WIT bindgen is a way to describe interfaces for Webassembly modules and export/import functions which can be used to generate stubs for multiple languages. The first implementation the team worked on was to move our initial hand rolled interfaces to WIT bindgen.

Pure Rust Runtime Implementations

New Rust Runtime on Devices

Our initial Runtime for mobile devices was implemented in C++. Updating this implementation required a lot of work and hampered our ability to iterate. This past week, we successfully released our mobile app update where the runtime is entirely defined in WIT. We are seeing slight improvements in memory! Most importantly, we can now use a single code base for all our runtimes (besides the web runtime).

Here’s a sneak peek from our Mobile Engineering Wizard! Our team has been playing with visualization to render ML pipelines directly in the app :) Neat, huh?

Bringing composability to Rune

From the beginning, Rune was built to provide a way to compose and reuse building blocks of ML pipelines (which we call processing blocks). Our initial version of Rune hadn’t quite hit this goal as we still needed to write all our processing blocks in Rust and everything was compiled into a single WebAssembly binary. Our team is excited to share that we are finally changing this!

We can now leverage multiple languages to write proc blocks as long as they fit our specific interfaces.

This means you could have processing blocks, models, and data ingestion written in languages from Go to Java.

We’ve accomplished this by compiling each processing block to WebAssembly individually and embedding both these compiled binaries and the pipeline DAG in the final Rune. The runtime then loads each processing block and chooses how to evaluate them based on the flow of data through the pipeline.

Expect to see more of a detailed breakdown of our runtime in the coming weeks.

Edge in-memory OLAP Analytics

Finally, our team is working hard on our ability to provide more value using portable computing. One of the directions we are exploring this is to address frustrations faced by inability to quickly run ML and ad-hoc queries in a rapid manner. To this effect, we are working on top of great technologies like Apache Arrow and DuckDB, to put together a way to run Runes directly on data on your laptop. No ETL, no Infra, and get started finding insights.

We want to work with you to make an amazing experience for Edge Analytics. Feel free to reach out to kartik@hotg.ai if this interests you!

Have a sneak peek at the video below!

Until then, keep building the future!

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Differential ETL | Part 1

This is a two-part series about an exciting area of work at hotg.ai. The second part will be a follow up post in a week. In the first part we want to touch upon a familiar concept of distributed map reduce but with a few twists. If you are wondering how this relates to the world of edge computing - read on …

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4 years ago · 2 likes · Akshay Sharma and Holly Ly

Alrighty! Let’s dive deeper into what Differential ETL means with a specific example. We will wrap up by highlighting other examples where this will become powerful.

Federated Analytics

Federated analytics is a case of differential ETL, where data is in multiple private silos, and the aim is to extract meaningful information from the data across the silos.

This paper talks about Federated Analytics in more depth if you are interested in a literature review. Google wrote a blog detailing how Federated Analytics can accelerate data science.

The diagram below shows a simple schematic of what federated analytics looks like.

According to the diagram above, this demonstrates data in several heterogeneous systems (databases, data lakes, files, etc.). The compute environments around these could also be different from each other (bare metal, Kubernetes, on-prem vms, phones, etc.). The goal is to send one or more workload to extract some key information back as result.

To sum up:

1. Data and app teams create a workload - think of this as the ETL pipeline. HOT-G’s Rune container technology is designed for this.

2. Optionally the security team(s) could review what is in the workload. This verification can happen programmatically with HOT-G tools.

3. Workloads are then federated / distributed to every participating compute node. This topographic setup can be dynamic too!

4. The workloads are run on each silo, and the resulting computed results (reduce) are aggregated back. This could happen in another trusted compute node or a 3rd part even.

It is important to note that the workloads don’t fetch all data back, instead they run some form of map ETL function.

The differential bits come in if you want to send different workloads to different nodes as the underlying data could be different.

Demo

Let’s look at this with the lens of a real-world example. Pharmaceutical companies are looking to monitor the drug on patients after it is launched. The monitoring could be as part of the post market surveillance of the drug or even during the trial to understand the survival rate. They are keen on measuring some critical information from several sites (hospitals / providers) to learn about the drug characteristics of patients.

Sites / Providers / Hospitals do not share the actual diagnosis and medical record data due to compliance, conflict, and regulation in general.

Federated analytics is a perfect solution for this problem. The top part of the schematic below explains the setup.

The bottom part of the above schematic shows the demo setup. We’ve thoughtfully chosen a simpler setup to show the demo. We are demonstrating silos of data across simulated data on desktop browsers and phone browsers.

We are computing the mean (average) and standard deviation of the age of the patients who have recovered. In fact, the filtering of the data itself could be part of the workload - for example: find the mean and median age of patients who have shown allergic reactions to the drug.

Watch the demo video below for the full experience.

Dynamic updates with HOT-G platform

If you saw the video, we changed the metric to track in the workload. The ability to dynamically change differential workloads and recompute other analytics is a powerful paradigm.

The self-explanatory sequence diagram below describes the operations in the demo.

This all happens without having to change a single line of code! This is the true power of HOT-G’s zero code build and deploy platform. Moreover, we can run the workload anywhere. This is because we are maximally portable as well.

Future extensions

With HOT-G’s platform, the possibilities are endless. Soon, our team will be supporting the following on our platform:

• Workload manager for security and infrastructure teams - the platform provides a zero-trust based workload management tool where security teams can decide the access privileges per silo. This is configured using OPA based policies.

• SQL like interface for querying - The workloads in our example were built using our zero code drag and drop interface. We are adding an SQL interface so analysts can run their favorite aggregate queries. Power to the people!

• Private and secure algorithms/workload - In many situations, the workload might need to be built using proprietary algorithms/models. Now, HOT-G can computationally protect this with our technology. We have written in the past about our original work in the space of Edge SecOps - protecting workloads on any edge. We are bringing it all home with these efforts!

Conclusion

To show how repeatable and big this market is, lets count the ways, shall we?

These are only feasible because we have highly portable container technology that can be easily secured, built, and deployed continuously with minimal effort.

• Analytics on streaming data such as video and audio - rune containers can do aggregation on rich edge data - without requiring any code.

• Data harmonization - data silos mean there are schema differences and data changes continuously. Now, you can reign in the complexity with Rune workloads that monitor and auto-normalize schemas (and data).

• Data mesh based unified analytics - Our platform lets you build data mesh where you can interoperate across data silos and vendors by not having to move the data.

• ESG reporting - Sustainability is a big market and ESG reporting is inherently hard due to how fragmented the data is. HOT-G can compute the metrics on data anywhere.

• AI ad-tech analytics - Runes workloads can be used to run simple small models on web pages to do dynamic triggers. Third party analytics are mostly focused on click-stream data, but with Runes you can make a first party rich analytics capture to personalize browsing experiences.

The possibilities are endless.

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In the first part we want to touch upon a familiar concept of distributed map reduce but with a few twists.

If you are wondering how this relates to the world of edge computing - read on :).

A brief primer

The classic paper from Google that introduced us to the world of map reduce pattern in 2004 was designed to address computing on large data where the computations need to be distributed across hundreds or thousands of machines to finish in a reasonable amount of time.

source: google map reduce paper

To simply put, you have a large data set in a central location that needs to be processed with well-defined computation that can be parallelized over a network of compute nodes. The mapped results are then reduced, again with a well-defined computation to a meaningful result.

This is a distributed computing implementation of the well-known MapReduce design pattern.

A well-known variation of this is using streaming data (Spark) as opposed to batch processing (Hadoop).

The conundrum of centralizing data

Every modern enterprise is creating new data across several touch points within the organization. This could be data created in ERP systems, sales systems, product systems, application data, monitoring data, vendor data, infrastructure, and much more.

ELT helps enterprises bring all raw data in a Data Lake, allowing them to build more refined datasets in a Data Warehouse. In return, the Data Warehouse becomes useful for downstream systems such as machine learning systems and analytics systems.

a16z has written what a modern enterprise’s unified data platform could look like.

source: https://future.a16z.com/emerging-architectures-modern-data-infrastructure/

There are hundreds of companies working in the “ETL space” that are iterating on ways to help enterprise centralize their data. a16z has a brief list as well.

source: https://future.a16z.com/emerging-architectures-modern-data-infrastructure/

If you are an enterprise, the goal to centralize ALL data in one place is an asymptote - it will be a never-ending catch-up game.

This leads to the conundrum:

What if you cannot centralize all the data?

A fragmented and distributed world

With most enterprises, there’s massive fragmentation of data that is accumulating in small silos that cannot easily centralize. Here are some key reasons as to why this is the case:

• Compliance and privacy - GDPR, CCPA, and every state, country, and region are moving towards creating unique laws around how citizen’s data should be handled

• Rapid rate at which new type of data is added - creating and maintaining new data ingestion is an expensive endeavor

• Security is a priority - sometimes, it is about permanent control on access to data for regulatory reasons

• Heterogeneity in where the data is generated and kept - a lot of times the data is sent via email, spreadsheets, FTP files, a local database, and so on. Places where you can forget about running a spark job.

From our point of view, the future of fragmentation looks something like this:

We will need to bring the compute to data

The data will not only be spread in data silos internally, but it will also be on user devices, across partners, and hybrid cloud and on-prem.

Embracing heterogeneous computing will become paramount for the success of an enterprise.

What will be even more interesting is that not only will each data silo look different, but it will also contain different fragments of data.

This means you cannot easily apply MapReduce patterns to gain computational advantages easily

We will have to bring compute to hostile environments that might have extremely limited capabilities. For instance, phones, bare metal computer running in a basement at a hospital, an on-prem data center VM, multiple cloud environments each with its own setup, and so on.

