u/Exotic-Titan

One thing I realized is that brands care more about seeing what you can create than how many followers you have.

I started making 15–30 second sample videos for products I already owned and added them to a simple portfolio. It gave me something to send during outreach instead of just saying, "I can create content."

Even if a brand doesn't reply, you still end up with more examples to show the next one.

For those who've landed paid UGC deals, what made the biggest difference for you—your portfolio, cold outreach, or networking?

I hope you guys will engage this post too and help me reach my goals

I've been reading a lot about careers in private equity, hedge funds, and venture capital, and one thing keeps coming up: everyone talks about networking.

At first, I thought having strong technical skills (financial modeling, valuation, accounting, etc.) would be the biggest factor. But after looking through a bunch of career stories, it seems like a lot of people got interviews because they reached out to alumni, attended events, or simply stayed in touch with people in the industry.

That got me wondering where the balance actually is.

If someone has:

• A solid resume
• Good technical knowledge
• Decent internship experience

...but doesn't have a strong network, are they already at a disadvantage?

On the flip side, can someone with average technical skills but a great network still land interviews and learn the technical side later?

For those of you already working in PE, VC, HF, asset management, or family offices:

• What mattered most when you got your first buy-side role?
• Was networking the deciding factor?
• If you were starting over today, what would you focus on during your last year of college?

I'd love to hear different perspectives because there seems to be a lot of conflicting advice online

I used to think a search API was basically just "Google but with code." After looking into it for a class project, I realized they're actually solving different problems.

For example:

• Exa API is nice when you want search results that are easy for an AI model to understand. Instead of just returning links, it tries to find content that's actually relevant to the question.
• Tavily API seems built for AI agents that need fresh information from the web before answering.
• Serper API is useful if you just want Google Search results inside your app without scraping the search page.
• Brave Search API is interesting because it has its own search index instead of relying on Google.
• Firecrawl API isn't really a search API. It's more for taking a webpage and turning it into clean Markdown or structured data, which makes it much easier to feed into an LLM.

One thing that finally clicked for me is that these APIs can work together.

For example:

1. Use a search API (Exa, Tavily, Serper, or Brave) to find relevant pages.
2. Use Firecrawl to extract the content from those pages.
3. Pass the cleaned content to an LLM so it can answer based on real information instead of guessing.

It made me realize that a lot of AI apps aren't using one magical API behind the scenes. They're chaining together search, crawling, retrieval, and an LLM.

I'm still learning this stuff, so I'm curious—if you've built an AI project, which search API did you end up using, and why did you pick that one over the others?

I was scrolling through AI news today and found something pretty interesting.

Researchers made a model that doesn't just generate images, but can actually use them while solving problems. So instead of converting everything into text first, it kind of "reasons" with visual information.

Imagine showing it a geometry question or a diagram. Rather than describing the image in words, it can work through the picture itself. That seems way more natural, especially for stuff like math, engineering, architecture, or even medical images.

I feel like this is one of those small updates that could have a big impact later. Most AI models today are really good with text, but humans don't think only in words. We also think in visuals, patterns, and mental images.

Curious to see where this goes.

Do you think AI that can reason visually will actually make a big difference, or is this just another research paper that'll be forgotten in a few months?