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How does Jev work? Jev takes two things: your input and an output schema. The schema gives the structure of the answer you want. I send an email. My schema asks for two fields: is spam, and a category from marketing, finance, or spam. Jev returns: - is spam:...

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The Startup Ideas Podcast (SIP) 🧃

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Profilbild von Joshua Pi'Rwot
Joshua Pi'Rwotvor 1 Monat

Writing the schema is where most of the real work sits, and it is easy to underrate. Once you can state the shape of a correct answer field by field, you have already decided what counts as correct. The model filling it in is doing the easier half. When output quality is poor it is usually because the schema was vague, and the prompt gets blamed for it. It also makes failures easier to spot. A missing field shows up immediately, where a weak paragraph can sit there for weeks with everyone assuming it is fine.

Profilbild von Jay
Jayvor 1 Monat

the schema is doing the real work here

Profilbild von AI Mastery Guide
AI Mastery Guidevor 1 Monat

Finally someone explains Jev simply

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What does it cost to sort 1,700 emails with Jev? I gave JEV each full email object: - Subject - body - sender No special changes. Jev sent back 4 values for each email: - Category: work, shopping, finance, security - Priority: low to urgent - Spam score: a percentage - Reply score: how much the email needs a reply from me One user wrote that their account had a violation. JEV gave it a 90% reply score. The run used 4.2 million input tokens and 500,000 output tokens. The final cost was 18 cents. The total time was 12 seconds.
2:48

What does it cost to sort 1,700 emails with Jev? I gave JEV each full email object: - Subject - body - sender No special changes. Jev sent back 4 values for each email: - Category: work, shopping, finance, security - Priority: low to urgent - Spam score: a percentage - Reply score: how much the email needs a reply from me One user wrote that their account had a violation. JEV gave it a 90% reply score. The run used 4.2 million input tokens and 500,000 output tokens. The final cost was 18 cents. The total time was 12 seconds.

The Startup Ideas Podcast (SIP) 🧃

11,588 Aufrufe • vor 1 Monat

Jev is a classifier Example: I asked Jev what color is this iPhone Jev gives me this: - 80% orange - 10% red - 10% blue The total is 100. I set 5 options: blue, orange, red, green, and yellow. The question and the options are the schema. Jev does not say "This is orange." It says it is 80% sure. Jev is a decision model
1:15

Jev is a classifier Example: I asked Jev what color is this iPhone Jev gives me this: - 80% orange - 10% red - 10% blue The total is 100. I set 5 options: blue, orange, red, green, and yellow. The question and the options are the schema. Jev does not say "This is orange." It says it is 80% sure. Jev is a decision model

The Startup Ideas Podcast (SIP) 🧃

12,999 Aufrufe • vor 1 Monat

Launching Jev on Command Code for free today. Jev x Command Code is super interesting. You can only use Jev in headless mode or Provider API. Also open sourced a mod "cmd-mod-jev-nudge" Use Jev for free to keep your agent running as long as the there's more work to do.
5:06

Launching Jev on Command Code for free today. Jev x Command Code is super interesting. You can only use Jev in headless mode or Provider API. Also open sourced a mod "cmd-mod-jev-nudge" Use Jev for free to keep your agent running as long as the there's more work to do.

Ahmad Awais

34,780 Aufrufe • vor 1 Monat

Jev is incredibly good! If you haven't heard, Jev is a new "System One" model optimized for decision-making. For example, you can give it a set of possible choices, and Jev will classify the input text based on those choices, including a confidence score for each. Best of all: Jev is really fast and cheap, so you can use it in all sorts of applications. The first thing I built with it is a classifier to pick the hotels I want to visit. I use Apify to collect thousands of reviews, then classify them with Jev based on specific criteria I care about. You just couldn't do this reliably, but now you can. Link to the repository:
6:57

Jev is incredibly good! If you haven't heard, Jev is a new "System One" model optimized for decision-making. For example, you can give it a set of possible choices, and Jev will classify the input text based on those choices, including a confidence score for each. Best of all: Jev is really fast and cheap, so you can use it in all sorts of applications. The first thing I built with it is a classifier to pick the hotels I want to visit. I use Apify to collect thousands of reviews, then classify them with Jev based on specific criteria I care about. You just couldn't do this reliably, but now you can. Link to the repository:

Santiago

17,898 Aufrufe • vor 1 Monat

10 Jev use cases a marketer can see: 1. Lead scoring - You get 100 new leads overnight. Jev can score which ones are most likely to buy. Sales gets the best ones first. 2. Email prioritization - Drop hundreds of emails into it. Jev can score priority, spam, category, and reply probability. 3. Support routing - A client reports a billing issue, another one has an API problem. Jev can instantly decide where each ticket should go. 4. Customer intent - A user visits pricing three times this week. Jev can score how likely they are to upgrade. 5. Marketplace matching - Someone explains exactly what service they need. Jev can score which provider is the closest match. 6. Lead qualification - A prospect fills out your website form. Jev can decide between sales, self-serve, or nurture. 7. Content clipping- Give it a 60-minute podcast transcript. Jev can score the moments most worth clipping. 8. Browser tasks - Tell an agent to find the right hotel. Jev can help choose the best one in 8 seconds. 9. Human vs AI routing - Jev can decide whether it goes to AI or a human. 10. Workflow decisions - Most workflows eventually hit the same question: What should happen next? That's probably the best place to use Jev.
0:22

