What I like about this approach is that it quietly reframes the problem from “detect AI” to “make abusive access patterns uneconomical”. A simple JS+cookie gate is basically saying: if you want to hammer my instance, you now have to spin up a headless browser and execute JS at scale. That’s cheap for humans, expensive for generic crawlers that are tuned for raw HTTP throughput.

The deeper issue is that git forges are pathological for naive crawlers: every commit/file combo is a unique URL, so one medium repo explodes into Wikipedia-scale surface area if you just follow links blindly. A more robust pattern for small instances is to explicitly rate limit the expensive paths (/raw, per-commit views, “download as zip”), and treat “AI” as an implementation detail. Good bots that behave like polite users will still work; the ones that try to BFS your entire history at line rate hit a wall long before they can take your box down.

Yeah, this is where I landed a while ago. What problem am I _really_ trying to solve?

For some people it's an ideological one--we don't want AI vacuuming up all of our content. For those, "is this an AI user?" is a useful question to answer. However it's a hard one.

For many the problem is simply "there are a class of users that are putting way too much load on the system and it's causing problems". Initially I was playing wack-a-mole with this and dealing with alerts firing on a regular basis because of Meta crawling our site very aggressively, not backing off when errors were returned, etc.

I looked at rate limiting but the work involved in distributed rate limiting versus the number of offenders involved made the effort look a little silly, so I moved towards a "nuke it from orbit" strategy:

Requests are bucketed by class C subnet (31.13.80.36 -> 31.13.80.x) and request rate is tracked over 30 minute windows. If the request rate over that window exceeds a very generous threshold I've only seen a few very obvious and poorly behaved crawlers exceed it fires an alert.

The alert kicks off a flow where we look up the ASN covering every IP in that range, look up every range associated with those ASNs, and throw an alert in Slack with a big red "Block" button attached. When approved, the entire ASN is blocked at the edge.

It's never triggered on anything we weren't willing to block (e.g., a local consumer ISP). We've dropped a handful of foreign providers, some "budget" VPS providers, some more reputable cloud providers, and Facebook. It didn't take long before the alerts stopped--both for high request rates and our application monitoring seeing excessive loads.

If anyone's interested in trying to implement something similar, there's a regularly updated database of ASN <-> IP ranges announced here: https://github.com/ipverse/asn-ip

It depends what your goal is.

Having to use a browser to crawl your site will slow down naive crawlers at scale.

But it wouldn't do much against individuals typing "what is a kumquat" into their local LLM tool that issues 20 requests to answer the question. They're not really going to care nor notice if the tool had to use a playwright instance instead of curl.

Yet it's that use-case that is responsible for ~all of my AI bot traffic according to Cloudflare which is 30x the traffic of direct human users. In my case, being a forum, it made more sense to just block the traffic.

I'm curious about whether there are well coded AI scrapers that have logic for "aha, this is a git forge, git clone it instead of scraping, and git fetch on a rescrape". Why are there apparently so many naive (but still coded to be massively parallel and botnet like, which is not naive in that aspect) crawlers out there?