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1 There's a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. This was eye-opening when I started my career, and every time I talk to an NLP engineer at @textio, it continues to strike me even now.

2 Research conditions are theoretical and/or idealized. A huge problem for so-called NLP or AI startups with highly credentialed academic founders is that they bring limited knowledge of what it takes to build real products outside the lab.

3 A product is ultimately a thing that people pay for - not just cool technology or user experience. But I’m not even talking about knowledge gaps in go-to-market work. I'm talking purely technical gaps: how you go from science project to performant + delightful user experience.

4 Most commoditized NLP packages solve well-understood problems in standard ways that sacrifice either precision or performance. In a research lab, this is not usually a hard trade-off; in general, no one is using what you make, so performance is less important than precision.

5 In software, when you’re making something for real people to use, these tradeoffs are a big deal. Especially if you’re asking those people to pay for what you’ve made (can’t get away from that pesky GTM thinking). They expect quality, which includes precision AND performance.

Maker movement is doing to startups what startups did to corporations.

Here is why 👇

Startups fixed the problem of innovation, that corporations lack.

In big, slow corporations, innovation is a RISK and distraction from the core $$$ profitable business.

Agile startups could launch, iterate fast and eventually stumble upon new growing market opportunities.

However,

When a startup reaches product-market-fit, it has to 🚀 "grow at all costs" and reach market dominance before some giant corporation can replicate their new product and distribute it to their existing giant customer base.

Startup's "growth at all costs" often means growth at the expense of charging customers $$$ money.

Hence, to be sustainable, startups have to constantly chase investor money.

Startup teams spend more time finding and pleasing investors, than finding and pleasing customers.

95% of startups die because they run out of (investor) money + no business model + crazy investor expectations.

Same way corporations die, when unable to adjust to new technology and market shifts.

one of the things i like about Batman is that it's fundamentally about being orphaned, and being an orphan — how this guy stays tethered to the world in his own weird, abstract, extrajudicial way when he feels that his link to it has been violently severed

Batman does this by permanently recapitulating his own trauma: solving crimes for other people, protecting them. But that's actually just the beginning; he also recapitulates his own experience of orphanness by essentially raising several orphans

Dick is an orphan in the traditional sense: dead parents. Batman sees it happen. Jason, less so: missing dad, known mom who eventually dies. Tim, even less so: parents are around a while, and involved, before both dying. Carrie, even less than that: negligent parents.

Steph: evil father, who openly works against her. And then there's Damian, whose parents are both alive and yet both have identities that totally obliterate their role as his parents. He doesn't relate to them as a mother and father; he can't, and neither can they.

In all these different cases, Batman tries all these different ways of making orphanness for them not what it was for him: Either by trying to teach them the (arguably poor) coping skills early that he learned over a very long time, or by literally legally adopting them, etc.