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What can you build with Jev? Practical use cases for fast, cheap decision models.

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Rubric Labs

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14,436 Aufrufe • vor 4 Tagen •via X (Twitter)

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computer use with openai's decisions api might be the next big thing. i built arc-cua - a fast computer use layer powered by decision models. >no screenshot + LLM call after each action. >supports jev & decisions api. open source. link below.
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computer use with openai's decisions api might be the next big thing. i built arc-cua - a fast computer use layer powered by decision models. >no screenshot + LLM call after each action. >supports jev & decisions api. open source. link below.

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We found the perfect use case for Jev: auto-routing GenAI models. The idea: Jev picks the most cost-effective models for your task, balancing speed and quality. All generations made with Higgsfield API.
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Why is nobody posting about this? This is hands down the BEST way to run Jev right now. You can use the new Jev Playground to start running decisions with Jev for FREE. You're welcome. →
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This is f*ckin awesome. A Stanford team pairs JEV with Claude Code to sort 75 billion data points every 11 minutes. JEV runs a cheap first pass on everything. Claude only gets the hard cases. Faster, cheaper, way less compute burned.
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This is f*ckin awesome. A Stanford team pairs JEV with Claude Code to sort 75 billion data points every 11 minutes. JEV runs a cheap first pass on everything. Claude only gets the hard cases. Faster, cheaper, way less compute burned.

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The `rebel-large` model is awesome for relation extraction 🔗 Paired with CUDA, it’s blazing fast ⚡️. With LlamaIndex 🦙 🦙, we can now build a knowledge graph over any text data super quickly! 🕸️ Full Colab notebook showing how you can use it:
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Jev + SERV is actually insane. We already showed you can increase Jev's performance with SERV Reasoning. Now we're taking it further, bringing Jev-powered Decision nodes into Graph Sharding with the upcoming SERV v3. Here's a breakdown of how it works: Jev is a decision-making model. Given a task and a set of options, it predicts which path is more likely. Think of the octopus that predicted World Cup results. Jev does that for your business, except it's not luck. It weighs every option and tells you how sure it is. It does this by assigning probabilities to outcomes. It doesn't generate text on its own, so you can't expect it to create a new outcome for you. But that's also what enables it to be lightning fast and dirt cheap. For example, in customer service you can ask Jev how to triage an incoming query and route it to the correct department. It can only select from the list of departments you provide it. This also means it can't hallucinate a new outcome outside the options it's given, which makes it incredibly interesting for OpenServ. In Graph Sharding, we take a single system prompt and break it down into multiple LLM steps with deterministic input and output shapes. Some of these steps require an LLM to produce new output, while others are simply decision routers that determine the next possible path. Traditionally, LLMs are slow and expensive. Breaking a single prompt into multiple steps increases accuracy and reliability by a ton, but it also introduces latency. Jev takes on those decision nodes, which are the backbone of a business process and therefore SERV graphs, and makes them super consistent and lightning fast, lowering the overall cost and latency of graph execution. SERV Reasoning on its own is a great force multiplier for Jev because, like all other models, it works by interpreting input instructions. The clearer those instructions are, the better the model performs. That's where SERV Reasoning comes into play. Just like amplifying any other model, we also amplify the accuracy and consistency of Jev's responses. And now we're bringing Jev-powered Decision nodes into Graph Sharding with SERV v3.
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