Latest / AI Fire Daily / #110 Robin: Why Jev is the Fastest, Cheapest LLM API for Autonomous Agents & "System 1" AI

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#110 Robin: Why Jev is the Fastest, Cheapest LLM API for Autonomous Agents & "System 1" AI · AI Fire Daily

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Tech giants are rushing a new AI into production.
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And well, the funny thing is, it can't write
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a single sentence. It can't generate a paragraph.
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It certainly won't chat with you. Yeah, it literally
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lacks the ability to explain its answers to you.
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Yet it might be the most important model today.
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Welcome back to another deep dive. Today, we
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are unpacking a fascinating new model called
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Jev. I have to admit, looking at modern AI, I
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still wrestle with prompt drift myself. Getting
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consistent outputs is exhausting. Oh, it really
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feels like wrestling smoke most of the time.
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You tweak one single word, suddenly everything
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breaks apart instantly. You add a comma, and
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the entire JSON format completely changes. Right.
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The model suddenly decides to add a friendly
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greeting. And, you know, that polite little greeting
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completely shatters your entire software pipeline.
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We have a massive stack of notes to cover today.
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We are going to explore exactly what this strange
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As GEV model is, we will apply Daniel Kahneman's
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system one thinking to AI architecture. And we
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will examine how GEV bypasses tech's generation
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entirely. Plus, we will dig into why developers
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are obsessed with its pricing. It's roughly 20
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to 200 times cheaper. That price difference is
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completely shaking up the tech industry. I mean,
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when you operate at massive scale, those fractions
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of pennies really matter. Let's start by dismantling
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what we think an LLM should do. Most frontier
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models are explicitly trained to talk to humans.
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They want to be helpful, so they write long essays.
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Yeah, you ask a simple question and get five
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dense paragraphs. They start with repetitive
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phrases like, you know, sure, I can help. They
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give you bullet points you never even requested.
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Exactly. Right. And it feels polite, but it's
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actually computationally disastrous for developers.
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Because your application pays for all those extra
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generated tokens. You literally pay for reasoning
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you never needed or wanted. It's like paying
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a philosopher to sort your daily mail. You just
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want to know if a specific letter is urgent.
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But the philosopher reads the letter and writes
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a long critique. Then they hand you the critique
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instead of a simple answer. It's wildly inefficient.
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It gets incredibly expensive at scale. Exactly.
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So Jeff takes a completely different route through
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the underlying data. He uses something called
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RLCD. That stands for Reinforcement Learning
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for Calibrated Decisions. Yeah, they spent two
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years developing this specific training method.
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The training focuses entirely on structured decisions,
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not prose. It strips away the conversational
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interface we're so used to. It learns how to
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map complex states directly to simple categories.
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Let's bring in Daniel Kahneman's famous psychological
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framework here. He wrote the classic book, Thinking
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Fast and Slow. Right. Kahneman describes two
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distinct modes of human cognitive processing.
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System two is our slow, deliberate reasoning
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mental engine. Like if I ask you to multiply
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24 by 17. My brain actually has to grind through
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those numbers slowly. I have to hold intermediate
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values in my short -term memory. Exactly. You
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feel the cognitive effort happening in real time.
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You burn actual physical calories trying to solve
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it. That's exactly how traditional large language
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models behave today. They generate their response
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step by step, right? Right. And they do it token
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by token very slowly. They build their thoughts.
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sequentially, just like we do. But Kahneman also
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defines system one thinking for us. System one
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is our fast, intuitive, and immediate judgment.
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If I throw a baseball at your head suddenly.
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I duck before I even realize what is happening.
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My body just reacts to the visual stimulus instantly.
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Yes. Your brain processed the visual data instantly.
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It made a split -second decision without generating
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an internal monologue. You didn't consciously
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calculate the velocity of the baseball. Wow.
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JEV is designed entirely around this System 1
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concept. It makes a fast judgment from the available
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information. It just returns that decision directly
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to your code. This brings up a major technical
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difference in the architecture. Traditional models
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have an autoregressive loop to build their answers.
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That means guessing the next word one by one.
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They guess the next token based on previous tokens.
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And that guessing game takes massive amounts
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of computational power. Every single token requires
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a full pass through the neural network. If an
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answer is 50 words, that's 50 complete passes.
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It creates a massive bottleneck in the processing
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pipeline. You're just sitting there waiting for
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the model to type. Right. Jev completely removes
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that normal autoregressive loop from the equation.
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It doesn't guess the next word in a sentence
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at all. It doesn't generate text sequentially
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in any capacity. Not at all. You send it a state,
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which is just your data. It evaluates your questions
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in parallel and answers instantly. It fires the
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entire neural network exactly one single time.
