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Short pieces on what the job actually is. Every one carries the date it was written, because a claim about a fast-moving field is only as good as its month.
2026-10-06
A new post, model or tool appears in this field every day, and trying to follow all of it is the fastest way to learn none of it. A handful of resources that genuinely help, and the stage of learning at which each one is worth reading.
Read2026-10-05
Writing code has never been this fast. But code produced quickly amplifies whatever structure sits underneath it. Why engineering matters more today, not less, and where to go deeper.
Read2026-10-02
In an ordinary web app every request costs roughly the same. In an app with a language model inside, it doesn't. Capping cost when you ship, using caching safely, watching the right things, and being ready when the provider retires a model.
Read2026-09-30
A chatbot demo impresses nobody anymore. Six signs that make an AI project credible, five project ideas that each show a different skill, and a README someone can read in two minutes.
Read2026-09-27
An application with a language model inside carries every security problem of an ordinary web app and adds new ones. Why prompt injection can't be fully prevented, how to limit the damage, protecting the endpoint, and why the model's output can't be trusted.
Read2026-09-23
You changed the prompt and the answers look better. Do they? Building a first test set, taking a baseline, seeing what the average hides, the limits of one model grading another, and watching the system once it's live.
Read2026-09-19
Training a model on your own data is an attractive idea. Most of the time it's the wrong tool. What fine-tuning teaches and what it doesn't, how it compares with prompting and RAG, data quality, leakage, LoRA, and licences on open models.
Read2026-09-14
Seven questions worth asking before you spend money and months on an AI engineering course. A short answer to each, and the concrete sign you can check in the syllabus before you buy.
Read2026-09-09
Handing a model a photo or a recording isn't the same as sending text. Request size, unreadable images, sentences invented in silence, generation that takes seconds, and an image paid for again on every turn.
Read2026-09-04
"Agent" is today's most requested and most easily misbuilt thing. The difference between a fixed workflow and an agent, why tool descriptions are critical, how to stop the loop, and how to protect irreversible actions.
Read2026-08-31
When a system that answers from your documents says something wrong, the model is the first suspect. Most of the time the culprit is the chunk it was given. Finding where the failure is, containing invention, citing sources and measuring properly.
Read2026-08-26
Most roadmaps in the search results describe a different job. What the AI engineers being hired today actually know, the order to learn it in, and what makes each stage "done".
Read2026-08-21
The first step people take after deciding to build semantic search is usually picking a vector database. Most of the time it's an early step. What an embedding is, what a similarity score doesn't tell you, and the real reasons to move to a database.
Read2026-08-17
A prompt that works in a chat window quietly breaks inside an application. Keeping prompts in the repository, separating user input from instructions, validating the answer against a schema, and shipping a change only after measuring it.
Read2026-08-12
Three errors you never saw in development arrive together in the first busy hour. Telling 429 from 5xx, retrying properly, setting timeouts, and keeping the product up when the provider goes down.
Read2026-08-07
The honest answers to the six questions that stop people before they start: maths, degrees, machine learning, Python, hardware and money. Short answers first, reasons after.
Read2026-08-07
Search "how to become an AI engineer" and the first page teaches linear algebra, CNNs and training loops. That is a different job. Here is what the work actually is, and what you can skip.
Read