Guides

AI research lands on arXiv first, reaches a conference like NeurIPS or ICLR months later, and a journal last. What each venue certifies, and what to check before you repeat a number from one.

Every serious AI course publishes a prerequisites page, and read together they settle the ordering argument: application layer first, mechanism second, internals only if the job asks. Plus four things worth skipping.

A Census survey of 15 business functions says 18% of US firms use AI, and 52% of adopters use it in sales and marketing. Function by function, here is what actually got measured: a 16.3% sales lift in one chatbot experiment, a 14% support gain concentrated among novices, a software trial that broke, and 2,044 court cases where it went wrong.

Ramp’s July 2026 spend data puts the median firm at $11.95 per employee a month and the top 1% at $7,400. We priced both halves of that bill from vendor pages: seat licences, token rates, and the surcharges that never reach the headline number.

Ten AI podcasts measured against their own RSS feeds: how often each one publishes, how long the episodes actually run, and which job each show is built to do.

Short, checkable, reversible steps clear the bar and long judgement-heavy ones do not. What the agent benchmarks, the vendor pricing pages and one annual report say about which workflows AI automation actually survives.

Two different skills go by that name and employers pay for both. One has free courses you can finish in a few months. The other has almost no curriculum, and a randomised trial says people badly overrate how good they already are at it.

arXiv’s cs.AI listing carried 4,730 entries in July 2026 against 514 in July 2020. Here is the small stack of sources that survives that volume, and what the Reuters Institute measured about using AI itself to get the news.

You can run a complete AI stack on hardware you control. Whether you should depends on what an hour of maintenance is worth to you.

The main thing between you and a working local setup is picking a model that fits your memory. Get that wrong and you will conclude local models are useless.

Fine-tuning teaches behaviour. It does not teach facts reliably, and confusing those two is where the money goes.

Nothing is composed. The image emerges everywhere at once, which is exactly why counting and text placement go wrong.