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.


We are Montreal AI

One of the great outcomes of the AIFest event in Montréal last weekend was an increased sense of unity among the different parts of the AI ecosystem here. We are all working together to make AI a new industry in this city. 

To that end, we have created an open letter to the applicants for the AI Supercluster program in Montréal. This is a federal program to invest in AI. We want to work with government, industry and academia to make the ecosystem a success. 

The letter is at wearemtlai.org. Fuzzy.ai’s co-founders have signed as well as dozens of leaders in the community. We encourage participants big and small to make their voices heard. We can only do this if we work together. 

Major update to the Python SDK for Fuzzy.ai

One of the benefits of having Fuzzy.ai on Product Hunt last week was getting a lot of additional eyeballs on our SDKs for different programming language. We got a lot of feedback on the Python library on github, so there’s now a new version of the fuzzy.ai package in pypi.

This version brings the Python SDK up to the same level of functionality as other SDKs, and provides support for Python 2 and 3, a better unit testing framework, and some other software engineering improvements.

Please kick the tires if you’re a Python developer. Issues to Github!

New Ruby SDK version for Fuzzy.ai

The Fuzzy.ai SDK for Ruby was updated to version 0.2.1 this week, based on feedback from Ruby developers who were building apps with the library. The gem is significantly better than previously, with a number of bugs fixed, additional features for managing agents, and improved integration with other gems.

You can get the SDK using gem install fuzzy.ai  from the command line or add gem ‘fuzzy.ai’ to your Gemfile.

More info in the docs and please file issues on the github project.