Its more and more seeming less like “Fuvk AI” and more and more like “I am bad at using AI and Im gonna blame my skill issues on everyone else”

This entire post sits in pure “git gud” territory, wait-

git: unrecognized command "good"

Dang

…wat?

LLMs are completely capable of invoking npm install, the fuck you smoking lol.

Theyre perfectly able to read docs and use existing libraries fine.

Arguably I find better, because when I bump into a bug in a lib, I can fork it, fix the bug, get my patch up, and use the patch in my project in like 15min flat, no longer having to even worry about “ugh is it worth the effort to fix it”

Yes, its so easy now. I just do it.

Furthermore agents can look up documentation in mere seconds and read it (better than Id say a lot of junior devs tend to approach problems ngl)

If the agent is equipped with adequate tools and instructions, its extremely productive under strict guidance.

The same way.

The result the LLM produces is a link to the relevant information directly I can click and go to it.

Example would be a giant collection of files, think like, 10gb+ of many pdfs, or more. I want any relevant sections on a topic, it quickly can aggregate on it and give me links I click to open up straight to relevant sections in specific files, and then read rhem.

This stuff comes up a lot in my industry (software dev) as we often inherit huge data pools of legacy documentation on massive codebases.

When I am tasked with fixing 1 small specific piece of the system, it could take me hours to find the specific stuff Im looking for on the (often poorly maintained) docs.

But also vector db setup to map to that data, and an LLM wired up to it, can search it in milliseconds and pull up relevant sections asap, and I can click and dig deeper from there as much as I need.

This sort of “fuzzy searching” vectorization of tokens is what an LLM does very well. Its part of how it produces its output, but you can reverse the process to create search indexes (effectively reversing the data through the LLM to turn the data into deterministic vectors)

What fault tolerance?

I tell it to find me the info, it searches for it via provided tools, locates it, and presents it to me.

Ive very very rarely seen it fail at this task even on large sets.

Usually if theres a fail point its in the tools it uses, not the LLM itself.

But LLMs often are able to handle searching via multiple methods, if they have the tools for it. So if one tool fails they’ll try another.

I have not had this experience tbh, Ive found summarizing to be one of the few things they are good at out of the box.

If your LLM summarizes something poorly you probably just fucked something up and got a “shit in, shit out” problem.

That sounds like a tooling problem.

Either your tooling was outright broken, or not present.

It should be a very trivial task to provide an agent with a MCP tool it can invoke to search for stuff like that.

Searching for a known specific value is trivial, now you are back to just basic sql db operations.

These types of issues arise when either:

A: the tool itself just gave the LLM bad info, so thats not the LLMs fault. It accurately reported on wrong data it got handed.

B: the LLM just wasnt given a tool at all and you prompted it poorly to give room for hallucinating. You just asked it “who has this license plate” instead of “use your search tool to look up who has this license plate”, the latter would result in it reporting the lack of a tool to search with, the former will heavily encourage it to hallucinate an answer.

They dont impact it at all, its not relevant to using MCP vector searching for info.

What Model Context Protocols have you tried that you had issues with?

Ive found most vector db search MCPs are pretty solid.

Hallucinations aren’t relevant as an issue when it comes to fuzzy searching.

Im not talking about the LLM generating answers, Im talking about sifting through vector databases to find answers in large datasets.

Which means hallucinations arent a problem now.

Can you come up with better ways to quickly search and summarize massive amounts of data?

Thats what I find their best use case is, and theres no better solution for it, so I use it for that heavily.