From the original post
How to use Jev, and where it actually gives you the 100x:
setup takes 10 minutes:
1. join the waitlist, people are getting approved same day
🔗 https://typesafe.ai
2. install the official skill so your agent writes correct calls:
- npx skills add typesafe-ai/skills --skill typesafe-ai
on Claude Code it's two commands, the marketplace add on its own doesn't install anything:
- claude plugin marketplace add typesafe-ai/skills
- claude plugin install typesafe@typesafe-ai
3. create an API key in the dashboard
4. in your prompt just say: "use the TypeSafe skill"
now the part nobody is posting:
the 100x isn't the model, it's where you put it
you don't get it by swapping your LLM for Jev
you get it by deleting the calls that never needed a language model
open your agent and find every call that just picks something:
> which tool next
> is this spam
> is this chunk relevant
> does this need a human
> is this diff risky
none of those are writing tasks
they're if statements you outsourced to a frontier model
here's the upgrade, in order:
1. replace each one with a typed question
Choice picks from up to 255 options, Score places it on a 2-10 level scale, Noul returns a raw 0-1
2. batch them
questions in one call run in parallel and barely move the latency, and output tokens are free
so ask every question you might need, including the ones you'll throw away
3. threshold on confidence, not on the answer
under 0.5 escalate to a big model or a human
0.85+ before anything irreversible
4. never let it invent options
build the candidate list in code, from the DOM, the retriever, the tool trace
then let it pick
5. put it in the loop, not next to it
router picks the cheap model, gate checks the tool call before it runs, judge verifies the output after
that's where the heaviest calls in your agent are hiding
6. start with compaction tonight
score every tool call, drop the dead ones, keep the survivors verbatim instead of a lossy summary
lowest effort win available and you'll see it on tomorrow's bill
the honest part:
text only right now, no images, no audio
and on broad benchmarks it loses to frontier models
but somebody ran 18,514 emails through it zero-shot and got 98.33%
against a TF-IDF classifier trained on 14,800 labelled examples that got 98.39%
no training data, $1.12 total
it wins on narrow, well specified decisions
which is most of what your agent is actually doing all day
today gonna share use case how i integrated it to content creation and how i find winning meta ads now in a seconds...
How Jev fits in the loop
- Agent collects runtime context or input state from external sources.
- Bounded decision options are formatted into Jev System One questions.
- Jev scores each candidate option and returns a probability for it.
- Deterministic code executes the selected action or routes to specialized models.
How to reproduce
- Read the author's original post on X at https://x.com/DeRonin_/status/2100917158922387537.
- Inspect the described workflow architecture and bounded decision points.
- Test Jev System One decision queries against the provided task context.
Why this build matters
Demonstrates real-world Jev Skills experimentation by @DeRonin_ with verified community reach of 469.4k views.
Limitations
- Implementation details are based on the author's public post on X.
- Performance figures and benchmarks are author-reported community claims.
- Production deployments may require custom calibration and policy thresholds.