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Jev AI examples, page 16
407 real Jev AI builds and public demos, updated daily. Original text, metrics, and a link back to each source.
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Public Jev AI examples and demos, not official TypeSafe docs. Clients and SDKs are in the Jev GitHub projects. Paths for jev-latest, Vercel, and OpenRouter are in the September 2026 channel map.
Agent-skill repositories are listed on Jev skills on GitHub.
OpenRouter model ids recorded for Jev AI are on Jev AI on OpenRouter. Rubric and eval posts are on Jev as a judge. How the company and the model fit together is on TypeSafe AI. The pricing calculator uses the published input rate.
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@0xkaushik_k built JevScope, which watches an agent and asks Jev whether each step is aligned, progress, repetition, or stuck, then plots that over time. They describe a coding agent stuck on a race condition where the curve shows it and no log line says “I’m stuck.” Code is said to be coming.
Got early access to @typesafeai Jev and spent a few hours building on it. The problem I keep hitting: agent traces tell you which tool ran, how long it took, wh…
Quoted @CompleteSkeptic
@BolhaDevs posts about the Jev model (Jevons paradox) and TypeSafe’s RLCD, reinforcement learning for calibrated decisions, with a link. The text does not describe a build.
o modelo jev (paradoxo de jevons) com RLCD (reinforcement learning for calibrated decisions) da typesafe ai https://t.co/aGwtQkbJyj
@Mnilax put Jev between GPT and themselves to kill drafts that broke their rules. Jev then stopped answering, and the agent treated silence as safer and sent nothing until a second agent unstuck it. They list branches for a quiet checker: pass it tagged unverified, hand the span to a big model, queue and retry, or wake a human if the action is irreversible.
it's f*cking insane Jev sat between GPT and me, killing every draft that broke my rules before i saw it. good setup. then Jev did not answer. the agent decided…
Quoted @Mnilax
@kenny_io demos Thally creating a GitHub docs repo tied to the products it documents. They say they are now exploring how TypeSafe could make that workflow more efficient. The post does not say Jev is already wired in.
One of the popular requests since launching @thallyio is a demo of how it works. So here it is! Thally can create a new docs repository on your GitHub account,…
Quoted @kenny_io
@yuntiandeng describes ProgramAsWeights: small neural programs for fuzzy judgments, compiled from English descriptions, combined with code for control flow. One example joins 30 programs and a decision tree into a course-website helper that looks like a chatbot but runs locally, including a question such as which specialist should receive the query.
I increasingly think future software will combine code for control flow with small neural programs for "fuzzy" judgments. That's what I've been exploring with P…
Quoted @CompleteSkeptic
@norbertbodziony built a moderation guard on TypeSafe Jev and links a live demo at guard-jev.vercel.app. They call it fast and accurate; the post does not include a measured score.
i have created moderation guard based on typesafe's jev its fast and accurate live demo you can: https://t.co/FquTJtWCzq https://t.co/PyNNZGu0iW
@mittalparth_ shows TypeSafe Jev playing Chrome Dino. They report about 280ms average response, 116M tokens spent, $4.65 cost, and decisions to jump, run, or duck with a confidence score from a snapshot of the runner. A repo link is in the post.
@typesafeai jev playing chrome dino! the model is incredibly fast! - avg response time: ~280ms - spent 116M tokens - cost? just $4.65. - the input here is a sna…
Quoted @thread
@dwhitedesign posts a 34-second explainer made after spending an evening making sense of Jev, and says they want to build with TypeSafe.
Spent the evening making sense of this, so here's Jev in 34 seconds. Super excited to build some incredible things with @typesafeai what a time to be alive... h…
@melvindvivas lists 9 use cases they thought of for Jev. The post text itself does not enumerate them.
9 Use Cases I thought of for Jev Hope you find it useful
@prakshaljain_ describes Yappy from MitosisLabs, a Mac agent that fills job applications, taxes, and forms, sped up with Jev. Their comparison versus a competitor is 1m 54s versus 13m 14s, 22 versus 104 LLM calls, and about $0.24 versus $4.24.
Jev by @typesafeai turns your Mac into an AI agent... what this means for You ? - Jev, a new model from a ChatGPT co-inventor, can operate computer tools in r…
@legitamit used Jev to make an audience of 100 personalities at 1v100.fun. Each blob has its own personality and makes a Jev call whenever you talk, to decide if it is bored. They say a round costs under $0.01.
I used Jev to make an audience of 100 personalities to yap to. Each blob has its own personality and makes its own Jev call every time you talk to decide if it’…
Quoted @thread
@SamGCoder tests TypeSafe as the decision layer for an AI game companion. It reads the player’s message and game context and returns an action, destination, and confidence. Game code moves the character; dialogue is prewritten.
