Notes from inside the loop.

Pop-psychology strips the conditions out of real science and sells the residue as a superpower. Case study #1: Flow. The mainstream “summon a magic zone” version inverts what Csikszentmihalyi actually found — flow emerges when you engineer three specific conditions on top of thousands of hours of expertise. It’s a reward for mastery, not a technique for reaching it, and there’s a three-question test that catches the laundering every time.

Point an AI at a codebase and it slop-codes itself into a context death spiral — the code gets so bad the model can’t reason about it anymore. The way out is three steps: state the general intent with a source to learn from, drive the design, and iterate. Plus the one move that shrinks the context (separate data from control) and the six behavior-preserving rules underneath it. Framework credit: Goju Tech Talk.

Tech decisions, vendor contracts, and spend each live in a different system, so nobody can see a decision’s blast radius before making it. Six problems no governance tool solves — the $15M time bomb under a growth curve, sunsetting that raises spend, the 87 vendors nobody owns, procurement brought in too late, invisible cross-contract bundling, and why a six-figure stack still can’t say what breaks. And what closing the loop looks like.

Researchers hijacked 26,000 AI agents with fake skills sold through Instagram ads, and Cisco, NVIDIA, and skills.sh all marked the package safe. Goju explains the trick: review the files, then swap the payload behind an external URL. The deeper problem is worse: a skill written in plain English can't be formally verified the way code can. Why marketplaces are shipping unsigned code, and what a formal skill compiler would actually fix.

Most “AI marketing” is a prompt in a template or a bet on an autonomous SDR, and the receipts are brutal: 95% of GenAI pilots show zero P&L impact, a wave of “AI” founders charged when the autonomous product turned out to be people in a call center, and 1,200+ court cases over hallucinated filings. The motion that actually moves pipeline rejects both lies: detect real buying pain early, instrument every agent like production infrastructure, earn access like an engineer, and measure pipeline, not activity.

A web version of the original cover-letter PDF: the biographical through-line, the refund-reduction and retention wins, the “conversational canvass,” Dr. Oz / HealthCorps proof, paid-versus-earned ad views, and the process maps behind how Maceo turns messy growth systems into something a team can actually run.

The one number I use to decide my day: an energy-aware, buffer-managed prioritization score, now denominated in dollars. Play with the live formula (it reads “$438 per focus-hour”), watch urgency rise with neglect even when nothing’s due, and slide the horizon lens where a cheap daily habit overtakes a big one-off at 18 months. Then read the full write-up: the two times an adversarial test broke it, and a 30-day simulation where it beats “do the most valuable thing” and “do the most urgent thing.” 0.17 missed deadlines a month vs 2.92.

The companion to DailyFlowScore. Everyone nods at “quantify your Cost of Delay,” then gets stuck, because the dollars were never in the task. They live one level up, in a plan most people never wrote. Work backwards: a round plan → a financial model → dollars on everything (even the workout and the post) → and the per-task number finally falls out in a unit you already understand. Five minutes, eight million dollars.