Hey all! YC just released their latest RFS which includes an interesting concept: B2A (Business to Agents) – software where customers are AI agents instead of humans. Full RFS: https://www.ycombinator.com/rfs Would love to brainstorm and hear your takes—what potential opportunities do you see in the B2A space?

Hi everyone! This post is my answer to a question someone asked me in one of my previous posts, and I thought it would be a good idea to talk about it. This was @Kevin Cranfield's message. Here’s what he asked: "Hi, what is your feeling about using ChatGPT/OpenAI to help you code? I started using it for little bits of help while building my Django projects. Then I figured out that I can get ChatGPT to write the whole project. I knew this was bad for my learning, but it helped me get the project done quicker." I’ll start by saying that I love ChatGPT! I think it’s an amazing tool, and I use it all the time. However, over-reliance on it can make you replaceable in the marketplace. If all you do is rely on ChatGPT to create, someone else can easily do the same. The real value comes from what you bring to the table—your ability to refine, adapt, and improve the outputs, making your work truly unique and personalized. The key is to leverage ChatGPT as a starting point while focusing on tweaking and enhancing its results to ensure your work stands out. One critical skill is learning to do things with ChatGPT that others can’t. Let me share an example from my own experience. A colleague and I were tasked with creating an automation in R. I had no idea how to do it, so I turned to ChatGPT. One thing I’m very good at is clearly expressing my ideas in writing, so I probably crafted an effective prompt. As a result, I had no trouble generating the script I needed and tweaking it, whereas the other person couldn’t get hers to work at all. Being able to write clear, effective prompts that deliver exactly what you need is a valuable skill. But there’s a downside to tools like ChatGPT: they can discourage us from learning deeply. Let me share another example. I heard a story about a wedding where the pianist didn’t actually know how to play the piano. She was there to press a button, and the music would play from the keyboard. But on that particular day, something went wrong, and the recorded music didn’t play. The violinist asked her to play manually, and she couldn’t do it! A similar situation happened to the same violinist on another occasion, but this time, the pianist knew how to play and immediately stepped in. The same principle applies to us: we need to understand the code we’re using and be able to recreate it if necessary. One way I approach this is by experimenting with changes to see their effects. I also try to write as much of the code myself as I can, and when I don’t understand something, I ask the AI to explain it, so I can better understand and learn.

