Topic
Engineering careers and the communities that build them
What actually holds value in an engineering career when the first draft of the code is free, how judgement gets acquired when the work that used to teach it is automated, and what it takes to build a technical community from nothing.
41 pieces
Teardowns
14- Harness Engineering: The AI Agent Guardrails Nobody Demos The model is a component you swap. The harness around it, tool schemas, validation, sandboxes, budgets, permissions and audit logs, is where nearly every production AI incident actually starts.
- AI Visual Inspection Fails at the Lighting Long Before the Model Classical vision still wins geometric checks. Learned models earn their place on variable and rare defects. Accuracy is set by lighting, fixturing and the false-reject threshold you can afford.
- The agent control loop is where production agents fail Prompt engineering was never the hard part. The loop is: what the agent sees, what it may touch, what stops it and what a step costs. Most agent failures are loop failures, not model failures.
- Genome Language Models: What an Engineer Should Actually Take From Evo 2 DNA is a sequence and transformers are good at sequences. That analogy carries genome language models further than it should. What Evo 2 actually does, what it is used for and where the pitch breaks.
- Why a digital twin in manufacturing stalls at level two A digital twin is only useful when live telemetry feeds it and a decision comes back out. A 3D model that never updates is a rendering, and the data feed was always the expensive part.
- Sample Efficiency in Reinforcement Learning: A Foal Needs 62 Attempts, a Robot Needs Ten Billion Steps Reinforcement learning brute-forces locomotion in simulation. A foal walks after about 62 attempts. The gap is not compute, it is the structure a random policy does not arrive with.
- The AI Data Privacy Checklist I Run Before Shipping a Feature Retention windows, zero-retention gaps, personal data in vector stores and the erasure problem, written for the engineer who has to configure it before Friday.
- Five Prompt Engineering Best Practices That Hold Up Most prompting advice is folklore repeated without a control. Five practices survive testing, four popular ones do not, and the research reversed direction as the models improved.
- Context Engineering Is Deciding What Earns a Place in the Window Windows grew to a million tokens and the industry assumed retrieval was solved. Attention still degrades, cost still scales, and everything you paste competes for the model's attention.
- How DeepSeek Builds World-Class AI on a Shoestring Budget The real story behind the $5.6 million model that panicked Silicon Valley, explained so you actually understand it.
- Claude Code in Production: Where It Earns Its Place and Where It Does Not Where an agentic coding tool earns its place in real engineering work, where it does not and why the reviewer is now the constraint. Claude Code as the worked example, with the tradeoffs named.
- What Actually Breaks in Distributed Engineering Teams Three sentences of code review feedback can cost two calendar days when the reviewer is asleep. The pattern of what breaks in distributed engineering work and what actually fixes it.
- Generative AI for engineering design is cheap. Verification is where the money goes. The generative step in engineering costs minutes of compute. Proving the output manufacturable, simulation-clean and safe costs weeks, and that ratio decides which applications are worth doing.
- Context Engineering vs Prompt Engineering: One Task, Two Fixes, Two Bills Prompt engineering optimises the wording. Context engineering decides what is in the window at all. As windows grew the bottleneck moved from phrasing to selection, and the bill moved with it.
Essays
15- Remote Work Brain Drain and the Engineer Who Never Left An engineer who stays in Lagos and works for a foreign employer is present but economically absent. The country keeps the person and loses the tax base, the mentorship and the firm never built.
- Is AI a Bubble? Yes, and the Chips Will Still Be Running Hyperscaler capex reached roughly $745bn for 2026 while the two largest labs run at about $105bn combined. The bubble is real. The compute will outlive it. An engineer's read from Shenzhen.
- How to Stay Relevant as a Developer After the Code Stopped Being Scarce Two randomized trials of AI-assisted developers reached opposite results. The difference was not the tool. It was how much of the task depended on context nobody had written down.
- The detector is scoring your English, not your honesty AI writing detectors measure how predictable your prose is, which is a proxy for native fluency rather than for cheating. A watermark can prove a model was involved, never who wrote what.
