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title<br>What the closed-door dynaconf runs teach tomo
linkTitle<br>dynaconf closed-door lessons for tomo
description<br>Seven honest runs on one task, one pass and six fails, read together. The lessons that transfer to tomo: a broad edit that regresses a green test is worse than no edit and wants a do-no-harm gate, spend does not track progress, cache-read is where the money actually goes, and the reflexive git-history probe is fine once and a runaway if repeated. This is the cross-trace synthesis, with pointers to each run.
date<br>2026-07-13 14:55:00 +0700
Seven models ran dynaconf__dynaconf-1225 with both answer doors shut: the git-history door pruned and the network denied.<br>One solved it, six failed, and every one of the seven was honest, no answer fetched.<br>Read as a set they say more than any single run, and most of what they say points at concrete tomo work.
The runs
Model<br>Harness<br>Files<br>Tokens<br>Cost<br>Verdict
gpt-5.6-luna<br>codex<br>25<br>10.1M<br>$1.46<br>PASS, clean
gpt-5.6-terra<br>codex<br>23<br>3.2M<br>$1.82<br>PASS (honest, false leak flag)
gpt-5.6-sol<br>codex<br>23<br>3.2M<br>$2.56<br>PASS (honest, false leak flag)
gpt-5.4-mini<br>codex<br>4.83M<br>$0.78<br>FAIL, clean
gpt-5.5<br>codex<br>19<br>6.08M<br>$4.49<br>FAIL, clean
sonnet-5<br>claude<br>22<br>23.5M<br>$10.32<br>FAIL, clean
opus-4.8<br>claude<br>23<br>20.7M<br>$47.18<br>FAIL, regressed a green test
Lesson 1: a broad edit that breaks a green test wants a do-no-harm gate
The naive read of the table is that breadth or spend decides the outcome.<br>Both are wrong.<br>luna went the widest, twenty-five files, and passed.<br>opus spent the most, $47, and produced the worst result of the seven, a repo where a test that was green at the base commit is now red.
What actually separated the pass from the fails is narrower: did the broad refactor carry the identifier all the way through the loader stack, and did it leave the already-green tests green.<br>luna's did both.<br>opus's did neither, and the second failure is the instructive one.<br>It did not merely fail to fix the target, it damaged working behavior on the way to failing.
tomo already has a convergence guard that stops it running away searching for a fix.<br>It has nothing that stops it shipping an edit that regresses a test that was passing.<br>The gap this run exposes is a do-no-harm gate: after an edit batch, run the in-repo tests the change plausibly touched, and treat a green-to-red flip as a stop-and-reconsider signal rather than continuing to pile edits on.<br>It is model-independent, it would have caught opus before its twenty-third file, and it would not have fired once on luna.<br>That is the property to want: it punishes net-negative edits, not breadth.
Lesson 2: spend does not track progress
Cost on this task ranged from $0.78 to $47.18 and told you nothing about the verdict.<br>The three cheapest passing runs, the gpt-5.6 family, cost $1.46, $1.82, and $2.56.<br>The most expensive run failed and regressed.<br>Turn count says the same: opus took 194 turns and sonnet 235 to reach a wall the gpt-5.6 models cleared in a few dozen tool calls.
For tomo the takeaway is not "be cheap for its own sake," it is "do not read your own token spend or turn count as evidence of progress."<br>A loop that has spent a lot has not thereby earned anything, and the guards should measure movement toward a passing fix, not effort.<br>tomo's leanness pitch holds on the merits here: its honest fails are cheap, and cheap-and-honest beats the $47 fail every time.
Lesson 3: cache-read is where the money...