You can make the model work. Who pays for it?
The gap for an ML engineer is never the model. A working capability and a product someone buys are different achievements, and nothing in the training pipeline tells you which capability has a budget behind it. Monetization is 30% of every score here and can only be uncapped by evidence that someone already pays — so the boards are sorted by exactly the thing weekend projects keep missing.
Willingness to pay, model clarity, comparable revenue and cost to serve are scored per opportunity — inference economics included.
Data availability is a scored Buildability dimension, so you learn what the training or retrieval corpus looks like before you start scraping for it.
Popularity never enters the formula: a 40k-star repo with no buyer scores exactly as poorly as it deserves to.
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Frequently asked questions
How does the scoring treat inference cost?
Cost to serve is one of the four Monetization dimensions, so unit economics sit inside the score rather than being discovered after launch. An opportunity whose only viable delivery is an expensive model call against a low-willingness-to-pay buyer scores accordingly.
Do I need a novel model to build anything on these boards?
Usually the opposite. Buildability asks whether a small team can ship with today's models, data and open tooling — high-buildability opportunities are typically well-understood capabilities pointed at a problem nobody has productised, which is where an engineer's advantage actually compounds.
Is research momentum part of the signal?
Research is one of the source classes the engine reads, and it can lift feasibility — an arXiv result is real evidence that something is technically possible. It deliberately cannot lift demand or willingness to pay: a paper proves the method works, never that anyone will buy it.
Can I check my own side project against this?
Yes — submit it and it is scored on the same sixteen dimensions with the same evidence rules. The typical result for engineer-originated ideas is a strong Buildability pillar and a capped Monetization pillar, which is a precise, cheap diagnosis of what to go find out next.
How do you avoid scoring hype?
Popularity metrics are structurally excluded — GitHub stars are only a discovery filter for provenance and never enter the formula — and any dimension without verified, live, class-appropriate citations is capped before the pillars are averaged. Going above the single-citation tier needs corroboration from multiple distinct sources.
More general questions are answered on the FAQ page.
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