@Argona0x: Chinese trader in Hangzhou with a 5-monitor setup who sells a $2,000 course on institutional order flow cleared $8,300 …

Chinese trader in Hangzhou with a 5-monitor setup who sells a $2,000 course on institutional order flow cleared $8,300 trading last quarter. his 214 students paid him $61,400 to learn the same method. three thousand miles away, an engineering team's highest cursor accept rate belonged to the engineer who owned 9 of their 11 production incidents that quarter. the monitors were the sale, not the signal. nobody bought the method - they bought the setup. 214 people handed over $2,000 because the configuration looked like what a serious institutional trader uses, and if the setup matches the story, the method must work the engineering floor runs the same script. swap the monitors for a cursor accept rate that looks exceptional on a quarterly ai adoption slide i watched it in real time last year one engineer: highest accept rate on the floor at 71%, loudest voice on ai-native development, $2,100 in quarterly anthropic api spend while the rest of us averaged $380 → PR sizes ran 52% above team median - every diff looked like enormous output → median time to first review on his submissions was triple the floor average, stacking up in the queue → he introduced a try/except: pass block in a payments handler that silently swallowed errors for 6 weeks → added an import that shadowed a local utility across 4 files - invisible until something failed downstream → never ran git diff --stat before committing - cursor's change summary was ground truth for him → by end of quarter: 9 of the team's 11 production incidents traced back to his merges the faros ai engineering report 2026 tracked the same pattern across thousands of teams under high ai adoption: bugs per PR up 54%, PR size up 51.3%, and median time in PR review up 441.5% a jetbrains study of 800 developers ran ide telemetry for two years and found ai users performed 100 extra delete and undo actions per month versus 7 for non-ai users - a 14x rework gap that half of the developers said they never noticed when surveyed directly the metr randomized trial found experienced developers took 19% longer to complete tasks with early 2025 ai tools than without them, and still believed they were 20% faster the gap between what you believe you're doing and what the logs show is the entire product the engineer got promoted before the incident retrospective was finished nobody connected all 9 back to the same source - they were spread across three sprint reviews and the pattern never surfaced he gave the best internal talk on ai adoption anyone had seen all year, and that's what leadership filed away the hangzhou trader has 214 students this quarter the engineer's slide deck is being circulated to three other teams right now the monitors are always the sale