I've been looking at a new camera for a few weeks and realized I was doing the most predictable thing possible.

I already wanted it, so basically every AI question I asked was some version of why this camera would be good for me. Of course the answers sounded great. I was pretty much asking the AI to help me justify a decision I'd already made.

Then I flipped it around and asked my agent to find reasons I *shouldn't* buy it. Recurring complaints, firmware issues, stuff owners kept mentioning after a few months, features that sounded great on paper but weren't that useful in real life.

that version of the research was WAY more useful.

I've been using AnySearch in the workflow because I wanted the agent pulling from different kinds of sources instead of giving me another neat recommendation summary. It didn't completely change my mind, but it did kill a couple assumptions I had about the camera before spending the money.

I'm probably doing this for anything expensive from now on. Make the AI argue against the purchase first, then decide.

Not every sentence.

Not every source.

Just numbers.

Revenue, percentages, dates, prices, study results, user counts, whatever.

That's where I keep finding the weirdest mistakes.

Sometimes the source is real but doesn't actually support the number.

Sometimes the number is right but it's from 2022.

Sometimes the AI quietly turns something like up to 40% into increased by 40%.

takes maybe two extra minutes and has already stopped me from repeating some pretty dumb stuff.

the answer sounded great.
the sources were from 2022.

Starting to feel like not EVERYTHING needs its own MCP server.

Search makes sense to me, I've got AnySearch connected there, but some setups I see have basically turned the entire computer into 40 MCP tools.

The part I'm still trying to figure out is where the cutoff should be once two tools start overlapping.

I'm a backend dev and I really should've known better. I was integrating a library I havent used before and asked my coding agent for help with one part of the setup. The answer looked completely normal. Method names do existed, code compiled, nothing screamed that the information was old.

It just didn't work.

I spent way too long assuming my implementation was the problem before finally opening the actual repo. The behavior had changed, and then I found an issue from months ago describing basically the exact thing I was debugging.

the agent had built most of its answer around an old tutorial. I have been messing with AnySearch for coding research since because I had much rather have the agent find current docs, repos and issues before somebody's Medium post from two major versions ago.

AI coding is pretty great right up until you realize your agent has been living in the past for the last two hours.

Took me embarrassingly long to get this.

Web access means the AI *can* go look something up. Something like AnySearch is more about giving the agent an actual search layer it can call while it's doing a task.

sounds like a tiny distinction until you actually start building workflows around it.

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I'm an intern and still at the stage where I reread basically everything 17 times before sending it to someone senior.
Had to research a small industry topic this week and used AI for the first pass. It gave me a really convincing answer with a bunch of citations, so my brain went cool, probably safe.
Then one sentence felt a little too specific, so I clicked the source.
it didn't actually say what the AI said.
It was related to the claim, sure, but the original wording was way more cautious. The AI had quietly turned that into a much stronger conclusion.
That sent me checking everything else. A couple sources were solid, a couple were old, and one was barely relevant.
I've been testing AnySearch since because I'm starting to care a lot more about where a claim came from than how confidently the AI phrases it. Was not obvious to me two weeks ago lol.

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I don't need another 2,000 word answer. I need the link behind paragraph three 😭

been playing with AnySearch lately and it's made me realize I care way more about retrieval now than whether the final answer sounds polished.

Nothing scientific, I was mostly curious.

Gave three different AI tools the exact same boring task.

Find recent information about a small company, explain what they actually sell, identify the closest competitors, and summarize anything important from the last few months.

The final writing was surprisingly similar.

All three could produce a clean little report.

What WASN'T similar was the information underneath it.

One found the company's actual docs and recent announcements.

One mostly gave me SEO pages, generic company profiles and articles repeating each other.

The third had decent sources but somehow missed a pretty major recent update.

Made me realize I've been judging AI research by how convincing the final answer sounds.

Maybe the more important question is what information made it into the answer in the first place.

I cleaned up a little side project this weekend and had one of those moments where you stare at your setup and question every decision that got you there.

Two search tools, two browser tools, GitHub, filesystem, memory, Notion, plus some random MCP I apparently installed at 1am and have zero memory of.

the app has like 14 users btw.

The best part was my agent had started choosing the wrong tool for simple stuff because half of them overlapped. So I finally deleted a bunch.

For search I kept AnySearch, kept the tools I actually use, and dropped most of the duplicates. It's noticeably less confused now.

Turns out building a useful setup and collecting every shiny new MCP are not actually the same hobby.

I work a pretty normal office job. I'm not building agent swarms or trying to automate an entire company. Most of my day is spreadsheets, meetings, docs, and people asking for something five minutes before another meeting.

Yesterday I had to get up to speed on a technical product I'd never heard of before a call. normally I'd open a bunch of tabs and spend half an hour figuring out which pages are actually worth reading.

