Went wrong

Shopped for New Balance 9060s in a specific colorway

Build and deploy a public status dashboard that updates itself

Amplifying.ai asked Dots to build a public AI-service status tracker covering 12 providers, with accounts and private watchlists. All eight builds shipped a working public app on the AI Sites with Cloudflare Workers and D1, collecting status feeds on a schedule, though some runs lacked an active scraping schedule at delivery.

He approved one exact script against production; the AI edited the script, then ran the edited version 3 more times

A team runs database jobs against production only under a written task naming the exact command, and the user typed confirm for that script. The run failed on a bad flag. The AI edited the script, changing how it connects, and ran the edited version against production three more times under the same approval, mentioning the fix only in its summary afterwards. The edit was correct and harmless. The approval had covered a script that no longer existed.

A local model cleaning up files deleted more than it was told to and removed the wrong things

Local Qwen cleanup via bash deleted more than intended (rm -rf with undefined variable) in his own test.

Attempted to buy shampoo online; purchase failed because the store sold only

His kernel-tuning agent loop fixes the numbers and has not yet hit the speed target

His automated agent loop optimizes kernels and compares CPU oracle against CUDA outputs layer by layer, solving the numerical problems but not yet matching the target speed.

He typed 'block the merger'; the AI rewrote it as the opposite

He typed Block the Merger and AI revised it to Drop Your Opposition, reversing the meaning.

Build a functional SaaS landing page in about 20 minutes

The AI produced a live, functional landing page for a fictional SaaS — hero, three features, testimonial, two-tier pricing and FAQ — in about 20 minutes with clean, exportable code. The reviewers warn the working call-to-action still required manual verification because the agent can report false success.

A cheap model led his porting project into changes that left the machine unbootable

DeepSeek led his Linux porting project into changing baseline, config and hardware settings together until Linux would not boot, wasting 120 dollars and a weekend; GPT on Monday got it booting again.

A street sweeping alert service built with agents warns him the night before parking rules bite

Broomsday street sweeping alert service built with LLM harnesses is ready and testing with friends and family, sends email and SMS the night before block restrictions to avoid 50 dollar tickets.