Official SDKs, third-party plugins, local experiments, and community demos, each linked to its repository or original post. Filter by category or search by name.
Introducing typesafe/jev-router: a cache-aware model router powered by Jev and @typesafeai The Jev Router picks the best model and reasoning effort for each request, balancing quality, speed, and cost. Here's how it works 👇🏻
Text classification in MotherDuck just got ~50x faster at ~1% of the cost. prompt_jev() is a SQL function powered by Jev, TypeSafe's new system one model. 100k rows: 40s, $0.50, frontier-LLM accuracy. The LLM took 32 min and $37. Read on:
Realtime sentiment analysis with Jev and ElevenLabs. Each phrase takes on the color of the emotion it carries while the caller is still talking. Six meters on the right track the mood of the call.
The bookkeeping services industry is dead. This weekend, I built a better solution using Jev from @typesafeai. I fed it 34 months of work a firm charged $20,000+ for. Jev did a better job in 20 seconds, for just $0.32 🤯
🎇 I made a small Chrome extension: a side panel that drives any site's WebMCP tools with Jev! When you type, on every keystroke it picks the relevant page's tool, fills in the arguments, and tells you how sure it is. Here it is grocery shopping:
built a real-time writing linter powered by jev 📝 write custom rules like “flag anything that sounds like a linkedin post” receipt printer included 🖨️ (includes audio)
Introducing jevgrep - a research agent CLI powered by jev from @typesafeai that reduces your coding agent cost by 40% (verified on SWE-bench) Make sure to use the built in skill so your coding agent knows to use jg for context collection
Realtime scam detection using Jev and ElevenLabs. ElevenLabs transcribes the caller as they speak, Jev calculates total scam risk and detects common red flags. Each request came back in ~180ms, and the 11 requests cost $0.00043.
The latest @ASIMOV_Platform release lands a built-in `--jev=NOUL` output filter, enabling agentic harnesses such as Hermes to offload simple natural language postprocessing decisions to Jev, a System One model Look ma, here just casually turning my LinkedIn network into JSON-LD
found the perfect use case for @typesafeai Jev: instant compaction in 2026, why is compaction still a summarization prompt? Jev can make it instant by scoring every tool call and dropping what’s irrelevant
Breaking: Browser Use + Jev = Ultrafast ⚡ Findings flights took 7s and cost only $0.0039 🤯 > new action space every step > DOM state space > small LLM fallback to type (this video is at 1x speed btw) Built a tiny open source browser agent. try it below ↓
I built a trading bot with Jev! Jev decides if it should "buy" or "sell", given the price feed of an asset pair, and executes real trades. It uses Monad to place the orders on Kuru's on-chain order book in every 300ms block. Demo link →
Jev is WILD for SEO audit 🤯 in 45.1 seconds it read all 586 pages on my site and rebuilt the internal link map. 584 links placed, 139 pages it refused to link because nothing honestly fit. total cost $0.21. Claude Opus 5, same 586 pages, same clock, got through 21 of them and