We call this “Differential ETL”

You could run different workloads in each silo, and still need to reduce the output of each node. Think of it as not trying to bring the data to one place but bringing the insights from the data to wherever needed.

We want to be clear in stating that centralizing the data with ETL technologies will continue to exist and grow, but there will also be a higher rate of fragmentation.

There is no easy way to run docker on these systems:

Part 2 | Prologue

Hopefully, we have whet your appetite for data fragmentation across a heterogeneous platform. We call moving compute to where the data silo is a problem of edge computing.

In part two of this series, we will go a bit deeper on the examples and use cases of differential ETL across a few industries. We will also couple that with some sneak peaks into some exciting work we have been doing in this space with our Rune container technology :)

Until then, keep building the future!

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It has been a busy month and the HOTG team has been plugging away across our platform. Let’s jump straight into it :D

Platform Updates

React Native

First, we have finally added support for React Native! React Native is a popular application development framework that uses a bridge over JS to call native modules such as librunecoral. Behind the scenes we had to work hard on memory copy over the bridge. However, with some elbow grease we were able to crack this problem and now have an initial version working. Have a look at an example app in action here.

Repo with example: https://github.com/hotg-ai/react-native-rune

Runtime: Rust first, iOS build & Tensorflow 3.8 Support

The Librunecoral (librc) project is our native bindings across various platforms and provides a consistent runtime for Rune to be executed on. Until this release librc was leveraging an older version of our runtime implementation built in C/C++. In this release we have moved over our entire runtime to Rust with bindings to Wasm3. This is a big deal as it means the Rust runtime from linux embedded, iOS, Android and more is less fragmented. We have had a Rust runtime implementation for several weeks now but were blocked by our ability to build on iOS.

As of 3 days ago our engineering team were finally able to get bazel and iOS builds working!

The cherry on top is that in the process of upgrading bazel builds we were able to add Tensorflow 3.8 support!

Rune ABI and WIT

Investing in better interface definitions

With more implementations with partners and developer we learned that our initial implementation of our Rune interface has begun to show limitations. Each iteration of the ABI required us to go an update a variety of runtime implementation across the ecosystem. Needless to say this really hamstrung our ability to iterate and release new ABI improvements. One solution to this are declarative interfaces that can be used to auto generate code bindings to the various runtimes. WIT bindgen is early stage project from the byte code alliance. Using WIT we are able to provide clear interface definitions for all of our Rune proc blocks. By making our interfaces declarative we can now begin work on updating our ABIs to support more features rapidly!

An example of how we use WIT is to provide tensor metadata.

/// Information about a tensor.
resource tensor-metadata {
    /// Create a new named tensor.
    static new: function(name: string) -> tensor-metadata
    /// A human-friendly description of the tensor.
    ///
    /// The text may include markdown.
    set-description: function(description: string)
    /// Add a hint that provides the runtime with contextual information about
    /// this node.
    add-hint: function(hint: tensor-hint)
}

This interface is then fulfilled with in our proc-blocks in rust as

let output = TensorMetadata::new("labels");
            output.set_description("The corresponding labels.");
            let hint = supported_shapes(
                &[ElementType::Utf8],
                Dimensions::Fixed(&[0]),
            );
            output.add_hint(&hint);

You can see the entire Rune interface defined in github.

What’s next

We plan on redesigning more of our ABI using WIT to iterate on ways to do graph-context sharing which will allow for better error reporting and ragged tensors. If you are curious about our work here please come chat with us on discord.

Let's chat about WIT

Hammer Studio Forge

Security, UX, Diagramming Engine and Documentation

We have been meeting many developers since our Feb8th launch and rapidly releasing improvements. Our release notes are published on studio show details on various improvements so far.

One specific update that I am particularly excited about is our diagramming engine.

On Forge Studio we have completely overhauled our rendering engine so that we can make designing pipelines less painful. The new diagram rendering engine provides better interaction support and eventing. Better eventing allows us to finally have visual validation

This allows ML pipeline designers to rapidly see issues in their transformations and tensors.

Documentation Improvements

Over on the documentation side we have put a ton of new articles from examples tutorials and more. One of our community’s favorite is building custom computer vision models for the edge.

Most read tutorial

TinyMLOps - Paper submission

One of the paper we authored with the Dr. Pieter Simoens from Ghent University has now been accepted for the Parallel AI and Systems for the Edge workshop (paise 2022) https://www.paise.org.

It is titled: TinyMLOps: Operational Challenges for Widespread Edge AI Adoption and you can read it all here: https://arxiv.org/abs/2203.10923

In a continuously fragmenting and distributed world, ensuring having a strategy for the Edge becomes critical. There are inherent production level challenges for the Edge such as:

• repeatability

• deployments

• security

• IP protection

• orchestration

Give the paper a read for a broad understanding of the challenges, and feel free to reach out to us if you want to discuss more :).

Forge App of the Month!

Game SDK for Mobile Web

If you made it this far you get to see an easter egg :D As you may have heard we have been getting into the unicorn business with Laguna Games :) We are putting together an SDK that will let you use Rune with Unity games :D

My best,

Kartik (f0rodo)

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• machine learning

• embedded computing

• application development.

Hidden with all of this is also the needs of infrastructure and DevOps which is not easy. 

To re-imagine the next generation of accessible on-device ML platform looks and feels like, we had to rethink what a developer with any background could do with the right tools. We built Hammer Forge Studio, our SaaS platform for any developer to build, test, deploy, and monitor on-device ML in minutes. 

In this post, we want to talk about how critical the design thinking and process was leading up to the launch. 

Art and design

We want to create no-code developing tools that allow our users to create apps that are fresh and at the forefront of edge computing. In our effort to increase UX-friendly features and improve our design, we have deployed the Glassmorphism and Flat design styles to achieve our goal. This post will expand on both designs and what it means for our foray into the future of making easy-to-use developer tools. It is about bringing accessibility and simplicity to modern development. 

Glass Morphism on Hammer Forge Studio

Glass-Morphism is a catchall term for a design aesthetic that you may have encountered before. It has a “frosted glass” feel and makes the product look fresher. This is important when you are trying to create the next generation of modern development. Users need to encounter something that is new to them. You may have seen this design used on the iOS’ pull-down menu. The effect and look of the design make the user experience feel fluid and responsive. When they open a new menu on top of an older one, they can still see the original menu behind the “frosted glass”. Doing so creates an element of cohesion, preventing users from feeling lost or disjointed from their main interface. Moving between menus feels modern, but then there is the element of staying accessible and consistent delivery to most users.

You can see that in the two examples below with windows and iOS.

Glass Morphism Design on Microsoft Windows 11

IOS 15.3.1 highly blurred Glass-Morphism Menu

Making it accessible

Accessibility and UI that is easy to grasp is at the heart of our design mission. This is why we couple our use of Glass-Morphism with elements of Flat design. It is a much older term than Glass-Morphism as it originated in the Bauhaus era of design in the 1930s.

Oskar Schlemmer (4 September 1888 – 13 April 1943) Bauhaus Design Area - Dancer

Ushering an age of modernism and efficiency, while keeping things clean and simple. Flat design utilizes a minimalist approach with flat menus and colors to streamline the user experience. This way, the user never feels lost in menus or while they are building a new app within our simple drag-and-drop builder. Therefore, this creates the main element of our design challenge. Enabling users to create on-device machine learning apps by only dragging and dropping components while conserving a futuristic feel yet keeping things simple.

Sample of Design System Development - based on Flat-Design Inspirations

We have succeeded in creating a design that feels futuristic while still being approachable and easy to use. Utilizing simplicity in design and manifesting it, we have achieved the look and feel while not losing functionality. When it comes to delivering edge computing developer tools, the front-end can often be neglected. However, the user experience and interface are just as important. The combination of Glass-Morphism and Flat design has allowed for the ideal mix of capable and accessible. You want tools that can do impressive things, but you also want them to be easy to use. We have succeeded, using this combo. The future is in no-code computing!

Runic mobile app also carries over the same design aesthetics

Now, you can also build your next idea using on-device ML using studio. Studio lets you drag and drop using out of the box models to build fully featured apps. Try it at https://studio.hotg.ai or share the link with any application developer that might find it useful!

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#1 Laguna Games

We announced our partnership with Laguna Games.

Hammer Of The Gods (HOT-G) is partnering with Laguna Games to bring AI to NFTs and blockchain gaming. With this partnership, users of Laguna Games can bring their Unicorn NFTs to life!

We are no strangers to blockchain as we have written about our ideas in the past:

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Metaverse And The Edge

We authored a post earlier titled "Decoupling intelligence" in which we articulated our long-term vision and hammered home the central theme - Intelligence will go where the data is. Over the coming decade, billions of sensors and edge devices will be shipped. 5G connectivity will increase the density of connected devices around us, and every industry we…

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5 years ago · 5 likes · Tinyverse

We are working on bringing real world AI to NFTs and games. Buried in that release is a hint at something we have been working on for a few months now :) - our web3 SDK for edge computing called DeepNFT.

DeepNFT - a name derived from adding neural nets to NFT is our skunks work projects that can “enhance” crypto assets on the edge with AI.

The key to this breakthrough is the fact that we have a portable ML container called Rune that can run on phones, browsers, mobile browsers, desktops, embedded devices, and pretty much standard popular computing devices.

This is the habitat of many games as well!

Here is a Rune with computer vision neural net edge pipeline for object detection running on Solana’s Phantom wallet app. This is all about portability of ML on the Edge!