10 Jev use cases a marketer can see: 1. Lead scoring - You get 100 new leads overnight. Jev can score which ones are most likely to buy. Sales gets the best ones first. 2. Email prioritization - Drop hundreds of emails into it. Jev can score priority, spam, category, and reply probability. 3. Support routing - A client reports a billing issue, another one has an API problem. Jev can instantly decide where each ticket should go. 4. Customer intent - A user visits pricing three times this week. Jev can score how likely they are to upgrade. 5. Marketplace matching - Someone explains exactly what service they need. Jev can score which provider is the closest match. 6. Lead qualification - A prospect fills out your website form. Jev can decide between sales, self-serve, or nurture. 7. Content clipping- Give it a 60-minute podcast transcript. Jev can score the moments most worth clipping. 8. Browser tasks - Tell an agent to find the right hotel. Jev can help choose the best one in 8 seconds. 9. Human vs AI routing - Jev can decide whether it goes to AI or a human. 10. Workflow decisions - Most workflows eventually hit the same question: What should happen next? That's probably the best place to use Jev.

Yum⋆₊˚

134,823 Aufrufe • vor 1 Monat

this was never supposed to leave the studio group chats Jev doesn't generate images or write code. it picks. layout A or B, serif or sans, cut here or there. code does the drawing. that's why it never ships something half-broken. want a whole design system from one sentence? -> jev-design: type "an 18th century printed book" and the dashboard becomes one a second or two later want full UI screens from a one-line prompt? -> jev-design-test: real shadcn screens in ~1-2 seconds, Jev never writes a line of JSX want Figma to follow plain sentences? -> jev-figma-design-plugin: Jev decides, code draws want a long video cut into shorts? -> jevcut: 62 clips out of a 97-minute podcast, about 3 cents of Jev per hour of video want a video editor with Jev inside? -> HyperEdit: FFMPEG + Remotion, powered by Jev the catch: Jev only chooses from options a human already built. no vocabulary, no design. and it's still behind a waitlist. the taste is yours. the thousand tiny choices don't have to be. bookmark this. pick one. ship something this week.
0:35

this was never supposed to leave the studio group chats Jev doesn't generate images or write code. it picks. layout A or B, serif or sans, cut here or there. code does the drawing. that's why it never ships something half-broken. want a whole design system from one sentence? -> jev-design: type "an 18th century printed book" and the dashboard becomes one a second or two later want full UI screens from a one-line prompt? -> jev-design-test: real shadcn screens in ~1-2 seconds, Jev never writes a line of JSX want Figma to follow plain sentences? -> jev-figma-design-plugin: Jev decides, code draws want a long video cut into shorts? -> jevcut: 62 clips out of a 97-minute podcast, about 3 cents of Jev per hour of video want a video editor with Jev inside? -> HyperEdit: FFMPEG + Remotion, powered by Jev the catch: Jev only chooses from options a human already built. no vocabulary, no design. and it's still behind a waitlist. the taste is yours. the thousand tiny choices don't have to be. bookmark this. pick one. ship something this week.

kiosa

15,484 Aufrufe • vor 1 Monat

TypeSafe's new model Jev is everywhere this week, and it doesn't even generate text. It returns decisions: typed scores your code uses directly, in a single pass. We put it to work in a real-time chat moderator, scoring toxicity, frustration, and spam in 300 to 400ms. Then we deployed the Node.js backend on Cloudways Velocity. It runs as a persistent process, so no serverless cold start eats the speed Jev just gave you.
0:47

TypeSafe's new model Jev is everywhere this week, and it doesn't even generate text. It returns decisions: typed scores your code uses directly, in a single pass. We put it to work in a real-time chat moderator, scoring toxicity, frustration, and spam in 300 to 400ms. Then we deployed the Node.js backend on Cloudways Velocity. It runs as a persistent process, so no serverless cold start eats the speed Jev just gave you.

Cloudways

26,136 Aufrufe • vor 1 Monat

People already built a browser agent, a Claude Code bridge and a LangGraph router on top of Jev. Here are all 10 repos. If you have API access and don't know where to start, save this: 1. jev-browser Real browser control through MCP, CLI or as a library. At every step Jev answers two things: which element to touch, and whether the goal is done or the run is stuck. > 2. jev-browser-agent Picks the operation and the target in a single request, around a second per step. Anything under 0.6 confidence goes up to a bigger model or back to you. > 3. typesafe-jev-bridge OpenAI-compatible bridge with zero dependencies. Typed yes/no, choice and score answers inside Claude Code, Cursor, Cline or any OpenAI SDK, via CLI or HTTP. > 4. pilot-typesafeai-jev Jev wired into LangGraph and Deep Agents. Three questions in one request, plain code chooses the route, and the chat model only spends tokens on the route that won. > 5. building-with-jev-skill Agent skill for writing code that calls Jev. How to phrase questions, structure state, set thresholds, and debug a question that keeps answering wrong. > 6. jev-skill Guided setup and eval workflows. Checks what's installed without changing anything, never swaps your provider behind your back, never makes up probabilities. > 7. awesome-jev (yibie) What people actually shipped with Jev, sourced and sorted by category, with the limitations written down for each entry. > 8. awesome-typesafe-jev SDKs, live demos, agent tools and independent evals. Shows where every vendor number comes from. > 9. awesome-jev-by-typesafe Use cases, patterns and starter code backed by evidence. The 70 to 500 ms latency figure is labeled vendor-reported, not measured. > 10. awesome-jev (cobanov) Biggest catalogue so far: SDKs for Go, Java and JS, MCP connectors, guardrail experiments and studies. > Copy these complete Jev blueprints - then read full Jev setup below ↓
0:36

