Explore public demonstrations of TypeSafe AI’s Jev model—what each builder made, which typed decision Jev handles, and where to inspect the original evidence. Every note is independently written and links back to the creator’s original X post.
Metrics and build-time claims are labeled as author-reported. Original diagrams load first; with one clear opt-in, every card can show its creator’s image or video through X’s official embed.
24 cases found
A try-on interface turns a spoken request plus the current outfit into a bounded wardrobe choice, then lets the surrounding application render the selected change.
The creator reports roughly $0.0011 per decision and about 620 ms latency. Those are demo-specific claims, not independently reproduced production benchmarks.
A spreadsheet column becomes a typed decision instruction: each row is evaluated against an intent such as urgency and receives a bounded rating rather than generated prose.
The post reports response time around 100 ms. Dataset size, batching, network conditions, and error handling are not established as a production benchmark.
The demo evaluates a bounded set of email candidates against a user’s intent, turning fuzzy relevance into an explicit decision that an inbox interface can rank or filter.
This is a demo pattern, not evidence that Jev should scan a large inbox directly. Real deployments need deterministic or semantic prefiltering, privacy controls, and evaluation.
A deterministic Tetris engine enumerates legal placements and supplies the board state; Jev chooses among those valid actions rather than controlling pixels or inventing moves.
The creator reports an average decision below 400 ms. Treat the result as a playful prototype; it does not establish optimal play, reliability, or general game-agent performance.
Multiple bounded color decisions run in parallel, while confidence affects brush width; a plain JavaScript renderer turns those choices into a growing pixel composition.
This is a creative experiment driven by a pure JavaScript renderer, not an image-generation model and not a claim of visual fidelity or artistic autonomy.
A drone simulator supplies world state and available tactics, then uses Jev for bounded decisions while the simulation remains responsible for movement, rules, and feedback.
The claimed 15-minute build and roughly $0.10 cost come from the author’s post. They are not independently verified and say nothing about real-world drone safety.
An emulator playground converts RAM and short lookahead into bounded controller actions, with a dashboard, logs, and recordings helping a developer inspect each decision loop.
According to the source post, screenshots were not sent to Jev. The post demonstrates an experimental playground, not a benchmark for general game playing.
OpenRouter’s launch demo shows Jev available in beta through its provider interface, preserving the state-plus-typed-question pattern and structured probabilistic response.
This source establishes a beta platform integration, not a performance, quality, availability, or cost benchmark. Current service details should be checked with the provider.
A driving experiment uses Jev as a rapid decision layer over simulator state while the surrounding program remains responsible for vehicle controls, physics, and execution.
The creator describes rebuilding a Tesla-style driving demo in under an hour. That is an author-reported prototype claim, not evidence of roadworthiness or real-world autonomy.
A deterministic hook asks Jev which local coding harness should receive a task, replacing fragile routing instructions that were previously buried in system prompts.
This is the creator’s local-stack workflow. It does not establish routing accuracy, task quality, or reliability across other harness configurations.
A small browser agent derives a fresh action list from each page DOM, lets Jev select the next operation, and reserves a generative fallback for text that must be typed.
The creator reports finding flights in seven seconds for $0.0039. Those are author-reported results from one demo, not a general browser-use benchmark.
In this WebMCP setup, Jev selects the appropriate tool while Mercury produces its arguments, splitting bounded routing from the smaller amount of free-form parameter generation.
The source reports 49 of 49 tasks solved and large cost advantages over other setups. These are author-run benchmark results and have not been independently reproduced here.
A computer-use prototype turns observable interface state into candidate operations and asks Jev to choose among them, keeping action execution in platform-specific code.
The creator reports about 155× lower cost than Opus 5 and roughly 20× higher speed. Those comparisons are author-reported and depend on the tasks and baselines used.
A remote-browser workflow feeds the page accessibility tree and available actions to Jev, then relies on Stagehand to carry out the chosen operation.
The creator reports near-instant execution and a $0.001 task cost. These figures come from the showcased task and are not an independently controlled comparison.
One request evaluates a code diff against a fixed checklist and returns probabilities for issues such as secrets or unsafe SQL, leaving merge policy to ordinary code.
The post reports half-second answers, $0.00007 per PR, and a cost comparison with Opus 5. These are author-reported figures from six examples, not a security validation.
The fx team evaluated Jev as a bounded classifier for an auto-mode safety decision and compared its speed and accuracy with the model used in their existing path.
The author reports roughly 5–18× higher speed and better accuracy than GPT-5.6 Luna. The dataset, thresholds, and full methodology are not published in the source post.
Instead of regenerating a prose summary, this compaction experiment scores prior tool calls and results so the application can keep, truncate, or remove each record.
The post calls the approach instant, but provides no published quality evaluation, token-retention curve, or comparison of downstream agent performance.
The experiment pairs json-render with Jev so interface decisions are selected from an application’s existing components, slots, and actions instead of arbitrary generated markup.
The creator describes millisecond rendering in an experiment. The post does not establish accessibility, layout quality, security, or performance across larger component catalogs.
A Minecraft system assigns quick moment-to-moment decisions to Jev while GPT-6 Astra handles longer-horizon planning, then coordinates both through the game controller.
The visible gameplay, including combat, is a result of the complete author-built system and should not be attributed to Jev alone. No controlled success-rate benchmark is provided.
The demo asks Jev for the snake’s next legal direction on every game step, then lets deterministic game code execute that move and calculate the resulting board.
The creator reports 200 requests costing about $0.02 and extrapolates roughly 10,000 steps per dollar. These are author-reported demo economics, not an independently reproduced benchmark.
Instead of a regular expression, a developer describes a semantic code smell such as swallowed exceptions or SQL string concatenation, and the tool ranks functions by Jev probability.
The post reports about $0.0005 per buffer. Repository scale, language coverage, recall, false-positive rate, and comparison with static analysis are not established.
A browser extension reads YouTube captions, estimates sponsor probability for timeline segments, visualizes the result, and skips only sections above an application threshold.
The creator reports a result in about 0.9 seconds and Chrome and Firefox support. Accuracy, caption availability, multilingual behavior, and false skips require independent evaluation.
The author shows a browser-based adversarial suite running many release checks in parallel, with Jev acting as an inexpensive decision layer over the observed outcomes.
The post says the suite costs pennies but does not publish scenario count, coverage, false-positive rate, repository details, or a reproducible evaluation. The workflow description is partly inferred.
The source presents Cloudflare AI Gateway as an alternate route for invoking TypeSafe AI’s Jev while an application preserves the same state-and-typed-question contract.
The screenshot does not establish latency, pricing, reliability, or continued availability. Current Cloudflare provider documentation should be checked before relying on operational details.