Local LLMs as constrained data transformers: Duolingo vocab transformed to Anki using Qwen 32B on MacBook M2 Max (45min run). Key insights: larger models sometimes over-help, Qwen 2.5 32B balances quality/instruction adherence. Practical iteration on consumer hardware. #LocalLLaMA #DataTransformation #LLMApplications #AnkiIntegration #Qwen32B

https://www.reddit.com/r/LocalLLaMA/comments/1ptfibf/using_local_llms_as_constrained_data_transformers/

12-factor Agents: Patterns of reliable LLM applications

https://github.com/humanlayer/12-factor-agents

#HackerNews #12factorAgents #LLMapplications #ReliablePatterns #AIdevelopment #SoftwareEngineering

What happens when a language model solves maths problems?

"If I’m 4 years old and my partner is 3x my age – how old is my partner when I’m 20?"
Do you know the answer?

🤥 An older Llama model (by Meta) said 23.
🤓 A newer Llama model said 28 – correct.

So what made the difference?

Today I kicked off the 5-day Kaggle Generative AI Challenge.
Day 1: Fundamentals of LLMs, prompt engineering & more.

Three highlights from the session:
☕ Chain-of-Thought Prompting
→ Models that "think" step by step tend to produce more accurate answers. Sounds simple – but just look at the screenshots...

☕ Parameters like temperature and top_p
→ Try this on together.ai: Prompt a model with “Suggest 5 colors” – once with temperature 0 and once with 2.
Notice the difference?

☕ Zero-shot, One-shot, Few-shot prompting
→ The more examples you provide, the better the model understands what you want.

#PromptEngineering #GenerativeAI #LLM #Kaggle #LLMApplications #AI #DataScience #Google #Python #Tech