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Jev: New frontier model 40-400x cheaper and 20-200x faster

TypeSafe AI launches Jev, an early-access 'System One' model delivering calibrated structured outputs claimed 40-400x faster and cheaper than LLMs.

TypeSafe AI, founded by former OpenAI researcher Diogo Almeida, released its first 'System One Model' called Jev in early access. Jev forgoes string generation and is trained with Reinforcement Learning for Calibrated Decisions (RLCD) to produce type-safe structured values with calibrated probabilities. The company claims 70-500ms response times (40-200x faster), input pricing of $0.042 per million tokens, and free output tokens via a parallel sampling architecture. Target use cases include AI-powered workflows, real-time applications, and verification/guardrail tasks.

Hacker News · security · 16h agoModel releaseHN 59↑ · 11 comments

Most Fraudulent Hires Receive Credentials Before Detection

HYPR report finds 42% of fraudulent hires pass screening and receive corporate credentials, averaging 5.73 days of unmonitored network access before detection.

A HYPR study of 500 US HR executives found 42% of fraudulent candidates pass pre-hire screening and get hired, with only 3% detected on their hire day and 20% remaining undetected up to three weeks. This gives fraudulent hires an average of 5.73 days of unmonitored corporate network access, and 98% of surveyed executives said they had experienced candidate fraud firsthand. The report follows a September 9 CISA update to its Insider Threat Mitigation Guide warning that malicious actors use AI tools to obtain remote IT jobs, a tactic long used by North Korean actors for data theft and extortion.

Infosecurity Magazine · 20h agoPhishing & fraud