Infinto · Skills diagnostics & practice · Singapore
Every skill you have is being re-priced. Infinto measures which parts of your work AI now does outright, which parts you'll be paid to supervise, and which parts compound — then keeps building the third kind, for as long as the ground keeps moving.
Half-life is how long before half of today's task list moves left. These figures are an illustrative model, not measured data — see how we derive them. Your audit uses your own task log.
First half — your skills decay
Skills don't disappear on a schedule — they get hollowed out from the inside. The routine middle of a craft goes first, quietly, while the title stays the same. What's left is thinner and harder: framing the problem, judging output you didn't produce, carrying responsibility for a decision a model can't be accountable for.
We say this plainly because most providers won't. A half-life of 2.4 years is not a marketing number — it's the reason a certificate earned this year is worth less next year, and worth arguing about now.
Second half — we don't stop building
A course ends. Decay doesn't. So the useful thing isn't a qualification that dates — it's a standing relationship with the question, re-run as often as the ground moves under you.
Infinto re-audits your work each quarter, because a band measured last March describes a job that no longer exists. Every re-audit produces a new starting point and a new track. Nobody graduates, and that's the point: the skills are finite, the building isn't.
How it works
You log a fortnight of real work, or connect a calendar and ticket queue. Each task is scored against what current models do unassisted. Output: your band, not a persona's.
Graded exercises in the compounding column: judging model output under time pressure, specifying work, spotting confident errors in your own domain. Reviewed by a practitioner.
You finish with a worked artifact and a scored record of the judgment calls behind it — the evidence a certificate was never able to give a hiring manager.
A new audit against current model capability, a new band, a new track. You can see what held its value and what didn't. This is the part that doesn't end.
Tracks
How to audit model output fast: sampling strategy, error signatures, when to stop trusting fluency.
Read the outline → SpecificationTurning a vague ask into constraints, acceptance tests, and a definition of done that survives delegation.
Read the outline → SystemsAssembling agents and scripts around your workflow without a platform team, and knowing when not to.
Read the outline → EvidenceEvaluation design for people who aren't researchers: baselines, sample size, and honest reporting.
Read the outline → RiskAccountability, disclosure, and the failure modes that end up in front of a regulator or a customer.
Read the outline → PersuasionMaking the case for a change in how work gets done, to people who will be judged on the result.
Read the outline →For organisations
Run the audit across a function and you get a coverage map instead of a completion rate: which judgment your team can back, and where one person is the entire safety net.
Start
The audit is free, takes about four minutes, and nothing is stored. It's the first of many — that's the whole idea.