By partnering with Laguna Games, we are taking this to market first. When we launch the GA version of DeepNFT web3 SDK, we will offer it as part of our enterprise tier offering on Hammer Forge.

#2 Urban AI

We also announced our partnership with Urban AI.

Urban.ai is partnering with Hammer of the Gods (HOT-G) to bring real time mapping of urban spaces and municipalities using the latest AI technology and edge computing. 

When we met the Urban AI team, we knew we had to work with them as their vision and mission is extremely compelling.

Our Mission

Urban space contains enormous amounts of information, however this information cannot be extracted using classical tools. Using artificial intelligence we allow cities to extract powerful data out of urban space, creating the smart-cities of the future.

We have gone deep into understanding the smart cities market tackling it from several different angles - climate change, urban planning, sustainability, and more. We wrote about it a while back as well:

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Smart Cities, Climate Change, and Sustainability

In our previous week’s update, we mentioned that we were a sponsor for a climate tech event called VERGE. The urgency of the climate crisis is accelerating — and so, too, is the opportunity to build a clean economy. We have a once-in-a-lifetime moment to transform society and create sustainable, just and resilient systems…

Read more

5 years ago · 1 like · Tinyverse

With Urban AI, we will be bringing the functionality of their cloud based urban AI mapping services closer to the edge devices, where we can significantly improve issues of latency, cost, and speed of mapping.

We are gearing up to launch our containers on embedded devices (such as raspberry pi) and other constrained devices that will be used for running the AI.

Here is a loom video from our co-founder/CTO, Kartik Thakore showing an early prototype of our containers running on Raspberry Pi.

Faster feedback loops with Hammer Forge Studio

The most important thing to know is that we will be keeping our developer experience the same no matter what device you intend to build on. This means:

• You don’t spend an enormous amount of time testing across different platforms

• You test sooner in the process - not after writing a lot of integrations (shift left testing)

• You continue to provide feedback to the model dev teams pre and postproduction

The key to this breakthrough is also because we have a portable ML container called Rune that can run on phones, browsers, mobile browsers, desktops, embedded devices, and pretty much standard popular computing devices.

The Journey

When Kartik and I started HOT-G, we were just enamored with the possibilities of what edge computing would offer. We had the first-hand experience building this in healthcare at doc.ai (which got acquired and then went public). We knew right then the possibilities were endless.

However, going from the anticipation of this space being big to something very concrete has been a meandering journey for the last 10 months. Now, we are ready to grow and go fast!

— Akshay

Now, you can also build your next idea using on-device ML using studio. Studio lets you drag and drop using out of the box models to build fully featured apps. Try it at https://studio.hotg.ai or share the link with any application developer that might find it useful!

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Our 1st place winners created an amazing app to use a food model to recommend recipes for all those needing help deciding what’s for dinner! Check out the neat CookHack demo above.

What has been your experience in building ML applications? 

Logan Hoogendijk:

I have built two machine learning applications, both times with a group of 5 people, including myself. The first is a computer vision model using K-Nearest Neighbours that predicts a handwritten digit's accuracy based on the MNIST dataset. The second is another computer vision model using Inceptionv3 that classifies an animal-based off on a picture.

Hunter Hoogendijk:

I have developed two machine learning applications with a team of 5, including myself. The first application was a prediction software using binary classification models to analyze the labels and data of COVID-19 to predict stock market changes. The second machine learning application is an NLP model that utilizes news articles to predict stock market changes.

Sam Thibault:

I did not have any previous experience in building ML applications. Exploring Forge was the first time I was introduced to EdgeML deployment.

Kevin Yu:

I don't have any previous experience in building ML applications; however, I'm currently taking an ML course on Udemy and learning the fundamentals such as linear regression, and reinforced learning.

Have you deployed any ML apps to the phone? 

Currently, we have not deployed any mobile applications.

Describe the idea and project you built on Forge:

The project we built on Forge is known as CookHack. CookHack is a full-stack web application that allows users to log in to a personalized account to browse a catalogue of unique recipes from our database and receive simple step-by-step instructions on cooking delicious homemade dishes. CookHack also provides the ability for users to add the ingredients that they have readily available using a picture with image detection and start cooking recipes with those associated ingredients. Lastly, CookHack encourages the idea of interconnection by allowing users to share their cooking experiences online with each other.

What was the overall experience using Studio?

The overall experience of using Studio is that it allows for seamless deployment of ML models to various applications. Also, Studio’s user-friendly UI made the process of deploying an application extremely easy.

What are some ML apps that you've used in the past that you also want to build? 

We would like to build some ML apps, including developing a traffic prediction ML model that helps predict the best time to travel on the highway.

After using tools to convert scanned notes to digital text, we thought of building an application that used text recognition models such as the East Text Detector available on Forge to suggest cable layouts for custom PC builds.

Share about your knowledge around edgeML and brainstorm new ideas you have for your applications. 

Our team was interested in implementing edgeML in CookHack since it greatly simplified the project stack. Deploying the recognition model locally via Forge addresses fundamental privacy concerns by mitigating the need to call external APIs.

Our application's new ideas were implementing an ML model that could recognize multiple ingredients in a photo rather than a single ingredient. Another idea was migrating our web application to a mobile application to allow users to use our application anywhere, anytime.

What were some highlights using Studio? What were some challenges? 

Some highlights of using Studio was the great UI that made it extremely easy to build and deploy the model. Additionally, Studio reduced the time it takes to develop the ML model allowing for more time to create other functions during the hackathon. Some of the challenges included incorporating the deployment key into our application as we used a different bundle than the template.

In your words, what does Studio mean for developers? Can you share what you see Studio is solving?

With machine learning applications significantly gaining traction over the past few years, Studio will solve the problem of developers being unsure how to deploy models to their applications. This is done by creating a simple and effective way to implement ML models into applications with only a few lines of code. Developers will now have the option to discard the significant amount of time it takes to develop a working ML model and instead use Studio to deploy the models to their applications instantly.

Now, you can also build your next idea using edge ML using studio. Studio lets you drag and drop using out of the box models to build fully featured apps. Try it https://studio.hotg.ai or share the link with any application developer that might find it useful!

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Our team will also move all bi-weekly updates to monthly updates from the technical team. Bi-weekly updates will be replaced with technical driven posts as well as some guest appearances from our circle of incredible networks. We are excited to bring this new change to Tinyverse. If you would like to guest blog, feel free to drop Holly a line, our Head of Operations & Community Relations at holly@hotg.ai.

I wanted to share our ethos of building HOTG.AI as an open-source product company, and our culture to rally around the community first.

First, a few things to note, that will help understand our thesis:

• Pandemic changed the culture of deep tech. You can be anywhere in the world and build things.

• The idea of time, timezone, and location is obsolete - in fact, you are giving up easy alpha not being distributed from get go.

• The creator and builder economy emerged almost immediately. Building a product without community is like licking the tip of a finger and sticking it into the air.

• The future is more decentralized due to talent distribution.

These are the same market forces that benefit us at HOT-G building the MLOps platform for the Edge.

Open source is about building trust. When you run workloads on servers, cloud, and even phones, you are putting a lot of trust in it. One way to scrutinize this trust is by auditing the code that runs on these devices - and open source makes this easy. Open source is more than this - it is a culture of building with a community and rallying around common beliefs. We also think

• Some of the best talent work on open-source projects

• Open source is already distributed, and setup correctly has every aspect of decentralized decision making possible

• For us open source is how we will get to scale. Building container technology which helps solve the MLOps for the edge, along with the surrounding infrastructure of security, reliable builds, and telemetry means we have to scale frameworks (TF, TF-JS, Pytorch ...) across a fragmented world of devices (mobile, web, desktop, sensors, MCUs, etc). Open source is how we become Pareto efficient.

Every engineer in HOT-G is pushing something that contributes to our open-source effort.

You cannot be an open-source company without making it a key investment upfront (money, time, and culture). The challenge that comes with open source is how do we manage our product lines (SAAS and enterprise) while keeping our roadmaps coordinated. In an ideal world they are all the same, but this is not the case. We usually see one or the other having a more recent tip of the tree. However, setup right, we know it starts to compound the value very quickly. For example, our security bits started enterprise first and only then into our OSS SDKs.

We have seen companies like Hashicorp and Databricks (for Spark) do an excellent job with this model.

What does this mean to you?

If you are an engineer, builder, maker, tinkerer, community stewards, or simply curious kind come join us in forging the field of Edge SecOps. We wrote about this in our newsletter -

tinyVerse

Introducing EdgeSecOps - Securing Models and Workloads on the Edge

Our team have some news to share! We’ve launched our new website: https://hotg.ai/! We can take a short intermission here for your browsing pleasure! What do we think? :) Don’t forget to add yourself to the waitlist if you want to be notified when our SaaS platform is live (it is almost here!). Now, onto this week’s discussion…

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4 years ago · 3 likes · Akshay Sharma, Holly Ly, Sam Leroux, and Jacek

We have re-launched hotg.dev to fully focus on our community and OSS offering.

Here are a few ways you can already contribute today by visiting hotg.dev:

• Give us github stars for our open-source container technology - https://github.com/hotg-ai/rune

• Fork or clone it and start running it. You can do all the things we do in our SAAS platform - https://studio.hotg.ai using the same CLI tooling. This is the power of OSS - you don’t have lock-ins - even with us!

• Even better - you can add support to another ML framework or a device - hit us up on discord on how to

• Join our discord https://discord.gg/gPCNNvRnF4 and chat with us, watch our community interactions, and be part of this new wave

Kartik and I as founders have a lot of plans to bring more collaboration features and build the dev community around this.

