17:33
Jev Founder, Diogo Almeida (ex-OpenAI): "The next era is not the...
Codez@0xCodez
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Sep 19, 2026
~1 min read
Jev Founder, Diogo Almeida (ex-OpenAI):
"The next era is not the Claude Code era. Claude Code is still part of the assistance era - Jev is the next step
Jev is x200 faster, x400 cheaper and has 0 hallucination. This is what comes after RLHF"
In 17 minutes he explains why every model you use has a human-shaped flaw baked into it and why Jev is better
worth more than a $500 Claude Code engineering course
watch today, then read how to actually boost yourself x100 with Jev in the post below
"The next era is not the Claude Code era. Claude Code is still part of the assistance era - Jev is the next step
Jev is x200 faster, x400 cheaper and has 0 hallucination. This is what comes after RLHF"
In 17 minutes he explains why every model you use has a human-shaped flaw baked into it and why Jev is better
worth more than a $500 Claude Code engineering course
watch today, then read how to actually boost yourself x100 with Jev in the post below
Ronin@DeRonin_
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
🔗 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...
setup takes 10 minutes:
1. join the waitlist, people are getting approved same day
🔗 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...
