Fuzzy.ai source code released

We’ve released as much code as possible on https://gitlab.com/fuzzy-ai .

This includes repositories for:

  • our fuzzy logic controller engine
  • our fuzzy learning libraries
  • our API front end server
  • our landing page
  • our IDE
  • all the microservices needed to make these work
  • our drivers for different programming languages
  • example programs

In general, we have released libraries and plugins under the GNU Lesser General Public License 3.0 , command-line programs under the GNU General Public License 3.0, and network servers under the GNU Affero General Public License 3.0. Some code was already available under the Apache 2.0 license, so we left it that way. Documentation and images were released under the Creative Commons Attribution-ShareAlike 4.0 International license.

We haven’t released any of the work we did for clients, whether or not that work was paid for. And we haven’t released configuration data or scripts for our production servers (even though most of these wouldn’t work now, anyway).

Still to come:

  • Our Docker images are still private. We’ll be making those public over the next week or so
  • We haven’t yet published a Helm chart for running your own version of the Fuzzy.ai API stack. Given that there’s about a dozen microservices needed to run the whole thing, it’s a lot easier to do with a script than trying to configure them by hand.

Fuzzy.ai API now off

As announced previously, the Fuzzy.ai API was turned off on December 31, 2018. If you have not received your data from the API service and you’d like a copy, please email support@fuzzy.ai.

We’ve started releasing code as Open Source bit by bit on https://gitlab.com/fuzzy-ai. You can expect a full stack to be available publicly in the next few weeks.

If you have any questions or comments, please let us know at the support email address above.

Fuzzy.ai API service to end on December 31, 2018

The last day of service for the Fuzzy.ai API at https://api.fuzzy.ai/ will be December 31, 2018. We will release the full stack for the API service under copyleft licenses. Data will be available for download on demand.

Unfortunately, we haven’t made enough money with the Fuzzy.ai API to cover costs and pay the development team, so we can’t justify its continued operation. The team has moved on to greener pastures at other companies; the founders will soon follow.


Developers who want to recover data from the API should send an email to support@fuzzy.ai. We will be able to service these requests through the end of the year, and on an best-efforts basis in Q1 2019.

Open Source

We want our users who have come to depend on the API to be able to set up their own servers for future use. (This should require only a small change in client code.) We continue to believe in the importance of artificial intelligence for all developers. We hope that an Open Source API will be a useful contribution to their toolset.

The software will be available under the following licenses:

  • LGPL 3.0 or later for our core fuzzy logic engine and adaptive fuzzy learning engines
  • AGPL 3.0 or later for the API, development IDE, and microservices needed to make the engine run

We will provide a Helm chart to make it easy to launch a new Fuzzy.ai API instance on your own Kubernetes cluster.

Watch this blog and https://gitlab.com/fuzzy-ai for news as we manage the release process.


Matt and I want to thank everyone who believed in our vision for  artificial intelligence available to all developers.

First and foremost, to the developers who have put our product to the test, made it part of their stack, stretched its envelope, and provided valuable feedback and Open Source contributions. We hope you continue to put your users first and make your software intelligent and adaptive.

Thanks also to our investors: Interaction VC, Real Ventures, iNovia Capital, 500 Startups, Mark Cuban, Julien Smith, Kai Gradert, Julien Genestoux and Stav Prodromou. Your capital and guidance let us put our case before the market. Thank you for providing that opportunity.

Thanks immensely to our startup and tech community in Montreal and San Francisco for the intros, suggestions, and cheerleading. Thanks to our advisers, friends and family for their help, support, and ideas. Thanks to my kids Amita and Stavy for being confident and believing in me. Special thanks to our wives Cassie Sera and Michele Ann Jenkins who have been proud, supportive and forgiving.

Finally, we want to thank our team: Kevin Fox, James Walker and Pablo Boerr. You put in intense effort, creativity and late hours to make a product that mattered. We can’t count the number of times we’ve accepted congratulations for your great visual design and friendly, usable software. We expect to see even more awesome work from you in the future.

We, the founders, will be moving on to other endeavours in 2019. I (Evan) will begin a new position as Product Manager for the API at the Wikimedia Foundation, the folks who make Wikipedia and other great wiki products. Matt will continue to share his experience and expertise as a consultant and mentor for other startups.

Democratization of AI at Mozfest 2017

A quick note that I will be talking about democratization of AI at the Mozfest 2017 event in London this weekend. Fuzzy.ai’s goal has always been to make it easier for developers to include artificial intelligence in more of their software, giving the ubiquitous experience we call casual intelligence. I think this is an important issue for technology and society, and I hope this session will help inspire software makers and thinkers to focus on more widespread and diverse AI development.

What should you do next?

I gave a talk last night at MTLDATA about using Fuzzy.ai for task prioritization. It’s a really interesting subject for me, because tasks are an atomic part of our life and our work. How and when we complete tasks is an important part of any person’s productivity, and artificial intelligence that helps us do that effectively is really valuable.

Most of us use a task management system — whether it’s part of a project management system like Github issues or JIRA, or a personal system like Todoist, todo.txt or Google Tasks. All of them have ways to define important data about our tasks, but very few of them make intelligent recommendations for what to do now.

Intelligent organizations are starting to work on this problem today. After all, having people inside your company work on tasks that are timely, impactful, actionable and personal can make a huge difference. Tying the performance of tasks to metrics within the organization can drive much better relevance tracking.

To test out these ideas, we built a cool task prioritization project using the Todoist API. If you log into the site at https://tasks.fuzzy.ai/ with Todoist authorization, it will automatically score and rank your upcoming tasks, putting the most relevant items first.

Todoist has a relatively simple schema for tasks. We used that data for building a fuzzy.ai agent that uses time, priority, and other features to determine the task’s relevancy.

We think there’s some interesting future developments here. Keyword matching with previously relevant tasks is a big one. Providing other backends, like Github issues, is another. (I use Todo.txt, so I’m especially interested in this.) Most of all, building in different kinds of performance metrics to see if your tasks really matter is going to be the biggest.

Our code is open source. Feel free to fork and try on your own, or send pull requests. We’re always interested in what people figure out to do with Fuzzy.ai.