Join us and have fun!

#opensource #ai #community #mlops

— Akshay

Community

QHacks: Hackathon

Our team had a blast at QHacks! Not only did we partner with QHacks, but we also had a grand time hosting a workshop using Forge Studio. The HOT-G’s contestants came out flying to take home HOTG Forge API Prizes with incredible ideas!

Our 1st place winners created an amazing app to use a food model to recommend recipes for all those needing help deciding what’s for dinner! You know, one of those days…or every day! Keep it locked here for a follow-up post where the QHacks 1st place winners talk about showcasing their work in the coming weeks!

The first-place winners from Queens University are:

1. Hunter Hoogendijk

2. Logan Hoogendijk

3. Sam Thibault

4. Kevin Yu

The 2nd place winner Benjamin Sun developed a proposal for project to detect neck pain using gesture recognition!

LightBulb Event

Lightbulb is an event held by students at Woodlands High School in Canada. A few months ago, we met the amazing hosts (Hi, Nathan & Justin!) at Hack the North (our first hackathon!) where they were the third-place winners of HOTG’s Rune API prizes. We kept in touch, and they asked if our CTO & Co-Founder, Kartik Thakore could make an appearance to discuss the future of computing and edgeML with students and teachers alike. The event was well structured in a Q&A format where students ask questions pertaining to burn out, entrepreneurship and even what kind of books Kartik was reading. Kartik had nothing but amazing things to say about the event. He loved talking to the students, especially about the importance of mental health and physical health, being a top priority for those who are looking to enter the entrepreneur startup world. Big shoutout to Woodlands High for following us onto discord! Our team love having you with us!

Rune SDK: Sprucing Up

In the last two weeks, we have made several improvements to support more platforms that coincide with the launch of Studio:

1. RuneSDK for web runtimes have gotten some simple fixes for certain versions of Safari that don’t support BigIntArray64.#411

2. We have also started to resolve MacOS builds for runecoral.

3. We have also started working on the ‘most asked’ for React Native bindings :)

Hammer Forge SDK:

Over on the ForgeSDKs there have been flurries of activities:

1. Support for Audio Capabilities is now added on the webSDK

2. Support for Flutter! You can now deploy Studio ML projects to Flutter for iOS/Android.

3. Support for DeviceGroups in deployments

4. Improving telemetry messages

Forge Runtime Examples

With the stable releases of Hammer Forge SDKs, we have put together examples that show how you can integrate in your deployments with your app!

*If you love our content, consider supporting our team by subscribing and sharing TinyVerse! We cannot wait for you to see what we have in store in the pipeline this year!

Our goal is to continue to provide a blank canvas with all the ML tools for artists who want to build something amazing for the edge. Studio, SDK, and examples are just the start - we are shipping multiple times daily to continuously improve this experience.

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HOT-G Socials:

Twitter @hotg_ai and @hammer_otg | LinkedIn | Discord

Our team has been working on this for many months now (seems like forever!), building with our early adopters, community, and key enterprise customers. Thank you so much for supporting us and taking us this far!

We are happy to announce the launch of Hammer Forge Studio - our SaaS low-code deployment and orchestration platform for edge computing. This first launch is geared towards the mobile phone, web, mobile-web, and desktop markets.

Take it for a spin - there is a free tier, and no credit card required. You can read more about our pricing here.

We also announced a press release and are sharing that verbatim here below for your perusal.

Press Release, Feb 8th, 2020

Palo Alto, Calif. - Feb 8, 2022 - Hammer Of The Gods (HOT-G) launched Hammer Forge, the low-code deployment and orchestration platform for secure edge computing and embedded machine learning.  While there has been an acceleration of cloud developer tools for Machine Learning (ML) and MLOps,  Hammer Forge is at the forefront what is only just beginning as the next technology super growth phase:  managing ML for billions of edge devices (e.g. phones, mobile browsers, Coral.ai, Jetson Nano, Raspberry Pi, IoT devices, and sensors). 

Analysts estimate¹ that the edge computing market has a CAGR of more than 19% and is expected to be $87.3 billion USD by 2026. The fragmentation in this market means that engineering teams have to support many types of devices and ML frameworks. HOT-G is making the developer experience (DX) consistent from building all the way to managing production deployments while taking security, monitoring, and observability into account upfront.

For the past year, the HOT-G team has been focused on the closed beta of their developer experience tools and have been working with the community of open source devs, students at several prominent hackathons, and early adopters in the areas of:

• securing edge deployments

• reliable builds for the edge devices

• and MLOps for the edge devices

We are at an inflection point where enterprises with sufficient data are now building machine learning models making them their IP. The data will be kept private on their environments, but distributing and safeguarding their IP (models) is still a hard problem. In this regard, Hammer Forge also serves as a licensible cloud native SecOps platform for enterprises to secure, protect, and deploy their IP on devices.

“The developer experience to deploy models on the Edge requires you to know several different frameworks from React-Native, Obj-C/Gradle, C++, Tensorflow, Python and more. With Hammer Forge, we are making EdgeML development accessible to more developers with a No-Code environment.”- Co-Founder and CTO, Kartik Thakore.

Hammer Forge is based on HOT-G’s open source framework called Rune, that runs on the edge devices. 

“Demand to move to the edge is surging from the need to address privacy, security, latency, and growth of capable hardware, but we are still at the very beginning of this Big Bang stage. HOT-G is looking ahead by solving problems we will run into managing secure production edge computing workloads while still making the developer experience simple and awesome.” - Co-Founder and CEO, Akshay Sharma.

About Hammer of the Gods (HOT-G)

Based out of Palo Alto, CA, HOT-G, is building the distributed infrastructure and developer experience tools paving the way for secure production-ready edge computing deployments. The founding team previously built several companies, including most recently doc.ai, a Palo Alto-based healthcare AI company which was acquired by Sharecare Inc. in 2021. The team previously developed federated learning, privacy preserving technologies, and zero-trust security infrastructure for healthcare.

The company has raised money from Quiet Capital, Amino Capital, S2 Capital, and other prominent Silicon Valley angel investors.

Visit hotg.ai and contact akshay@hotg.ai for more information.

[1] The edge computing market size is expected to grow from USD

We can take a short intermission here for your browsing pleasure! What do we think? :) Don’t forget to add yourself to the waitlist if you want to be notified when our SaaS platform is live (it is almost here!). Now, onto this week’s discussion!

Security out of the box for Edge Computing

In the previous post, our co-founder talked about the foundations of our edge computing platform. You can read more about it in the excerpt link below:

tinyVerse

Foundations of Our Edge Computing Platform

My cofounder wrote a compelling piece earlier this month on building for the edge and beyond. It chronicles an honest journey we have undertaken from our experience building for the cloud, deploying real world AI in domains outside of ad-tech, and into the world of edge computing. This post highlights the hacks we have had to deal with to make things wo…

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4 years ago · 3 likes · Akshay Sharma and Holly Ly

In that post, we articulated that Security was a critical foundational component that our platform should offer. To recap, we shared that:

Many enterprises collect unique data that can be used to train models which then end up becoming intellectual property (IP).

Development cost of production version of *good* AI models can easily reach millions of dollars. Therefore, these models are very valuable assets which must be well protected. In the centralized server solutions, those models are well hidden behind firewalls, the external world has only API access to those models.

On the contrary, deploying these models to edge devices means opening to several types of new attacks, such as:

• Direct model theft: Here the attacker steals the exact weights of the model to use it for their own benefit.

• Indirect model theft: Here the attacker queries the model repeatedly to automatically generate a labelled dataset. They can then train their own model to replicate the behavior of the original model without spending a lot of effort on labeling a dataset himself.

• Supply chain attacks

• GAN attacks to reverse data from model

• A whole bunch of attacks on the machine learning models including identifying personally identifiable info (PII)

In this post, we want to lay down the foundation of how to think about these security issues at the process, platform, and cultural level when working on edge computing. We will also briefly touch up on the solutions and research our team is building. We will also introduce you to the concept of DevSecOps and our take on it for edge computing called EdgeSecOps.

EdgeSecOps

DevSecOps is beginning to be well known in the world of cloud computing, and is described by RedHat as following:

DevSecOps stands for development, security, and operations. It's an approach to culture, automation, and platform design that integrates security as a shared responsibility throughout the entire IT lifecycle.

It is about bringing the culture of security mindset to DevOps.

An excerpt from the excellent DevSecOps book refers to cloud native security also known as 4C’s include the following:

• Code security

• Container security

• Cluster security

• Cloud security

ref: Kubernetes 4Cs

If we want to extend this to edge computing, henceforth christened EdgeSecOps the equivalent will be the following from our point of view:

Note: This is a concept architecture and not the actual implementation

The primary concern of EdgeSecOps is to secure the computation on the device. It has less to do with physical device security and device monitoring which are critical in its own way, but some of this is being addressed at the firmware level.

At HOT-G, EdgeSecOps is a key area of focus, where we are building some techniques to help protect against attacks under some threat models. The important thing to know is that the threat models for each use case drive the solution.

To summarize: EdgeSecOps is the process, culture, and tools for securing models and workloads on the edge device.

HOT-G’s research and solutions in EdgeSecOps

We are building several edge native and cloud native solutions for the EdgeSecOps space. We are rolling some of these features in our enterprise offering, which you can find more details here: https://hotg.ai/pricing/. Several of the items below are still in early exploratory phase at HOT-G.

