New blog post 📝 "Buzzword Engineering" Prompt Engineering, Context Engineering, Harness Engineering, and now Loop Engineering — at least four "Engineerings" have been born in just a few years since LLMs arrived. Why does the next name keep arriving before the previous one has matured into a real methodology? 🤔 In this post, I name and dissect this phenomenon: Buzzword Engineering — a mode of knowledge production in which methodologies are named and shipped faster than verification can digest them. The root cause is an asymmetry of speed. LLMs drove the cost of proposing methodologies to nearly zero, and are even becoming proposers themselves. Verification, however, completes only when products are used by real users — it remains rate-limited by human behavior. Proposals move at machine speed; verification moves at human speed. Names pile up in the gap as a backlog ⚙️ But this is not a piece that sneers at buzzwords. As Schumpeter's "swarms" and the hype cycle show, proliferation is written into the standard timetable of every technological revolution — it is the first step of knowledge creation, coordinating the attention of engineers worldwide. What I propose instead is a gearbox connecting two clocks: the weekly clock of methodology and the yearly clock of product value. That gearbox is xOps. Inside it: evaluation assets that compound over time, an "autonomy budget" for operating agent delegation by observation, and one norm — if you coin a name, attach falsification conditions and an eval. Methodologies depreciate; evaluation assets compound 🚀 If you're tired of chasing new names, this one is for you. #BuzzwordEngineering# #TechTrends#

Instead of treating prompts as static, Agentic Context Engineering (ACE) explores how context can evolve over time through generation, reflection, and curation. Read the paper ⬇️

We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a solid 1M-token context. GLM-5.2's new capabilities include: Solid 1M Context: A solid 1M-token context that stably sustains long-horizon work Advanced Coding with Flexible Effort: Stronger coding capabilities with multiple thinking effort levels to balance performance and latency Improved Architecture: We propose IndexShare, which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9× at a 1M context length. We also improve GLM-5.2’s MTP layer for speculative decoding, increasing the acceptance length by up to 20% Pure Open: An MIT open-source license — no regional limits, technical access without borders Supporting long-horizon tasks starts with making long context engineering-usable: the model must maintain quality across long, messy coding-agent trajectories, not just accept more tokens. A 1M context is easy to claim, but much harder to keep reliable under real engineering pressure. To this end, we substantially expanded 1M-context training for coding-agent scenarios, covering large-scale implementation, automated research, performance optimization, and complex debugging. The result is a long-context system that is not only wide in scope, but solid in execution: a practical substrate for sustained engineering work. This capability is reflected in GLM-5.2's performance on three long-horizon coding benchmarks. FrontierSWE measures whether an agent can complete open-ended technical projects at the scale of hours to tens of hours, spanning systems optimization, large-scale code construction, and applied ML research. On this benchmark, GLM-5.2 trails Opus 4.8 by only 1%, while edging out GPT-5.5 by 1% and Opus 4.7 by 11%. On PostTrainBench, where each agent is given an H100 GPU and evaluated by how much it can improve small models through post-training, GLM-5.2 outperforms both Opus 4.7 and GPT-5.5, ranking second only to Opus 4.8. On SWE-Marathon, an ultra-long-horizon software engineering benchmark covering tasks such as building compilers, optimizing kernels, and developing production-grade services, GLM-5.2 still has room to grow, trailing Opus 4.8 by 13% while remaining second only to the Opus series. Across all three benchmarks, GLM-5.2 is the highest-ranked open-source model, showing that its 1M context has translated into practical long-horizon delivery capability.