Tailor: websites that adapt to every visitor, powered by Jev

Tailor shows every website visitor the version of your site that fits them, and updates its read on every click. THE PROBLEM Dev-tool websites serve developers, buyers, privacy reviewers and investors with one page. Developers get "Book a demo", buyers get code samples, privacy reviewers hunt for the data policy, and most of them leave. Existing personalization tools segment once per visit using company data and fixed rules. WHAT IT DOES Tailor is a React SDK plus a stateless decision API. Your team writes a version of each section per audience. Tailor watches on-site behaviour (clicks, reading time, code copies) and Jev decides who the visitor is. The page then re-renders headlines and buttons, reorders sections and highlights the right pricing plan. - It never hides anything: every page and section stays reachable, and a "View as" switcher lets anyone pick a view. - It never changes what you're reading: swaps happen off-screen or on the next page. - It only acts when Jev is confident; otherwise you see the normal page. - A visitor can match several audiences at once (e.g. a technical investor). HOW WE USE JEV - Atomic yes/no (noul) questions, one per audience: "is this visitor a developer who would write the code that adds Tailor to a website?" - Evidence sources judged separately: where the visitor came from (referrer/UTM, asked once per session and cached across visitors) and what they did on the site (asked after each meaningful action). Each request's state holds only its own evidence. - Code owns policy: it blends the two reads, weighting behaviour more as it accumulates, and applies a confidence cut-off with hysteresis so the page doesn't flip-flop. Pinned choices always win. - A separate talk_interest question flags visitors who want a conversation (0.9+ on "Book a walkthrough" / "Talk to the founders"). - Live numbers: about 0.5 s server-side per decision, about 1.2k input tokens across two parallel Jev calls, roughly $0.00005 per decision. Per-click personalization with an LLM would cost seconds and cents. DOGFOODED Tailor's own marketing site runs on Tailor. The Lens overlay shows each evidence source, the combined read, latency and cost live. /demo has entry links and QR codes (Hacker News, LinkedIn ad, GDPR search, VC newsletter…), so judges can watch the site adapt to their own clicks. ARCHITECTURE - packages/core: pure TypeScript for normalization, question building, combining, policy, validation and decide() (43 tests) - packages/react: TailorProvider, Slot/Variant (re-render), SlotGroup/Section (reorder), Emphasis (highlight), Signal, AudienceSwitcher, TailorLens - apps/site: Next.js 16 on Vercel; POST /api/tailor/v1/decide calls Jev server-side (the key never reaches the browser). A Jev failure keeps the current page. PRIVACY Session-only behaviour, one first-party cookie, no personal data, no fingerprinting, no inference of protected traits. Visitors can see and reset every choice. WHAT'S NEXT Built-in holdout groups to measure lift per audience; a script tag for Webflow/Framer/WordPress; LLM-drafted versions approved by the team; onboarding flows and chat agents that adapt the same way. Live: https://tailor-five-indol.vercel.app Demo launcher: https://tailor-five-indol.vercel.app/demo

View source

// latest_project_recording.mp4

// Project brief

// Built with

Browserbase

TypeSafe AI

// Watch & explore

Open project recording

// More from this event

Keep exploring what builders shipped.

All projects →

manubaba

AgentGate

**AgentGate** is a safety loop for code that agents write. A builder opens a pull request, CodeRabbit reviews it, and Jev is the only thing allowed to merge. If CodeRabbit still has a critical or security finding, or the tests are failing, the pull request stays blocked and goes back to be fixed. On a live GitHub pull request, CodeRabbit flagged a refund endpoint that dropped the payment lookup, so any payment id could receive a completed refund, and that never checked the amount. Jev’s decision on that evidence is fix, at risk 9.9 out of 10. After a clean review the same gate can say merge, at risk 2.7. Nothing ships while a blocking finding is open. Built for JEVATHON with CodeRabbit and Jev. Repo: https://github.com/manvendersingh21/PR-slayer