People already built a browser agent, a Claude Code bridge and a LangGraph router on top of Jev. Here are all 10 repos. If you have API access and don't know where to start, save this: 1. jev-browser Real browser control through MCP, CLI or as a library. At every step Jev answers two things: which element to touch, and whether the goal is done or the run is stuck. > 2. jev-browser-agent Picks the operation and the target in a single request, around a second per step. Anything under 0.6 confidence goes up to a bigger model or back to you. > 3. typesafe-jev-bridge OpenAI-compatible bridge with zero dependencies. Typed yes/no, choice and score answers inside Claude Code, Cursor, Cline or any OpenAI SDK, via CLI or HTTP. > 4. pilot-typesafeai-jev Jev wired into LangGraph and Deep Agents. Three questions in one request, plain code chooses the route, and the chat model only spends tokens on the route that won. > 5. building-with-jev-skill Agent skill for writing code that calls Jev. How to phrase questions, structure state, set thresholds, and debug a question that keeps answering wrong. > 6. jev-skill Guided setup and eval workflows. Checks what's installed without changing anything, never swaps your provider behind your back, never makes up probabilities. > 7. awesome-jev (yibie) What people actually shipped with Jev, sourced and sorted by category, with the limitations written down for each entry. > 8. awesome-typesafe-jev SDKs, live demos, agent tools and independent evals. Shows where every vendor number comes from. > 9. awesome-jev-by-typesafe Use cases, patterns and starter code backed by evidence. The 70 to 500 ms latency figure is labeled vendor-reported, not measured. > 10. awesome-jev (cobanov) Biggest catalogue so far: SDKs for Go, Java and JS, MCP connectors, guardrail experiments and studies. > Copy these complete Jev blueprints - then read full Jev setup below ↓

Asteri

16,629 Aufrufe • vor 1 Monat

Jev dropped barely a week ago and traders are already doing some wild sh*t with it. live order books. real-time signals. trading bots. entire AI hedge funds. here's what happened: > jev-trader · every ~0.3s asks Jev “buy or sell?” on Monad and trades > jev-trade · reads Hyperliquid order book -> Jev picks -> code sends the order > beebots · 3 OKX trading “bees”; every choice is Jev (paper trading by default) > QuantDinger · full AI trading OS; optional Jev gate = “should we trade at all?” > ai-hedge-fund · multi-agent “fund” with a Jev adapter for fast yes/no calls and that’s the funny part: Jev isn’t here to think. it’s here to make the call. the market moves -> Jev picks LONG / SHORT / HOLD -> code executes. no essays. no chain of thought. no expensive model debating every tiny market move. big models figure out the strategy. Jev handles the split-second calls. code makes it real.
0:27

Jev dropped barely a week ago and traders are already doing some wild sh*t with it. live order books. real-time signals. trading bots. entire AI hedge funds. here's what happened: > jev-trader · every ~0.3s asks Jev “buy or sell?” on Monad and trades > jev-trade · reads Hyperliquid order book -> Jev picks -> code sends the order > beebots · 3 OKX trading “bees”; every choice is Jev (paper trading by default) > QuantDinger · full AI trading OS; optional Jev gate = “should we trade at all?” > ai-hedge-fund · multi-agent “fund” with a Jev adapter for fast yes/no calls and that’s the funny part: Jev isn’t here to think. it’s here to make the call. the market moves -> Jev picks LONG / SHORT / HOLD -> code executes. no essays. no chain of thought. no expensive model debating every tiny market move. big models figure out the strategy. Jev handles the split-second calls. code makes it real.

kiosa

111,388 Aufrufe • vor 1 Monat

This is completely f*cking wild. A Stanford AI researchers gave Claude Code a second brain with JEV. The result? 80+ billion data points processed every 14 minutes. here's how it works: Jev stands at the door. every row has to get past it first. the easy ones it waves through on the spot. only the ones it can't call get sent upstairs to Claude. the old way: everything -> Claude -> big bill the new way: everything -> Jev -> the tricky few -> Claude Jev also tells you how sure it is on every call. that number decides who goes upstairs. just don't pick it out of thin air. test it first. the 20-minute setup: 1. take 200 real rows from your data 2. run them through Jev AND Claude 3. sort Jev's answers by how sure it was 4. find the point where Jev stops disagreeing with Claude. that's your cutoff 5. above it, Jev's answer is final. below it, the row goes to Claude then do the math. on a public benchmark Jev cost $0.015 per 1,000 rows and Claude cost $0.80. if Jev settles 80% of your rows: $0.015 + (20% × $0.80) = $0.175 per 1,000 rows instead of $0.80 that's ~78% off, with Claude still handling every hard case. 3 rules that keep it working: -> ask all your questions in one Jev call. topic, urgency and sentiment together, not three separate requests -> once a week, send 50 of Jev's "confident" answers to Claude anyway. if they start disagreeing, raise the cutoff -> never let Jev make the final call on anything you can't undo. refunds, bans, deletions go to Claude or a human where this pays off right away: -> support tickets: route by topic and urgency -> leads: hot, warm or skip -> comments: fine, spam or needs a look -> spreadsheets: fix category labels across thousands of rows Claude thinks. Jev picks. code ships.
0:21

