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the new jev model is insane.. it can check your AI's work and make decisions inside software for $0.042 per million input tokens, with free output here are the first things you should use jev for: 1. second brain 2. content workflow 3. post analysis 4. SEO article review typesafe reports up to 193.6× faster results and 444.6× lower costs than the LLMs in its workflow tests you give jev information and specific questions. it returns choices, scores or probabilities that your software uses to decide what happens next 1. second brain when a document, message or meeting note enters your second brain, give jev the content and your categories identify what it is, classify the topic and check for duplicates against existing notes your software checks required fields, saves the content with its source and verifies that it was saved correctly works well with the karpathy LLM wiki framework 2. content workflow give jev your draft, and then reference your anti slop rules and voice DNA file (which defines how you write) it will check for generic phrasing, repeated points and differences from your voice. use previous content and its results to estimate performance potential then send results to the writing agent for revision, compare the performance estimates with results after publishing 3. post analysis jev can compare a draft with previous posts and their results to estimate how well it could perform with your audience evaluate the hook, topic and format against defined criterias. and compare those scores with the performance after it goes live 4. SEO article review give jev your article / page draft, a target keyword and the articles currently ranking for that keyword (in the top 10 SERP) compare how well they answer the query, cover the topic and provide useful information. use that comparison to estimate your article's ranking potential then your LLMs can revise weak sections before publishing, and compare the estimate with actual rankings so many more usecases, we will see many new upgrades to previous concepts and workflows we´ve read about now

We had Jev score Elasticsearch hybrid search results and let a short Python policy do the reranking. • nDCG@10 went from 0.9351 to 0.9565 (250 Amazon Queries) • Latency: 70–500ms end-to-end • Cost: $0.042/M input tokens (outputs free) No text parsing, just structured probabilities feeding business logic.

A company called ClarityCheck markets itself as a tool to detect catfishing and fake dating profiles. Its website tells users: "your reverse image search is private and secure." researcher Jeremiah Fowler just found 9,042,977 images sitting in an unsecured Amazon S3 bucket acessible to anyone who knew where to look. 450 gigabytes of faces. stored in folders labeled "faces" and "profiles." adults. teenagers. children. The most concerning thing that makes this different is that most of the people in that database never used ClarityCheck. The service lets users upload someone else's photo to identify them. that means every person whose face got uploaded by a stranger, someone trying to find out who they are, where they're from, whether they're real had their biometric data indexed and stored without ever consenting to it. without ever interacting with the platform.. ClarityCheck's response after being contacted: the data was in a "temporary storage" bucket and an "ordinary member of the public" would not have come across it. the URL to the bucket was embedded in their own website's source code. Fowler also found a second misconfiguration. entering a name into a manipulated website URL returned email addresses, physical addresses, and phone numbers for anyone in their database.

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Deep|LLM: Tiered Model Pricing Is Broadening AI Adoption; Limited Impact by Jev Pricing cuts accelerate adoption: Anthropic launched Claude Opus 5.5 at $4/$20 per million tokens, 20% below Opus 5, and estimates typical workload costs are down 40%. OpenAI’s GPT-6 Sol at $2/$10 and GPT-6 Luna at $0.10/$0.50 are 50–58% below comparable GPT-5.6 prices, making more everyday AI tasks economical to automate. Tiered models expand use cases: Frontier models such as GPT-6 Astra and Claude Opus 5.5 still command premium pricing for complex tasks, while lower-cost models like Luna support extraction, routing, and consumer-agent workflows. The same application budget can now support more users, more model calls, or longer agent workflows. Jev is useful but narrow: TypeSafe’s Jev offers structured decisions at $0.042 per million input tokens with free output, averaging $0.0004 per call and 0.4-second latency in TypeSafe’s evaluation. It can replace some classification and guardrail calls, but it does not perform long-chain reasoning, so its impact on frontier-model demand should be limited. Compute demand remains supported: Over the next 6–12 months, broader AI deployment is expected to increase both usage and compute demand, even as inference efficiency improves. Application companies are the clearest beneficiaries, while model developers that compete mainly on low API prices face greater margin pressure, especially standalone Chinese players. Detailed Report

📢 TypeSafe AI · Jev Is Now Live on API As the first System One model from @typesafeai, Jev is built for fast, structured decisions inside software. It does not generate text: send app state plus a typed question, and get a typed decision with probability and confidence—no JSON prompting, no output parsing. It evaluates Choice, Score, and Noul questions in parallel, responds in about 70–500ms, and costs $0.042 per million input tokens (output free), making it a fit for ticket routing, moderation, risk scoring, and agent branching. Now available on the API (access via Jev-1.13.0 or Jev-Latest)! 👉 Try now: 🔗 Learn more: