Most people use LLMs.
Very few actually understand how they work under the hood.
If you want to go from prompt user → real AI engineer, study these 9 concepts in order:
1️⃣ Transformers — attention, tokens, self-attention basics
https://t.co/5YmBhXQrpu
2️⃣ Transformer tricks — what makes them stable & scalable
https://t.co/QFvPbLVuMt
3️⃣ From Transformers → LLMs — how scale changes behavior
https://t.co/1mbXcogTGF
4️⃣ LLM training — where “intelligence” actually emerges
https://t.co/4PnlOTPjbT
5️⃣ Instruction tuning & alignment — why fine-tuning matters
https://t.co/r5XbxsJvpu
6️⃣ LLM reasoning — why models fail + what improves them
https://t.co/0wzxbMtIIk
7️⃣ Agentic LLMs — models that plan, call tools, and act
https://t.co/oG0VaEWqp0
8️⃣ LLM evaluation — measure beyond demos & vibes
https://t.co/nLtvJW4n6W
9️⃣ What’s next — trends that actually matter
Bookmark this. Study step-by-step. Your prompts will level up — and so will your builds.
New art project.
Train and inference GPT in 243 lines of pure, dependency-free Python. This is the *full* algorithmic content of what is needed. Everything else is just for efficiency. I cannot simplify this any further.
https://t.co/HmiRrQugnP
UN NUEVO CÁPITULO PARA KRÜ VISA 🤟
Denle la bienvenida a nuestros nuevos guerreros para 2026:
🇦🇷 @Dantedeu5
🇧🇷 @mwzera
🇦🇷 @SaadhakW
🇧🇷 @silentzfps
🇧🇷 @lessvlr
Debut del equipo: En enero, con el kickoff de #VCTAméricas