TypeSafe's Jev gets a curated directory: 33 reviewed picks, from chess to code review
Jev – a curation of Jev demos on X, tools, skills, and integrations
A new community directory called awesome-jev curates 33 source-reviewed projects built on TypeSafe's Jev, a model for typed decisions. It separates verified demos from raw discovery, covering SDKs, browser agents, games like Jev Chess, and tools like tisco. The goal is to connect flashy demos to actual code and reusable skills, with clear evidence labels and safety caveats.
A compelling clip often leaves the useful questions unanswered: what did Jev decide, where is the implementation, and what can I reuse?
- davidweatherall
https://x.com/dWeaths/status/2102415625301717065 - Here's my use case of jev, being able to accurately detect nouns (with adverbs, adjectives etc.) in realtime as the user is typing it, genuinely feels like it's running locally with how fast it comes back. I've sent over 1000 requests to Jev and its cost me $0.01 (probably rounded up!).
- levmiseri
I had an idea for a 'write without space, basicallylikethis' and Jev (given how cheap it is) just being asked after every key press where inserting spaces (or making typo correction) would make sense.
The demo is here: https://levmiseri.com/nospace
- liquicity
Initial personal use case (on my side project https://mealplannr.io/lists) is lists have a "smart categorise" button to group items into 20 or so preset lists.
Previously this took up to ~30-60 seconds using deepseek v4 flash (even with a medium list size) - Jev is <1 second @ same cost with typesafe guarantee
As a bonus I can also instantly categorise new items - rather than sending them into an "unknown" category (and waiting for user to have to click "categorise" again)
- suraj_phanindra
i built wellposed as a plugin/skill which works with any agent (https://github.com/suraj-phanindra/wellposed) to ensure your agent understands how to choose the right kind of jev request and format it correctly not just for syntax but for completeness and correctness. it is well-documented in the "jagged-ness" docs (https://docs.typesafe.ai/model-jaggedness/jev-1.13) that typesafe include on their docs page that the absence of essential options can cause jev to choose the wrong option with high confidence (it cannot choose what it cannot see in the request) - so the responsibility to ensure whatever the intent behind your jev request is - it is captured correctly with the right states and options for jev to pick from falls on the user. wellposed should ideally make your agent better at converting NL intent into jev requests. please try and give me feedback. appreciate it!
- lewisjoe
Can someone ELI5 me why Jev class models matter?
Note: Not the technical side, but as an end user of LLM APIs.