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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 🧩

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54,859 Aufrufe • vor 2 Tagen •via X (Twitter)

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Quality, speed, efficiency. Now you get all three, without having to choose. Intelligent Model Routing picks the best model for every task, so you don’t have to. That means top tier output for up to 65% less cost. 0 effort spent switching models.

Quality, speed, efficiency. Now you get all three, without having to choose. Intelligent Model Routing picks the best model for every task, so you don’t have to. That means top tier output for up to 65% less cost. 0 effort spent switching models.

Replit ⠕

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we built blazing fast computer/browser use with Jev + Stagehand. this task cost $0.001 and executed at near instant speed (in a remote browser btw) the loop: observe the page, send a11y tree as state + actions as questions, Jev decides the next action, then Stagehand executes it.

we built blazing fast computer/browser use with Jev + Stagehand. this task cost $0.001 and executed at near instant speed (in a remote browser btw) the loop: observe the page, send a11y tree as state + actions as questions, Jev decides the next action, then Stagehand executes it.

Kyle Jeong

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I've nearly ditched closed source models for image generation (in creative workflows). For most tasks, the cost/speed ratio doesn't justify closed models anymore. Open models have caught up on quality and I can try 15 new images in 1-2 sec. Iteration speed matters. Sharing the few models I currently use ⬇️

I've nearly ditched closed source models for image generation (in creative workflows). For most tasks, the cost/speed ratio doesn't justify closed models anymore. Open models have caught up on quality and I can try 15 new images in 1-2 sec. Iteration speed matters. Sharing the few models I currently use ⬇️

Victor M

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Is Jev is a game changer for robotics? We gave Jev a robot body and handed it complex tasks across navigation, spatial reasoning, and world geometry 120 different real + simulated tasks and environments benchmarking performance against Dimcode, Astra, Fable, Opus, and 5.6 We graded against speed, cost, tokens, # collisions, path quality Code, Data, and Paper dropping tomorrow. The results were surprising.

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stash

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I used Jev to classify 1,018 AI research papers. The result: $0.08 total cost and 256ms median end-to-end latency per paper. The pipeline was: 1. Summarize each paper with DeepSeek V4 Flash 2. Send the title + summary + 24 possible topics to Jev 3. Use Jev to classify each paper 4. Visualize everything on The summaries cost $3.99 on Together AI. The classifications cost $0.08 on TypeSafe AI. So for just over $4 of inference, I ended up with a pretty useful way to explore the top AI research papers from the past year. I think this is where things are heading: different models for different parts of the workflow, instead of using one model for everything. I’m running evals on the Jev classifications before replacing the current ones, but the site is already live:

I used Jev to classify 1,018 AI research papers. The result: $0.08 total cost and 256ms median end-to-end latency per paper. The pipeline was: 1. Summarize each paper with DeepSeek V4 Flash 2. Send the title + summary + 24 possible topics to Jev 3. Use Jev to classify each paper 4. Visualize everything on The summaries cost $3.99 on Together AI. The classifications cost $0.08 on TypeSafe AI. So for just over $4 of inference, I ended up with a pretty useful way to explore the top AI research papers from the past year. I think this is where things are heading: different models for different parts of the workflow, instead of using one model for everything. I’m running evals on the Jev classifications before replacing the current ones, but the site is already live:

Hassan

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Meet #DBRX: a general-purpose LLM that sets a new standard for efficient open source models. Use the DBRX model in your RAG apps or use the DBRX design to build your own custom LLMs and improve the quality of your GenAI applications.

Meet #DBRX: a general-purpose LLM that sets a new standard for efficient open source models. Use the DBRX model in your RAG apps or use the DBRX design to build your own custom LLMs and improve the quality of your GenAI applications.

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You don’t have to use one model (or one provider!) for everything. With Workshop, you can combine frontier and local models in the same workflow. For example: Opus can be the main agent, and delegate specific tasks to Gemma 4 via subagents. Better quality where it matters. Better privacy, speed, and cost where it counts. One workflow, best model for each task.

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JEV is INSANE. We gave it 400 companies and one candidate profile. In 12 seconds, it predicted which jobs the candidate had the highest chance of getting, assigned a confidence score and detected job-candidate mismatches. All for just $0.0005 It can also score companies, analyse your experience, match you with the right roles and identify the opportunities you’re most likely to get based on your profile. Coming soon to Backdoor Comment “JEV” for early access.

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Kling 2.6: A Real Killer of Other Video Models on Higgsfield. Upgrade your workflow with 70% OFF on Higgsfield + UNLIMITED image models for a year. Only for Cyber Week. Forget about manual cleanup. Kling 2.6 delivers professional results straight out of the gate. Sharper, faster, and completely unrestricted on Higgsfield AI 🧩

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Kiro

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