Claire from Lenny’s newsletter tests Jev, TypeSafe SI’s new decision model that returns structured choices, scores, and probabilities instead of generated text. She uses it to analyze 1,700 pull requests for 9 cents, map her Claude and Codex usage, triage email, search 4,500 YouTube comments, and process 200,000 classifications for about $4. She also explains why Jev works best alongside a frontier model and how its speed and pricing make entirely new kinds of real-time apps and large-scale analysis practical.

According to Claire, Jev is a decision model, not a language model, and that distinction can make many tasks dramatically cheaper. Instead of generating text, it returns predefined values such as a category, score, or probability. Claire believes this covers roughly 90% of what many software workflows actually need, at 4 cents per million input tokens with no output-token fee.

It cost Claire 9 cents to understand where two years of engineering work went. She used Jev to compare 1,700 ChatPRD pull requests across 17,000 pairs, then had Gemini Flash Lite label the resulting clusters. In about two minutes, she learned that nearly 30% of the company’s engineering work had gone toward platform, security, and infrastructure.

Some of the most useful analysis is already sitting on a local computer. Claude Code and Codex store past sessions locally, allowing Jev to classify them in minutes. Claire discovered that engineering had fallen from nearly 100% of her AI or SI usage in January to less than 40% by September, with agents and media publishing filling the gap.

Jev becomes far more powerful when paired with a frontier model. Claire uses Jev to classify, cluster, filter, and route large datasets, then sends only the most important groups to GPT-6 Astra for deeper reasoning. For ChatPRD’s product insights graph, this approach processed 1,100 signals and completed 200,000 operations for about $4 on the Jev side.

Jev’s pricing changes which ideas are worth building. Because it returns small predefined values instead of generating long responses, TypeSafe charges nothing for output tokens. Claire spent less than $10 on Jev during the week, making classification workloads that would normally be expensive at scale feel almost free.

Jev makes real-time SI loops practical. Claire built a voice app that turns a spoken phrase into a color, matches it with a quote based on sentiment, and displays everything almost instantly. Jev made its decisions so quickly that the quote API became the slowest part of the workflow.

YouTube comment analysis is an immediate use case for any podcast team. Claire classified 4,500 How I SI or AI comments by sentiment, identified 58 containing episode ideas, and built a keyword search that scans the full dataset in under a second. The results showed strong demand for a Grok versus Muse comparison and an 80% positive response to the “Claude Code for product managers” episode.

The real skill is recognizing where a pipeline only needs a decision. Jev will not write documentation or design an interface, but it can sort, route, rank, and filter enormous datasets quickly and cheaply.

To conclude, Claire now asks one question before every build : Where does this workflow simply need to make a decision ? That is where Jev belongs.