A new term is echoing through AI circles these days: Loop Engineering 🧶
Do you know what it actually means? ⬇️
For the last 2 years, the workflow looked like this:
You write a prompt 👨💻, AI gives an answer 🤖
You write another prompt 👨💻, AI gives another answer 🤖
You were in the loop 👦
Now the idea is different.
You build a system that:
- Finds work 📋
- Prompts the agent 🤖
- Checks the results 🧪
- Updates memory 📝
- Decides the next step 🔄
Without waiting for you. You're no longer prompting the AI. You're designing the loop that prompts the AI.
The leverage point has moved. 🎯
Prompt Engineering taught us how to talk to models. 🗣️
Loop Engineering is about teaching models how to keep working when we're not talking to them. 😶
And that's a much bigger shift than most people realize. Have you tried it? 🧐
Ever wondered how LLMs understand negation if they read text one token at a time? 🧶
Consider this sentence: ⬇️
I don't think the movie was terrible.
As the model reads:
👉 I don't think...
it starts expecting a negative opinion?
Then it sees:
👉 terrible
Now things look even more negative.
But the actual meaning isn't strongly negative at all.
In many conversations, "I don't think it was terrible" is closer to:
✅ It was okay.
✅ It wasn't that bad.
So how does the model get this right? 🥶
The trick is that LLMs don't lock in meaning as each token arrives. 💿
Every new token can change how previous tokens are interpreted. 🧶
When the model sees words like don't, not, never, or hardly, it uses attention to connect them with other relevant words in the sentence. 🤯
In this case, attention helps the model understand that don't modifies the idea of terrible, changing the overall sentiment. 👨🏼🍳
Meaning doesn't come from individual words.
It comes from how words attend to each other. 💡
And that's why a tiny token can completely flip the meaning of a sentence. 🧠
Bcz Attention is All You Need. 🤯
We've all heard that modern LLMs have billions of parameters. 🧶
Some of the latest models are rumored to have over a trillion. 🤯
Now think about that for a second.
If a model has 1 trillion parameters, and each parameter takes 4 bytes, that's 4 TB just to store the weights. 🥶
And every time you ask the model a question❓, those weights need to be available to the GPUs performing inference. 📐
So how is it possible for AI companies to serve millions of requests without needing racks of GPUs per conversation? ⬇️
The answer is quantization (mapping floating point weights into lower-bit integers like 8-bit or 4-bit using scale + zero-point to approximate values during inference) Just like instead of full value for pi we just limit to 3.14. 💡
Most weights don't actually need 32 bits of precision. You can often represent them using 8 bits, 4 bits, or even fewer, while preserving most of the model's quality. 🤖
Suddenly that 4 TB model becomes 1 TB.
Or 500 GB.
Or less.
Same model.
Same knowledge.
Far less memory and bandwidth.
The funny thing is that many people think AI inference is limited by compute. 💿
In reality, a huge part of the challenge is simply moving model weights around fast enough. 🚢
Quantization works because the model usually doesn't need perfect numbers.
It just needs numbers that are accurate enough.
And that simple idea is one of the reasons trillion-parameter models are practical at all. 🫱🏻🫲🏼
Spent the weekend shipping with the latest MiniMax M3 model 🤖 for $0
A few observations: ⬇️
- Very good at spec-driven development
- Strong backend engineering capabilities
- Great at troubleshooting and debugging
- Frontend is okay so far.
Have you tried it? What's been your experience? 👀
Do you know how a reasoning model actually works? 🤔
Does it have some special reasoning engine hidden inside? 🧩
Not really. ⬇️
Imagine asking two AI models:
"What is 47 × 23?"
The first one instantly replies: 1081
The second one says:
47 × 20 = 940
47 × 3 = 141
940 + 141 = 1081
Same answer. ✅
But this time you can see how it got there.
That's essentially what a reasoning model is. It's not a new architecture.
It's the same LLM, trained to think step by step before answering.
How?
🧠 Chain of Thought → learn to break problems into smaller steps.
🎯 Reward Models → reward good reasoning, not just correct answers.
🔄 Reinforcement Learning → reinforce reasoning paths that lead to better outcomes
✅ Self-Critique → review and fix its own mistakes before responding
The result?
Instead of making a fast guess, the model learns to spend extra tokens thinking.
Same brain.
Different training.