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It's like traditional LLMs or essayists pondering
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a prompt. Meanwhile, Jev is a referee making
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an instant split -second call. Beat. So without
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generating tokens, how exactly does it handle
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complex inputs? Well, it reads the state and
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fires back an immediate structured decision.
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And crucially, it includes a highly calibrated
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confidence score. No token -by -token weighting.
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Just instant structured decisions. That confidence
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score is a massive feature for developers. It
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tells your code exactly how much to trust the
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decision. If the confidence is too low, you just
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route to a human. Okay, let's move past the theoretical
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architecture. We need to see how this fast referee
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actually works. What do these whistleblows look
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like in an actual code base? The request format
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is beautifully simple to understand. You give
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Jev a state and a specific question type. A state
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could be an email or a user profile. It could
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be transaction details or a customer support
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history. It's basically any context your application
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already has. Yeah, it's the messy raw data sitting
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in your database. Jav mainly works with three
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specific question types. The first type is a
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simple multiple choice question. You ask it to
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pick billing, technical, or sales. The second
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type is a score or a probability. It rates something
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on an ordered scale for you. Like rating customer
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frustration from 1 to 10, right? Exactly. The
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third type is a basic yes or no question, but
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it returns that answer with a specific probability
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attached. It might say yes with a 90 % probability.
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Let's upgrade our examples to something developers
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actually encounter. If you pass it a basic JSON
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payload asking for a color, you give it an enum
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of red, green, or baby blue. It doesn't write
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you a poem about meteorology. Right. It just
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returns baby blue with an 81 % confidence score.
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And that response comes back. in a matter of
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milliseconds. Your application gets exactly what
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it needs right away. There's no need to parse
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a long messy paragraph. You don't need complex
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regular expressions to find the real answer.
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No. OK. So identifying a color is neat for a
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playground. But if I'm an enterprise dev, I care
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about messy data. Does this hold up when a customer
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is screaming in support? Consider a complex help
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desk triage situation. Oh, absolutely. The state
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includes a really frustrated customer support
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complaint. They say their Wi -Fi issue was completely
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ignored all week. They're threatening to cancel
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their entire service plan immediately. JEV also
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receives the user role and physical location.
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It also knows the specific channel the message
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came through. From that single state, it makes
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several rapid decisions. It instantly assigns
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a medium priority to the ticket. It routes the
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ticket straight to the correct technical team.
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It estimates a 5 % probability of a strict deadline.
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It even selects a ticket status tool to use automatically.
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One request handles multiple decisions without
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needing extra prompts. It extracts the sentiment,
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the priority, and the routing simultaneously.
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If you used a normal model for this specific
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task, you would need several API calls or complex
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extraction logic. Traditional software relies
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entirely on hard -coded deterministic logic loops.
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If X happens, then the software must do Y. If
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the text contains the word cancel, route to retention.
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But that works great until human fuzziness enters
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the system. People use sarcasm or misspell crucial
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words all the time. A message sounds urgent without
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actually using the word urgent. A transaction
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looks shady despite passing every single fixed
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rule. Jeff steps in when things get incredibly
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fuzzy computationally. But I have to ask the
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obvious question here. Does this really replace
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those traditional deterministic rules in a developer's
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software? Not at all. You keep your hard -coded
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logic completely intact. But Jeff slots in to
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handle the fuzzy, judgment -based micro -decisions.
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You still want strict deterministic rules for
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processing the actual payments. Right! You just
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use Jeff to decide if the user sounds angry.
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It bridges the gap between fixed code and fuzzy
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human logic. It acts as the intuitive connective
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tissue in modern software. And you can test all
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this easily in the JEV console. The console includes
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a really fantastic testing playground environment.
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You can test your state and your criteria out
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safely. You verify the structured output before
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going to live production. It even integrates
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directly with tools like Claude Code. JEV handles
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the small tool selection steps for coding agents,
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instead of asking a massive, expensive model
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for every decision. Jeff takes the fast steps
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and returns structured results. The pattern becomes
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incredibly clear once you see it working. State
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goes in. Strongly typed decisions come out. Your
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code handles the very next action without pausing.
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Sponsor. Sponsor placeholder provided separately.
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Do not pull from newsletter text. We've seen
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how this works on a micro level. Now let's scale
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it up for massive enterprise companies. We need
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to understand why developers are aggressively
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switching over. Well, the economics of AI are
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experiencing a massive fundamental shift. We
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are moving away from needing the smartest possible
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answer. The new metric is cost per useful decision
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made. That fundamentally changes how we evaluate
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these AI tools today. We used to just look at
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complex academic benchmark scores. Vercell actually
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provides a fantastic case study for this shift.
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Yeah, Vercell had been using Gemini 2 .5 Flashlight
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previously. They used it primarily for their
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automated classification tasks. Classification
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is tricky because it requires reading complex
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data payloads. You have to sort varied inputs
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into very specific buckets. Using a text generating
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model for this is inherently wasteful. It's like
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using a lo - luxury sports car to plow a field.