This is completely f*cking wild. A Stanford AI researchers gave Claude Code a second brain with JEV. The result? 80+ billion data points processed every 14 minutes. here's how it works: Jev stands at the door. every row has to get past it first. the easy ones it waves through on the spot. only the ones it can't call get sent upstairs to Claude. the old way: everything -> Claude -> big bill the new way: everything -> Jev -> the tricky few -> Claude Jev also tells you how sure it is on every call. that number decides who goes upstairs. just don't pick it out of thin air. test it first. the 20-minute setup: 1. take 200 real rows from your data 2. run them through Jev AND Claude 3. sort Jev's answers by how sure it was 4. find the point where Jev stops disagreeing with Claude. that's your cutoff 5. above it, Jev's answer is final. below it, the row goes to Claude then do the math. on a public benchmark Jev cost $0.015 per 1,000 rows and Claude cost $0.80. if Jev settles 80% of your rows: $0.015 + (20% × $0.80) = $0.175 per 1,000 rows instead of $0.80 that's ~78% off, with Claude still handling every hard case. 3 rules that keep it working: -> ask all your questions in one Jev call. topic, urgency and sentiment together, not three separate requests -> once a week, send 50 of Jev's "confident" answers to Claude anyway. if they start disagreeing, raise the cutoff -> never let Jev make the final call on anything you can't undo. refunds, bans, deletions go to Claude or a human where this pays off right away: -> support tickets: route by topic and urgency -> leads: hot, warm or skip -> comments: fine, spam or needs a look -> spreadsheets: fix category labels across thousands of rows Claude thinks. Jev picks. code ships.

kiosa

13,948 Aufrufe • vor 18 Tagen

Everyone is talking about JEV. Almost nobody knows how to actually use it. These 10 GitHub repos fix that. 1. browser-use/jev-ultrafast (~16.6k⭐) Jev for browser agents: pick the next action without another expensive LLM call. 2. tamaratran/fast-jev-compaction (~6.5k⭐) Uses Jev to decide what context still matters and what can be dropped. 3. TheoLeeCJ/SemIf (~4k⭐) Open-source Jev-style decision making you can run locally. 4. jarrodwatts/jev-trader (~2.1k⭐) Real-time trading loop where Jev decides buy, sell or wait. 5. kerpopule/hermes-jev-skills (~670⭐) Jev routing, memory, compaction and skill selection for AI agents. 6. devagrawal09/jev-review (~570⭐) Code review powered by Jev decisions instead of one giant prompt. 7. thruwire/foreman (~520⭐) Jev as a supervisor for coding agents: continue, retry, verify or stop. 8. superagents-lab/jev-search (~400⭐) Jev decides where and how an agent should search. 9. droidrun/mobile-jev (~350⭐) Jev for Android agents choosing the next app action. 10. gargpratyush/jev-router (~340⭐) Routes agent tasks to the right model with Jev. After looking through these, the pattern is pretty clear: LLMs generate. Agents execute. JEV decides. That decision layer is what most agent stacks are still missing. Save this if you want to integrate Jev into your workflow👇
1:11

Everyone is talking about JEV. Almost nobody knows how to actually use it. These 10 GitHub repos fix that. 1. browser-use/jev-ultrafast (~16.6k⭐) Jev for browser agents: pick the next action without another expensive LLM call. 2. tamaratran/fast-jev-compaction (~6.5k⭐) Uses Jev to decide what context still matters and what can be dropped. 3. TheoLeeCJ/SemIf (~4k⭐) Open-source Jev-style decision making you can run locally. 4. jarrodwatts/jev-trader (~2.1k⭐) Real-time trading loop where Jev decides buy, sell or wait. 5. kerpopule/hermes-jev-skills (~670⭐) Jev routing, memory, compaction and skill selection for AI agents. 6. devagrawal09/jev-review (~570⭐) Code review powered by Jev decisions instead of one giant prompt. 7. thruwire/foreman (~520⭐) Jev as a supervisor for coding agents: continue, retry, verify or stop. 8. superagents-lab/jev-search (~400⭐) Jev decides where and how an agent should search. 9. droidrun/mobile-jev (~350⭐) Jev for Android agents choosing the next app action. 10. gargpratyush/jev-router (~340⭐) Routes agent tasks to the right model with Jev. After looking through these, the pattern is pretty clear: LLMs generate. Agents execute. JEV decides. That decision layer is what most agent stacks are still missing. Save this if you want to integrate Jev into your workflow👇