• Watermarking models and registering models: Watermarking allows companies to check if other companies are using their exact models. Watermarking, together with registering models, allows companies to prove that their models were stolen, and it may significantly help companies in legal proceedings.

• Cryptographic and obfuscation techniques to protect IP:
  These techniques make it much more difficult to steal the model. Ideally, those techniques make reverse engineering (RE) of a model unsustainable - the cost of RE is similar to the cost of building a model including collecting the data.

• Zero Trust: Zero Trust is quickly emerging as a security model as a “perimeterless security” measure - that is to defend against attacks when the origin of the attack is inside the traditional perimeter-based security (on cloud or on-prem typically). With Zero Trust, we can bring identities to workload (example SPIFFE for cloud). Once we have cryptographically signed identities, this means anything running on a “machine” has been verified by a trust system. We can then extend this model from identities to agent-based policies to control granular level permissions (example OPA for cloud). Under certain threat models, Zero Trust provides an acceptable level of security. We are evaluating how to bring these concepts to the edge devices and most of this is now tractable because we have container-based technology for the edge.

• Observability: We talked about observability before. Observability refers to being able to monitor the ML model as it is making predictions. Specifically for security, this allows us to detect anomalous input patterns that might indicate an attack on our system. An abnormally large number of requests, for example, might be caused by an attacker who is trying to automatically label his own dataset to train their model. We are also interested in monitoring the input data distribution to detect inputs that deviate from the typical inputs. We could then, for example, detect adversarial attacks where the attacker is using specially crafted inputs to fool the system.

EdgeSecOps is an exciting field we are trying to establish and drive, to bring the type of security mindset needed when deploying AI to the edge.

If you are a security researcher or AI researcher in this space and want to collaborate with us, reach out to us at akshay AT hotg.ai.

*If you love our content, consider supporting our team by subscribing and sharing TinyVerse! We cannot wait for you to see what we have in store in the pipeline this year!

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Twitter @hotg_ai and @hammer_otg | LinkedIn | Discord

tinyVerse

To the Edge and Beyond

In 2017, I was looking for a solution to solve privacy issues with Healthcare AI. At doc.ai, my colleagues and I had put a lot of effort into figuring out how to build computer vision models for medical research. This involved collecting highly sensitive images for training and inference. The privacy and regulatory issues were a nightmare. In an attempt…

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4 years ago · 3 likes · Kartik Thakore

Engineers, Product Managers, CTOs, and CSOs coming from the world of cloud computing are used to a plethora of tools to make their life less complicated, but many of these are missing for running production edge computing.

Six foundation pieces

When we started building HOT-G, we laid out a set of six key foundational principles that a new stack of edge computing must include. We’ve talked about them in our past posts independently, and in this post, I want to bring it all home to provide a clear and succinct vision of how we build things.

1. Developer experience (DX) centricity

2. Repeatability to reduce developer overhead

3. Reliability for builds and deployments

4. Monitoring and Observability as a first-class citizen

5. Audit-ability with open source

6. Security out of the box

#1 Developer experience (DX) centricity

What I mean by developer experience is the sum total of how developers interface with their tools, end-to-end, day-in and day-out.

- Jean Yang from a16z post Developer Experience (DX)

There are many tools available for building machine learning models. Building for edge devices, albeit harder, is getting a lot of attention from some very good companies.

Developer experience extends beyond training. It encompasses collaboration during testing, deploying, and all aspects of the control plane - what we call the whole “Orchestration on the edge”

tinyVerse

The Future of Orchestration on the Edge

The world of software has advanced - where we can deploy models continuously, train them with new data, federate back the gradients, average an aggregated model, validate the new model, and push this new model all in matter of minutes. None of this orchestration chain exists for the world of TinyML…

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5 years ago · 2 likes · Tinyverse

Our first foray into learning and getting feedback on our DX mindset was in our first hackathon - Hack the North.

tinyVerse

Hack the North: Developer Experience (DX) Learnings

Experience is one thing you can't get for nothing. - Oscar Wilde We consider ourselves lucky because we experienced something amazing a week ago. Something that cannot be gamed, which cannot be bought, but can only be earned by doing good work - not for nothing…

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5 years ago · 2 likes · Holly Ly and Tinyverse

We have come a long way since that hackathon and have incorporated those learnings into our SaaS low-code environment called Hammer Forge which is launching shortly very soon!

Forge Studio - our low-code environment:

#2 Repeatability to reduce developer overhead

Repeatability for us means two things:

1. Isolation of builds from host environments

2. Minimizing developer effort to target multiple platforms

We are addressing both these items using our open-source containerization technology called Rune for edge computing.

Containerization makes the builds portable, which means developers dev/test environments are not dramatically different from production deployment environment.

Additionally, Rune also provides pipeline capabilities on the edge to perform MLOps, which means the effort to retarget different devices becomes manageable configurations.

We have discussed the origins of container technology being inspired by the cloud computing world below.

tinyVerse

The Future of Orchestration on the Edge

The world of software has advanced - where we can deploy models continuously, train them with new data, federate back the gradients, average an aggregated model, validate the new model, and push this new model all in matter of minutes. None of this orchestration chain exists for the world of TinyML…

Read more

5 years ago · 2 likes · Tinyverse

In addition, we described our take on portable edge computing:

tinyVerse

HOTG & Wasmer: Portable Edge Computing

We are very excited to be featured by Wasmer for bringing portability to AI/ML applications to the edge and beyond. Similar to what we do, Wasmer, is a rust company building on web assembly and a great open-source champion. It is our pleasure to build the future of edge AI and open source software with them. We are particularly excited to contribute to …

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5 years ago · 3 likes · Tinyverse

#3 Reliability for builds and deployments

The hallmark of a good production orchestration system includes repeatable builds, traceable deployments, and automated workflows.

The traditional workhorse for this in cloud computing world is the Continuous Integration / Continuous Delivery CI/CD system. Edge computing adds additional layers of complexity because sometimes the triggers are not based on code changes, but updates to models, or edge pipelines.

Hammer Forge (our SaaS) platform comes bundled with Foundry* - a CI / CD system for setting up repeatable and continuous deployment for edge computing.

We have briefly touched on this in one of our recent posts:

tinyVerse

Updates from the Tinyverse - January 10 2022

Happy New Year! We hope everyone had a great holiday, celebrating with your loved ones! Our team is back with a vengeance and cannot wait to share what we have been up to! Our focus has been closing out orchestration and deployments on the web mobile edge platforms. Check out the platform updates below…

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4 years ago · 2 likes · Holly Ly and Kartik Thakore

*Note: Foundry is an enterprise cloud native product available for enterprise customers

#4 Monitoring and Observability as a first-class citizen

Building and deploying models to run on edge is just the beginning. Models are workloads that run on devices placed in a specific environment. There are constant changes to the environment which affect the model performance. For example, an embedded computer vision ML model could have been trained to recognize objects in an environment, but the device could be placed where there is poor lighting, causing the models to predict incorrectly.

Models on the edge need to be monitored and observed continuously - just like production cloud workloads.

We articulated our thoughts on Monitoring and Observability here.

tinyVerse

Observability for the edge

Welcome to another brand-new Monday, and we hope you had a good Thanksgiving / holidays! Machine Learning models are not your “develop once and be done” type of software. Even the phrase “if it ain't broke, don't fix it” might not apply to them. They need constant attention, monitoring, and observation to examine if they are behaving correctly in the pro…

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5 years ago · 2 likes · Tinyverse

Hammer Forge is launching with monitoring features, and we are adding observability features later this year to help identify model and data drifts.

#5 Audit-ability with open source

Workloads that run on the edge should have verified software, and the best way to do this is by having open-source technology. Our container technology, Rune is completely open source. All the SDKs that help integrate into the devices are open source.

The tools we use for cloud are mostly open source because it allows us to audit and leverage the community as needed. Edge computing should be no different.

Our ethos for open source comes deeply from our developer culture, coming from the Rust community.

tinyVerse

Rust and Tinyverse

The Tinyverse is a family of projects from HotG which brings machine learning into the mainstream. Our flagship project is Rune, a tool for packaging up machine learning pipelines and allowing them to be deployed anywhere, be it the server, a mobile device, or the IoT…

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5 years ago · 4 likes · Michael-F-Bryan

We are building our community around open collaboration. The fragmentation of devices means it is impossible for HOT-G to cover every type of device and platform out there. We will strongly rely on the community coming together and rallying around Rune to extend to several platforms in the future.

If you would love to contribute, join our discord!

Discord

#6 Security out of the box

The final guiding principle is something that is often an afterthought in most products - Security.

Many enterprises collect unique data that can be used to train models which then end up becoming intellectual property (IP). Deploying these models to edge devices means opening to several types of attacks, such as:

• Direct model theft

• Supply chain attacks

• GAN attacks to reverse data from model

• A whole bunch of attacks on the machine learning models including identifying personally identifiable info (PII).

Having built and deployed ML for sensitive information such as healthcare, security, and legal teams can kill products from launching if these are not addressed.

Security is a deep topic with several layers of attack vectors and merits its own post which we will be publishing in a couple of weeks. So, stay tuned.

While it is impossible to address/thwart every type of attack vector, the important thing to do is have tools and systems that help identify them.

Hammer Forge is launching with a few key security features which we will talk about in this next post.

Building this stack of interoperable tools takes time, effort, and resources. We have been diligent in building them with early adopters and champions from our community, students, and pilots to optimize our learning. This year we are ready to commercialize and continue adding missing pieces of our edge computing stack quickly.

