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I’m Gal, co-founder at Checksum (https://checksum.ai). Checksum is a tool for automatically generating and maintaining end-to-end tests using AI.
I cut my teeth in applied ML in 2016 at a maritime tech company called TSG, based in Israel. When I was there, I worked on a cool product that used machine learning to detect suspicious vehicles. Radar data is pretty tough for humans to parse, but a great fit for AI – and it worked very well for detecting smugglers, terrorist activity, and that sort of thing.
In 2021, after a few years working in big tech (Lyft, Google), I joined a YC company, seer W21, as CTO. This is where I experienced the unique pain of trying to keep end-to-end tests in a good state. The app was quite featureful, and it was a struggle to get and maintain good test coverage.
Like the suspicious maritime vehicle problem I had previously encountered, building and maintaining E2E tests had all the markings of a problem where machines could outperform humans. Also, in the early user interviews, it became clear that this problem wasn’t one that just went away as organizations grew past the startup phase, but one that got even more tangled up and unpleasant.
We’ve been building the product for a little over a year now, and it’s been interesting to learn that some problems were surprisingly easy, and others unusually tough. To get the data we need to train our models, we use the same underlying technology that tools like Fullstory and Hotjar use, and it works quite well. Also, we’re able to get good tests from relatively few user sessions (in most cases, fewer than 200 sessions).
Right now, the models are really good at improving test coverage for featureful web-apps that don’t have much coverage (ie; generating and maintaining a bunch of new tests), but making existing tests better has been a tougher nut to crack. We don’t have as much of a place in organizations where test coverage is great and test quality is medium-to-poor, but we’re keen to develop in that direction.
We’re still early, and spend basically all of our time working with a small handful of design partners (mostly medium-sized startups struggling with test coverage), but it felt like time to share with the HN community.
Thanks so much, happy to answer any questions, and excited to hear your thoughts!
I run a US company who controls and processes data of EU citizens.
My legal counselor advised me that my engineering team in South America (Chille, Brazil) cannot access my production database, as GDPR does not allow to export data to these countries (and if they access production database, the data will be streamed to their local machine).
I'm not looking for legal advice - just curious if anyone else has heard the same thing? If so, what do they do about it?
FP is about learning by thinking. You analyze deeply a situation from it's core foundations to arrive to a comprehensive plan.
Lean Startup is about learning by doing. Quick hypotheses. Iterations from the current state. Full of assumptions and let reality teach you what's true.
So in a way they are contradicting philosophies.
Agree?
They call out several companies that uses dark patterns, but fail to mention how hard it is to unsubscribe to the New York Times (only by talking to a person).
That's unfair. It's ok for the NYT to write an article about it. I'm sure the reporter is not responsible for NYT's unsubscribe flow. But if you call-out other companies, have the decency to mention your own company.
https://www.nytimes.com/2021/04/30/opinion/dark-pattern-inte...
Mainly asking about a self-funded business
Do you have any methodical process to make sure your decisions are as accurate as possible? is it helpful? Do you use any tools in the process?
Context: I'm thinking about building a lightweight tool that will help startups introduce some research-driven construct to their process.
The goal of this tool is to help with 20% of the process that account for 80% of the gain in terms of making sound, unbiased hiring decisions. What do you think about that?