Ten weekend-scale AI builds for people who already write code and want to attach a model to something, rather than learn ML from scratch.
Fifteen BTech AI projects plus a heavier advanced set, each naming the stack it needs and what it shows someone reading your CV.
Dissertation-scale AI builds, each scoped to be defensible: why that architecture, what the baseline was, and how the numbers were checked.
Starter AI builds grouped by the skill each one teaches, from chatbots and prediction models through to computer vision projects.
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Everything else on the blog, grouped by the thing it helps you get done.
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9 postsLong enough to be actually useful
Practical writing about AI tools — what they do, where they fall over and whether the paid tier is worth it.
Written to still be useful next year
These are explainers and walk-throughs, not news. Where a post depends on a price, a limit or a version number, it says so in the text rather than leaving you to find out it has moved.
Every post has a name on it
The byline is a link to that writer’s other posts and to what they actually do. Nothing here is published anonymously.
Reading times are counted
Counted from the body of the post at 220 words a minute — the same rate the news desk uses. A post with no body to count shows no estimate rather than a guessed one.
“Featured” is a curation, not a ranking
It is the weight an editor set on a post. The sort menu says so, because a reader has no way to know that from the label alone.
Filters that admit when they are empty
Every count is measured with your other filters already applied, so the number beside an option is what you will actually get. Options that cannot narrow anything are not offered.
Updates are rewrites, not touch-ups
When something changes enough to matter, the post is rewritten and re-dated. A post that has only had a typo fixed keeps its original date.
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