For those who are already getting consistent UGC work, did you choose a niche from the beginning, or did you just take different projects and figure it out along the way?
Curious what worked better for you. 👀
When I first started looking for UGC work, I had no idea what to charge.
I kept seeing completely different numbers online, and it made me think I needed to figure out the “perfect” rate before even applying.
Eventually, I stopped overthinking it and started looking at each project separately.
Things like:
That made pricing feel a lot more realistic.
I also realized that a $100 project isn't necessarily better than a $50 project if the $100 one takes three times as long. 😅
For other beginners here: how did you decide on your first UGC rate, and did you change it after getting more experience?
I went a little overboard when I first started using MCP.
GitHub? Connect it.
Files? Connect them.
Search? Connect it.
Random APIs I barely use? Also connected.
It looked impressive, but after a while I realized I was just giving Claude a huge list of tools without thinking about whether I actually needed them.
So I tried the opposite approach.
I kept only the MCP servers I use regularly and removed everything else.The difference was surprisingly noticeable. My prompts felt simpler, I spent less time wondering which tool Claude was going to use, and debugging became much easier.
Now I’m treating MCP servers more like actual software dependencies rather than a collection of cool things to install.
Curious if anyone else has run into this. How many MCP servers do you actually keep enabled at the same time?
I’ve seen creators mention things like super specific scripts, endless revisions, unpaid “test” videos, or brands asking for way more content than the original deal included.
It made me wonder where everyone else draws the line.
What’s the weirdest, most unreasonable, or just plain funny UGC request you’ve gotten from a brand?
I've been messing around with search + AI retrieval lately, and something I didn't really expect was that more results don't necessarily mean a better answer.
I tested giving an LLM the top 5 results vs. top 10–20 results. Sometimes the extra results helped, but other times they just introduced duplicate articles, outdated information, or pages that were technically related but didn't actually answer the question.
It made me wonder if the problem isn't always the search API itself. Maybe we're just giving the model too much stuff to work with.
I’ve been looking into buy-side careers lately, especially private equity, asset management, and hedge funds, and I’m honestly a little overwhelmed by how competitive the whole space seems.
A lot of the advice online is basically “go to a target school, get IB experience, network, and have a perfect resume.” But I’m curious about what actually made a difference for people who eventually got in.
Also curious whether there are realistic paths into the industry for someone who doesn’t come from a traditional finance background.
This started as a joke.
A friend bought a bunch of houseplants and said she had no idea what to name them. I gave each one a ridiculous name based on its personality — Gary, Susan, Kevin, etc.
She posted a picture of them, and a few people started asking me to name theirs too.
So I started offering it for a few dollars.
Now people send me pictures of their plants and I give them a name + a little backstory. 😂
The weird part is that people actually seem to enjoy it. I’ve named everything from tiny desk plants to someone's giant monstera that apparently needed a “more sophisticated” name.
It obviously isn't making me rich, but it's probably the strangest money I've ever made online.
The funniest request so far was a cactus that the owner insisted looked like a divorced accountant.
I named him Greg.
I’m curious what people actually focused on when preparing for investment banking or other sell-side roles.
There’s so much advice online saying you need to know everything from accounting and valuation to financial modeling and current market trends, but I’m wondering what really matters in interviews.
I’ve been looking into search APIs lately, and one thing I noticed is that it’s pretty hard to compare them just by looking at their feature lists.
A search API can look great on paper but still give very different results depending on what you’re searching for.
Curious what other people here use - Exa, Tavily, Firecrawl, Brave, or something else? And what kind of queries are you using it for?
I came across a recent study that tested something I’ve been wondering about: how close are AI agents to actually doing independent research?
Researchers gave AI agents six days and thousands of dollars in compute to work on two unpublished NeurIPS research problems. The agents were able to handle the engineering side without human help — coding, running experiments, etc.
But they struggled when it came to the harder parts of research.
What I find interesting is that this doesn’t really sound like “AI can’t do research.” It sounds more like AI is getting pretty good at executing a research workflow, but still has a hard time deciding what is worth researching in the first place.
I’ve been looking into search APIs recently, and I realized I was mostly comparing them based on how good the search results looked rather than how useful they actually were for my use case.
There are quite a few options now Exa, Tavily, Firecrawl, Serper, Brave Search, etc. and they seem to approach search pretty differently.
One thing I’ve been paying more attention to is what happens after you get the search results.
For those who’ve been doing UGC longer: What’s the biggest mistake you made as a beginner, and what advice would you give to someone just starting out?
A few years ago it felt like every major breakthrough came with code, weights, and detailed research papers.
Today, the biggest frontier models are increasingly proprietary, while open-source models are catching up at an incredible pace.
I'm curious where people think this trend goes over the next five years.
Will the best AI eventually be open source, or will closed labs always stay one generation ahead?