Hi all, I'm guessing that most of you here want to start freelancing or their bussines in AI/ML/DS. At least I am, and one problem we all face is the chicken and egg problem. How to get that first client without having a references or previous client's success stories. I've been thinking about this and I want to share my take on how to navigate that first stage. This dilemma is common and other industries have attempted different strategies to overcome it. So I studied them and proposed some ideas that could work in freelancing: 1. Ride-sharing industry: Uver and Lyfr initially faced this problem, drivers wouldn't sign up without passengers, and passengers wouldn't sign up without drivers. They solved this by offering finnacial incentives to both parties. 𝗜𝗻 𝗳𝗿𝗲𝗲𝗹𝗮𝗻𝗰𝗶𝗻𝗴, 𝗼𝗻𝗲 𝗰𝗼𝘂𝗹𝗱 𝗼𝗳𝗳𝗲𝗿 𝗱𝗶𝘀𝗰𝗼𝘂𝗻𝘁𝗲𝗱 𝗼𝗿 𝗲𝘃𝗲𝗻 𝗳𝗿𝗲𝗲 𝗶𝗻𝗶𝘁𝗶𝗮𝗹 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝘁𝗼 𝗮 𝗳𝗲𝘄 𝗰𝗹𝗶𝗲𝗻𝘁𝘀 𝗶𝗻 𝗲𝘅𝗰𝗵𝗮𝗻𝗴𝗲 𝗳𝗼𝗿 𝘁𝗲𝘀𝘁𝗶𝗺𝗼𝗻𝗶𝗮𝗹𝘀. 𝗜 𝘁𝗵𝗶𝗻𝗸 𝘁𝗵𝗶𝘀 𝘄𝗼𝘂𝗹𝗱 𝘄𝗼𝗿𝗸 𝘄𝗶𝘁𝗵 𝘀𝗼𝗺𝗲 𝗽𝗲𝗼𝗽𝗹𝗲 𝗶𝗻 𝗺𝘆 𝗻𝗲𝘁𝘄𝗼𝗿𝗸. 2. Social media platforms: Facebook and Twitter had to get users to join without much existing content. They focused on small, tightly-knit communities to generate initial activity and then scaled up. 𝗜𝗻 𝗳𝗿𝗲𝗲𝗹𝗮𝗻𝗰𝗶𝗻𝗴, 𝗼𝗻𝗲 𝗰𝗼𝘂𝗹𝗱 𝘀𝘁𝗮𝗿𝘁 𝗯𝘆 𝘁𝗮𝗿𝗴𝗲𝘁𝗶𝗻𝗴 𝗮 𝗻𝗶𝗰𝗵𝗲 𝗺𝗮𝗿𝗸𝗲𝘁 𝗼𝗿 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝘄𝗵𝗲𝗿𝗲 𝘀𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁 𝗶𝗺𝗽𝗮𝗰𝘁 𝗰𝗮𝗻 𝗯𝗲 𝗺𝗮𝗱𝗲. 𝗢𝗻𝗰𝗲 𝘀𝘁𝗿𝗼𝗻𝗴 𝗿𝗲𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗯𝘂𝗶𝗹𝘁, 𝗲𝘅𝗽𝗮𝗻𝗱 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝘁𝗼 𝗮 𝗯𝗿𝗼𝗮𝗱𝗲𝗿 𝗮𝘂𝗱𝗶𝗲𝗻𝗰𝗲. 3. Online marketplaces: eBay and Etsy initial had to attract both buyers and sellers. They often started with a focus on unique or high-demand items to draw in users. 𝗜𝗻 𝗳𝗿𝗲𝗲𝗹𝗮𝗻𝗰𝗶𝗻𝗴, 𝗼𝗻𝗲 𝗰𝗼𝘂𝗹𝗱 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲 𝗶𝗻 𝗮 𝗵𝗶𝗴𝗵-𝗱𝗲𝗺𝗮𝗻𝗱 𝗼𝗿 𝘂𝗻𝗶𝗾𝘂𝗲 𝘀𝗸𝗶𝗹𝗹 𝘀𝗲𝘁 𝗶𝗻 𝗔𝗜/𝗠𝗟 𝘁𝗼 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝘁𝗲 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝗼𝗻. 𝗣𝗶𝗰𝗸 𝗮 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗮𝗻𝗱 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝗲 𝗶𝗻 𝘁𝗵𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝗔𝗜/𝗠𝗟. 4. Payment Platforms: Paypal and Venmo faced the challenge of getting users to trust a new way of handling money. They built trust through partnerships. 𝗜𝗻 𝗳𝗿𝗲𝗲𝗹𝗮𝗻𝗰𝗶𝗻𝗴, 𝗼𝗻𝗲 𝗰𝗼𝘂𝗹𝗱 𝗽𝗮𝗿𝘁𝗻𝗲𝗿 𝘄𝗶𝘁𝗵 𝗼𝘁𝗵𝗲𝗿 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹𝘀 𝗼𝗿 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 𝘁𝗼 𝗴𝗲𝘁 𝘆𝗼𝘂𝗿 𝗻𝗮𝗺𝗲 𝗼𝘂𝘁 𝘁𝗵𝗲𝗿𝗲. 𝗝𝗼𝗶𝗻 𝗳𝗼𝗿𝗰𝗲𝘀 𝘄𝗶𝘁𝗵 𝗼𝘁𝗵𝗲𝗿 𝗳𝗿𝗲𝗲𝗹𝗮𝗻𝗰𝗲𝗿𝘀 𝗼𝗻 𝗮 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝘁𝗼 𝗴𝗮𝗶𝗻 𝗶𝗻𝗶𝘁𝗶𝗮𝗹 𝗲𝘅𝗽𝗼𝘀𝘂𝗿𝗲. 5. Subscription services: Netflix and Spotify offered free trials to get users hooked on their services before requiring payment. 𝗜𝗻 𝗳𝗿𝗲𝗲𝗹𝗮𝗻𝗰𝗶𝗻𝗴, 𝗼𝗻𝗲 𝗰𝗼𝘂𝗹𝗱 𝗼𝗳𝗳𝗲𝗿 𝗮 𝗱𝗲𝗺𝗼 𝗼𝗿 𝗣𝗢𝗖 𝗼𝗳 𝘆𝗼𝘂𝗿 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀/𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗯𝗲𝗳𝗼𝗿𝗲 𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝘆𝗼𝘂 𝘂𝗽 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀.

Hey everyone, I just completed a new course for you: "Data Science Accelerator". This course will be unlocked, together with "Building Applications with LLMs" at level 3. How to level up? Just interact with the group, get likes and comments, and watch your level go up!

Hi Friends, can someone please suggest few good starting AI and Data Science projects which I can look into and do reverse engineering with the hope to quickly learn and get my hands dirty. A little background about me: I am an experience software engineer. I also finished course about Python so I believe I am ready to get my hands dirty. Thank you in advance to my wonderful community!