The Batch n°350 — How Coding Agents Accelerate Different Types of Software Work (Andrew Ng) + GLM-5.1, Digit chez Schaeffler, anti-data-center revolt, assistant axis
Editorial by Andrew Ng in The Batch #350 laying out an **acceleration hierarchy driven by coding agents** by type of software work: **Frontend (max) > Backend (moderate) > Infrastructure (low) > Research (minimal)**. The rationale rests on implicit *verifiability* (fluency in TypeScript/JavaScript + an autonomous agent–browser loop on the frontend) and on the LLMs' blind spots (corner cases / security / DB migrations for the backend, opaque network tradeoffs for infra, irreducible hypothesis formation for research). The issue also covers 4 structuring news items: **GLM-5.1 (Z.ai)**, a 754B/40B-active-parameter MIT-licensed model capable of 8-hour autonomous tasks (SWE-Bench Pro leader at 58.4%); **Digit (Agility Robotics) at Schaeffler**, the first industrial deployment of humanoids (5'9"/143lb, $10–25/h vs $20/h for a human); the **anti-data-center revolt** (~$64B in projects blocked May-2024 / March-2025, Maine moratorium on 20MW+, a molotov cocktail at Sam Altman's home); and the **"assistant axis"** (Christina Lu, MATS / Oxford / Anthropic), which reduces persona drift and jailbreaks (Qwen3 32B: 83%→41%; Llama 3.3 70B: 65%→33%) without degrading IFEval/GSM8k/MMLU-Pro/EQ-Bench.
Andrew Ng (édito principal — fondateur DeepLearning.AI, Stanford, ex-Google Brain, ex-Baidu) ; rédaction The Batch (DeepLearning.AI) pour les sections actualités