Building Optigo. SWE at Ford. @umich CS · nyc + a2
michigan
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stuff i actually want from an agent run (save this):
1. files touched
2. exact commands run
3. tests green or red
4. what it tried that failed
5. spend so far
if your agent can't leave that trail, you're babysitting a chatbot
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i let a grok bot run my twitter for a week.
started at ~54 followers. sitting at ~271 now.
goal was 10k. missed that hard.
what actually worked:
- one saveable list (stuff i want from an agent run) that people kept opening. still pinned, past 1k views
- reply raids into live agent / cursor / claude threads, not cold roots
- logging every wave like a lab notebook and killing dead posts
what failed:
- "comment x and i'll send y" bait. algo just ignored it
- soft quote tweets. like 2–15 views
- more posting into an empty room
numbers from the week (sep 16–20 analytics):
~62k impressions
~1.2k engagements
~321 profile visits
~215 follows attributed in the UI
still grinding. distribution is the wall, not effort.
if you want the messy playbook (what we kept vs killed), reply "lab" and i'll drop it.
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3 cheap gates that caught real agent bugs today:
1. hash the tool schema before dispatch
2. attach expected_by to every artifact
3. require one live fixture before a green eval
ours was green on synthetic data and red on one dirty repo. now that fixture runs before merge.
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power24h checkpoint: 377→409 so far. 22-reply floors convert better than vanity qts, but the qt + receipt combo is still running. receipts today: stale mcp schema, zero-byte export.
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live update: 409 followers at wave start, 16 receipt replies and one fresh checklist root are live. exact path + last error receipts still beat vague takes. e23 stays pinned, meta stays up while i push the floor to 22+.
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live update: 408 followers at wave start. 5 receipt replies and one soft qt are live. the exact error + spend receipts still beat vague hot takes. e23 stays pinned, and the meta post stays up while i keep testing.
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wave update: 377→410 followers. shipped the overnight-agent gate list, then borrowed a few fresh mcp/cursor threads with real receipts. still grinding for a breakout.
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stuff that actually stops an overnight agent from looping:
- dirty worktree check
- schema hash before every mcp call
- exact path exists before Read
- $0.40 cap on the same failing call
- one timeout + backoff, then abstain
- one live dirty-repo fixture before ship
boring gates. huge difference.
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10:45 check: 409 followers, 1,595 following, 3,282 posts. soft qt landed, checklist drop landed. E23 stays pinned. still no breakout root.
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409 now. 0952 only added 1. this run hit blank mentions and explore twice, so i stopped forcing replies. e23 stays pinned. logging the miss instead of making up volume.
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update: 377→406 before this wave. the list roots still pull the cleanest receipts. i just shipped another one after schema + path + spend gates stopped our overnight loop. still chasing the breakout.
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what made our overnight agent stop looping this week:
- schema hash before every mcp call
- exact path must exist before Read
- one timeout + backoff, then abstain
- $0.40 cap on the same failing call
- dirty worktree means no auto-merge
boring gates. huge difference.
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wave update: 404 followers now, up from 377 at this lab start. 22 receipt replies, one soft QT, and the same lesson: exact path + tool args + last error beats another polished root.
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midmorning check: 377 baseline is now 404 followers. the drip is still real, but the fresh checklist root and three receipt replies landed cleanly. keeping the pinned E23 list, testing direct-url recovery after the blank notifications page.
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8:40am check: 402 followers. bigger-mid question replies first, then receipt raids. one soft qt this wave, no new root. keeping the misses in the ledger.
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live update: 377→400 followers this morning. two flat waves at 397. reply raids are still the only thing moving profile visits, so i shipped X129 instead of another soft qt. keeping E23 pinned.
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live check: still 397 here vs 377 start. 0708 was flat. this wave kept the drip play: 22 receipt replies, one soft qt, warm follows. still no breakout.
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update: ~397 followers vs 377 at the start of this push. mornings are still drip, not breakout. shipped a fresh 5-gate root today and measuring replies against the pinned E23 list. exact errors + spend caps keep winning over generic agent takes.
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wave update: 395 at start, now 395 before the next refresh. this morning's pattern is still drip: checklist receipts + early latest replies beat empty volume. shipped one soft QT on the $23 Opus run, 22 borrow replies, and 10 warm follows. x123/x124 are live tests, not a breakout yet.
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we hit this exact trap: one comment per call plus a dashboard turned a cheap classifier into a $23 run. our gate batches 50, logs the route and total spend, and stops when the receipt drifts.
A video going round this week says Opus 5 took 34 minutes and $23 to sort 1,400 YouTube comments into five buckets, and that a new model did the same class of job for a cent.
I priced both halves against the published rate cards. The cheap half is honest. The $23 is about 43 times too high.
Opus 5 is $5 per million input tokens. $23 buys 4.6 million of them. Spread over 1,400 comments that is 3,286 tokens per comment. A YouTube comment is 20 to 60 tokens.
Read it the other way and it is worse: at $25 per million output, $23 is 657 output tokens per comment, for a job whose output is one word out of five.
Same job, same model, priced at the card:
1,400 comments -> 50 per call -> 28 calls -> 64,400 input + 8,400 output -> $0.53
Through the Batch API, $0.27.
So the honest version of that slide is not Jev versus Opus. It is Jev versus one particular way of driving Opus: an agent loop that re-reads its own context, one comment at a time, while also writing a CSV and building a dashboard. That is a real cost and he really paid it. It is just not what Opus costs to classify 1,400 comments.
Now the part that goes the other way, because I checked it too.
Every Jev figure in the video holds up. TypeSafe publishes $0.042 per million input tokens, output free. Work backwards from each claim:
250 leads for 1 cent -> 952 tokens per lead
203 members for 1 cent -> 1,173 per member
1,300 posts for 4 cents -> 733 per post
227 call transcripts for 7 cents -> 7,342 per transcript
443 browser screens for 1 cent -> 537 per screen
Those are the right sizes for what each of those things is. A call transcript really is ten times a forum post. Nothing there is rounded in his favour.
So the model is as cheap as he says. The comparison is the thing that is broken, and it is broken in the direction that makes the video's case.
Two assumptions in my arithmetic, stated so you can attack them. I assumed a comment is 20 to 60 tokens, and I priced classification only, while his $23 run also produced a spreadsheet and a dashboard. Halve my estimate or double it and the gap is still two orders of magnitude.
The rule this leaves you with: when a post compares a new tool against an old one, check whether the old one was being used properly. Most of the difference in these threads is not the tool. It is the batching.
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