unicode

20,745 Aufrufe • vor 1 Monat

this is f*cking gold 20 GitHub repos with 500K+ combined stars that will level up your JEV workflow AGENTS > jev-ultrafast: Jev picks every click and DOM target, a small LLM only types > hermes-jev-skills: routing, memory, compaction and skill picks in one pack > typesafe-computer-use: OCR reads your Mac screen, Jev picks the next click MEMORY > fast-jev-compaction: scores every tool call keep, truncate or drop instead of summarizing > jevmem: project memory for Claude Code, Cursor and Codex, updated every turn > jev-second-brain: your Obsidian vault, Jev judges which notes duplicate, revise or contradict SAFETY > jev-guard: a gate before every tool call, Jev scores the risk, you set allow, ask or deny TOOLS > skills: the official TypeSafe skill for Claude Code and Codex > system-one-adapter-python: dry-run your questions on an ordinary LLM before you burn a Jev key > jev-mcp: claim checks, screening and ranking as MCP tools > typesafe-mcp: plug Jev into any MCP client > json-render: Vercel's generative UI, where Jev picks the components OPEN MODELS > SemIf-OpenJev: semantic ifs from frozen open models kev: Jev-like models on Qwen that run on your MacBook > laya-mlx: the Laya decision engine on Apple silicon > clm: an open System One model with Choice, Noul and Score > jevlike: train your own Jev-like model TRADING > jev-trader: one buy or sell decision per Monad block START HERE > awesome-jev: the biggest map of everything built on Jev > awesome-jev-by-typesafe: use cases, patterns and starter code bookmark it before your next build
0:22

this is f*cking gold 20 GitHub repos with 500K+ combined stars that will level up your JEV workflow AGENTS > jev-ultrafast: Jev picks every click and DOM target, a small LLM only types > hermes-jev-skills: routing, memory, compaction and skill picks in one pack > typesafe-computer-use: OCR reads your Mac screen, Jev picks the next click MEMORY > fast-jev-compaction: scores every tool call keep, truncate or drop instead of summarizing > jevmem: project memory for Claude Code, Cursor and Codex, updated every turn > jev-second-brain: your Obsidian vault, Jev judges which notes duplicate, revise or contradict SAFETY > jev-guard: a gate before every tool call, Jev scores the risk, you set allow, ask or deny TOOLS > skills: the official TypeSafe skill for Claude Code and Codex > system-one-adapter-python: dry-run your questions on an ordinary LLM before you burn a Jev key > jev-mcp: claim checks, screening and ranking as MCP tools > typesafe-mcp: plug Jev into any MCP client > json-render: Vercel's generative UI, where Jev picks the components OPEN MODELS > SemIf-OpenJev: semantic ifs from frozen open models kev: Jev-like models on Qwen that run on your MacBook > laya-mlx: the Laya decision engine on Apple silicon > clm: an open System One model with Choice, Noul and Score > jevlike: train your own Jev-like model TRADING > jev-trader: one buy or sell decision per Monad block START HERE > awesome-jev: the biggest map of everything built on Jev > awesome-jev-by-typesafe: use cases, patterns and starter code bookmark it before your next build

NO1ennn

18,445 Aufrufe • vor 1 Monat

Someone said: I have created 5 Gmail accounts and Yahoo Mail but all the cold emails I send goes to the spam folder. Why is this so? My Answer: Did you test your cold email for spam triggers before sending? Before you send your next cold email, check if there are spam words. Words as simple as "For You" can send your email to spam folder No need for stories Let me show you how to Test your draft email for spam triggers Even your chatgpt and clause emails are not safe. Test it first Thank me later
17:44

Someone said: I have created 5 Gmail accounts and Yahoo Mail but all the cold emails I send goes to the spam folder. Why is this so? My Answer: Did you test your cold email for spam triggers before sending? Before you send your next cold email, check if there are spam words. Words as simple as "For You" can send your email to spam folder No need for stories Let me show you how to Test your draft email for spam triggers Even your chatgpt and clause emails are not safe. Test it first Thank me later

Richard U. | Web Developer

25,922 Aufrufe • vor 2 Monaten

Jev is cool. So is it's OSS companion, Laya. The Latest Cool Thing In AI™ tends to get a lot of hype, sometimes without everyone even understanding it. So... what is this thing? Jev is an AI model that consumes input and produces output VERY differently than chat, claude, grok. The input is two things: 1) Text state to assess. Email, html, code, whatever. 2) A set of questions which will be asked about the attached state. The canonical example from TypeSafe's docs is to identify the urgency of a support ticket. We pass the model the customer text + a single noul question "is this urgent?". Jev returns a full set of JSON. This JSON is not generated with token-by-token autoregression. Jev is not trained to produce sequences of text tokens, rather to answer questions, and guarantees well-formed responses. In the example below, we see it produces a 0.99 probability (on a 0-1.0 scale) that the answer is "yes." Jev supports exactly three types of questions (seconds example in video): a) Noul: 0–1 probability that the answer to a yes/no question is "yes." b) Choice: Ask question with pre-defined set of answers. Jev chooses the best and assigns probabilities to each. c) Score: Ask question with pre-defined scale of answers. Jev produces a position on the scale. Jev computes answers for all questions in parallel, making responses super fast even for many questions in a single request. This might seem like a narrow set of capabilities, but in the right contexts leads to incredible potential. It also makes for a useful API / primitive for programming, since the outputs are... *ahem*... type-safe and predictable in structure. Jev is not going to replace LLMs for writing your code, auto-generating your docs, or being at the core of an agent harness. But Jev IS incredibly cool, and will be used to build a lot of amazing tech. Hope this helps.
1:02

