0 active · 0/20 queued
Browser: one start every 1.2 seconds (at most 50/minute), up to 2 active calls and 20 waiting. Shared by examples in this tab. Waiting requests cancel when their run is stopped; a full queue rejects new work without an API call.
Server: 60 requests per rolling 60 seconds and 2 concurrent calls per key, per server instance. Other tabs using the same key share that instance’s allowance. This in-memory safeguard resets on restart and is not a distributed or account-wide quota.
Source: playground policy in lib/requestRateLimit.ts. TypeSafe may impose separate limits. Provider HTTP 429 pauses calls until its reported reset; without a reset time, retry requires your action. HTTP 402 is a budget/billing failure, not a pacing limit. No automatic retries or background replay.
lib/requestRateLimit.ts
Import your key to override the shared server default.
Saved on this browser until removed or site data is cleared. Masked on screen; sent only with Jev requests through this app. The saved value is never displayed.
Paste a conversation and see where a response would matter most.
Rank messages and find the person your bot should answer next.
A conversation transcript and experiment choice: select a reply recipient or compare how a changed detail affects the final message.
Reply selection evaluates each speaker’s latest message; comparison modes evaluate the final message. Jev returns reply probabilities, and the configured probability threshold controls selection.
A proposed recipient, the other candidates, and supporting scores. Changing the threshold recalculates the displayed decision locally.
Load a sample or paste messages, then choose the experiment.
Run Jev to rank messages against the reply policy.
Review the selected message, other candidates, and the reply-probability threshold.
Load the sample, pick a recipient, then raise the threshold and watch when the selection becomes uncertain.
Live classification only. The lab does not write or send replies.
Paste from Discord, or use Name: message.
Do they have a desktop app?
Can you prototype the codebase search flow? I need exact context, line-by-line search, and AST support.
That sounds great 👀
Labels are scored in A/B and final-message modes.
Run the conversation to compare reply probabilities and identify a recipient.
Valid labeled runs in this tab. Repeated runs count again; this is not a benchmark.