typesafes
10 stories and discussions about typesafes, aggregated from every source we track.
TypeSafe's Jev is a genuine breakthrough – snap judgments with calibrated probabilities instead of generated text. My bet is OpenAI is already figuring out how to copy it, and then embed it inside its own models where…
kicking the tires on jev with 2048. GitHub Gist: instantly share code, notes, and snippets.
For the last few weeks my timeline has been nothing but Jev. TypeSafe AI shipped it, and within days...
TypeSafe's Jev is pitched at exactly the decisions that sit around a phone call. We put it on three real jobs against 17 models: it is 1.7 to 3.3 times faster everywhere, level with the best on judging and extraction,…
Benchmark of TypeSafe's Jev against Sonnet 5, GPT-5 nano and local LLMs on 770 Reddit AITA verdicts: Brier scores, latency and cost - dchristopoulos/jev-aita
TypeSafe's Jev against four shipped prompt-injection detectors on 7,803 labeled messages and 296 conversations. It wins the curated benchmark and the newest attack set, loses two older ones, and its probabilities track…
Open-weight GLiNER2.5-Decide in MLX Swift versus TypeSafe’s hosted Jev: what each offers, what the published benchmark compared, and local measurements: 7.6 ms per request at 0.85 GB with INT8.
TypeSafe's Jev answers a closed question with a fixed answer type plus a number saying how sure it is. We moved our four decisions over to it. What worked, what didn't, and one thing we didn't expect.