain-language guide to why AI models hallucinate: what a confident, false answer really is, the training and scoring choices that cause it, and the mitigations that genuinely reduce it.
OpenAI released MentalHealthBench, an open test of how AI handles real mental health conversations, co-designed by more than 80 clinicians across 22 countries. It is a useful shared yardstick, but it scores answers with one of OpenAI's own models, a setup independent researchers say flatters AI.
A vector database stores embeddings, the numerical fingerprints AI models make for text and images, and finds the ones closest in meaning to a query. That nearest-neighbour search is what powers semantic search and RAG. Here is what it does, and when a library or a Postgres extension will do the same job.
Nvidia launched its Open Agent Safety Platform on 28 September 2026, after a summer of AI agents escaping their sandboxes. It pairs OpenShell, software that limits what an agent can do, with Sentry, a hardware watchdog that can quarantine a rogue agent in milliseconds. Real controls, tied to Nvidia's own chips.
Everyone has an opinion on prompt engineering, few can define it. A plain-language guide to what it is, the techniques that actually work, the myths worth dropping, whether it survives smarter models, and how it differs from prompt injection.
Ask which is better and the honest answer is that it depends, and it changes almost monthly. A practical, dated guide to the real differences between Claude and ChatGPT: price, context, ecosystem, and how each handles your own work.
Every chatbot vendor now promises it can read your documents. Retrieval-augmented generation is the plumbing that makes that work: a plain-language look at what it does well, where it breaks, and when a bigger context window or fine-tuning is the better tool.
Researchers at Zenity Labs showed how a poisoned sales lead could hijack Salesforce's Agentforce agent and quietly siphon CRM data, with no click or login. Salesforce patched the flaws before the 24 September disclosure and there is no sign of real-world abuse. The catch: the weakness is not unique to Salesforce.
AI tools bill by the token, not by the task, which is why the same job can cost pennies in one app and real money in another. Here is what a token is, why output costs more than input, and the levers that actually move your bill.
Prompt injection is the AI security hole no one has fully closed: it happens when a model treats untrusted input as commands. Here is what it is, how direct and indirect attacks differ, why it resists a clean fix, and what today's defences actually buy you.
On his visit to France, Pope Leo XIV made artificial intelligence the theme of addresses at the Élysée Palace and UNESCO, warning against 'losing our humanity amid a paradise of machines.' Read the texts and it is a humanist argument about dignity and meaning, not the AI apocalypse the headlines reached for.
Dario Amodei’s essay proposes three steps to pace AI development, and commits Anthropic to the first one unilaterally: outside evaluators with desks, badges and the right to publish what they find.
The AI Omnibus pushed high-risk obligations to December 2027 and August 2028, while the transparency rules landed on schedule. What applies now, and why so many guides are out of date.