Announcing Jev Detector The world's fastest AI slop detector, built on jev from @typesafeai ~10,000 words scanned for slop in ~2 seconds Best part, its free, no sign up required, enjoy! jevdetector.com
API & Models
Retry policy
The SDK configuration for retrying failed requests: attempt count, retryable statuses, backoff, and retry-header handling.
- Category
- API & Models
- Also known as
- —
- Related terms
- 4
- Directory entries
- 2
- Docs
- docs.typesafe.ai
- Added
- 2026-09-24
Definition
Both official SDKs retry automatically with a default policy and expose a RetryPolicy type for tuning attempts, retryable statuses, and backoff. The docs point at 429 and 529 as the statuses worth retrying.
Using a default policy is the recommended path for rate limits, because it already implements the exponential backoff the API expects and reads the retry-after header when the response carries one.
Tagsapisdk
Related terms
Definitions that connect to this one.
Exponential backoffThe retry strategy the docs recommend for 429 and 529 responses: wait longer after each attempt instead of retrying immediately.API & ModelsRate limitThe throughput caps — 250,000 tokens per second and 1,200 requests per minute — that return 429 when exceeded.API & ModelsAPI errorThe four documented failure statuses — 401, 422, 429, and 529 — with retry guidance for the last two.API & ModelsClient SDKThe official typed clients for the API — Python and JavaScript/TypeScript — with retries and inferred answer types.API & Models
From the directory
Usage guide for typesafe-sdk: sync and async clients, uv and pip installs, and one request mixing Noul, Choice, and Score questions with answers grouped by type.
Repos & SDKsDocs#official#docs#sdk
Official
Quickstart for @typesafe-ai/sdk on Node 20+: answer types inferred from your questions, a choice() helper, and one systemOne call to classify a support ticket.
Repos & SDKsDocs#official#docs#sdk
Official
From the community
Posts from builders shipping with Jev right now.
Jev Detector scans ~10,000 words for slop in ~2 s
Computer use without screenshots
Okay so Jev can actually do computer use really well Without any screenshots, or LLMs and no Pixels leave my mac I dont even read the Dom elements A local CoreML model segments every button and UI element on screen. On-device OCR reads the labels. That text is all Jev gets. Show more
@CompleteSkeptic
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x
Jev plays Subway Surfers
jev is insane 🤯 Here is Jev playing subway surfers at super human speed, and also playing 50 games at once. cost less than a cent to do this run. Jev does not replace llms like astra or fable, but opens up an entirely new world of capabilities.
Agentic browsing in Chrome
I built a Chrome extension for agentic browsing using Jev by @typesafeai, fx.sh including AI Gateway by @vercel. Now agents can browse, click, and interact with websites directly in your browser. Cost effective and fassst. Decision-making by Jev.
@CompleteSkeptic
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x
724 competitor ads, broken down
jev is INSANE. in 40 seconds it broke down 724 live ads from 37 brands. every hook. every format. offer. cta. awareness stage. landing page mismatch. used 9 cents of tokens. (will be avail in @stealads + mcp)
A chat bot with no LLM
I built a chat bot with jev, no LLM at all! Responses are instant, no hallucinations. I hooked it up to web search, wikipedia, weather, todoist and home assistant. Jev decides what tool to call and what args to use based on the prompt. Instant answers cite sources as well!