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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 views • 2 days ago •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 ⠕

13,822 views • 24 days ago

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

63,359 views • 3 days ago

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

39,333 views • 8 months ago

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.

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.

stash

27,722 views • 2 days ago

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

166,913 views • 4 days ago

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.

Databricks

327,800 views • 2 years ago

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.

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.

Workshop AI

962,487 views • 5 months ago

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.

Sarvagya Kulshreshtha

145,595 views • 2 days ago

1-bit Kimi K3 performs at Opus 5 level on 3D physics! We ran our Atomic Chat quant of Kimi K3 locally on 4x B200 against three cloud models and gave them all the same task, to build a giant anvil drop test as a single HTML file with real physics Outputs: K3 1bit (local): 15.8K tokens, $0 API cost Kimi K3 (API): 15.3K tokens, $0.30 API cost Opus 5: 22.8K tokens, $0.77 API cost GPT 5.6: 14.5K tokens, $0.72 API cost All four got the physics right. But only Kimi made a working winch. The drum turns and the chain drags the flat car off the pad. Opus 5 drew the most detail, road markings and sparks on the hit. And you can run a model at this level on your own box now. That still feels insane to us

1-bit Kimi K3 performs at Opus 5 level on 3D physics! We ran our Atomic Chat quant of Kimi K3 locally on 4x B200 against three cloud models and gave them all the same task, to build a giant anvil drop test as a single HTML file with real physics Outputs: K3 1bit (local): 15.8K tokens, $0 API cost Kimi K3 (API): 15.3K tokens, $0.30 API cost Opus 5: 22.8K tokens, $0.77 API cost GPT 5.6: 14.5K tokens, $0.72 API cost All four got the physics right. But only Kimi made a working winch. The drum turns and the chain drags the flat car off the pad. Opus 5 drew the most detail, road markings and sparks on the hit. And you can run a model at this level on your own box now. That still feels insane to us

atomic.chat

53,845 views • 1 month ago

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 🧩

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 🧩

Perks

98,231 views • 9 months ago

Jev + GrokBot is the best AI agent system I’ve built in my life It just made my setup CHEAPER and FASTER than what 95% of people are running... setup takes literally 7 minutes: prompt → GrokBot → Jev decision → GrokBot execution → result step 1 → open TypeSafe AI , create API key (keep it off chat paste) step 2 → tell Grok Bot: store TYPESAFE_API_KEY in the secure field step 3 → prompt Grok Bot: install typesafe-sdk on Agent Computer + smoke system_one (Choice) step 4 → tell Grok Bot: build the usage lab (router, dry-run, config, logs) - or clone Github below step 5 → add skill jev-usage-router: before browser / research / retry / extra bot → call the router, honor action step 6 → stay shadow first, read logs, then active when you trust it - kill switch: bypass jev or enabled: false step 7 → flip active: GrokBot obeys route - Jev decides - GrokBot executes - humans control irreversible actions the result: Jev + GrokBot the best and fastest agent running directly on your computer rn, I’ve already tested it on routine tasks - and the results are genuinely incredible You can come up with endless ways to use Jev + GrokBot - but the most important thing is to install it as soon as possible Copy this 2028 setup, explore my repo below - then read the full Jev deep dive ↓

Jev + GrokBot is the best AI agent system I’ve built in my life It just made my setup CHEAPER and FASTER than what 95% of people are running... setup takes literally 7 minutes: prompt → GrokBot → Jev decision → GrokBot execution → result step 1 → open TypeSafe AI , create API key (keep it off chat paste) step 2 → tell Grok Bot: store TYPESAFE_API_KEY in the secure field step 3 → prompt Grok Bot: install typesafe-sdk on Agent Computer + smoke system_one (Choice) step 4 → tell Grok Bot: build the usage lab (router, dry-run, config, logs) - or clone Github below step 5 → add skill jev-usage-router: before browser / research / retry / extra bot → call the router, honor action step 6 → stay shadow first, read logs, then active when you trust it - kill switch: bypass jev or enabled: false step 7 → flip active: GrokBot obeys route - Jev decides - GrokBot executes - humans control irreversible actions the result: Jev + GrokBot the best and fastest agent running directly on your computer rn, I’ve already tested it on routine tasks - and the results are genuinely incredible You can come up with endless ways to use Jev + GrokBot - but the most important thing is to install it as soon as possible Copy this 2028 setup, explore my repo below - then read the full Jev deep dive ↓

codila

296,951 views • 1 day ago

Open weight models are now available in Kiro. Three open weight models are available in the IDE and CLI, so you don’t have to default to the largest model for every task. Choose the right model based on speed, cost, or long-running agentic workflows. What model would you like to see supported next? #Kiro #BuildwithKiro #AICoding

Open weight models are now available in Kiro. Three open weight models are available in the IDE and CLI, so you don’t have to default to the largest model for every task. Choose the right model based on speed, cost, or long-running agentic workflows. What model would you like to see supported next? #Kiro #BuildwithKiro #AICoding