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They swapped that model out and plugged JEV in
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instead. The resulting system became roughly
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six times faster overall. But surely you lose
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accuracy, right? You trade that deep reasoning
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for pure speed. That's the crazy part. Performance
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stayed right near the top of their evaluations.
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Wow. They didn't sacrifice accuracy to get that
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massive speed boost. That speed increase is absolutely
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massive at enterprise scale. Time is literally
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money. If you process millions of items, speed
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is absolutely everything. It forces us to look
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beyond raw token pricing models. You have to
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ask what one useful decision actually costs.
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A model that avoids unnecessary text completely
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changes the economics. There are some wildly
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impressive demos popping up online too. The developer
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community is really pushing this model's boundaries.
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Oh, definitely. People are categorizing hundreds
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of messy emails in literal seconds. Smart home
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commands are executing in just 185 milliseconds.
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That latency is faster than human perception
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can even register. It feels like turning on a
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physical light switch. Yeah. Someone navigated
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Wikipedia from DNA to a distant page. That complex
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navigation took only 1 .7 seconds to complete.
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OpenCode also tested Jev with browser -related
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automated tasks. incredibly fast performance
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across the entire board. Low latency decisions
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helped these AI agents move so much faster. Gamers
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even hooked it up to Starcraft and Doom. The
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gaming applications are actually my favorite
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benchmark here. Doom ran continuously for an
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hour with massive request volume. Let's linger
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on that Doom example for a second. Playing Doom
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requires massive amounts of split -second decisions
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continuously. Every single frame is a new highly
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complex state. The agent needs to decide to shoot,
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Move or dodge. Exactly. Normally an AI agent
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looks at the screen state and thinks, you know,
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I see an enemy I should probably shoot. Then
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it generates that text. Then a script parses
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the text. which takes seconds. Right. And in
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Doom, you're already dead by then. The latency
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would make the game completely unplayable. And
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the token costs for traditional LLMs would bankrupt
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you. But Jev just outputs the action code instantly.
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The game runs smoothly. It kept receiving a highly
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structured game state constantly. It knew where
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the enemies were on the map. It kept sending
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back structured actions to the engine. That entire
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hour of continuous processing cost about seven
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dollars. It proves Jev handles high volume structured
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actions perfectly well. It never bogs down. trying
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to generate text blocks. The sheer volume of
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decisions it handles is staggering. It's wild.
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Whoa. Two -sec silence. Imagine scaling to a
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billion queries without the latency of generating
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text. It completely changes what we can realistically
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build at scale. Think about complex fraud detection
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systems in modern banking. They can score risk
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profiles instantly on every single transaction.
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Support systems can route thousands of tickets
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without breaking down. Coding workflows can decide
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which model handles the next tasks. With JEV
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handling all this, What happens to the massive
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AI agents we've been relying on? Well, agent
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systems stop getting bogged down by trivial tasks.
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You save the massive models for deep reasoning
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work. You let JEV act as the cheap, fast reflex
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system. Right. An agent usually asks itself many
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questions before acting. It asks which tool to
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use or if a prompt is safe. Those micro decisions
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add massive latency to the entire system. Large
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models handle deep thinking. JEV handles the
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quick reflexes. Exactly. You stop using a supercomputer
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to sort the mail. You let the specialized routing
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system handle the daily traffic. it just decides.
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Let's step back and synthesize this core takeaway
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today. Jev truly flips the script on modern AI
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utility. We have been so obsessed with building
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smarter conversational bots. But for developers
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building complex systems at massive scale, the
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true bottleneck is rarely a lack of raw intelligence.
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The bottleneck is almost always latency and operating
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cost. When you need guardrails, routing, and
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constant tool selection, you absolutely do not
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want a paragraph of thoughtful prose. No. Jeff
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proves that sometimes your code just needs speed.
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It needs a fast, highly confident decision to
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proceed smoothly. It's a profound shift in software
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engineering philosophy overall. We are building
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systems that act intuitively and instantly now.
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They replicate our own human cognitive reflex
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pathways beautifully. I want to leave you with
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a lingering question today. Look at your own
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daily workflows and digital systems. Look at
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the complex pipelines you interact with daily.
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Which of the complex agonizing decisions inside
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your system could actually be stripped down into
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a simple choice, a score, or a yes -no probability?
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If the answer is a lot, get ready for change.
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The entire way you interact with technology will
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shift. Everything's about to get significantly
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faster very soon. Thank you for taking this deep
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dive with us. Sometimes the most powerful AI
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is the one that stays quiet. It doesn't write
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essays or try to chat with you. It just blows
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the whistle and keeps the game moving.