#IRCTC-
@dtbhat https://t.co/C3bABWssNW

1. Now that Stan Lee is being eulogized far and wide, it's important to remember how marginal Stan Lee and his collaborators (Jack Kirby and Steve Ditko) were in the 1950s and 1960s.

2. Marvel Comics (before that Atlas) was just a cog in the machine of a bottom pulp publisher run by Martin Goodman, the husband of one of Lee's cousins. It was the lowest of the low in the publishing world.

3. Now Mario Puzo (not yet the author of the Godfather) shared offices with Stan Lee in the 1950s and 1960s. Puzo wrote for garrish men's adventure magazines and, like Lee, dreamed of writing a novel & breaking out. But Puzo looked down on Lee.

4. Flo Steinberg, 1960s secretary at Marvel: "They were always making jokes about us. They'd come in and giggle. mario Puzo would look in and would see us all working on his way to the office and say, 'Work faster, little elves. Christmas is coming.'"

5. When JFK was killed, the whole office of Magazine Management was stunned and quiet. Except Lee. He continued working. "He was still working on the comic books," Puzo said. "Like that was the most important thing in the world."

The 2018 State of JavaScript survey is out. They got 20,000 responses and have some delicious, delicious data. I'mma thread in some highlights:

First up: JavaScript flavors. There's TypeScript at 47% adoption, a tad higher than npm's own survey results (which said 46%). npm's survey is coming up again soon and will ask about TypeScript in a lot more detail.

I really, *really* like SoJ's "would not use again" question, which lets people who've abandoned a tech self-identify. This is noticeable in the graph above with Flow users -- 41% of people who've used Flow say they wouldn't use it again.

Now JavaScript frameworks. It's been a full year since npm's survey, so these numbers differ from ours in the directions I expected:
React 65% (vs. 60%)
Vue 29% (vs. 24%)
Ember 5% (vs 4%, I was expecting a bigger rise)

But there's a shocker in here: Angular.

npm's survey had Angular at 40% last year and SoJ has it at either:
- 58% (if you include those who don't want to use it again)
- 24% (if you count only those who like it)
Since npm's question didn't ask if they intend to *continue* using it I think that might explain this.

Boss is always Boss @Mitesh_Engr

Mitesh Sir's FUTURES BREAKOUT TRADES 101:

• How to pick stocks at right time?
• What to look for?
• Importance of BO post consolidation
• How to manage SL
• How to get huge profits
• Multiple Examples

In collaboration with @niki_poojary

Importance of Trading only BO or BD stocks by @Mitesh_Engr Sir: 👇

•Two questions to always ask:

~Why stock is going up/down and when?
~ Who is behind big up/down and when?

• Trade only stocks with Momentum
• Instant results
• Small SL or huge profits

Sir's take on how to Trade: 👇

• Observe fight between buyer & seller
• Position will be created within first 3 hours
• Operators will win
• Just find BO or BD
• Look for huge volume support
• Give company to operators
• ATP importance explained

How to take trade in Breakout/Breakdown stocks by @Mitesh_Engr Sir: 👇

• Check Support & Resistance
• Wait for 2-3 months consolidation
• Surge in volumes
• Trade on 3 min charts
• Small SL
• Keep trailing
• Always pyramid in profit

When and Why to Pyramid? By @Mitesh_Engr Sir

• Adding to winning positions
• Signals of continued strength
• Potentially large profits
• An Example of pyramiding in one of his trades

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.