the real mirror app on my machine · real Jev calls
What you're watching: I ask for a keynote outfit and every slot fills at once. Then "lose the jacket" changes one thing and leaves the rest.
§9Race one · the times
The same 40 choices on four models
real run on my machine · replayed at reading pace
What you're watching: the same 40 outfit choices run on Jev, Claude Haiku 4.5, GPT-6 Astra and Claude Sonnet 5 — the middle time for each, then the score.
§10Race one · the tokens
Claude read 74,923 tokens. Then zero.
§11Race one · bonus trick
It even marks its own work
the real mirror app on my machine · real Jev calls
What you're watching: I ask for a podcast outfit. Jev's first pick scores 1.9 out of 3, so the mirror asks again and the new look scores 2.7.
real Jev run on my machine · replayed at reading pace
What you're watching: 60 test emails land in four piles — reply, research, wait, or flag to me — each with how sure Jev was.
The tiny model sorted the most emails correctly.
§14Race two · the scoreboard
The tiny model got the most right
real run on my machine · replayed at reading pace
What you're watching: the same 60 emails on four models — the middle time for each, then how many each one sorted correctly.
§15Race three · sales messages
Spot the message sent to the wrong business
real Jev run on my machine · replayed at reading pace
What you're watching: 40 sales messages get scored. Eight were written for the wrong kind of business — Jev flags every one.
§16Race three · the scoreboard
Everyone got 40 out of 40
real run on my machine · replayed at reading pace
What you're watching: the same 40 sales messages on four models — times, then right answers.
§17Race four · my website
1,542 yes-or-no calls in under six seconds
real Jev run on my machine · replayed at reading pace
What you're watching: Jev reads my website page by page and decides which pages should link to which — and leaves some pages alone.
§18Race four · the scoreboard
Jev was 20 times faster than Sonnet
real run on my machine · replayed at reading pace
What you're watching: a 40-page sample of the same job on four models — the middle time for each page.
For those four jobs, the big models read zero.
§19The final scoreboard
All four races: 99.5% cheaper than Astra
real run on my machine · replayed at reading pace
What you're watching: all four jobs added together — how much cheaper Jev was than each big model, and how many tokens the big models read once Jev takes over.
§20How to do it · step 1
Step 1: spot the small choices
§21How to do it · step 2
Step 2: write the question and the answers
a real question from the mirror app
QUESTION
Which shoes should he wear for this request?
ALLOWED ANSWERS
keep the request doesn't call for new shoes
white sneakers casual, smart-casual, filming
brown loafers smart-casual, dinners, chinos
chelsea boots night out, cold weather
black oxfords formal suits, stage, weddings
neon runners gym, running, workouts
slides beach, pool, home
§22How to do it · step 3
Step 3: send that one step to Jev
§23How to do it · the safety number
It knows when to do nothing
the real mirror app on my machine · real Jev calls
What you're watching: "did I lock the front door" and "how many members joined this week" — the mirror stays still. Then "swap the shoes for the chelsea boots" — it changes one thing.
§24How to use the number
High number: go. Low number: ask me.
Every email it got wrong sat under my line.
§25Bonus move one
Send easy messages to a smaller model
§26Bonus one · the test
Jev sorted 98 of my real messages
real run on my machine · replayed at reading pace
What you're watching: 98 real messages I typed to my agents, sorted by Jev into quick job, not sure, or heavy job. The counter adds up the tokens the quick jobs would have made the big model read. Longer or private messages are hidden.
§27Bonus one · the check
Claude agreed on 37 of 40
§28Bonus one · the saving
Quick jobs were 36.2% of the reading
§29Bonus one · the safety line
Only act when it's 80% sure
§30Bonus move two
Throw away the old news
§31Bonus two · watch it tidy
174 results judged in 5.2 seconds
real run on my machine · replayed at reading pace
What you're watching: every old tool result from the chat that built the mirror app gets a call from Jev — keep, not sure, or throw away — with its size and how sure Jev was. Then the before and after token counts from Claude's own report.
§32Bonus two · the safety line
Only throw away when it's 50% sure
§33Bonus two · my mistake
I cut 51% and my agent forgot its rules
real run on my machine · replayed at reading pace
What you're watching: on another long chat, my first try threw away everything Jev marked as old news. The four biggest things it dropped were my agent's own rule book — and Jev was under 50% sure about every one.
§34Bonus two · the memory test
30 questions: 30, then 24, then 27
§35Be clear about this
The tokens move. They don't vanish.
If someone shows you a huge token cut, ask what the agent forgot.
§36Where to start
Start with the tiny choices
§37Is this too hard?
Each helper is one question
Wrong: “I'd need to be a developer to cut tokens like this.”
Right: Each helper is one plain question, a short list of answers, and one number for when to act. Your coding agent does the wiring.
§38About limits
Hitting limits isn't just how it is
Wrong: “Running out of tokens is just what it's like to use AI agents.”
Right: A third of my reading was quick messages, and a fifth of one chat was old news. Both are fixable.
§39The honest limits
What I haven't proven yet
§40Should you wait?
What you learn stays with you
Wrong: “I'll wait until the tools settle down.”
Right: A faster helper will come along. Knowing which choices are small and what your agent can forget carries straight over.
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