Last year, I made a meme that very few understood. But I feel my fellow #RWRI participants will appreciate it 😅

Building software on top of (unpredictable) LLMs can feel more like praying to the ML gods than engineering 🙏. But it doesn’t have to be that way…. In a recent blog post, I showed how error analysis resolves the guesswork by giving us a systematic way to identify and improve

I just received 3 baguettes from Sathish on @buymeacoffee!

RT @HamelHusain: Over the last 2 yrs, I've helped 35+ companies improve their AI products. I distilled my approach into this guide, which….

RT @TDataScience: @deepseek_ai @OpenAI @ShawhinT

RT @TDataScience: @deepseek_ai @OpenAI @ShawhinT

5) Seek sophistication . Data science has endless interesting technical ideas that can applied to endless applications. Exploring these is fun and a big reason why I love data science. However, as an entrepreneur, I must fight this urge and instead bias toward simple.

4) You’re done when you deploy . As a data scientist, once the project was built and deployed, I could relax. As an entrepreneur, this is only the start. After deploying, I need to let people know about it and (hopefully) pay for it.

3) Building (rather than buying) just because I can . Before, I had a tendency to avoid paid software tools/services when I felt I could implement them myself. Now, I bias toward paying for solutions that can help me move faster.

2) Building for “prod” with 0 users . When working at an established company, ML projects might immediately impact thousands of customers and millions of dollars, so you have to build for scale from the start. However, as an entrepreneur, you’re more often seeking.

1) Progress = learning more tech skills. Going from a good to a great coder won’t move the needle as much as going from bad to mediocre in key non-tech skills (i.e., sales, marketing, writing, customer calls, etc.).

5 things I had to un-learn to go from Data Scientist → Entrepreneur 🧵.

RT @TDataScience: Follow along @ShawhinT's five-step roadmap to gain a solid foundation in AI's current landscape.

Technical founders love to code, but that alone won't make you successful. Here are 15 things I had to learn to become a (data) entrepreneur. 1. Talk to customers.2. Identify pain points.3. Sell.4. Sell to many people (i.e. marketing).5. Make a website.6. Build a landing page.

Last month, I booked my most expensive mentorship call yet at $700/hr 😬. While this might sound like a big number, the person on the other side of the call makes $100k/mo as a solo AI entrepreneur. Here are my key takeaways from this conversation. 1) Focus on customer pain.

RT @TDataScience: Multimodal RAG systems can synthesize knowledge stored in a variety of formats, expanding what’s possible with AI. @Shawh….

RT @TDataScience: Multimodal embeddings unlock countless AI use cases that involve multiple data modalities. Explore more mapping text and….

RT @TDataScience: What are multimodal models and how can you start using them? @ShawhinT presents an accessible, hands-on introduction to o….

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Physics majors when they heard @DarioAmodei say @AnthropicAI hired a bunch of theoretical physicists.

RT @PortkeyAI: 🚀 From 0 to 2Bn+ requests on Portkey’s AI Gateway!. Listen to Rohit Agarwal (@jumbld) as he shares lessons from building a b….