A FIELD GUIDE TO JEV / VOL. 01
Yang terbaik dari Jev: proyek nyata, panduan praktis, dan ide dari seluruh internet.
THE OFFICIAL SIGNAL 4 HIGHLIGHTS
Diogo Almeida@CompleteSkepticLAUNCH POSTDiogo Almeida memperkenalkan Jev, model keputusan terstruktur dari TypeSafe. Tonton film peluncuran dan eksplorasi thread asli untuk pendekatan model, contoh, dan kinerja yang dilaporkan penulis.
TypeSafe AI@typesafeaiOFFICIALPengumuman peluncuran resmi TypeSafe memperkenalkan laboratorium dan mengarahkan pengembang ke Jev. Termasuk film peluncuran Diogo Almeida dan pengantar asli.
TypeSafe AI@typesafeaiOFFICIALTypeSafe mengumumkan akses publik ke Jev tanpa daftar tunggu. Konsol resmi adalah titik awal untuk mencoba keputusan bertipe dalam aplikasi Anda sendiri.
JEVLAB ARTTypeSafe AI@typesafeaiOFFICIALTypeSafe menyoroti pendekatan Jev untuk output bertipe: aplikasi menyediakan state dan pertanyaan, lalu bertindak berdasarkan probabilitas, pilihan, atau skor. Pengumuman terkait juga mencakup integrasi API Venice.
JEVLAB ARTStart with the Launch Post. Explore four official signals.
7 sumber
AI-assisted summaries and translations. Check original sources for context and performance claims.
A guide explaining Jev's approach to making typed decisions rather than processing text.
JEVLAB ART
EnglishSource-linked curation
1,800-point thread debating whether typed decisions replace LLM calls for classification, routing, and scoring.
JEVLAB ARTEnglishSource-linked curation
Jev is utilized for decision-making processes on Cloudflare, including support routing and risk management.
JEVLAB ARTEnglishSource-linked curation
A LangChain package with TypeSafeClassifier for integrating Jev into applications.
JEVLAB ARTEnglishSource-linked curation
Short practical intro with a Python ticket-triage example.
JEVLAB ARTEnglishSource-linked curation
A guide on using Jev to route between models and block risky tool calls.
JEVLAB ARTEnglishSource-linked curation
One direct Jev question per row against 12–14 Jev-scored dimensions with locally fitted weights on three classification tasks: 5,477 test rows, 25,174 Jev calls, $1.43. Decomposition wins on Japanese NLI (0.9076 vs 0.8373) but flags about 25× more hard benign rows as attacks (37.2% vs 1.5%).
JEVLAB ARTEnglishSource-linked curation
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4 HIGHLIGHTS
Decisions & routing
Source-linked curation