Kiro

22,214 views • 7 months ago

I shared 10 Jev use cases for marketers. Here are 10 more: 11. Ad creative scoring - Feed it 100 ad variations. Jev can score which hooks, headlines, or angles are most worth testing first. 12. Social post filtering - Monitor thousands of posts. Jev can flag the ones worth replying to, reposting, or using as sales signals. 13. ICP detection - Give it a company, profile, or website. Jev can score how closely it matches your ideal customer. 14. Buying signal detection - Someone posts that they're switching tools, hiring, raising money, or struggling with a problem. 15. Comment prioritization - Get hundreds of comments across LinkedIn, X, YouTube, or Product Hunt. Jev can score which ones deserve a reply first. 16. Review analysis - Feed it thousands of customer reviews. Jev can classify sentiment, complaints, feature requests, and purchase intent. 17. Influencer matching - Give it 5,000 creators. Jev can score which ones best match your product, audience, and campaign. 18. Sponsorship qualification - Feed it newsletters, podcasts, or creator media kits. Jev can score audience fit, relevance, and whether they're worth reviewing. 19. UGC selection - Give it dozens of videos, screenshots, and testimonials. Jev can score which ones are strongest for ads or landing pages. 20. Product Hunt monitoring - Scan launches, comments, and makers to find competitors, customers, partners, or interesting products. The more repetitive marketing decisions you have to make at scale, the more interesting Jev becomes.

Yum⋆₊˚

291,743 views • 1 day ago

A highly-requested feature: web search now returns citations in the API 🌐 We've standardized them for all models, including native online models like OpenAI's web tool and Perplexity:

A highly-requested feature: web search now returns citations in the API 🌐 We've standardized them for all models, including native online models like OpenAI's web tool and Perplexity:

OpenRouter

17,451 views • 1 year ago

Image generation on Lovable now supports images with transparent backgrounds; perfect for your website assets! It also supports the latest, highest-quality image generation models, contributing to making your app even more beautiful

Image generation on Lovable now supports images with transparent backgrounds; perfect for your website assets! It also supports the latest, highest-quality image generation models, contributing to making your app even more beautiful

Benjamin Verbeek

10,302 views • 6 months ago

🚀Introducing The LLM Inference Provider Leaderboard - a live-updated, unbiased eval of API Inference products. Featuring: Abacus.AI, Anyscale, DeepInfra, Decart, Fireworks, Lepton AI, Together AI, Perplexity, Replicate, as well as OpenAI and Anthropic models For each provider's Mixtral-8x7B and Llama-2-70B-Chat public endpoint, we benchmark cost, rate limit, P50 & P90 of throughput & TTFT, and average daily collections overtime for long term tracking. At Martian, we route each API request to the best LLM to reduce cost, reduce latency, and get the best performance. So finding the best providers is an important problem for us. We found that there's a > 5x cost difference, > 6x throughput variation, and even larger rate limit discrepancies among providers! Choosing between different LLMs is only part of the equation -- the selection of different inference endpoints is also crucial to get the best performance for your use case. See highlights of provider performance in🧵👇

🚀Introducing The LLM Inference Provider Leaderboard - a live-updated, unbiased eval of API Inference products. Featuring: Abacus.AI, Anyscale, DeepInfra, Decart, Fireworks, Lepton AI, Together AI, Perplexity, Replicate, as well as OpenAI and Anthropic models For each provider's Mixtral-8x7B and Llama-2-70B-Chat public endpoint, we benchmark cost, rate limit, P50 & P90 of throughput & TTFT, and average daily collections overtime for long term tracking. At Martian, we route each API request to the best LLM to reduce cost, reduce latency, and get the best performance. So finding the best providers is an important problem for us. We found that there's a > 5x cost difference, > 6x throughput variation, and even larger rate limit discrepancies among providers! Choosing between different LLMs is only part of the equation -- the selection of different inference endpoints is also crucial to get the best performance for your use case. See highlights of provider performance in🧵👇

Martian

128,984 views • 2 years ago

Working with multiple models in Chat? The model picker in VS Code is now organized by provider, making it easier to browse, search, and switch between models. You'll also see provider names next to your recent models for quicker recognition. 💡 Tip: Use /models for quick access.

Working with multiple models in Chat? The model picker in VS Code is now organized by provider, making it easier to browse, search, and switch between models. You'll also see provider names next to your recent models for quicker recognition. 💡 Tip: Use /models for quick access.

Visual Studio Code

32,974 views • 4 months ago

SOUL IS FREE FOR EVERYONE!!! The most realistic image model ever made is wide open. Try to beat Higgsfield Soul in the comments! Drop your photo and we'll reply with the UPGRADED version. See why Higgsfield Soul is the MOST HUMAN AI ever made. Best photo wins 1 YEAR of Creator Plan! Retweet to get the MOST INSANE Soul Guide in DMs

SOUL IS FREE FOR EVERYONE!!! The most realistic image model ever made is wide open. Try to beat Higgsfield Soul in the comments! Drop your photo and we'll reply with the UPGRADED version. See why Higgsfield Soul is the MOST HUMAN AI ever made. Best photo wins 1 YEAR of Creator Plan! Retweet to get the MOST INSANE Soul Guide in DMs

Higgsfield AI 🧩

180,031 views • 1 year ago

Cyber Week walked, so Cyber WEEKEND could RUN! 70% OFF - Spend the ENTIRE 2026 with ALL the BEST image models UNLIMITED for you or your loved ones for 365 days. Kling O1 AND Kling 2.6 are now UNLIMITED, exclusively on Higgsfield. FOLLOW, retweet, & reply for 216 credits in DMs

Cyber Week walked, so Cyber WEEKEND could RUN! 70% OFF - Spend the ENTIRE 2026 with ALL the BEST image models UNLIMITED for you or your loved ones for 365 days. Kling O1 AND Kling 2.6 are now UNLIMITED, exclusively on Higgsfield. FOLLOW, retweet, & reply for 216 credits in DMs

Higgsfield AI 🧩

1,004,108 views • 9 months ago