Testing @typesafeai Testing TypeSafe as the decision layer for an AI game companion. It reads the player’s message and game context, then returns an action, des…
@maybe_im_sam wired TypeSafe into a three.js action game as an AI co-op partner. It reads game state and chooses when the bot should engage, regroup, survive, or escape. They say the demo was about an hour of work and about 10 minutes of playtesting cost about $0.02.
Woke up with early access to @typesafeai and was super excited to try. Got a demo cooking before work this morning where typesafe drives an AI co-op partner in…
@kraayenJon says Jev checked a website for 35 tells of AI slop in 243ms for $0.00015, including purple gradients, emoji headers, the word “seamlessly,” fake testimonials, and bento grids. The free tool is at madewithjev.com/free-tools/ai-slop-detector.
jev is INSANE. in 243 ms it checked a website for 35 tells of ai slop. purple gradients. emoji headers. "seamlessly". fake testimonials. bento grids. the works.…
Quoted @CompleteSkeptic
@ashutoshftw built a semantic circuit breaker so agents that try different actions from the same wrong assumption still get stopped. In one stuck run they report 12 model calls down to 4 and 19.9K tokens down to 5.1K.
Jev unlocked a new way to stop AI agents from spinning. Agents do not always repeat the same action. Sometimes they try different things based on the same wrong…
@rohanpaul_ai explains the Jev launch: a model for software decisions rather than text, returning a typed choice or score with probabilities instead of token-by-token generation. The example is a support app asking whether a ticket is urgent, whether it violates policy, and which queue should get it. They cite TypeSafe figures of 20–200x faster, 40–400x cheaper with free output tokens, and 70–500ms responses.
Another brilliant launch for developers: and its 20-200x faster than LLMs because it skips token-by-token generation entirely. TypeSafe AI just launched Jev, >…
Quoted @CompleteSkeptic
@nielsmouthaan built a Grammarly-like Mac app with Jev, which they describe as TypeSafe’s first System One model for fast structured decisions rather than text. The point of the app was to feel that speed.
Built a Grammarly-like Mac app with Jev. Jev is TypeSafe’s first “System One Model”, built for fast, structured decisions rather than generating text like an LL…
@eltokh7 posts another Jev demo for nearly instant variable generation and links a video.
another demo for jev for ~instant variable generation https://t.co/ddjvvHXpai
@kyamilass used TypeSafe Jev to rank AI models in a tier list from S+ to F for general intelligence, weighting intelligence and performance. They say an agentic tier list is next.
So I ranked every AI model till date in a tier list from S+ to F using Jev from @typesafeai into an ULTIMATE AI TIER LIST for General Intelligence, giving a lot…
@AIMind_Ai quotes ChatGPT co-inventor Diogo Almeida saying Jev is 193x faster than AI. The post explains that Jev never writes text: context in, a typed yes or no out, instead of paying a chat model to think for 9 seconds about a yes-or-no.
ChatGPT's co-inventor Diogo Almeida says his new model Jev is 193x faster than AI. Jev by TypeSafe never writes text. Context in, typed decision out. YES or NO…
@injaneity says TypeSafe Jev is 5x faster and 1000x cheaper than Astra at playing 2048, and that it clearly does not match Astra or Fable at reasoning.
@typesafeai jev is 5x faster and 1000x cheaper than astra at playing 2048! though it (clearly) does not excel at reasoning compared to state of the art models l…
Quoted @injaneity
@tarat_211 describes zero-shot robot tasks with the TypeSafe API: a goal in plain English, and Jev chains hard-coded primitives so the arm acts. They say no training was required.
Zero-shot robot tasks with the @typesafeai api Give it a goal in plain English. Jev chains a set of hard-coded primitives. The arm does the thing. No training r…
@Mnilax wired Jev into the routing layer of a build that had been running on Astra and says the same task finished 37 minutes sooner with no malformed responses to retry. The post also cites TypeSafe’s published figures: $0.042 per million input tokens with free output, 67.8% matching Sonnet 5 on their eval at 294x less cost and 195x the speed, 0.114s versus 8.566s for GPT-5.6 Terra on the same demo, and about 10 decisions per second in the Doom demo.
man who co-invented ChatGPT just shipped a model that does in 0.114 sec what GPT Astra does in 8.5. i wired Jev into the routing layer of a build i was running…
Quoted @CompleteSkeptic
@zadescoxp built a trading agent with TypeSafe Jev at jevtrades.zadescoxp.com, BYOK, also on GitHub. They say it took a 2.405 ETH trade and about a 0.23% profit in their test.
I built a trading agent with the all new @typesafeai Jev. You can try it here : https://t.co/e7bLjnCfTj. Just put your own API key and start playing or clone it…