If you love our content, consider supporting our team by subscribing and sharing TinyVerse! We cannot wait for you to see what we have in store in the pipeline this year!

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Don’t forget to join the Hammer Forge’s Waitlist - we will be onboarding selected customers soon!

Join the Hammer Forge’s Waitlist

HOT-G Socials:

Twitter @hotg_ai and @hammer_otg | LinkedIn | Discord

Our focus has been closing out orchestration and deployments on the web mobile edge platforms. Check out the platform updates below!

Enabling Repeatable Builds

Our primary focus has been wrapping up our integration between Forge and Rune, to enable repeatable builds. We have been working with our customers and partners to iterate and develop the @hotg-ai/forge and @hotg-ai/rune typescript libraries. The current versions are now deployed on npm!

A quick refresher on the terminologies:

• Rune is our open-source container and edge MLops technology for devices

• Forge is our SAAS product built for making orchestrations with Rune easy, launching soon!

Rune SDK: 0.11.X releases

One big change you will see is that we are changing how the rune SDKs will be agnostic to ML Frameworks. To this end, we have now added support for several Tensorflow (tf) backends. Currently, we have released two backends for Tensorflow JS (tfjs). We will be adding Tensorflow lite (tflite) and onnx support soon! Rune container mimetypes have also been updated to reflect this work. This work will prepare us to be truly ML Framework agnostic and bring us closer to the vision we set out for Rune.

Hammer Forge SDK: 0.11.X early release

We have also released @hotg-ai/forge ahead of our Forge Studio SaaS launch. This library will allow our SaaS users to soon do reliable deployments with telemetry and security for the mobile web platform!

Community

Our team has another hackathon coming up w/ Queen’s University on January 28th to January 30th! Due to the spike in COVID cases in the Kingston, Ontario area, the hackathon will be moved to a virtual platform only.

We will be hosting a workshop called, “Rapid Prototyping for EdgeML Applications using Forge.” We want hackers to create three different ML applications (audio, NLP, and image), to showcase how developers can build ML applications for the mobile web and Forge Studio SaaS! If you have been to HOTG’s hacking party, you know our team have a grand old time hacking alongside you. Make sure you join our discord to hack alongside our incredible engineers!

Our team also want to send a big shoutout to Astasia Meyers who is a Founding Partner at Quiet Capital for publicly showing our company support! We are beyond thrilled to be mentioned alongside notable ML companies.

Don’t forget to join our Hammer Forge Waitlist! We are onboarding select customers this year!

Join the Hammer Forge Waitlist!

If you love our content, consider supporting our team by subscribing and sharing TinyVerse! We cannot wait for you to see what we have in stored in the pipeline this year!

Subscribe now

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HOT-G Socials:

Twitter @hotg_ai and @hammer_otg | LinkedIn | Discord

Throughout my career, I have always been the “hack-anything-with-glue-or-bolts” guy. My approach to most hacks has been to find the quickest path from a rough idea to a Rube Goldberg machine. I am driven to just get something working “end to end”. Hack first, then understand later has always been my ethos. So it came as a surprise to me that this was very difficult to do in TinyML and EdgeML.

I admit it, I am spoiled coming from a background of device-agnostic development that has a plethora of amazing developer tools. The openness of the web and cloud have created a massive playground where developers can build almost anything.

I started my development journey (interestingly) when I was studying for my MCATs as a Biology major at the University of Western Ontario. Much to the dismay of my parents, I fell in love with writing code. I was addicted to the feeling of telling a computer to do something and seeing it happen. The developer feedback loop was my first dopamine hit and I have never looked back.

Similar to many of my colleagues, I started working on games and soon fell deeply in love with the Perl community and kick-started the open-source PerlGameDev community. The Perl community instilled in me the core principle of Tim Toady (TIMTOWTDI). TIMTOWTDI stands for “There is more than one way to do it.” Larry designed Perl to be a language where developers were free to do almost anything in any way they wanted to do it. I strongly believe in that freedom of expression in computing and even more importantly, the freedom to compute. 

14 years later, I have built products, companies and hacked on everything from DevOps, frontend development, federated learning, and large mission-critical ML pipelines that serve 140 million users to small hacks during the pandemic. Throughout all of these projects and products, I have never been as excited as I am for the upcoming year in Edge and EdgeML.

I started playing with the edge deeply in 2018, tinkering in my backyard shed with Arduino devices, tiny FPGAs, and piezoelectric ceramic sensor devices, trying to shove ML models into places they shouldn’t go.

The prototype below was my first attempt at making an ML system that can program itself using signal data and containers stored in a local device. Using the Arduino Nano BLE 33 I was able to reprogram a TinyFPGA BX to run various models deployed purely in the form of a web-assembly container. The implications of an entire ML deployment pipeline running on various chipsets opened my eyes to the future.

Needless to say, the development process was pure hell. The precursor to Rune (our open-source containerization tech) was several scripts and tools I developed to make deployment possible. The dopamine hit of a fast feedback loop between model building and seeing it work on the device was lackluster before having containers working. In the process of improving the development workflow for my hacks, I built HOTG’s technology.

I dreamed of a world where I could use declarative languages to have my ML application work on any computing device with no constraints. We are well on our way to accomplishing that dream and 2022 will accelerate that in significant ways.

The future of Tiny/EdgeML will be accelerated with advances in ML Compilers which will create problems of their own.

The number of AI-enabled devices is exploding and the variety of different architecture and especially different software, powering them. Each of these classes of devices has detailed nuances that require deep expertise to build complete applications. From CoreML to HS4ML, there is an explosion and wide surface area of knowledge an application developer would need to know. The success of your application is now tied to the hardware it runs on.

Deploying to production in the cloud is fairly simple. It is a matter of beefing up your infrastructure and tweaking your terraform scripts. Deploying to production on the edge is a complex iterative process of selecting the best model, quantization, processing unit, and sensors. This is especially complex when we start seeing product teams working on mission-critical systems.

A big part of the coming innovation is focused on ML Compilers from a plethora of companies and open source initiatives. This hotbed of competition is poised to change how ML models are optimized for various devices. However, this still doesn’t solve the problem for the application developer. The joyful, tight iterative loop from idea to production and users using your EdgeML application still doesn’t exist in this ecosystem. ML compilers are a fundamental technology that needs to be solved and 2022 is the year for this! There will be many more challenges that need to be solved before EdgeML is at the same stage as CloudML.

I am confident that the amazing EdgeML space that is growing now will solve a lot of these technical issues. The space is indeed well funded and I expect this to grow.

On a side note, I am also particularly excited about Mythic AI which is working on analog matrix processors to take on the king of deep learning, the GPU. Indeed, this is an extremely innovative space, and the software stack that follows these chips will be just as diverse and innovative.

Analog computing provides the ultimate compute-in-memory processing element. The term compute-in-memory is used very broadly and can mean many things. Our analog compute takes compute-in-memory to an extreme, where we compute directly inside the memory array itself. This is possible by using the memory elements as tunable resistors, supplying the inputs as voltages, and collecting the outputs as currents. We use analog computing for our core neural network matrix operations, where we are multiplying an input vector by a weight matrix.

https://www.mythic-ai.com/technology/

The revolution/evolution in hardware, computing, and compilers will lead to a very natural conclusion.

Fragmentation is here to stay. Fortunately, we already know how to solve this problem.

For the foreseeable future, fragmentation will be the norm in the EdgeML landscape. As reported by @ChipPro in her recent blog post, both the device backends and ML frontends are rapidly expanding.

EdgeML fragmentation is very analogous to the early mobile development landscape where the iOS and Android market. In the last 5 years alone several tools and frameworks such as React-Native have solved this problem. I anticipate the same happening with projects like Microsoft’s ONNX and Apache TVM. In preparation for such frameworks, HOTG is planning on a stack agnostic solution to provide the best-in-class experience for product teams. I strongly believe, that at some point there will be more edge computing products than cloud or internet-connected devices. In such a world, product teams will need a way to rapidly iterate, collaborate, secure and deploy EdgeML applications.

Beyond 2022 to 202X: Product teams building applications on top of Edge/TinyML in a multi compute environment

At HOTG.AI, we are focused on what comes next for the edge - frameworks, and tools that allow teams to deploy production ML applications and products. With our enterprise offering, Hammer Forge, we plan on releasing tools to allow developers and product managers to collaborate on building amazing EdgeML products from designing ML frameworks to rapid prototyping to deploying with security and telemetry backed in.

If you want to learn more about Hammer Forge, join the waitlist below!

Join our Hammer Forge Waitlist!

Want to geek out with me on all things EdgeML? Let’s connect at kartik (at) hotg.ai or on my twitter @KartikThakore

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So this is Christmas
And what have you done
Another year over
And a new one just begun
And so this is Christmas
I hope you have fun
The near and the dear ones
The old and the young

A very Merry Christmas
And a Happy New Year
Let's hope it's a good one
Without any fear

John Lennon - Happy Xmas (War Is Over)

2021 has been the most unusual and difficult year for most to say the least. While 2020 had a brief period of relative normalcy, 2021 proved that the pandemic has dominated our lives since the start of the year. While we all had to adapt to this “new normal,” we also experienced a remarkable breakthrough where humanity came together to build SARS-CoV-2 coronavirus vaccine in a record time.

Admittedly, we still have a long way to go to get the world in order; at the same time, this year proved that our world is more connected than ever. Our team at HOT-G is hopeful and positive that things will be looking up. If anything, 2021 gave us all a preview of a more digitized future. There is a lot of potential for experimentation with opportunities to rethink things and find out what isn’t working and what’s working. In the last year, we have learned a lot about what didn’t work, but we also learned a ton about what worked and really honed in on those skills to accomplish the things we set out to do as a team.