CodeRabbitTypeSafe AI

HackerSquad project

Elevator Sense

# Elevator Sense TypeSafe calls Jev "programmable common sense." I tried giving some to a hospital elevator. Live demo: https://jev-elevator-dispatch.vercel.app Code: https://github.com/tanishkgovil/jev-elevator-dispatch ## The problem A normal elevator dispatcher knows where you're going, not who's waiting. In a hospital, a trauma patient on a bed, a nurse on break, and a family heading to the cafeteria are all just "a hall call." LLMs can read that meaning, but at seconds per call they're too slow to sit inside a control loop. ## What I built A simulated 10-floor hospital with two identical elevator banks side by side: same building, same stream of requests, same dispatch algorithm. The only difference is that on the right, Jev reads each free-text request first (e.g. "Bed transport ER to ICU, pt intubated, need to go NOW"). ## How Jev is used One Jev call per request, three typed questions, ~100–200 ms: - Priority (Choice): stat / urgent / routine / visitor - Load (Choice): bed / wheelchair / cart / walking - Needs its own car? (Noul): probability 0–1 Plain code turns the answers into rules: a bed fills a whole car; emergencies go first and get an empty car. The same ETA-based dispatcher then picks the car. Jev never picks the elevator or does arithmetic, because TypeSafe's docs say it's weak at numbers, so it supplies only the meaning. Live incidents typed into the demo ("Car C door keeps sticking", "Trauma incoming to the ED in 5 minutes") go through a second Jev call (event type, floor, car). The physical effect happens in both buildings, but only the Jev side responds. ## Results Mean over 16 randomized traffic scenarios that were NOT used for tuning: - Emergency patients wait: 46s → 13s (−71%) - Patient beds wait: 80s → 24s (−70%) - Everyone, on average: 31s → 19s (−38%) - Trips where a bed didn't fit: 120 → 3 Jev came out ahead in 14 of 16 scenarios. ## Limitations - It's a simulation; the physics and traffic mix are assumptions. - I wrote the test requests myself, so Jev's accuracy on real hospital language is untested. - The baseline is a textbook dispatcher, not a commercial controller. The claim is "same dispatcher with vs. without understanding." ## What's next Test on requests written by real clinical staff; use Jev's confidence scores to handle uncertain requests conservatively; compare against published dispatch algorithms; generalize to other fleets (hospital porters, ambulances, warehouse robots).

TypeSafe AI

jevolution

Jevolution

Jevolution - Explore the ecosystems we can’t experiment on Understanding what keeps a species alive requires understanding its interactions: where it finds food, how it avoids predators, and how it responds when its environment changes. For many species, researchers lack enough observations to assess even their extinction risk. A 2022 study predicted that 56% of the 7,699 Data Deficient species it assessed were threatened. https://www.nature.com/articles/s42003-022-03638-9 We can’t freely experiment on endangered ecosystems. Simulations offer a way to explore possible outcomes—but their usefulness depends on the assumptions and behaviors they capture. The problem Rules-based simulations are fast and can produce complex emergent patterns, but extending their behavior often requires researchers to design and maintain additional rules. Generative AI offers more flexible decision-making, but calling a language model for every animal’s decisions introduces latency and cost. The 2023 Stanford-led Generative Agents project reported spending thousands of dollars in token credits to simulate 25 characters over two in-game days. Scaling that approach to an ecosystem presents a significant challenge. https://arxiv.org/html/2304.03442v2 What we built Jevolution is an interactive ecosystem simulator exploring a more efficient approach: Jev agents that select structured actions from their local observations. In our predator–prey demo, every rabbit and wolf has its own Jev-powered controller. Rabbits choose how to forage, move, flee, and signal. Wolves choose how to search for and pursue prey. The simulation engine handles movement, energy, reproduction, inheritance, and the consequences of those decisions. This separation lets us explore flexible agent behavior within consistent environmental rules, while measuring the cost and responsiveness of each controller. What the demo reveals Individual decisions accumulate into population-level patterns. Rabbits can warn nearby animals about danger, advertise food, and respond to signals. Our demo shows rabbits gathering around resource patches without a centrally scripted colony formation sequence. Communication actions are available to the agents; the interesting question is when they use them, how others respond, and what collective patterns follow. Offspring also inherit and mutate five predefined traits. As populations change, users can inspect which trait distributions persist alongside changes in food availability, predation, and population size. Built to investigate Jevolution makes these interactions visible and inspectable: - Rewind a run and follow individual animals. - Inspect traits, observations, and recorded model decisions. - Track births, deaths, predation, and population changes. - Compare supported controllers using recorded API latency and estimated cost. - Explore environmental disruption, including drought. The result is a working platform for asking questions about behavior and testing simulation assumptions. Where we’re going Our next step is to ground the simulator in a specific wildlife system: species-specific biology, measured environmental conditions, and validation against field observations. From there, researchers could explore habitat loss, changing resources, and other threats across many scenarios. Jevolution currently produces illustrative simulated trajectories; establishing their scientific usefulness requires that calibration and validation. Our ambition is to make adaptive ecosystem experiments affordable enough to run repeatedly—and transparent enough to understand. Live demo: https://jevolution.world

TypeSafe AI