Seconding this. PML is high quality, active, and well documented.
54 karma · joined August 26, 2009
https://rosebud.ai/ powers a suite of apps to help creatives make content. https://tokkingheads.com/, our most popular app (2 million IOS downloads, all organic, high retention) allows any portrait/photo/face to be animated in seconds with no skill. Creators use Tokkingheads to make memes, deepfake parodies, NFTs, and most notably make their family and friends feel special with photos of past loved ones animated (1.8M views #tokkingheads hashtags on TikTok).
We are looking to use Flutter for web- and Android-platforms. The job includes:
- quickly prototyping new user experiences in Flutter;
- porting React.js and Kotlin apps to Flutter;
- getting pixel-perfect match between Flutter apps and Figma designs;
- architecting Flutter apps where connectivity and business logic are shared between platforms;
- collaborating with API engineers to guide the design of APIs which drive user-facing apps.
If this sounds interesting please reach out to dzmitry[æt]rosebudai[dot]com
https://rosebud.ai/ powers a suite of apps to help creatives make content. https://tokkingheads.com/, our most popular app (2 million IOS downloads, all organic, high retention) allows any portrait/photo/face to be animated in seconds with no skill. Creators use Tokkingheads to make memes, deepfake parodies, NFTs, and most notably make their family and friends feel special with photos of past loved ones animated (1.8M views #tokkingheads hashtags on TikTok).
We are looking for an experienced DevOps engineer to:
* help ML engineers containerize and deploy models and ML pipelines;
* maintain the production Kubernetes cluster: including upgrades, monitoring, and troubleshooting;
* instrument and configure the cluster for analytics, profiling, scaling, and other purposes as defined by business-goals, backend-team and ML-team;
* support, manage and troubleshoot (remotely) dedicated physical servers used by ML-team for model-training;
* analyze and optimize infrastructure cost;
* keep logs and documentation of infrastructure changes and decisions.
Our stack: AKS with node-autoscaling and custom replica-scaling logic, Skaffold, basic Istio, and a little Helm. The cluster is mostly stateless (model weights are in Azure Files). We use GPU-nodes and care a lot about using resources efficiently.
https://rosebud.ai/ powers a suite of apps to help creatives make content. https://tokkingheads.com/, our most popular app (2 million IOS downloads, all organic, high retention) allows any portrait/photo/face to be animated in seconds with no skill. Creators use Tokkingheads to make memes, deepfake parodies, NFTs, and most notably make their family and friends feel special with photos of past loved ones animated (1.8M views #tokkingheads hashtags on TikTok).
Responsibilities and what we are looking for:
* Build Great Products. We care deeply about what our customers want. We constantly iterate on our products and prototype new ideas. You must be very good at interpreting customer requests, translating them into great user experience, and implementing it in code.
* Tackle Challenging Problems. A lot of our underlying technology is based on machine-learning algorithms. ML-algorithms are often not designed to run in real-time and require unique runtime environments. You must have experience with cloud-based service-oriented asynchronous systems. You must also understand how to bridge the gap between asynchronous algorithms and synchronous user interactions with web and mobile applications.
* Prototype New Features. We meet our customers where they are. This means rapidly building prototypes end-to-end, including storage, business logic, and user experience.
* Productize Exciting Research. Our team includes engineers developing new machine-learning algorithms. You should be able to understand the constraints and requirements of algorithms and participate in productizing them.
Our stack: Flutter; React.js with TypeScript; Node.js with TypeScript running on Firebase Functions; Firestore and Google Cloud Storage; PyTorch wrapped in Flask and running in a Kubernetes cluster.
Awesome "About" section, it answered all my other questions.
https://rosebud.ai/ powers a suite of apps to help creatives make content. https://tokkingheads.com/, our most popular app (2 million IOS downloads, all organic, high retention) allows any portrait/photo/face to be animated in seconds with no skill. Creators use Tokkingheads to make memes, deepfake parodies, NFTs, and most notably make their family and friends feel special with photos of past loved ones animated (1.8M views #tokkingheads hashtags on TikTok).
Responsibilities and what we are looking for:
* Build Great Products. We care deeply about what our customers want. We constantly iterate on our products and prototype new ideas. You must be very good at interpreting customer requests, translating them into great user experience, and implementing it in code.
* Tackle Challenging Problems. A lot of our underlying technology is based on machine-learning algorithms. ML-algorithms are often not designed to run in real-time and require unique runtime environments. You must have experience with cloud-based service-oriented asynchronous systems. You must also understand how to bridge the gap between asynchronous algorithms and synchronous user interactions with web and mobile applications.
* Prototype New Features. We meet our customers where they are. This means rapidly building prototypes end-to-end, including storage, business logic, and user experience.
* Productize Exciting Research. Our team includes engineers developing new machine-learning algorithms. You should be able to understand the constraints and requirements of algorithms and participate in productizing them.
