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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 görüntüleme • 4 gün önce •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.
0:11

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.

shiv

15,904 görüntüleme • 4 gün önce

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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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.

Higgsfield AI 🧩

59,547 görüntüleme • 24 gün önce

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. →
0:12

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. →

Miles Deutscher

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You can now benchmark Qwen3.8 27B with Optima. See how a model that can run on your laptop compares on performance, cost and speed for your custom use cases We're also offering credits and free advisory from an Artificial Analysis team member to enterprises looking to build benchmarks for their use cases. Let us know if you’re interested: Build and run your custom benchmarks today at
0:11

You can now benchmark Qwen3.8 27B with Optima. See how a model that can run on your laptop compares on performance, cost and speed for your custom use cases We're also offering credits and free advisory from an Artificial Analysis team member to enterprises looking to build benchmarks for their use cases. Let us know if you’re interested: Build and run your custom benchmarks today at

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What’s MedGemma? 🤔 It’s our collection of open, multimodal medical models that are designed to help developers build AI tools for healthcare, such as analyzing radiology images or summarizing notes for physicians. We built this demo using MedGemma to help showcase the possibilities of the model. What other use cases can you foresee for this technology?
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What’s MedGemma? 🤔 It’s our collection of open, multimodal medical models that are designed to help developers build AI tools for healthcare, such as analyzing radiology images or summarizing notes for physicians. We built this demo using MedGemma to help showcase the possibilities of the model. What other use cases can you foresee for this technology?

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the range of what you can build with Inco is incredible in this comprehensive guide, we provide 30+ examples of Inco use cases 🏦 onchain finance 💸 payments 🃏 gaming ➕ much, much more get the full guide ⬇️
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the range of what you can build with Inco is incredible in this comprehensive guide, we provide 30+ examples of Inco use cases 🏦 onchain finance 💸 payments 🃏 gaming ➕ much, much more get the full guide ⬇️

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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.
0:19

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.

kiosa

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Revamped our Codex use cases page! If you're curious about all the different ways you can use Codex, check out suggested use cases.
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Revamped our Codex use cases page! If you're curious about all the different ways you can use Codex, check out suggested use cases.

Katia Gil Guzman

125,394 görüntüleme • 4 ay önce

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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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:

Jerry Liu

181,829 görüntüleme • 3 yıl önce

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.
0:13

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.

Armagan Amcalar

365,108 görüntüleme • 19 gün önce

Jev + Kimi K3 for fraud detection! TLDR: Jev classified 100 emails in 1.42 seconds, then I routed the uncertain cases to Kimi K3. The full pipeline got 96/100 correct for only ~$0.07. Video is not sped up, check out the live run! Here was my process: I gave Jev 100 emails to classify (a mix of 50 legit & 50 fraudelent emails). It classified all of them in 1.42 seconds. An underrated feature about Jev is it will give you the confidence score for a classification, so I routed any prediction under 95% confidence to Kimi K3 to be fully sure. 31 emails fell below that threshold. After routing those to Kimi K3, the combined pipeline reached 96% accuracy. The full run took 16 seconds & ~$0.07 in inference costs: - $0.068 from Kimi K3 on Together AI - $0.003 (1/3 of a cent) from Jev on TypeSafe AI. I think this is a really interesting pattern: use a fast specialized model like Jev for the narrow task, then route the uncertain cases to a larger LLM. I feel like this kind of approach could be a game changer for use cases like fraud or anything realtime. You can use the speed & low cost of Jev while having a larger LLM as a fallback to ensure high accuracy.
0:12

Jev + Kimi K3 for fraud detection! TLDR: Jev classified 100 emails in 1.42 seconds, then I routed the uncertain cases to Kimi K3. The full pipeline got 96/100 correct for only ~$0.07. Video is not sped up, check out the live run! Here was my process: I gave Jev 100 emails to classify (a mix of 50 legit & 50 fraudelent emails). It classified all of them in 1.42 seconds. An underrated feature about Jev is it will give you the confidence score for a classification, so I routed any prediction under 95% confidence to Kimi K3 to be fully sure. 31 emails fell below that threshold. After routing those to Kimi K3, the combined pipeline reached 96% accuracy. The full run took 16 seconds & ~$0.07 in inference costs: - $0.068 from Kimi K3 on Together AI - $0.003 (1/3 of a cent) from Jev on TypeSafe AI. I think this is a really interesting pattern: use a fast specialized model like Jev for the narrow task, then route the uncertain cases to a larger LLM. I feel like this kind of approach could be a game changer for use cases like fraud or anything realtime. You can use the speed & low cost of Jev while having a larger LLM as a fallback to ensure high accuracy.

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What are the potential use cases for this device? 🤔
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What are the potential use cases for this device? 🤔

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0:15

What used to take lights, props, and a full set can now be done inside an agent. This was created using a Glif agent with Kling 2.6 Motion Control and a Nano Banana reference image to lock the look. No physical setup needed. Glif has agents for different creative use cases, so you can pick what fits your workflow and move fast. Check the agents used for this below 👇

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Today, we're releasing GLM-5.3 Fast: one of the most intelligent open-weight models ever at an even higher TPS. Designed for real-time use cases that demand consistent performance. Get access here:
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Today, we're releasing GLM-5.3 Fast: one of the most intelligent open-weight models ever at an even higher TPS. Designed for real-time use cases that demand consistent performance. Get access here:

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