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? ...

A selection of my music production work from November 2025. Browse other months All Tracks

Traktor MX2: A New Beat in DJ Creativity The DJ world is buzzing, and for good reason: Native Instruments has dropped a controller that’s both familiar and forward-thinking. The Traktor MX2 isn’t just another piece of DJ gear. It’s a compact performance instrument that wraps pro-level tools into a friendly two-channel format - perfect for bedroom DJs stepping up their game, vinyl lovers exploring digital workflows, or seasoned selectors looking for a portable second rig. ...

A selection of my music production work from October 2025. Browse other months All Tracks

Suno has launched Suno Studio, a cloud-based platform the company is calling the first generative DAW - a digital audio workstation built around AI from the ground up. Where a traditional DAW like Ableton Live or Logic Pro treats sound as something you record, sample, or program, Suno Studio treats sound as something you can ask for in plain language and then shape on a timeline. What it actually is Suno Studio sits in the browser. You can prompt for a stem - a drum loop, a bassline, a vocal hook - and drop it onto a multitrack arrangement. From there you get the controls a producer expects: tempo, pitch, volume, basic effects, and the ability to slice and rearrange. Stems can be exported as audio so the project can continue in a conventional DAW if you want to mix it properly later. ...

If you’ve been following Polkadot, you’ve probably heard “Agile Coretime” mentioned alongside “Elastic Scaling” and “Asynchronous Backing.” It sounds technical, important, and confusing. This post explains what it actually is, why it matters, and what it means for the network. The short version: Polkadot used to allocate blockspace like reserved parking spots. Agile Coretime makes it more like a parking meter - you pay for what you use, when you use it. ...

A selection of my music production work from August 2025. Browse other months All Tracks

A selection of my music production work from June 2025. Browse other months All Tracks

TL;DR A growing collection of interviews with Sergey Brin, Google co-founder and an active voice on AI now that he is back hands-on at Google working on Gemini His perspective spans foundational models, open-source strategy, AI robotics, AGI timelines, and the future of human-computer interaction Brin’s return to coding in 2024 makes him an unusually direct signal of how Google is positioning itself against OpenAI, Anthropic, and the rest of the frontier lab field Sits alongside the Geoffrey Hinton interviews and other founder-level commentary on where AI is headed Page will be expanded as new interviews appear - this is a working index, not a finished archive About Sergey Brin co-founded Google with Larry Page in 1998 while a PhD student at Stanford. He stepped back from day-to-day operations at parent company Alphabet in 2019, but returned to active engineering work on Gemini in 2024 as the competitive pressure from frontier AI labs intensified. ...

TL;DR An “emergency debate” framing the case that AI agents will displace large parts of the workforce inside a 24-month horizon Contributors disagree on speed but agree the direction is settled - the question is which sectors move first, not whether they move Near-term pressure is on roles built around predictable, repeatable cognitive work; durable roles cluster around judgment, taste, and accountability Education and training systems are slower to adapt than the technology, which creates a real workforce mismatch in the meantime Worth watching as a snapshot of the 2025 conversation - useful frame even where you disagree with the specific predictions About This debate explores urgent questions about AI’s impact on employment and the workforce. Contributors discuss the timeline for AI-driven job displacement and the societal preparations needed to adapt to rapid automation. ...