Do I Still Have a Job?
STATUS: YES
One question, run as an experiment: what happens if you take the human out of the loop? I built every reference here by steering an AI — the leaderboard is the same models, on the same brief, with me gone. The distance between them is the job.
Every model launch swears it can "replace your senior engineers." Adorable. So we took the inixiative ecosystem's hardest primitives — the small, brutal ones that take real engineering to get right — handed each agent the same product brief a human gets from a kickoff, and let it cook. Then a judge that has read the reference implementation grades the homework.
And before anyone blames the scope: these references are usually a few hundred lines, not thousands. That's the trap. Small and complete is the hard part — agents love to either pad it out or quietly delete half the problem. The reference did neither.
The industry wants to hand these things the keys and walk away. Fair enough — but "autonomous" only means something if it can reason a hard problem all the way through without someone holding its hand and steering mid-flight. So we don't steer: one brief, one sandbox, zero hints, zero "are you sure?". Off the leash. Let's see how far they get before they need an adult in the room.
And it's a fair fight: every reference was written 100% with AI too — same models, same tools the agents get. The only variable is whether a human is in the loop, steering. That's the whole point — it isn't human-vs-machine, it's the same machine with and without an architect. Autonomy is the dividing line — and so far, the cliff.
Getting the tests to pass is the participation trophy. We score the part that's hard. Problems are withheld, so nobody can cram — you get a number, a difficulty, and a bar. The agents get perspective.
Test #1
architect golden · 175 loc 12 contendersTest #2
principal golden · 415 loc 10 contendersBar length = the selected metric (rescaled per test). On score & corners, color = completion: green complete, amber partial, red fail; cost metrics (tokens, seconds) run neutral. On LOC, the dashed mark is the golden's size (1×) and color reads economy — green at or under it, amber up to 2×, red beyond. self = the difficulty the model thought it was facing. golden = what the run did to our confidence in the reference (so far: more convinced, not less).
Wait — what are these problems?
They're drawn from inixiative: a set of small, sharp, open-source primitives — an authorization core, a rules engine, a lifecycle guard — each built to be minimal and complete. Apparently that's the hard part. Come see what we're building.
Interested in the bench — running it, contributing problems, or the methodology behind the numbers? Get in touch →