Our stack: Flutter; React.js with TypeScript; Node.js with TypeScript running on Firebase Functions; Firestore and Google Cloud Storage; PyTorch wrapped in Flask and running in a Kubernetes cluster.
https://rosebud.ai/ powers a suite of apps to help creatives make content. https://tokkingheads.com/, our most popular app (2 million IOS downloads, all organic, high retention) allows any portrait/photo/face to be animated in seconds with no skill. Creators use Tokkingheads to make memes, deepfake parodies, NFTs, and most notably make their family and friends feel special with photos of past loved ones animated (1.8M views #tokkingheads hashtags on TikTok).
We are looking for an experienced DevOps engineer to:
* help ML engineers containerize and deploy models and ML pipelines;
* maintain the production Kubernetes cluster: including upgrades, monitoring, and troubleshooting;
* instrument and configure the cluster for analytics, profiling, scaling, and other purposes as defined by business-goals, backend-team and ML-team;
* support, manage and troubleshoot (remotely) dedicated physical servers used by ML-team for model-training;
* analyze and optimize infrastructure cost;
* keep logs and documentation of infrastructure changes and decisions.
Our stack: AKS with node-autoscaling and custom replica-scaling logic, Skaffold, basic Istio, and a little Helm. The cluster is mostly stateless (model weights are in Azure Files). We use GPU-nodes and care a lot about using resources efficiently.
Miniaturization of compute is insufficient to implement such devices. And even if compute could be miniaturized to render interactive web pages with a coin-sized device, the sorry state of battery technology would render it impractical.
With 5G we only need enough compute and power to run a modem and a Mightyapp renderer.
At Rosebud AI we believe all image and video creation will be done via generative methods in 5 years. It will enable visual storytelling at the speed of thought. We're building that future. Join us to turn research for generating videos and images into commercial products.
Our stack: Flutter; React.js with TypeScript; Node.js with TypeScript running on Firebase Functions; Firestore and Google Cloud Storage; PyTorch wrapped in Flask and running in a Kubernetes cluster.
At Rosebud AI we believe all image and video creation will be done via generative methods in 5 years. It will enable visual storytelling at the speed of thought. We're building that future. Join us to turn research for generating videos and images into commercial products.
Our stack: React.js with TypeScript; Node.js with TypeScript running on Firebase Functions; Firestore and Google Cloud Storage; PyTorch wrapped in Flask and running in a Kubernetes cluster.
At Rosebud AI we believe all image and video creation will be done via generative methods in 5 years. It will enable visual storytelling at the speed of thought. We're building that future. Join us to turn research for generating videos and images into commercial products.
Our stack: React.js with TypeScript; Node.js with TypeScript running on Firebase Functions; Firestore and Google Cloud Storage; PyTorch wrapped in Flask and running in a Kubernetes cluster.
Join us to turn research for generating videos and images into commercial products.
Our stack: React.js with TypeScript; Node.js with TypeScript running on Firebase Functions; Firestore and Google Cloud Storage; PyTorch and Tensorflow wrapped in Flask and running in a Kubernetes cluster.
At Rosebud AI we believe all image and video creation will be done via generative methods in 5 years. It will enable visual storytelling at the speed of thought. We're building that future.
Join us to turn bleeding-edge research for generating videos and images into commercial products.
Our stack: React.js with TypeScript; Node.js with TypeScript running on Firebase Functions; Firestore and Google Cloud Storage; PyTorch and Tensorflow wrapped in Flask and running in a Kubernetes cluster.
I mean, a stand you can attach to a chair or fix into the ground - tripod style.
I understand it's much more stressful than permanent guaranteed UBI, but it's truer to the real intent of UBI (which I believe is reducing the friction when deciding to try new job or state or whatever in a pursuit of self-actualization).
Self-driving trucks have no such probabilistic deterrent.
The service itself is certainly useful.
You can use it to manage all variable things in your app, including content itself, of course.
It literally takes less than a minute to get started, because you can create an "anonymous" dropconfig without signing-up.
DropConfig is a version-control and hosting for configuration-files. Our goal was to liberate constants from the code and let them live their own separate lives. DropConfig does that and requires no new infrastructure and no new dependencies in your code.
After working on many projects over the years, we noticed that every time we hand the project off, we reinvent a super-simple "back office" UI allowing users to change some aspects of application "on-the-fly".
For example:
- language-translation files
- web-widgets settings (IDs, colors etc)
- temporary banners ("down for maintenance", "new release" announcements)
- CSS rules
After talking to our friends we realized that other developers run into the same problem all the time. For them, for ourselves, and for you HN, we built DropConfig :)
DropConfig is for all cases where you have a JSON configuration file, which is updated more often than you are comfortable making app-releases. Think of it as something between Firebase and Github: immediate availability of Firebase yet change-audit and access-permissions of Github. Plus speed and reliability of CDN.
It helps me to understand something new if I can controllably break it. In other words, I progress by predicting the edge-conditions when something shouldn't work - and then testing if algorithm indeed experienced expected type of failure. Transparent algorithm implementation is key for this.
One thing, which I immediately checked in the spinningup-repo is if it uses TF Eager. And it doesn't. @OpenAI what's your reasoning for that?
With carefully tuned Transformer (matmul-heavy!) I could only make twice as fast as 1080ti (at 4 times the price).
The only undisputable benefit was using double batch size.
Have you considered adding the feature of listing the conversations which contributed to the score?