5 Rules for Running AI Agents Without Wasting Tokens @WhatsAI

Uploaded June 2026 | Updated September 2026, 1 hour ago

Yesterday I explained loop engineering. Today, how I actually use it.

Unlike the guys at Anthropic, I don't have infinite tokens.

So I start the loops myself, keep my skills in indexed markdown files, cap my spending, and never let an agent run when I'm not around.
Because at the end of the day, you're responsible for everything it does.

My full setup and the mistakes to avoid, in the video 👇 #short

5 Rules for Running AI Agents Without Wasting Tokens

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$Qwen3-Next: 10x faster, 10% cost ! Efficiency breakthroughs don’t come often, but this one looks real! Alibaba just dropped Qwen3-Next-80B-A3B, an 80B MoE model with only 3B active parameters at inference. The result? Really fast generation at 10% of the cost of a small model (eg. Qwen 32B). What’s new here: • Ultra-sparse MoE with 512 experts (Instruct + Thinking variants). • Hybrid attention with a gated mechanism, giving speedups where most models slow down. • Multi-Token Prediction to supercharge speculative decoding. • Trained on 15T tokens at a fraction of Qwen3-32B’s cost. • “Thinking” variant even outperforms Gemini-2.5-Flash-Thinking in benchmarks. • 256K native context (scalable to 1M). Why it matters: This model challenges the efficiency ceiling set by Mixtral and DeepSeek, while rivaling Qwen3-235B in reasoning with only 3B actrive parameters(!!). If these claims hold up outside benchmarks, we’re looking at a new efficiency baseline for frontier open LLMs. Which model do you want me to cover next? Drop it in the comments and I’ll tag you. I’m Louis-François, PhD dropout, now CTO & co-founder at Towards AI. Follow me for tomorrow’s no-BS AI roundup 🚀 #AIresearch #LLMs #ainews #short$

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