The same post scheduler, built twice, in front of an audience.
Florian Darroman
@floriandarroman
Jev is INSANE.
I asked 100 Indie Hackers to build a post scheduler with:
Jev vs Fable 5.1.
The results are unexpected 🤯
(You can clearly see Jev is faster at doing stuff)
Florian Darroman asked 100 indie hackers to build a post scheduler twice, once with Jev and once with Fable 5.1. His read of the results is that Jev is visibly faster at doing things.
how to add a decision model to your AI workflow
1,000 yes/no decisions can cost around:
~$10 on GPT-6 Astra
~$4 on Claude Opus 5.5
~$0.04 on JEV
use language models for planning, writing and coding, and decision models for decisions
a decision model can:
> check your agent’s output before it ships
> approve a draft or request revisions
> route a task to the right agent or tool
> classify leads, tickets or comments
> score fit, quality or priority
> check whether content follows your brand rules
> pick an option from a shortlist
> decide whether to continue, stop or request a review
at a much faster speed as well than any regular LLM
here’s how to add one
1. find the decisions in your workflow
look for repeated steps that need a choice, classification or score
start with one decision you can compare against examples you’ve already reviewed
2. define the question and allowed answers
give the model the information and references it needs
for a content workflow, that could include your draft, voice DNA file, anti slop rules and approved examples
ask it to return a choice such as approve, revise or human review
3. connect the answer to an action
your software uses that answer to decide what happens next
approved drafts move to your review queue, revision requests return to the writing agent, and uncertain cases go to you
4. test before letting it act
compare its answers with your reviewed examples
if it returns confidence scores, check whether those scores match how often it gets the decision right
start with actions you can easily reverse
5. put simple checks first
use code for fixed rules, such as required fields and character limits
then use the decision model for checks that need context, before starting more expensive work
6. track quality and cost
measure cost per completed task, incorrect decisions and how often another model or a person needs to review
use the failures to improve your questions, references and allowed answers
1/ Jev, a decision model by @typesafeai, sparked a burst of projects and discussion. We tested it using Ori Eval against popular LLMs on OpenRouter at judging.
Jev was >5x faster than the next fastest model, and even its slowest requests beat every other model's median.
Also have been playing with @typesafeai Jev, insane!
So many immediate use cases and new apps are possible. What a time to be a builder!
Sharing some experiments here starting with:
Keystroke oracle / predictive launcher:
Your launcher ranks by aliases, fuzzy match, and habit.
Jev reads intent: type "the pdf I just downloaded" and the newest PDF is already the top hit with a full confidence on every keystroke, in ~100 ms
