> I find it frustrating that the eslint recommended presets don't document why they are recommended.
That’s a good point. While this doesn’t address the root problem, I can share some context here as a former maintainer: browsing the core rules [1], you should see that recommended rules flag cases that are highly likely to be bugs or unnecessary constructs. Recommended rules should have false positives only in exceptional cases and be objective or near-universal consensus opinions.
Plugins, of course, are free to choose their own threshold for their recommended configs.
> Why isn't it in the "recommended" preset of lints?
It will be added to recommended in v9! [2] The rule was written during one of the v8 minor versions, and adding to the recommended config is always a breaking change.
For new projects, both at work or on the side, I like to imagine I have a “new stuff budget.” That forces me to spend it wisely.
If I over-spend my new stuff budget, I take on debt, much like tech debt, that I’ll have to pay down later. I’ll make slower progress because I’m trying to learn too many things at once, and I’ll have to go back and fix suboptimal tradeoffs where I didn’t have enough experience to make the right tradeoff at the time.
Conversely, if I don’t spend the new stuff budget at all, then I won’t learn anything new and might miss out on a tool that would have made the problem easier.
For side projects where the whole point is learning, sometimes I’ll spend my new stuff budget by e.g. learning a new frontend framework first, writing the backend in something I know. Then once I’m familiar with everything and have built back up the new stuff budget, I’ll go back and reimplement the backend in the new language I wanted to use, leaving the rest of the stack unchanged.
Drive Capital | Software Engineer, Data Scientist | Columbus, Ohio | ONSITE | Full-time
Come build the platform that powers Drive Capital as we invest $2.2B in startups between the coasts in the US and Canada.
We've partnered with more than 70 startups so far.
Some you might've heard of, like https://www.duolingo.com/ and https://greenlight.com/, and some you probably haven't, like https://www.koho.ca/ and https://www.path-robotics.com/.
Who's next?
That's partially up to you.
Our platform, affectionately called Herbie, surfaces promising startups to investors, makes connections to the next great customer, and helps recruit the most impactful team members.
We replaced the traditional army of analysts by learning the business as engineers to build software instead.
Drive Capital | Software Engineer, Data Scientist | Columbus, Ohio | ONSITE | Full-time
Come build the platform that powers Drive Capital as we invest $2.2B in startups between the coasts in the US and Canada.
We've partnered with more than 70 startups so far.
Some you might've heard of, like https://www.duolingo.com/ and https://greenlight.com/, and some you probably haven't, like https://www.koho.ca/ and https://www.path-robotics.com/.
Who's next?
That's partially up to you.
Our platform, affectionately called Herbie, surfaces promising startups to investors, makes connections to the next great customer, and helps recruit the most impactful team members.
We replaced the traditional army of analysts by learning the business as engineers to build software instead.
My employer, a venture investor, is looking for data scientists who understand finance. Our product uses Python, Django, and React like you mentioned. I don’t know much about being a Quant, so maybe we could help each other calibrate. My email is in my profile. Yours isn’t, so I’m posting this publicly, and anyone similarly curious should feel free to email me.
Drive Capital | Software Engineer, Data Scientist | Columbus, Ohio | ONSITE | Full-time
Come build the platform that powers Drive Capital as we invest $2.2B in startups between the coasts in the US and Canada.
We've partnered with more than 70 startups so far.
Some you might've heard of, like https://www.duolingo.com/ and https://greenlight.com/, and some you probably haven't, like https://www.koho.ca/ and https://www.path-robotics.com/.
Who's next?
That's partially up to you.
Our platform, affectionately called Herbie, surfaces promising startups to investors, makes connections to the next great customer, and helps recruit the most impactful team members.
We replaced the traditional army of analysts by learning the business as engineers to build software instead.
Our platform, affectionately called Herbie, surfaces promising startups to investors, makes connections to the next great customer, and helps recruit the most impactful team members.
We replaced the traditional army of analysts by learning the business as engineers to build software instead.
We typically invest at earlier stages, so I can't say firsthand what diligence looks like at series F. If you mean Fast, Gergely Orosz had a good writeup at https://newsletter.pragmaticengineer.com/p/the-scoop-fast. Sounds like that was more attributable to hiring ahead of revenue than infrastructure spend.
We've built a platform that helps us run the firm and includes tools for our investors and portfolio companies. At a high level, there are proactive features we want to improve or build and plenty of analysis to help inform our decisions. There's a bit more detail at https://drivecapital.notion.site/Data-Scientist-e05614596225....
Thank you for showing this! My employer is actively hiring investors, and I’ll share this with the person heading that search.
Relevant to the HN crowd, we’re also hiring data scientists and software engineers. It’s rare for a VC firm to have its own engineering team building a product, which is part of what makes it so fun. Do you think these technical jobs would be relevant to your board’s audience?
I lead engineering at a VC firm, and while we do sometimes help in diligence, it’s a tiny portion of our time, <1%.
Your second paragraph is spot on, and I hinted at a similar idea in a sibling to your comment. Especially at series A and later, customers are an existence proof for the product not being vaporware.
So when we are involved in diligence, it’s usually near the end, and we’re getting to know the engineering leaders to see if we can help them speedrun a few iterations by sharing best practices or introducing them to other leaders in the portfolio who have been there and done that recently.
As an engineer, I used to think the same. From vicarious experience, I’ve learned that software execution is far down the list of reasons startups don’t work. The wrong market, the wrong approach to the market, or the wrong team approaching the market are all far more likely and harder to change. By comparison, a tech stack that won’t scale is much easier to fix on the fly.
Source: I lead engineering at a VC firm. We’re hiring data scientists and software engineers! https://drivecapital.com/join for more, email in profile.
Our platform, affectionately called Herbie, surfaces promising startups to investors, makes connections to the next great customer, and helps recruit the most impactful team members.
We replaced the traditional army of analysts by learning the business as engineers to build software instead.
This book made me understand pointers. As I read it, I followed along building all the circuits in Logisim [1] from half-adders to latches to multiplexers all the way up to a full CPU.
Many will probably recognize the author, Charles Petzold [2], from his Windows programming books.
Matt Levine at Bloomberg had an excellent explanation of the dynamics at play in his newsletter recently [1]. The bankers are incentivized to inflate the valuation in order to win the IPO mandate and all the fees that come with it. They then run into the awkward step of walking back that number to something that the market might accept.