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RHRhea Vandermeer3d ago
I put off making a product video for about six months because every previous attempt turned into an afternoon in an editor. Recorded the screen capture, dropped it in, picked a template and had something I was not embarrassed by in under an hour. The part that actually saved me was the automatic captions — most people watch these on mute and I would not have bothered otherwise. My one complaint is that the template choice matters more than it looks like it does; the first one I picked made everything feel like an advert.
Typeframes
TOTobias Lindqvist9d ago
Set this up for a small open source project after people kept asking where they could send something. Took about ten minutes and the first support came through the same week. What works is that supporters do not need an account — that single thing removes most of the drop-off I saw with the alternative I tried first. Payouts have been on schedule. Marking it four rather than five because the analytics are thin; I would like to know where people are arriving from and I cannot tell.
Buy Me a Coffee
MAMarguerite Osei15d ago
Been using this for about four months for content planning. It is genuinely good at surfacing things I would not have thought to look for, and a couple of those became our best-performing posts of the quarter. Worth being realistic about what the numbers are though. The growth figures are directional rather than exact, and I have seen a few topics flagged as rising that were really one large news cycle. I treat it as a list of things to go investigate rather than a list of things to go write, and on that basis it earns its cost.
Exploding Topics
DEDevashish Raman22d ago
I have used this on every backend project for years and it is still the first thing I install. Collections shared across the team save a genuine amount of onboarding time, and the scripting is powerful enough that we run a decent chunk of our integration checks through it. The honest criticism is that it has grown a lot. What was a fast request client now takes a while to start and pushes cloud features I do not want. It is still the tool I reach for; I just notice the weight now in a way I did not three years ago.
Postman
PRPriyanka Balachandran1mo ago
Moved a marketing site and two documentation sites over last year. The thing I did not expect to care about was deploy previews on every pull request — being able to send a designer a working link instead of a screenshot changed how our review process works. Rollbacks are one click and have saved me twice. Build minutes are the thing to watch on the pricing side; our docs site rebuilds often and we hit the limit faster than I planned for, so check that before committing if you deploy frequently.
Netlify

AI Search

Semantic search over your own documents and data.

Nothing launched in this category yet.

About AI Search

Search over your own material is the most quietly useful application of language models, and it is also the one where the difference between a good and a bad implementation is most visible. The promise is simple: ask a question in your own words and get an answer from your documents, your notes, your codebase or your company's accumulated writing. The failure mode is equally simple — a confident answer that is not in any of those places.

The single most important feature in this subcategory is citation. A tool that shows you which document and which paragraph an answer came from is one you can use for work that matters, because you can verify in five seconds. A tool that returns a fluent paragraph with no provenance is asking you to trust it on questions you asked precisely because you did not know the answer. Given two otherwise similar tools, take the one that cites.

The second thing that separates implementations is what happens when the answer is not there. Good systems say so. Weak ones assemble something plausible from adjacent material, which is worse than a null result because it is harder to detect. Test this deliberately: ask something you know is absent and see what comes back.

Beyond that, the practical differences are about ingestion and freshness. What formats does it read — PDFs with tables, scanned images, spreadsheets, code, email? How does it handle a document that changed yesterday? Systems that reindex continuously stay correct; ones that index on upload quietly drift out of date, and the answers get worse in a way nobody notices for weeks.

Where the index lives is the privacy question. Some tools embed and store your documents on their servers, some keep the index local and only send the query, and some run the whole pipeline on your machine. All three are reasonable choices for different material; what is not reasonable is not knowing which one you picked. For local options see [local models](/categories/local-models).

If you are evaluating one of these for a team rather than yourself, permissions are the thing that will bite you. A search system that ingests everything and answers everyone will happily surface the salary spreadsheet to whoever asks the right question. Tools that respect the source system's access controls at query time are doing meaningfully harder engineering, and it is worth paying for.