Quickly figured out that there's a problem with just applying "big numbers = green, small numbers = red" uniformly, though. On my fantasy football team page, "number of fumbles" should show green when a player has few / red when they have many.
When TypesafeAI's Jev decision model was released, it seemed like a really good fit for being able to solve this sort of problem. With some context (like "does `cost` here seem like a good thing or a bad thing, given the title of the table and adjacent columns), it is great (and quick!) at determining expected order.
Was already doing the work of parsing numbers from cells, so things like "$50-60k" becomes comparable and so do cells with adjacent text (like "32.4% Xhigh effort").
It's all open source[0] and you can BYOK (for Jev or Clef) or use it for free (with some healthy limits)
I hope you like it!
Practical Data is a Chrome extension that turns `<table>`s on the web into data visualizations by way of conditional formatting. It makes it easy to see patterns in the data in front of you.Importantly, it uses a decision model (like Jev) behind the scenes to get around the gotchas normally associated with this, like "green-to-red scale, but high numbers for this column are actually bad",...
[0] Practical Data (https://practicaldata.dev/ / https://chromewebstore.google.com/detail/practical-data/oeoo...)
Our mission is to empower the world to communicate with context and insight. The Nylas Cloud APIs powers email, calendar, and contacts features in SaaS products ranging from CRM and marketing automation to recruiting tools, scheduling assistants, legal and real estate platforms, and more. The Nylas email API integrates with 100% of email service providers, allowing bidirectional email sync between SaaS apps and your email client.
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Tech Stack: Python, MySQL, Redis, Kinesis, HAProxy, Linux, Javascript, React, Redux, Flux, Flow, Elasticsearch, AWS, Jenkins, MyPy, nginx
Affinio is building a data platform that lets users run unsupervised machine learning against large datasets, like social networks and permutations thereof. We're looking for data-minded back-end engineers, architects, and QA devs.
Scala (Play, Sangria) with a graphQL API layer. Data dashboard is in Ember. Lots of D3, lots of statistics, lots of type safety.
We're funded and have about 20 open positions today: http://www.affinio.com/careers/
Reach out to me at https://twitter.com/phil_renaud if you've got questions!
Affinio is a well-funded Data Science startup that helps users make predictions and gain insights about really complicated graphs, such as social networks like Twitter and Reddit. We have a stack that spans from Spark to Scala to Ember, expose a GraphQL API, and write a lot of serverless-style code.
To see all open positions visit http://www.affinio.com/careers/
Come help us build tools to let users get a really, really deep understanding of social networks and their connections. Our backend is written in Scala (Play) and we've recently converted our API to GraphQL (which has been a dream to work with tbqh).
Even if you're new to Scala but your Java is up to speed, we definitely want to talk to you.
Check out the job listing here: http://www.affinio.com/careers/backend-developer-platform
Affinio is a marketing intelligence platform that leverages social graphs to understand consumers. We help agencies, media companies, publishers and brands come up with and evaluate their creative marketing strategies.
Our stack is Scala (Play) and Javascript (Ember.js) with a healthy amount of Java, Python and PHP. We're GraphQL-friendly and generally pretty buzzword compliant.
We're building a new Integrations team and have several positions open -- head over to www.affinio.com/company to apply.
Affinio builds data products that revolve around social graphs and the clustering thereof. We're looking for QA testers with experience with JS single-page apps and web-facing API testing. http://www.affinio.com/company/quality-assurance/ to learn more!
Affinio analyzes complex network graphs to come up with concepts like influence, trends, and density for groups on social networks. If you love working with clustering algorithms and graph data sets, we'd love to talk to you.
Find out more: http://affinio.com/engineering