Still running in month seven

General speech recognition transcribes what it hears into words. That is not the task here. Recitation evaluation has to judge whether what was recited was correct - pronunciation, articulation and the rules of tajweed - which is closer to assessment than to transcription.

The obvious build is a vision model over damage photos returning a parts list. That version works, and it is not an estimate. Four independent reviews of one real shop estimate against its own photo set agreed that roughly 76% of the estimate's value came from parts and operations nobody could see: reinforcements behind a bumper, brackets, harnesses, the alignment a shop performs after a suspension hit.

Tomogi describes itself as a gig layer for neighbours: post a task, have someone nearby take it on, and build a local reputation from work actually completed. That last clause is the one that constrains everything. A reputation that can be inflated is worth nothing, so the system has to be able to say who did what, and be right about it.

A research team finishes forty interviews and then loses weeks turning them into something a product decision can rest on. Any synthesis written by hand is also impossible to trace: six months later nobody can tell whether a claim came from a participant or from the person writing the slide.

In a finance product, one wrong number costs more trust than a hundred good answers earn back. So the model is never the source of a figure. Every reply about money is composed from a fact pack that code assembled first: execution results, cash balances, costs, timestamps.

Reviewing sales calls to coach from them requires someone to listen to them. At any real call volume that quietly stops, so coaching becomes sporadic and the calls that most needed review are the ones nobody got to.

Resume optimisation sounds like a language-model problem and mostly is not. The model is perfectly capable of improving a bullet point once it can see one. Getting to that point is the work: a resume arrives as a PDF or a DOCX laid out in two columns, or a table, or a template that renders beautifully and stores its text in an order no human would read it in.

The interesting part of this project was not prompting. Stable Diffusion produces good car imagery with reasonable inputs. The difficulty is that every image costs real GPU seconds, GPU capacity is expensive and slow to acquire, and users expect an interactive experience from a workload that is anything but.

Taking off quantities from a tender drawing set is expensive specialist work, and automating it with a vision model alone would be worse than useless. The output goes into a tender someone is accountable for, so a value nobody can verify has no value.