I Trained a 6.4M-Parameter Transformer From Scratch to Talk About Recipes
Article summary
Quick briefing — cleaned from the original RSS feed
Every LLM-powered app I'd built up to this point followed the same recipe (pun intended): call an API, write a good prompt, wrap it in a nice UI. That's a legitimate way to build things, but at some point I wanted to actually understand what was happening inside the model I was calling and not just how to prompt one. So for my recipe app Rasaveda , I decided to skip the API entirely. Intially, I had one made, but then I felt like I was not making any clear progress in actual machine building.…
1Key Takeaways
- Every LLM-powered app I'd built up to this point followed the same recipe (pun intended): call an API, write a good prompt, wrap it in a nice UI.
- That's a legitimate way to build things, but at some point I wanted to actually understand what was happening inside the model I was calling and not just how to prompt one.
- So for my recipe app Rasaveda , I decided to skip the API entirely.
- Intially, I had one made, but then I felt like I was not making any clear progress in actual machine building.….
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that every LLM-powered app I'd built up to this point followed the same recipe (pun intended): call an API, write a good prompt, wrap it in a nice UI.
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