Topic
Digital transformation, and the difference between the three words
Digitisation converts a paper record to a digital one. Digitalisation changes the process to use it. Transformation changes what the business does. Most programmes claim the third while delivering the first, and knowing which one you are actually doing is most of the battle.
52 pieces
Teardowns
1Essays
6- China Humanoid Robots Shipped 19,100 Units in Six Months. The Reducers Matter More. An ageing workforce and an explicit industrial policy point the same direction. Beneath the humanoid demos sits a component supply chain that quietly changed hands: reducers, sensors, actuators.
- The Four Levels of AI Fluency and Why Level Three Never Arrives on Its Own Most organisations are staffed entirely at level one and are surprised when level three does not happen by itself. The four levels of AI fluency are separated by what you own when it breaks.
- How 3 billion yuan of bubble tea manufactured AI adoption in China China's mass AI adoption was manufactured by firms owning payments, delivery and a super-app, not by consumer curiosity. Subsidy bought usage. Whether it bought habit is a separate question.
- AI for Small Business: Six Boring Wins and Three Ways to Waste Money The AI wins for a small business are boring: quoting, document extraction, translation for China trade. The waste is predictable: platforms bought before anyone defined the process.
- Digital Transformation in Manufacturing Is Three Different Projects Digitisation, digitalisation and digital transformation describe three different amounts of work. Most plants claim the third while doing the first, and that gap explains the disappointment.
- AI Productivity Paradox: the Model Was Never the Bottleneck Every general purpose technology took decades to show up in the productivity statistics, because the technology was never the bottleneck. Reorganising the work around it was.
Tools
12 of 45- ChatGPT review OpenAI's assistant and API, now on the GPT-5.6 Sol, Terra and Luna tiers, with a 1M token context window on all three and reasoning effort you set per request.
- Databricks Mosaic AI review The AI layer inside Databricks: Agent Bricks, model serving, vector search and MLflow, all resolving back to Unity Catalog governed data.
- Dify review Open-source LLM app platform from LangGenius for agentic workflows and RAG pipelines. Modified Apache 2.0: self-host freely, multi-tenant SaaS needs a commercial licence.
- E2B review Firecracker microVM sandboxes for running AI-generated code. Python and JS SDKs, 24-hour sessions, pause and resume with full memory state, Apache 2.0 core you can self-host.
- FunASR review Alibaba DAMO's industrial ASR toolkit. MIT code, Apache 2.0 Fun-ASR-Nano weights, 1.80% Chinese CER, plus VAD, punctuation, diarization and an OpenAI-compatible self-hosted server.
- Instrumental review AI inspection and failure analysis for electronics assembly. Cameras capture every unit at each build state, then models flag defects no golden-sample rule ever encoded.
- Lark CLI review MIT-licensed official CLI for Lark and Feishu (飞书), from ByteDance. 200+ commands and 24 agent skills over Docs, Base, Sheets, Calendar and approvals, built for AI agents.
- OpenManus review MIT-licensed open agent framework from the MetaGPT team, prototyped in three hours as the open answer to Manus. 58,100 stars and still pushing commits in August 2026.
- PaddleOCR review Baidu's Apache 2.0 OCR toolkit (飞桨 PaddleOCR). PP-OCRv6 ships at 1.5M, 7.7M and 34.5M parameters, and PaddleOCR-VL-1.6 is a 0.9B document parser covering 109 languages.
- Promptfoo review Open source CLI for LLM evaluation and red teaming. Generates adversarial cases against your prompt templates and agents, runs them in CI, reports vulnerabilities. MIT licensed.
- Qwen3-Coder review Alibaba's open-weights coding model family plus the Apache 2.0 Qwen Code terminal agent, separate artifacts from the hosted Qwen Chat product. Qwen3-Coder-Next runs 80B total, 3B active.
- Roboflow review End-to-end computer vision platform for labelling, training and deploying detection models to cloud or edge hardware like Jetson, with a visual builder for multi-step vision pipelines.