Question only for those who followed the thread in quoted tweet.

Did you manage to learn the names of 27 Nakshatras from this thread? Are you revising the names even now?

1/ So here is a thread on how I turned $32,000 into $1.2m and back to pretty much zero (once taxes are paid).

Just note, I am not bitter or salty in any way at all, the last 2 years have been an amazing ride - travelled the world, been wealthy, been poor.

2/ Dec '16, my advertising agency folded, I had a little bit of money left and I put $32k into Bitcoin and Ether. As it started to go up I diversified into everything, Monero, Dash, this that, any crap - even Ripplecoin. Everything just kept going up.

3/ By March I think I had around $300k and $500k by the summer. I used to take 25% out but towards the end of the summer I got greedy and put it all back in and by December it was $1.2m.

4/ Thinking I was an absolute genius I decided to start a bunch of businesses. As silly as it sounds I had this goal of making $5m as I wanted to buy Bedford Town Football Club and get them in the league, and as Crypto was going up forever I needed 6 months.

5/ So:
- Trading (income 1)
- Podcast (income 2)
- Mining (income 3)
- Mining pool (income 4)
- Consulting (income 5)

Yes - all of the above as a one-man army :)

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.