Jev is cool. So is it's OSS companion, Laya. The Latest Cool Thing In AI™ tends to get a lot of hype, sometimes without everyone even understanding it. So... what is this thing? Jev is an AI model that consumes input and produces output VERY differently than chat, claude, grok. The input is two things: 1) Text state to assess. Email, html, code, whatever. 2) A set of questions which will be asked about the attached state. The canonical example from TypeSafe's docs is to identify the urgency of a support ticket. We pass the model the customer text + a single noul question "is this urgent?". Jev returns a full set of JSON. This JSON is not generated with token-by-token autoregression. Jev is not trained to produce sequences of text tokens, rather to answer questions, and guarantees well-formed responses. In the example below, we see it produces a 0.99 probability (on a 0-1.0 scale) that the answer is "yes." Jev supports exactly three types of questions (seconds example in video): a) Noul: 0–1 probability that the answer to a yes/no question is "yes." b) Choice: Ask question with pre-defined set of answers. Jev chooses the best and assigns probabilities to each. c) Score: Ask question with pre-defined scale of answers. Jev produces a position on the scale. Jev computes answers for all questions in parallel, making responses super fast even for many questions in a single request. This might seem like a narrow set of capabilities, but in the right contexts leads to incredible potential. It also makes for a useful API / primitive for programming, since the outputs are... *ahem*... type-safe and predictable in structure. Jev is not going to replace LLMs for writing your code, auto-generating your docs, or being at the core of an agent harness. But Jev IS incredibly cool, and will be used to build a lot of amazing tech. Hope this helps.

Ben Dicken

42,183 Aufrufe • vor 1 Monat

HOW TO STOP YOUR COLD EMAIL FROM GOING TO SPAM. (100% WORKING) Someone asked: All my cold emails are going to Spam folder. How can I solve this permanently? Is it possible? My answer: We can always make sure our cold emails do not get to spam. Infact in this video I will show you what I do and how I avoid spam folder. If you try this method fully and your email still goes to spam, just pack your bags to your village or go and beg your village people... This is a sure method, I guarantee you If your email still enter spam after this, then cold email is not for you. Watch this video Thank me later
19:37

HOW TO STOP YOUR COLD EMAIL FROM GOING TO SPAM. (100% WORKING) Someone asked: All my cold emails are going to Spam folder. How can I solve this permanently? Is it possible? My answer: We can always make sure our cold emails do not get to spam. Infact in this video I will show you what I do and how I avoid spam folder. If you try this method fully and your email still goes to spam, just pack your bags to your village or go and beg your village people... This is a sure method, I guarantee you If your email still enter spam after this, then cold email is not for you. Watch this video Thank me later

Richard U. | Web Developer

98,348 Aufrufe • vor 2 Monaten

I couldn't f*cking believe this Stanford AI research team gave Claude Code a human brain setup. JEV is the fast gut brain. It scans everything in milliseconds and makes the easy calls. Claude is the slow thinking brain. It only wakes up for the hard ones. Here are the commands that wire the two together: /plugin marketplace add rashedInt32/jev-mcp /plugin install jev@jev-mcp -> Claude gets Jev as tools: classify, score, check npm install -g @dzhng/jevgrep jg "how are payments retried?" ./my-project -> ask what the code does, get the right files back. the author measured ~30% lower cost on the same tasks npx skills add altryne/jevify --skill jevify -> teaches your agent to hand the bulk yes/no work to Jev on its own > and fast-jev-compaction swaps Claude Code's summary for Jev decisions: it drops stale tool calls and keeps everything else word for word. Faster runs, smaller bills, barely any compute wasted. Save this before your next session.
0:27

I couldn't f*cking believe this Stanford AI research team gave Claude Code a human brain setup. JEV is the fast gut brain. It scans everything in milliseconds and makes the easy calls. Claude is the slow thinking brain. It only wakes up for the hard ones. Here are the commands that wire the two together: /plugin marketplace add rashedInt32/jev-mcp /plugin install jev@jev-mcp -> Claude gets Jev as tools: classify, score, check npm install -g @dzhng/jevgrep jg "how are payments retried?" ./my-project -> ask what the code does, get the right files back. the author measured ~30% lower cost on the same tasks npx skills add altryne/jevify --skill jevify -> teaches your agent to hand the bulk yes/no work to Jev on its own > and fast-jev-compaction swaps Claude Code's summary for Jev decisions: it drops stale tool calls and keeps everything else word for word. Faster runs, smaller bills, barely any compute wasted. Save this before your next session.

