KennyFromIT

Everyone's* got ideas they think are great and startup-worthy. What are some interesting reasons you chose NOT to pursue your idea?

*Of course not literally everyone, Jeff.

Building product has never been easier. But what about actually finding people who want your product? I understand the methods of customer discovery, but what about distribution beyond the first few - what worked for you?

Providing a chat bot to a large user base means you spend a lot of money on similar requests. I'm looking for best practices or lessons learned from implementing LLM-enabled apps at scale. Thanks in advance.

Not sure which one is the best option for my future...

Did you struggle with this (or similar) decision? Do you have a framework for deciding? Please share.

Parents of HN... What do you kids like to watch on YouTube? What do they like to watch that you also like to watch? Looking for a collection of interesting, educational, or just generally entertaining channels that will help me battle the kids toy and gamer influencer channels that they're hooked on.

The HN community is full of capable and driven individuals. Many can solve almost any problem they put their mind to. When this is the case, it's often important to focus on the most important things worth working on. I'm wondering, how do you chose what's worth your time and energy?

I use postgres and it has pgvector - why should I consider using Chroma, Pinecone, or any other dedicated vector db for my llm-based application that needs to store embeddings information?

As a casual observer of HN it feels like there's an uptick in open source projects that aim to become companies. This is obviously a customer acquisition strategy, but I'm struggling to think through the long-term implications of this pattern on the market as a whole. What does this mean for customers/users of the projects? What does it mean for the companies? Their employees?

I have a hunch that many of them will go out of business the moment they are asked (by their investors) to start thinking of how to generate revenue. At the same time, I recognize that I could have blindspots that prevent me from understanding something that will make them all work. Am I missing something?

I've been a long-time Linux user and generally locked in to the Google ecosystem. My flavors are usually Fedora and stock Pixel devices whenever available. However, lately I've become interested in this AI revolution, specifically LLMs. I'm interested in the prospect of running these inference models locally at some point.

Do you think it's necessary/worth it to make the switch to a modem MacBook/Air or should someone in my situation stick with cloud services and Dell XPS or Lenovo line of PCs?

It's all the rage right now, but what can the past (and your experience) teach us about the future of prompt engineering? I'm trying to figure out if this is fleeting or if there's really something to it.

I have found countless ways to incorporate AI into my everyday workflow, but my experience may be unique, so I'm trying to get a better idea for how this all could play out.