Double opt in intros are obsolete and too slow for the pace of modern business.
Just make the intro.
After 20 years leading engineering, product, and as CEO, and nearly 15 years working in the bay area with the best engineering leaders in the world, I’ve come to appreciate how quickly management culture has evolved in the past decade, and how little has been written about the operating details of practical modern engineering management. This post tries to shed some light on basic roles, responsibilities, and practices on a modern engineering team with distributed responsibilities, and is hopefully especially helpful to all those right now learning how to work in a more distributed and remote setting during the COVID19 crisis. The content has benefited from review and input from more than ten of the best folks I’ve encountered over the years who currently own large chunks or all of engineering at places like Google, Facebook, Linkedin, Twitter, and fast growing startups, all thanked at the end of the post.
Mortgage banking is a huge market, but it’s been a notoriously bad market for startups. Late-funnel-high-intent keywords for real estate are among the most lucrative and efficient in online ads. So the only real way to build a great business is to find a more cost effective way to scale acquisition. One approach is to go earlier in the funnel, where there may be more inefficiency in indirect intent, and try to build products that solve problems earlier in the home buying process.
Startup CEOs and Product leaders often fancy themselves as Steve Jobs types, launching the iPhone on stage to a wave of shocked and adoring applause. When startups conflate the destination (the marketing launch) with the journey (the product build), we call it the Big Bang Launch.
PM fit, or zero to one, is an important but murky concept. I think that ‘one’ is supposed to refer to ‘repeatability of sales’ in the case where there is a clear product that meets a market need and scales well enough to create a venture-backable startup.
Low level task-based AI gets commoditized quickly and more general AI is decades off. In the meanwhile, will new AI startups succeed or will the value accrue to Google, Facebook, and Amazon?
With AI in a full-fledged mania, 2017 will be the year of reckoning. Pure hype trends will reveal themselves to have no fundamentals behind them. Paradoxically, 2017 will also be the year of breakout successes from a handful of vertically-oriented AI startups solving full-stack industry problems that require subject matter expertise, unique data, and a product that uses AI to deliver its core value proposition.
This guide aims to present you with an easy way to understand and apply empathy better. It will hopefully be useful to everyone, and is written especially for leaders.
As quantitative finance has matured and the importance of computation has exploded, it’s time to use machine learning to harvest the new low hanging fruit. Traditionally, quants might work alongside engineers and computer scientists — the quants provide the statistical expertise, and the computer scientists and engineers provide the computational expertise. Machine learning folks combine statistics and computation in one brain to build models that leverage new levels of scale and richness to generalize better to unseen data and tackle new problems.