undefined

points

[-]

This is not meant as an insult, but have you actually LLM/vibe coded anything that used a fast(-ish) moving library or framework? Try asking your favorite LLM with say Jan 2025 knowledge cutoff (or pretraining data cutoff, whatever you want to call it) to work on something using a framework that had a big rewrite later that year (which would make it one year old now, which is like ages in the LLM coding era)... It's a nightmare full of wrestling with the LLM when you try to tell it the version of the framework and that it changed a lot from the previous version and yadda yadda long story short down the thread when context runs out and/or is compressed it begins to forget detailed instructions and just falls back to pulling out old patterns it "remembers" from pretraining. And so you need to constantly remind it what you work with and "oh hey this doesnt work because we're working with react router v7 in framework mode, remember? not react router v6". Or try to use the latest non-lts/breaking version of a library, at first it looks it up online, but again as you get deeper into the weeds and little details, the struggle begins.

So, as far as I'm concerned, training cutoff is still a big deal.

by dinfinity4 hours ago|

parent|

[-]

> It's a nightmare full of wrestling with the LLM when you try to tell it the version of the framework and that it changed a lot from the previous version and yadda yadda

Tip: Add a default instruction to look at the actial downloaded source code of the dependencies used (assuming you're not dealing with closed source dependencies). Have the agent treat it as your own (readonly) source code instead of relying on model training data and possibly mismatching documentation on the web. Then it just greps for the exact function signatures and reads the file based documentation.

by reconnecting9 hours ago|

prev|

[-]

Until they prefer not to search. Let me explain using the example of the open-source security framework (1) our team is working on.

If you ask Gemini what you should use to integrate fraud prevention or account takeover protection into your product, there will be no mention of our open-source project. Five years in development, 1.3k stars, over 140 pull requests — all this isn't enough to make it into the training data. From this perspective, any technology that emerges after 2024 is simply invisible to LLMs.

The answer is: without being in the training data, LLMs basically don't understand what they're searching for.

1. https://github.com/tirrenotechnologies/tirreno

by ordersofmag7 hours ago|

parent|

[-]

I just put the terribly generic query "what tools would you recommend to integrate fraud prevention or account takeover protection into my product" into both Claude (Sonnet) and Gemini (3.1 Pro) via the standard web interface and both took the first step of searching the web. That's consistent with my past experience -- the usual harnesses typically will search the web in cases where I might expect/want them to. Now whether you product has good web visibility or not in those searches and how the LLM's weigh the relative merits of open-source tools versus commercial offerings in deciding what to highlight in their responses is a different issue. As is the change in what constitutes effective SEO in an era where bots, rather then human eyes are the proximal important target. But I don't think the core issue with folks finding your products is the move away from user-driven search toward using models with out-of-date training cutoffs.

FWIW while neither model included your product in it's initial response, when I followed up with "what about open-source" both did another search and Claude's response included your tool....