kiosa

86,755 Aufrufe • vor 29 Tagen

LLMs vs. Jev, clearly explained! LLMs are great, and the ceiling is one you can watch scroll past: an LLM writes the answer one token at a time. give it a failed deploy and four decisions, and it produces a small JSON object where every token depends on the one before it. token nine cannot exist until token eight does, so four decisions that had nothing to do with each other just stood in a queue. then your code parses it, validates the shape, and retries when the shape is wrong. Jev fixes this without being a smaller or faster model: it removes the order. one turn on that deploy has to know: → whether the incident is urgent → which team owns it → whether the next command is risky → whether the task is actually done you declare the questions and the answer type upfront, and all four come back together, typed, with a probability on each. three primitives cover almost every fork in an agent: 1. **Choice** picks one of up to 255 options you define, like engineering, billing or sales. 2. **Score** places the state on an ordered scale you define, like low, medium or high risk. 3. **Noul** returns the probability that a yes-or-no condition is true. here is the sentence that resolves the whole confusion: text is a line you have to walk. an answer space is a room you see all of at once. ↳ generation: one order you cannot change, one string at the end, a shape you hope holds ↳ evaluation: no order at all, typed answers, a probability on every option Prompts → Agents → Loops → Graphs → Jev the probabilities matter more than the answer. ↳ engineering at 0.91 against billing at 0.09 is a route you can automate ↳ 0.52 against 0.46 is a coin flip wearing a label, and the label alone never told you which one you got that last one catches careful people. an LLM would have said "engineering" in a confident sentence and given you no way to know the race was that close. thresholds live in your code, one per action, scaled to what being wrong costs. it works when the options are known and the call depends on meaning. it is not for writing, code, arithmetic, or anything where question two needs the answer to question one. and the one that eats whole nights: type safety prevents malformed output, not incorrect judgment. Jev cannot return an option outside your schema, and it can still pick the wrong valid one with confidence. a schema-valid mistake refunds the wrong customer just as fast. an LLM writes new language when the answer space is open. Jev evaluates known paths when the answer space is closed. below i have quoted my full breakdown on Jev. it covers the three primitives, the parallel battery, the thresholds, and where it does not belong. save this and read it below ↓
0:35

LLMs vs. Jev, clearly explained! LLMs are great, and the ceiling is one you can watch scroll past: an LLM writes the answer one token at a time. give it a failed deploy and four decisions, and it produces a small JSON object where every token depends on the one before it. token nine cannot exist until token eight does, so four decisions that had nothing to do with each other just stood in a queue. then your code parses it, validates the shape, and retries when the shape is wrong. Jev fixes this without being a smaller or faster model: it removes the order. one turn on that deploy has to know: → whether the incident is urgent → which team owns it → whether the next command is risky → whether the task is actually done you declare the questions and the answer type upfront, and all four come back together, typed, with a probability on each. three primitives cover almost every fork in an agent: 1. **Choice** picks one of up to 255 options you define, like engineering, billing or sales. 2. **Score** places the state on an ordered scale you define, like low, medium or high risk. 3. **Noul** returns the probability that a yes-or-no condition is true. here is the sentence that resolves the whole confusion: text is a line you have to walk. an answer space is a room you see all of at once. ↳ generation: one order you cannot change, one string at the end, a shape you hope holds ↳ evaluation: no order at all, typed answers, a probability on every option Prompts → Agents → Loops → Graphs → Jev the probabilities matter more than the answer. ↳ engineering at 0.91 against billing at 0.09 is a route you can automate ↳ 0.52 against 0.46 is a coin flip wearing a label, and the label alone never told you which one you got that last one catches careful people. an LLM would have said "engineering" in a confident sentence and given you no way to know the race was that close. thresholds live in your code, one per action, scaled to what being wrong costs. it works when the options are known and the call depends on meaning. it is not for writing, code, arithmetic, or anything where question two needs the answer to question one. and the one that eats whole nights: type safety prevents malformed output, not incorrect judgment. Jev cannot return an option outside your schema, and it can still pick the wrong valid one with confidence. a schema-valid mistake refunds the wrong customer just as fast. an LLM writes new language when the answer space is open. Jev evaluates known paths when the answer space is closed. below i have quoted my full breakdown on Jev. it covers the three primitives, the parallel battery, the thresholds, and where it does not belong. save this and read it below ↓

Hanako

44,263 Aufrufe • vor 1 Monat

Jev Founder, Diogo Almeida (ex-OpenAI): "The next era is not the Claude Code era. Claude Code is still part of the assistance era - Jev is the next step Jev is x200 faster, x400 cheaper and has 0 hallucination. This is what comes after RLHF" In 17 minutes he explains why every model you use has a human-shaped flaw baked into it and why Jev is better worth more than a $500 Claude Code engineering course watch today, then read how to actually boost yourself x100 with Jev in the post below
17:33

Jev Founder, Diogo Almeida (ex-OpenAI): "The next era is not the Claude Code era. Claude Code is still part of the assistance era - Jev is the next step Jev is x200 faster, x400 cheaper and has 0 hallucination. This is what comes after RLHF" In 17 minutes he explains why every model you use has a human-shaped flaw baked into it and why Jev is better worth more than a $500 Claude Code engineering course watch today, then read how to actually boost yourself x100 with Jev in the post below

