Zeplin
Invision
Axure
Balsamiq
Proto.io
Flinto
Webflow
Moqups
Lambda Face Recognition API
Mattermost
Vast.ai
ipinfo.io
Grafana
Platform.sh
PostHog
Causal App
Zeplin
Lambda Face Recognition APIZeplin is best suited for designers and developers working in teams where clear design specifications and organized collaboration are critical. It's particularly beneficial for teams using Figma, Sketch, or Adobe XD who want to ensure precise design implementation and reduce misunderstandings between design and development departments.
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Lambda Face Recognition API might be a bit more popular than Zeplin. We know about 27 links to it since March 2021 and only 23 links to Zeplin. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Tools like Zeplin's AI layer, Supernova, and the emerging generation of Figma plugins with AI backends do this today. The output is not a PDF full of screenshots. It is structured data: exact values, token references, and context โ the kind of thing a developer can act on without a follow-up conversation. - Source: dev.to / 5 months ago
Zeplin.io AI-powered design-to-dev handoff. - Source: dev.to / about 1 year ago
Additionally, thank you to all our community launch partners across the frontend ecosystem for helping us bring Storybook 8 to the world! Thanks to Chromatic, Figma, ViteConf, Omlet, DivRiots, story.to.design, StackBlitz, UXpin, Nx, Mock Service Worker, Anima, Zeplin, zeroheight, kickstartDS, and Kendo UI. - Source: dev.to / over 2 years ago
Designers would often use separate tools like Zeplin or Invision to handoff the designs to developers.๐ฎ. - Source: dev.to / over 2 years ago
Zeplin โ Designer and developer collaboration platform. Show designs, assets, and style guides. Free for one project. - Source: dev.to / over 2 years ago
Setup time matters too. The delta between Runpod and bare-metal providers like Lambda Labs is large. Reaching an equivalent setup on a bare VM requires provisioning the instance, configuring the OS and CUDA drivers, installing Docker, setting up your orchestration layer (Kubernetes or Slurm), deploying your inference container, configuring autoscaling rules, and wiring up your load balancer. Thatโs a realistic... - Source: dev.to / 5 months ago
Let's do the math for a representative setup: GPT-OSS-120B via Together.ai ($0.15/$0.60) vs self-hosting on H100s from Lambda Labs at $2.99/hr ($2,183/mo). A single H100 running a 70B model produces roughly 50 tokens/second on average, which works out to about 130M tokens per month. - Source: dev.to / 6 months ago
How does this compare to https://lambdalabs.com/. - Source: Hacker News / about 3 years ago
Another option is to pay for AWS server with a beefy GPU and enough RAM. It's not too cheap, but isn't expensive either if you aren't planning to run it 24/7. Or get a GPU cluster from a company that offers stuff for ML specifically, it might be easier to set up compared to AWS and in some cases cheaper. Like, for example, lambdalabs that offers H100 gpu for 2 bucks per hour. Source: about 3 years ago
I used some of the cloud GPUs on Vast.ai, but I also tried Lambda Labs, and these days I have my own docker container setup which can be deployed to a VM on Google Cloud and used more programatically. Source: over 3 years ago
Invision - Prototyping and collaboration for design teams
Mattermost - Mattermost is an open source alternative to Slack.
Axure - The most powerful way to plan, prototype and hand off to developers, all without code. Download a free trial and see why professionals choose Axure RP 9.
Vast.ai - GPU Sharing Economy: One simple interface to find the best cloud GPU rentals.
Balsamiq - Balsamiq. Rapid, effective and fun wireframing software.
ipinfo.io - Simple IP address information.