Career

TL;DR Career-Ops is an open-source tool built on Claude Code that inverts the job search power dynamic - giving candidates AI-powered evaluation and application tools to match what companies use to filter them Each opportunity is scored across 10 weighted dimensions on an A-F scale, producing a structured comparison that replaces the ad hoc spreadsheet most candidates rely on The system generates ATS-optimized resumes dynamically tailored to each job description and auto-discovers new postings from 45+ pre-configured job boards A key design principle is human-in-control: nothing auto-submits, the AI recommends and the candidate decides, making it a decision-support system rather than an automation Career-Ops is a clean example of the broader pattern of AI tools that amplify individual judgment rather than replace it - worth studying for its architecture as much as its use case The job search has long been a one-way mirror - companies deploy AI to filter applications while candidates manually juggle spreadsheets, tailor cover letters, and hope their resume gets past the automated screener. Career-Ops flips that script entirely. Built on Claude Code, it’s an open-source system that gives job seekers their own AI advantage: intelligent evaluation of opportunities, automated customized applications, and systematic candidate strategy. ...

TL;DR AI has collapsed the middle rungs of the engineering ladder by automating execution - the junior-to-architect progression no longer works the way it did The emerging split is two human roles: Architects who decide what to build and why, and Builders who turn architectural decisions into precise, testable specifications Neither role exists to write code - code-writing is incidental to both, and AI handles the bulk of implementation The two paths require genuinely different skills that do not build cleanly on each other; taste for architectural judgment and clarity for specification are separate capabilities If you are a junior engineer in 2026, you need to choose your path now - the traditional ladder is a trap, and “I write good code” is no longer a sufficient value proposition For forty years, the engineering career ladder has looked like this: ...

TL;DR Modern knowledge work has quietly built identity on producing things - and AI pressure makes that fragility visible without you having to lose your job to feel it History (Keynes’ 1930 prediction) suggests freed-up capacity defaults to “more work”, not leisure - the shift to meaningful work has to be chosen deliberately What stays valuable when execution gets cheap: deciding what is worth doing, taking responsibility, sitting with other humans, craft for its own sake, and growing other people The “everyone will do deeper work” narrative ignores the dignity problem - for many people, work is structure and belonging, not just a vehicle for meaning Put your meaning somewhere that does not depend on being the cheapest producer of an artefact - it was never a secure place to put it, and agents are just making that clearer This is another “thinking out loud” post, in the same spirit as the agent-first architecture piece. I do not know how any of this is going to land. I am writing it partly because the question has been rattling around in my head for months, and partly because I suspect a lot of people in and around software are quietly wondering the same thing without quite wanting to say it out loud. ...

TL;DR The work AI now automates - boring tickets, bug hunts, boilerplate - was the unspoken apprenticeship that turned juniors into seniors The skills that work built (pattern recognition, systems intuition, taste, calibration) are built by doing, not by reading - and that doing is now cheapest to delegate The new apprenticeship shifts toward reading over writing, debugging agent output, earlier architectural decisions, and deliberate practice of things agents do badly There is a coordination problem: individual organisations rationally skip junior investment in the short term, but the senior pipeline thins industry-wide a few years later If you are starting out today, optimise for proximity to a great senior engineer above salary, title, or any other variable The views in this post are my own personal reflections on the industry as a whole, written in my own time. They are not about any specific employer, team, or colleague, past or present. ...

TL;DR When AI handles execution, the jobs that survive are those built on judgement, empathy, embodied skill, accountability, and taste - none of which AI can fully replicate The five durable categories are: human relationship professionals, AI wranglers and system architects, skilled trades, creative producers, and human trust and accountability roles Skilled trades (plumbers, electricians, HVAC engineers) are particularly resilient because general-purpose robots that handle unpredictable physical environments remain a hard unsolved problem AI wranglers - people who set objectives, constraints, and guardrails for AI systems - are a new and growing category driven by regulation like the EU AI Act and NIST’s AI Risk Management Framework The meta-pattern is interpretation vs execution: AI excels at execution, so high-value humans are those who bring judgment and responsibility to the question of what should be built and why Assume AI is “everywhere” - what still needs actual humans? ...