Codez

193,792 Aufrufe • vor 1 Monat

This is absolutely f*cking insane. A Stanford AI research group combines JEV with Claude Code to sort 78+ billion data points every 12 minutes. so the trick is stupidly simple: Jev runs a cheap first pass on everything. Claude only gets the hard cases. instead of: all data -> Claude -> $$$ it's: all data -> Jev -> only the unsure ones -> Claude and here's the part people miss. every Jev answer comes with a probability. that's your filter: -> Jev is 90%+ sure? keep the answer, done -> below that? send it up to Claude you set the cutoff once. the boring stuff never touches the expensive model. where this pays off right away: -> tagging support tickets -> sorting your inbox -> filtering leads -> cleaning a messy spreadsheet -> moderating comments faster. cheaper. way less compute and tokens burned. Jev sorts the mail so the genius only opens what matters. LLMs think. Jev decides. code does the job.
0:32

This is absolutely f*cking insane. A Stanford AI research group combines JEV with Claude Code to sort 78+ billion data points every 12 minutes. so the trick is stupidly simple: Jev runs a cheap first pass on everything. Claude only gets the hard cases. instead of: all data -> Claude -> $$$ it's: all data -> Jev -> only the unsure ones -> Claude and here's the part people miss. every Jev answer comes with a probability. that's your filter: -> Jev is 90%+ sure? keep the answer, done -> below that? send it up to Claude you set the cutoff once. the boring stuff never touches the expensive model. where this pays off right away: -> tagging support tickets -> sorting your inbox -> filtering leads -> cleaning a messy spreadsheet -> moderating comments faster. cheaper. way less compute and tokens burned. Jev sorts the mail so the genius only opens what matters. LLMs think. Jev decides. code does the job.

kiosa

18,441 Aufrufe • vor 21 Tagen

Jev is now multimodal. It can see. Here’s how it works and what you can build with it: Jev is a decision model. You give it information, ask questions, and get answers your code can use directly. Previously, that information was text or JSON. Now it can include images. Start with how a Jev request works. You send a “state,” which is simply the information you want Jev to examine. Then you ask questions about it. Each question specifies the kind of answer you need. → Choice picks from options you define, such as “thumbs up” or “fist.” → Noul returns the probability that something is true, such as whether a hand is visible. → Score rates something on a scale you define, such as how open the hand is. With multimodal Jev, images can be part of both the state and the questions. Put a camera frame in the state, and your questions can ask about what’s visible in it. Put a reference photo in a question’s instructions, and you can ask whether an object in the camera frame matches the one in that photo. Jev can now also identify an object and locate it for you. The new Box type brings a YOLO-like capability to Jev. Describe what you want to find, such as “the hand in this image,” and it returns a bounding box around it. Your app can use those coordinates to highlight the object or crop it. One detail matters here. Box returns coordinates even when the target is absent, so pair it with a Noul presence check before using the result. My demo asks about presence, gesture, and location together. Jev answers these independent questions in parallel, and the app combines the results. These capabilities open up prototypes for gesture controls, locating buttons in screenshots, and checking product photos against visual references. Jev’s focus on fast decisions also makes it promising for interactive applications where response time matters, with image latency worth testing in your own workflow. Images and Box are still experimental and require the images “danger zone” header. I cover how it works, the API, and the demo app in the detailed video below. Chapters 0:00 Intro 0:36 Live demo 1:41 What's covered 2:22 How Jev works 3:06 Question types 3:57 Playground demo 6:59 Demo app walkthrough 10:30 Using Multiple images 11:25 Parallel questions 12:40 Danger zone headers 13:07 Outro If you’re new to Jev and want to understand how decision models work, I’ve written a detailed article about it. It’s quoted below.
13:17

Jev is now multimodal. It can see. Here’s how it works and what you can build with it: Jev is a decision model. You give it information, ask questions, and get answers your code can use directly. Previously, that information was text or JSON. Now it can include images. Start with how a Jev request works. You send a “state,” which is simply the information you want Jev to examine. Then you ask questions about it. Each question specifies the kind of answer you need. → Choice picks from options you define, such as “thumbs up” or “fist.” → Noul returns the probability that something is true, such as whether a hand is visible. → Score rates something on a scale you define, such as how open the hand is. With multimodal Jev, images can be part of both the state and the questions. Put a camera frame in the state, and your questions can ask about what’s visible in it. Put a reference photo in a question’s instructions, and you can ask whether an object in the camera frame matches the one in that photo. Jev can now also identify an object and locate it for you. The new Box type brings a YOLO-like capability to Jev. Describe what you want to find, such as “the hand in this image,” and it returns a bounding box around it. Your app can use those coordinates to highlight the object or crop it. One detail matters here. Box returns coordinates even when the target is absent, so pair it with a Noul presence check before using the result. My demo asks about presence, gesture, and location together. Jev answers these independent questions in parallel, and the app combines the results. These capabilities open up prototypes for gesture controls, locating buttons in screenshots, and checking product photos against visual references. Jev’s focus on fast decisions also makes it promising for interactive applications where response time matters, with image latency worth testing in your own workflow. Images and Box are still experimental and require the images “danger zone” header. I cover how it works, the API, and the demo app in the detailed video below. Chapters 0:00 Intro 0:36 Live demo 1:41 What's covered 2:22 How Jev works 3:06 Question types 3:57 Playground demo 6:59 Demo app walkthrough 10:30 Using Multiple images 11:25 Parallel questions 12:40 Danger zone headers 13:07 Outro If you’re new to Jev and want to understand how decision models work, I’ve written a detailed article about it. It’s quoted below.

Akshay 🚀

26,160 Aufrufe • vor 25 Tagen