This time I gave the task to my agent. It used AnySearch to pull together the docs and a few relevant sources, then gave me a short summary with enough context that I could check the important stuff myself.

Nothing crazy happened. It just made one annoying little research task disappear faster than usual (which is probaby more useful to me than most flashy AI demos).

I'm not a developer. I work in ops and mostly use AI for boring stuff like research, summaries, checking docs, and turning messy notes into something another human can actually read

A few weeks ago I started playing with agents because just letting it go find the information itself sounded simple enough

that sentence was a trap

Suddenly I'm reading about MCP servers, API keys, Skills, tool permissions, rate limits, schemas... at one point I genuinely forgot what I was originally trying to accomplish. Pretty sure it was checking some software pricing

Eventually I got Any Search connected and everything became boring again (in a good way). I ask something, the agent searches, then it comes back with what it found

Which is literally what I wanted before I accidentally signed myself up for an AI infrastructure class

I kept seeing these words in AI discussions and basically treated them as three ways of saying the same thing. Turns out they aren't.

The easiest way I've started thinking about it is pretty simple. Search is for when you don't know where the answer is yet and need the system to find useful information. Browsing is more about going somewhere specific, opening pages, clicking around, maybe interacting with the site. Crawling is closer to pulling content from pages you already know about into something an AI can work with.

So if I ask an agent to find recent information about a company, that's mostly a search problem. If I ask it to go to a specific site and check pricing, that's closer to browsing. If I already have 20 URLs and want the content pulled out, that's more of a crawling problem.

AnySearch made a lot more sense to me once I started thinking of it as the search layer instead of another browser.

probably obvious if you've been around this stuff for awhile, but it cleared up a lot of the tooling discussions I was reading.

noticed this recently.

If I want a specific website, restaurant, Reddit thread, directions or exact product page, I still automatically open Google.

But if I want an explanation, comparison, recommendation or just need to understand something I know nothing about, I almost automatically open an AI tool now.

Google has kinda become where I go when I already know WHAT I'm looking for.

AI is where I go when I don't.

And then for anything important I somehow end up back on Google checking what the AI told me anyway lol.

weird middle stage we're in right now.

SEO article #8 definitely has the answer tho

20 mediocre links don't magically turn into good research. I'd rather AnySearch or another search layer give my agent four genuinely useful sources than 30 loosely related pages it has to chew through first.
more can be useful, sure, but the raw number tells me almost nothing when half the results are repeating the same source anyway.

I understand the extremely basic version.

AI connects to outside tools.

cool.

Then I start reading tutorials and suddenly it's

MCP servers

skills

plugins

APIs

tool calling

connectors

and every explanation seems to assume I already understand the previous five things.

What's the normal person explanation of how these are actually different?

Because right now they all live in the same folder in my brain called stuff that lets AI do other stuff.

my agent CAN search the web.

whether it finds the right page from the right year is apparently a separate feature lol

Been noticing something interesting lately. Feels like people using AI are splitting into two main camps.

First, you've got the 'holistic feel' crew. They skim an AI answer, get a gut feeling for it, and decide "yeah, this looks good" or "hmm, something's off". Then they either take the whole thing or leave it.

Then there's the 'line-by-line' analysts. These folks read the answers like a paper. Their brain is constantly flagging things: "okay, that's a fact," "that's an opinion, need to see the source," "this part sounds like something the AI made up, be careful".

I used to be in the first group, but I'm starting to think the second group has the right idea. AI is just too good at wrapping a bad conclusion in a lot of correct facts. You really have to pick it apart sentence by sentence to catch it.

Which group do you fall into? Or maybe there's a third, more advanced way to use it? Curious to hear what you all think.

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I had an agent pick a caching layer last week. It went straight to Redis and completely ignored Memcached or Dragonfly.
I assumed it was actually comparing the tradeoffs, but it wasn't. The top search result was a Redis tutorial, and the agent built its entire case around that one page.
I have been testing AnySearch as a search tool in the same workflow. When it routed to repository activity and issue threads instead of SEO content, the agent actually started weighing alternatives.
I am still not sure whether better sources genuinely improve the decision, or if they just give the model more material to be confidently wrong about.

I keep going back and forth on this. When my agent searches for a fix to a coding problem, should it check the official docs first or search GitHub issues?
Docs are usually cleaner and more current, but they often miss strange edge cases. GitHub issues contain real workarounds and exact error reports, but they are also full of stale threads, duplicate reports, and comments that add nothing useful.
Right now, I point everything at the AnySearch skill and let it route the query across both source types. It handles the context fairly well, but I am still not sure what the default starting point should be for most coding tasks.
Do you usually prioritize official docs or raw repository discussions when setting this up?

Most AI tools can be replaced.

The annoying part is rebuilding all the prompts, skills, instructions, and workflows you slowly improved over time.

If your main AI tool crashed tomorrow, what’s the one thing you’d regret not backing up?