A video demo of a macOS app that monitors the Downloads folder with Jev: it checks whether a downloaded file is an invoice, then moves matching files into a folder with a corrected filename, using no other LLM calls.
Play the video in the post belowRead from X on . Counts change daily.
Marcel Pociot built a macOS app that monitors the Downloads folder and applies a customizable set of rules to whatever lands there. The example rule in the post: is the downloaded file an invoice? If so, move it to a special folder with the correct filename.
The decisions run on Jev alone. Pociot says no other LLM calls are involved.
The 1,978-point, 513-comment launch thread. The top comment calls the speed comparison misleading, since Jev only returns structured output, and disputes the cannot-hallucinate claim because a valid type can still hold a wrong value. Others note ordinary LLMs can already be forced into structured output.
The founder's launch post, around 76k likes, arguing that RLCD-trained decision models are a shorter path to economic value than chat models. Submitted separately to HN as item 49716682.
Diogo Almeida (@CompleteSkeptic)OpenDiogo Almeida's Jev launch post (X) on x.com39M viewsSpeed 20-200x fasterCost 40-400x cheaper
A video demo pairs json-render with Jev to render generative UI components in milliseconds, using the viewer's own components, actions, and design system.
A video demo of copy and paste made 'smart' using Jev from TypeSafe, with no further detail on the mechanism given in the post text.
A demo breaking down 724 live ads from 37 brands in 40 seconds for 9 cents, covering hook, format, offer, CTA, funnel stage and landing-page mismatch for each ad.
Matthew Berman (@TheMattBerman)OpenBreaking down 724 ads from 37 brands with Jev (X) on x.com1.1M viewsSpeed 724 ads/40sCost 9 cents
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