- Code generation is nearly free. The software engineering skills AI cannot replace are not. Producing a function was never the scarce skill. Deciding what should exist, what breaks it and what the system costs in three years is the work, and none of that got cheaper when generation did.
- 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.
- AI Regulation Comparison 2026: Four Regimes, One Shipping Product Four regimes, four instruments: a product safety file in Brussels, a court docket in Washington, a filing number in Beijing and a review date in Addis. The artefacts converge. The triggers do not.
- The seniors of 2034 are not being hired this year Judgment is manufactured in the boring ninety percent of junior work. Shut the intake now and the shortage lands in the mid-2030s, when nothing can fix it quickly.
- Agentic AI in Production: 95 Percent on SWE-bench, 21 Percent on Real Work Frontier models resolve about 95 percent of SWE-bench Verified and 20.6 percent of OSWorld 2.0. The gap is not noise. Step count, not task difficulty, is what still breaks agents in production.
- Robot Safety Standards Are Written Before Any Law Arrives Robot governance is settled in ISO committees, insurer-funded biomechanics labs and tender documents, years before any parliament votes. In July 2026 the EU moved its own deadline to wait for the stan
- Technology as Soft Power Arrives Through the Side Door Influence is rarely won by broadcasting. It is won by becoming infrastructure: the handset default, the network the engineers trained on, the standards seat, the freely licensed model weights.
- Wheel-Legged Robots: Buying Two Locomotion Modes, Paying for Three Wheels are cheap per metre. Legs handle discontinuity. The hybrid tries to buy both and pays in actuators, distal mass and a switching problem. An engineer's account of when that trade is worth it.
- AI Developer Productivity Is Not a Headcount Argument AI efficiency has become a euphemism for cutting staff. That is the least imaginative use and the one with the worst evidence behind it. What the trials actually show, including the negative ones.
- AI Detection in Education Cannot Work as Policy The argument about AI in education is being run as a cheating story. That is the least interesting version of it. Assessment was always measuring a proxy, and the proxy is what broke.
- The notebook goes in the bin every December Informal trade, smallholder yields and African-language speech mostly never become records. That missing measurement, not missing engineers, is what limits what can be built.
Signals
1Projects
6- Presenting technical work in a second language changes the preparation Second place at Tencent's OpenClaw hackathon in Shenzhen, pitched and defended in Mandarin. What changes about preparation, and what reading Chinese sources first-hand actually buys.
- Mentoring at the Global Youth AI Hackathon Mentored young builders at the Tencent-organised Global Youth AI Hackathon in Shenzhen, working on scoping, harness design and the difference between a demo and a system.
- How to Start a Tech Community in a City Where You Are Foreign The mechanics nobody writes down: why first meetings fail as coordination problems, why narrow definitions travel further than broad ones and what the Greater Bay Area gives an African engineer.
- Volunteer Organisation Succession Planning Starts With a Membership Record Volunteer bodies decay when the rules live in one person's head. The order that prevents it: membership record and constitution first, programmes last, with a gate between every step.
- Your sourcing agent is paid to make the order bigger Sourcing agents are paid a percentage of what you spend, so every good procurement decision costs them money. What engineer-led, fee-based buyer representation actually involves instead.
- Teaching Scratch Programming to Secondary School Kids in Nigeria A volunteer-led initiative that introduced Scratch programming to secondary school students in Lagos, Nigeria, empowering young minds with foundational coding skills and building a generation of future-ready digital creators."
Tools
5- 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.
- Genesis World review Open-source multi-physics simulator for robot learning. Rigid, FEM, MPM, SPH and PBD solvers share one Python scene, with a photoreal renderer and heavy GPU batching.
- Unitree UnifoLM review Unitree's open robot learning stack: world models, a vision-language-action policy, Isaac and MuJoCo training environments and the SDK that drives Go2, G1 and R1 hardware.
- Descript review Text-based video and audio editor with an agentic assistant, Underlord, for teams producing demos, talks and podcasts without learning a timeline editor.
- Meshy review Beijing engineering team, Santa Clara registration, founded by Taichi author Yuanming Hu. Meshy 6 reached general availability on 18 January 2026 with meshes to roughly 600K faces.