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Introducing Remocn Studio. Waitlist for early access. This is what I've been working on for the past several months. I made every video in Claude Code, and over and over I kept repeating the same thing. "Why not build myself an app that gets rid of this," I thought... So I built it. Realized it would help a lot of people. This isn't just another AI video editor. There are no timelines, keyframes, or any of the other stuff every editor has. You can't trim a video by clicking on a timeline, you can't manage tracks. All you can do is prompt Remocn Studio is the director's chair. Here, you direct the people making the video. You watch the process. "Fix the text here" "The animation is too fast here" "Why the f*k did everything break here"show more

Remocn

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318,428 views • 10 days ago •via X (Twitter)

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Qullamaggie Wisdom

14,653 views • 1 month ago

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I built an agent that answers machine-learning questions. It's autonomous, and the best part is that I built the whole thing without writing a single line of Python code. Here is what I did and how I did it: Over a year ago, a friend and I built a site that publishes multi-choice questions. You get a new one every day. I decided to have GPT-3.5 answer questions. Here is what I needed to build: 1. Connect to the site's API to retrieve today's question 2. Extract the question and the potential choices 3. Connect to OpenAI's API and ask GPT-3.5 to answer the question 4. Parse the answer from the model 5. Submit the answer back to the API to get the score Not difficult. Likely several hours of work. But I didn't have to write any code. I built the whole thing by dragging and dropping components using Vellum is a YC-backed platform for developers to build LLM applications. They are the only ones I've seen offering this functionality. They sponsored this post, and their team helped me with all my questions while I built this. I created a workflow. The platform supports several node types to build whatever you have in mind. I show how I put the whole thing together in the attached video. The only code I had to write was a few lines of Jinja to parse and transform the API and the LLM results. There are three lessons I want to share from this experience: First, the best possible code is the one you didn't write. I'm a big fan of no-code tools because they help me materialize my ideas fast. They help product people, designers, and no coders collaborate on the solution. Second, Large Language Models are sensitive to how you prompt them. Small changes to prompts can make a big difference in results. This is more pronounced when you are building a multi-step workflow. Third, automated testing and evaluation for prompts is critical. There aren't many companies thinking about this. They'll have a hard time moving from a demo phase. The attached video will show you what I did.

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