Here is a proud recap of our accomplishments this year:

• Our team launched our open-source tool, Rune and related command line tools.

• We raised our seed round with some of the best investors in Silicon Valley who believed in our vision for edge computing.

• Our company partnered with Hack the north and Hack Princeton to run two very successful hackathons.

• Our team kicked off work on building our SaaS platform called Hammer Forge (launching in early 2022), which is continuously improving our open-source tools.

Join our Hammer Forge Waitlist!

• Our team grew from two guys in a garage setup to a whooping fourteen team members with a primary focus in engineering of course!

• Our TinyVerse Community is consistently growing with our blog read by many incredible investors, entrepreneurs, and engineers.

• We are a proud strategic partner with the TinyML Foundation and also provide support to the Webassembly community.

• We started working with some amazing enterprise customers to bring secure and observable edge computing.

• Our team is having a grand kicking-ass time working hard in a company we believe in!

We are walking into 2022 with a great sense of optimism where our ideas are primetime ready for the mass markets!

One more thing

At HOT-G, we are big believers in decentralization, and the technology that supports the blockchain. Our team has been tiptoeing around the metaverse; however, we plan on doing and bringing a lot more to this market in 2022.

Here’s a little teaser:

We are working on integrating edge computing, AI, and blockchain, that helps bring AI to the chain in a way that can truly lead to full decentralization.

With HOT-G’s products launching in 2022, decentralized verification of Real-World-Events, using edge computing stack will be possible.

Want to collaborate & invest? Contact Akshay — akshay@hotg.ai

Bon voyage to a bittersweet 2021! Our team believes in the magic of new beginnings because the first step towards getting somewhere is to decide you’re not going to stay where you are. And we intend to take HOT-G to new heights! Thank you for sticking around and believing in us! 2022 is about to get rowdy!

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Hammer Forge Studio

We are very stoked to share some sneak peeks of our upcoming enterprise launch! The team has been hacking through long nights in the Studio. Below are a few teases of our Studio UI that can now be used to build your edge ML pipelines from conception to deployment!

Studio is a simple drag and drop interface for Edge Computing

We are also working on improving the ability for you to share and collaborate on EdgeML deployments using our test feature. Forge will be addressing earlier issues we had with Rune from the learnings we got from previous hackathons. For instance, our team learned that the build system was way too complex. Additionally, for certain personas like application developers and ML engineers, a simplified UI is needed to cut down the feedback loop from model to deployment. We are building Forge to help accelerate development of their ML powered applications. Our team is still in the process of refining this. We cannot wait to share more information once we conduct user interviews and further quality assurance!

Studio makes it easy to collaborate with your team for edge apps

Our team is looking forward to share more in the upcoming weeks! If you would like early access to Forge, just add your information below - we plan to onboard select customers in 2022!

Join our Hammer Forge Waitlist!

Rune Core

The core Rune team have decided to skip this release cycle so they can dedicate their time to bringing Hammer Forge to life.

We’ve learned a lot more about how Runes can be constructed and used in the real world and are excited to take these lessons back when we start working on Rune again.

Community

Our team is excited to announce that we will be partnering with Queen’s University for their annual hackathon from January 28th-30th! We will be hosting a workshop as well as having a sponsored table at the event! This will be the first hybrid hackathon we are sponsoring in-person and virtually! More details will be revealed so make sure you follow us on all of our socials to keep apprised on all things HOT-G!

In the meantime, if you’re a hacker, be sure to join us for an unforgettable experience! We look forward to meeting you in Canada next year!

Join our community!

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Machine Learning models are not your “develop once and be done” type of software. Even the phrase “if it ain't broke, don't fix it” might not apply to them. They need constant attention, monitoring, and observation to examine if they are behaving correctly in the production system.

In the case of ML models on the edge, the problem only gets worse.

What makes machine learning different?

To understand why machine learning models need to be carefully monitored and observed, we need to grok what is encoded in the model.

To grossly oversimplify, in traditional programming your code reflects the rules/logic of the algorithms implemented. At runtime, your program provided with sufficient time and memory, will execute for the given inputs, to arrive at desired results.

In machine learning, you have copious amounts of data, which is labeled, and the inputs that led to them. However, you don’t know what logic or rules map the inputs to the outputs. The machine learning program produces a “model” which (in an ideal world) encodes these rules. The schematic below explains the simple (albeit trivialized) mental model (pun intended).

Source: https://futurice.com/blog/differences-between-machine-learning-and-software-engineering

A more nuanced understanding is required why machine learning is quite different. Andrej Karpathy has written a good post on what makes machine learning (specifically deep learning) software 2.0.

In contrast, Software 2.0 is written in much more abstract, human unfriendly language, such as the weights of a neural network. No human is involved in writing this code because there are a lot of weights (typical networks might have millions), and coding directly in weights is kind of hard (I tried).

Source: https://karpathy.medium.com/software-2-0-a64152b37c35

As you can see, monitoring machine learning is different because you are required to monitor whether the output program (the model) is behaving correctly, as opposed to whether the program is behaving correctly.

Monitoring vs Observability

We have been using a few technical words such as monitor and observe, and we would like to define them better to differentiate and arrive at the crux of this post.

Monitoring:

Monitoring is about having a clear view on the operational aspects of a system. It typically answers questions such as:

• Is my system working currently?

• Is the performance of the system acceptable?

According to the Google - Site Reliability Engineering handbook monitoring is defined as:

Collecting, processing, aggregating, and displaying real-time quantitative data about a system, such as query counts and types, error counts and types, processing times, and server lifetimes.

Observability:

Observability is a derived information to help understand if the system is behaving correctly. The “correctness” of a system is context specific.

Observability is the ability to measure the internal states of a system by examining its outputs. A system is considered “observable” if the current state can be estimated by only using information from outputs, such as sensor data. 

Observability for machine learning

Observability for AI system is critical to ensure the correctness of the system behavior continuously

Observability becomes extremely important for ML systems (along with monitoring). The rationale for this should be straightforward given that ML models are programs themselves as opposed to results.

ML systems have a few additional nuances that make observability important, as described below.

Concept drift

A shift in the actual relationship between the model inputs and the output. An example of concept drift is when macroeconomic factors make lending riskier, and there is a higher standard to be eligible for a loan. In this case, an income level that was earlier considered creditworthy is no longer creditworthy.

Prediction drift

A shift in the model’s predictions. For example, a larger proportion of credit-worthy applications when your product was launched in a more affluent area. Your model still holds, but your business may be unprepared for this scenario.

Label drift

A shift in the model’s output or label distribution.

Feature drift

A shift in the model’s input data distribution. For example, incomes of all applicants increase by 5%, but the economic fundamentals are the same.

The drifts are even more at play in the case of models on the edge. The environments on the edge are not static, and hence your models cannot be as well. Therefore, observing ML systems becomes critical to measure the drifts, and continuously improve the model.

Princeton hackathon talk

Observability is a deep topic and requires more than a post to go into further details. Our Co-Founder, Kartik Thakore recently gave a talk at the Princeton’s hackathon, touching on this fascinating topic. You can watch the whole video where he goes into more details while discussing the open issues in the field of edge computing.

Observability with HOT-G

Our team has extensive experience deploying real world ML systems in healthcare, finance, grids, and more. We believe that building the model and deploying it is only the first part of the puzzle. To truly manage a complex ML system requires tools and systems for both monitoring and observability.

Observability will be a key feature on our Hammer Forge platform sometime in 2022.

If you would like early access to Forge, just add your information below - we plan to onboard select customers in January 2022!

Join For Early Access

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At Hammer of the Gods, one of our proudest moments as a company is being able to work with team members spanning across the entire globe from the United States to Europe to Canada to India to Australia, to name a few. With a diverse remote-first company such as ours, we are very fortunate to be able to celebrate all the different amazing holidays that are near and dear to our team members’ hearts. With that being said, Thanksgiving is upon us and is widely celebrated in the US. Our team had to take this moment to send our tremendous gratitude to our wonderful community for the incredible support you have given us. 

When HOT-G started, our founding members had a vision to push the limits of what was possible with AI outside of the cloud. In order to bring this vision to life, we had to build a winning team which required working with really creative hackers who have special skills such as valuing empathy over ego, talent over title and collaboration, collaboration, and more collaboration. Did we say collaboration? We’re honored to work with team members who are experts in what they do and know why they do it and pay it forward to their fellow team members. 

When culture and values align within the company, we all win! This win led us to build our open-source technology called Rune, an orchestration tool for specifying how data should be processed, with an emphasis on the machine learning world, in a way which is portable and robust. The main goal our team had with Rune is to give developers in the fields of ML and data processing a way to declare how data should be transformed using a high-level, declarative language. As you’re aware by now, we are particularly focused on making rune containers easier to deploy. Deploying applications in the tinyML universe is a hassle and experts agree. 

Our team has been working hard in the last several months to make development and deployments easier! Enter, Hammer Forge!

Hammer Forge is a rapid development service that can build Runes, collect telemetry in environments completely owned by you and deploy Runes back to devices. Forge is part of our toolkit for embedded machine learning application developers, to focus more on making secure and private AI rather than building data collection traps. If you want to learn more of how this enterprise offering came about, read more here.

We are launching Forge sometime soon next year with folks already waiting in line to get early access into what our product can do. Sign up below for exclusive information you will not find anywhere else! 

