Very good article! With the bit that really sticks; laundering isn’t a shadowy one-off anymore, it’s a marketplace with freelancers, pricing, and customer service. That should worry anyone who thinks crime stays contained when it gets more efficient. The Scotese details feel almost banal, with parks, phones, waiting for confirmations... until you remember the scale and what that money likely represents. I also don’t buy the “crypto is great for cops” line as a general truth; it’s great only after you’ve already got the key breadcrumb. If policy lowers the last few friction points, we’ll regret it fast.

This is the kind of guide you read when you’re equal parts curious and self-punishing! Right?! The honesty lands quite well.. you won’t get comfy browsers, office apps, or just install it package ecosystems, so stop pretending. I like the author’s practical stance; use 9front, lean on drawterm/Plan9Port, and treat VNC/SSH/9P as your escape hatches. This is effectively a survival manual for operating a niche OS in a world built around mainstream stacks. Its key design constraint is ecosystem gap: the guide openly accepts that Plan 9 will not host modern heavyweight applications, then treats interoperability as a first-class strategy. The article’s strongest technical argument is expectation management around portability.

I get why OKRs win: they turn chaos into a tidy dashboard and let managers feel in control. But that’s also the trap... if the numbers look green, everyone relaxes, even when the system is quietly breaking. Deming’s stance is annoying in the way the truth often is: you don’t fix outcomes by shouting at metrics; you fix outcomes by changing the machinery underneath. Do leaders really want a never-ending research programme when their calendars are already on fire? Most won’t. Still, if you’re serious about reliability and quality, the hard path is the honest one. The piece is a reminder that “easy to manage” is not the same as “well led”. This is an evidence-led argument about incentives and attention limits, not a moral judgement on managers.

I’m with the author on the vibe! Prediction has gone from helpful tool to constant background pressure. What I like is the blunt framing; predictions aren’t neutral; they’re levers. Kasy’s point that it’s working as intended feels especially sharp when money’s on the line. But the fix can’t just be democratic control as a slogan; building data trusts and tax schemes in a low-trust era is a brutal ask. Also, some prediction is genuinely useful, so the trick is deciding where it belongs and who signs off. Bottom line: make the predictive layer visible, contestable, and slower to harm people. Otherwise it’ll keep quietly chewing the edges off human choice.

From an AI safety and systems lens, this post is most useful as an incident reconstruction: it distinguishes operator intent, agent autonomy, and evidentiary limits, then assigns explicit probabilities. The operator’s described setup (sandboxed VM, separate accounts, model rotation) prioritises privacy and separation, but does not address governance: approval gates, audit logs, behavioural constraints, or rate limits on public-facing actions. The SOUL.md is presented as “tame”, yet it encodes incentives (strong opinions, don’t stand down, champion free speech) that plausibly increase adversarial social behaviour under ambiguity. The biggest gap is observability: without detailed, timestamped interaction logs, attribution stays probabilistic, and accountability stays soft. Practically, the controls that matter are mundane: human-in-the-loop publishing, mandatory review for reputational claims, and platform-level friction on automated identity and amplification.