The cookbook uses regexes to find candidate emails, phone numbers, and amounts, then offers them to a Choice question — plus a none hatch — so the returned value is copied verbatim from the source. A receipt email was picked at 0.98 and an invoice total read as $1,315.50.
find() over-finds and dedupes while pick() offers the spans as options. The limit is the 255-option Choice cap, and candidates without a regex, such as names, need a roster, NER, or a generative model.
Regex finds candidate emails, phone numbers, and amounts; Jev picks the requested span so code copies a verbatim value that cannot be invented, with no free-text extraction required.
Text-only classifier for 261 federal tax forms that sends each PDF page's text to Jev, which returns a probability over forms and page kinds behind a confidence gate; no model is trained.
A two-stage extraction cascade: a mini model extracts, a Noul battery verifies each field in one request, and a 0.7 gate escalates to a reasoning model, sitting on the cost/quality frontier.
Extract dates by asking seven Choice questions about a date's shape and parts, then resolving them in code: five of six examples auto-accepted and missing dates flagged at 0.46 confidence.
Turn wine tasting notes into 67 numeric columns for a CatBoost regressor: five rounds of reading its own errors cut held-out RMSE from 3.09 to 1.77 across 800 unseen reviews.
built `jev-review` @typesafeai
it's an experimental, local-first MCP plugin that gives coding agents a score quality feedback loop across different metrics.
agents call jev while they work, get scored, make improvements, and repeat the loop
try below 👇
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400x
Now using @typesafeai Jev in aiseotracker.com, linkdr.com, genppt.com, etc
AI ends up vibe coding so much AI regex slop if you don't read the code, so I can finally move all this hard-coding to Jev and it's insanely fast!
Also for regular LLMShow more
got @typesafeai's new model Jev as a chief of staff for bots
Jev reads the task, wakes the right teammates off the bench
and gives each one the right model
It is possible on OpenMausBot as it supports all the LLMs from your existing subscriptions
Jev as a decision engine isShow more
I built a browser extension with Jev @typesafeai that can hide/collapse posts on X based on natural language.
It's so fast that it's not noticeable and insanely cheap...this must be the future of "ad blockers" and content firewalls.
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400x