Join for Early Access

Computers are one of the most complex inventions humans have come up with and arguably the best compliment you can give a piece of software is that it just works. There are a large number of factors that contribute to the “it just works” experience, and here at HOT-G, we are dedicated to applying them to each and every aspect of the Tinyverse. 

We are hammering home the central theme which is intelligence will go where the data is. We are about to witness a fork in the world of intelligence, a decoupling from centralized systems to a distributed intelligence system. To that end, our team is already working hard to bring our vision about Metaverse in the aspects of computation, decentralization, and the need for verifiable edge computing with AI, to materialize a fully decentralized Metaverse. Our open-source ethos means we will be building many of these items openly which aligns very well with the crypto community. 

As you can gather, our only limit is our imagination and speed of execution. As we begin to explore and build the future of the Tinyverse, we know that our imagination will only grow and we cannot wait to grow with you! Thank you for coming along the ride and giving us a reason to stick around! The journey has only just begun! Wait till see you what we got cooked up for 2022! 

May the magic and thrill of the holiday season forge on ;)

Akshay, Kartik, Michael, Jacek, Dinesh, Geert, Sam, Meelis, Mohit, Anugrah, Amin, and Holly

PS - our team will take a holiday break to recoup and will resume with our regular Tinyverse programming on Monday, December 6 with a post you do not want to miss!

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The urgency of the climate crisis is accelerating — and so, too, is the opportunity to build a clean economy. We have a once-in-a-lifetime moment to transform society and create sustainable, just and resilient systems.

Our team learned a lot attending the conference and had an incredible opportunity to connect with key leaders in the space of tech intersecting with climate change. It was refreshing to see the enthusiasm to solve some hard problems using tech.

It is clear to us the role edge computing and AI will play in getting us there.

We wanted to share a few videos from the conference that we feel are relevant to the topic of edge computing and AI.

Pulling Carbon from Atmosphere

Direct Air Capture (DAC) can play a critical part in climate strategies if the technology scales and costs fall.

Healthy, Clean Buildings for the 21st Century

How to balance comfort, health, and emissions goals in commercial spaces.

Using Data to Build Smarter Cities

Pioneering programs in Norway share their people-first approach to smart cities.

You can watch the entire series here, but this is a great segue to the topic of this post - smart cities.

Cities - Exponential Growth

According to the United Nations, the urban population worldwide will reach 68% by 2050. With population density increasing, cities are presented with difficult infrastructural challenges such as security threats, public health, crowd and traffic management, operational efficacy, resource management, climate change, and urban development.

source

We are at a precipitous moment in the history of humans, where the number of people living in a finite geographical area is growing at an unprecedented rate. Naturally, this means there is a contention for every resource, and issues that occur with scale.

In his seminal book SCALE, physicist Geoffrey West, articulated the epoch we are witnessing:

The term Anthropocene has been suggested as the name of this most recent epoch in the history of our planet during which human activities have significantly affected the earth’s ecosystems. This process began more than ten thousand years ago with the discovery of agriculture and the subsequent transition from the mobile hunter-gatherers to the sedentary communities and eventually to the emergence of the first cities.

…

In that spirit we should also acknowledge that we have already made a sharp transition out of the purely Anthropocene to what could be considered yet another epoch characterized by the exponential rise of the cities which now dominate the planet. I would like to introduce a new term and suggest the name Urbanocene.

Urbanocene will be adding an enormous stress to every system we can think of. Therefore, there is a big rally to make the cities smarter.

Smart Cities - Sustainable at Scale

What exactly does smart mean? We can define smart (in this context) as being able to measure, observe, and act proactively on every resource that is required to sustain a city at scale. The resource could include (but not limited to): infrastructure, security, public health, crowd and traffic management, water and energy management, resource management, climate change, urban development, and more.

Economic drivers to invest in Smart Cities

The total addressable market (TAM) for smart cities is estimated to be at 2.5 trillion by 2026, growing at a CAGR of 22.5%. This is an incredibly massive market with room for many players to establish themselves across various verticals.

Mayors of many cities are pushing mandates as part of the Climate Action Compendium, making actionable governance in communities/cities. Some examples:

NYC, NY

• 40% reduction in carbon-based emissions by 2030 on 1990 baseline; 80% by 2050 on 2005 baseline. Target a 30% reduction by 2025 in buildings

• Govt. buildings to be retrofitted for energy efficiency by 2025

Bay Area, CA

• Greenhouse gas reduction: 40% by 2025; 80% by 2050 (1990 baselines)

• 100% renewable energy by 2030

• Build or retrofit 50 million sq. ft. of green buildings

• Divert 100% of waste from landfill 

Most cities

• Net zero greenhouse gas emissions by 204x

• Zero waste by 203x

• 100% clean, renewable electricity by 203x

• Divert 100% of waste from landfill and convert waste to energy by 204x.

Read the full PDF here

Role of edge computing and AI

The TAM for just the edge computing market is at $7.1 Billion by 2026, growing at a CAGR of 28.4%. The market for smart cities includes deploying several key AI and edge computing technologies, with the following considerations:

• IoT and sensors deployed to gather continuous data around cities

• Smart meter and smart grid for predictive energy and resource usage

• Monitoring for threats, as they are increasing across the stack of hardware, software, policies, and operations

• Privacy first solutions and inclusive participation from the public to be expected - this means most things will move to the edge

We should expect a Cambrian explosion of devices to measure everything. We should demand privacy preserving methods to safeguard sensitive and classified information. It should not be under the control of centralized entities.

Edge computing and decentralization are the two techniques to get us there.

Opportunity for HOT-G

As a company, we care about climate change and sustainability. We believe to build the future for humanity, there needs to be progress and move from systems that are causing long term issues to our planet, and still support the economic progress of bringing billions of people to experience growth and opportunity.

The biggest impact we can have in the smart city vertical is to bring monitoring, privacy, security, and observability to the systems. To that end, we are building our SaaS product, Hammer Forge, which will be adding vertical features for smart cities sometime in 2022.

Monitoring and observability of every aspect of a smart city will become the critical need of the network infrastructure. HOT-G is building the key resources to make this tractable.

If you would like early access to Forge, just add your information below - we plan to onboard select customers in January 2022!

Join For Early Access

Follow us on our socials:

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Community

Our discord is buzzing with new developers dropping by! One of our users on discord have shown interest in local deployment because he wanted to see if he can embed rune! He was able to run a BERT demo from here. Like our team, he’s an NLP enthusiast, building open source project for medical literature using knowledge graphs using Redis. His project won RedisLabs Hackathon this year! Happy to have you joining us, Alex!

Our team is also thrilled to announce that we are a Gold Sponsor for this year’s HackPrinceton which commenced this weekend, Friday, November 5th till Sunday, November 7th! For 36 hours, hackers have the opportunity to collaborate and build out brilliant, innovative and impactful ideas. We were beyond excited to support young minds who are shaping the Future of technology! If you have been with us since the beginning, you know we love a good hacking party so be sure to join in on future adventures via our Discord and twitter @hotg_ai!

Our Co-Founder, Kartik Thakore, closed out the opening ceremony alongside Ethereum’s Co-Founder, Joseph Lubin, talking about the future of edgeML. He also led two talks showing 1. Tutorial on Rune and 2. Observability on the Edge and for AI. You can check out the 2nd workshop here. 

We had pretty great project submissions using our open source technology, Rune. The one project that really stood out was a project on Brain Health which uses a privacy preserving model for patients to get risk understanding of their mental health. One of things we loved about the team was that they used custom models on their own data and were able to deploy rune end to end. You can check out the dev post here! 

Thank you to HackPrinceton Hackers for making this weekend an enjoyable one! Our team had a great time chatting with all of you and look forward to seeing what other hacks you have in stored for the future!

Rune Core

We hit a big milestone in this release with the publishing of our web runtime to NPM. To accompany this, we’ve also developed a tutorial that walks developers through integrating Rune into their web apps. If you’ve ever wanted to use Rune in the browser, this is something you should definitely check out!

Try the Tutorial

The above tutorial is just one of many that we’ve developed in the lead up to HackPrinceton.

A point raised time and time again by Rune’s users is that sometimes you just want to try out a new technology without going through the hassle of installing software or setting up a development environment. Well, we’ve taken that advice onboard and now every tutorial in our new tutorial series has a big “Open in Gitpod” button which will open a VS Code window in your browser that is connected to a fully provisioned development environment in the cloud.

Verge

Our team was a proud sponsor of VERGE 21, one of the key events in the climate tech space.

The event focused on industry leaders and startups working to address the climate tech change. As noted on their website:

The urgency of the climate crisis is accelerating — and so, too, is the opportunity to build a clean economy. We have a once-in-a-lifetime moment to transform society and create sustainable, just and resilient systems.

This was an opportunity for our team to learn and highlight how edge computing (including AI on the edge) will make an impact in addressing climate change. A full debrief of this will require a separate post, which we intend to publish next week, especially as HOT-G is starting to build vertical solutions in this space.

Hammer Forge

We had a chance to showcase a preview of our upcoming NO/LOW-CODE product called Hammer Forge, which will allow anyone to come and build edge computing apps. While we have a long way to go to mature our product stack to make it incredibly easy for anyone to manage and observe edge computing, this is our first attempt at building a drawing canvas for edge artists :). You can watch the sneak peak video here:

The team has been hard at work building a cloud service that will make it easier for Rune lovers to make and monitor their applications. As of now, we have started putting together our initial UI, build services and other infrastructure.

If you would like early access to Forge, just add your information below - we plan to onboard in January 2022!

Join For Early Access

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