I asked, "What’s the best free tool for startups?"

I received 200+ replies.

Here are 10 free tools every startup must know:

Again, at such a key moment in UK and at such high political levels, evidence of deep misunderstanding of EU: The PM is under pressure in some quarters to delay #Brexit vote to allow her first to “improve” deal in Brussels BUT EU is determined not to budge at this stage

2) All EU leaders watching tomorrow’s vote. Of course they want to avoid a no deal #Brexit BUT they feel the deal - so painfully negotiated between EU and UK - is too fresh to re-open. Clarifications, yes but fundamental renegotiations, no.

3) The EU’s international reputation as a tough deal-maker is at stake here too. If Brussels now suddenly “gives in” to U.K. on EU red lines (eg backstop) then that would weaken the EU’s reputation in future trade negotiations with other non EU countries

4) Also on backstop the EU is unlikely to cave in to favour a relationship with a leaving member state (UK) against the explicit wishes and ignoring the deep concerns of a continuing member: Ireland.

5) Both the EU and the PM signed up to a guarantee to protect the border between Northern Ireland and the Republic of Ireland. If either the U.K. or EU can leave the backstop unilaterally then this clearly is no longer a guarantee.

You want to buy a house.

You take a loan from the bank.

The bank asks you, “what if you don’t pay back?”

You say, “take my home and sell it”.

(Thread)

That makes sense. The bank gives you a loan and in case you don’t pay, the bank will take over your house and sell it and recover its money.

That’s collateral.

Banks also lend money to big corporations. With them, banks don’t always have collateral.

So a loan where the bank has collateral is considered safer for the bank.

In the early 2000s, the banks in the USA were giving out loans to people for buying houses.

The economy had just recovered from the tech bubble in 2000 where many banks had given out loans to businesses that had shut down.

In that backdrop, the banks found giving home loans safer.

If u are looking to enter a scrip just before the breakout
what would u look for?
How would u identify scrips just before breakout?

Here are a few pointers
Carefully understand the them and use at ur own discretion
(All comments r compiled from a question i posed)

1/23

Price is testing the same resistance repeatedly since long.
But now in recent times it's forming multiple candles near the same resistance with low volume.
Observe it and see for opposite move.
As soon as u get a positive candle, get in to it.

2/23

1. Volume buildup (vol > when resistance point created) along with, increase in OI, increase in delivery % on HTF
2. On LTF, price stalling around breakout area for at least 1-2hr.
3. No major events.

3/23

Stock reaching breakout LVL with HL-HH
Contraction of price and volume near it especially near 20 days ma.
If there is any breach of the latest HL it should be followed by a bullish candle with good volume.
Left side of the chart has a rally with a very steep slope of ma.

4/23

Number of wide range green bars with high volumes.
If stock is at its ATH then it adds more conviction bcz there wont b supply after that point

Consolidation below the resistance, volume of the candles & strong bullish candle closing at near highs just below the resistance

5/23

This post is pretty bizarre, but it manages to hit on so many false beliefs that I've seen hurt junior data scientists that it deserves some explicit

(1) The notion that R is well-suited to "building web applications" seems totally out of left field. I don't feel like most R loyalists think this is a good idea, but it's worth calling out that no normal company will be glad you wrote your entire web app in R.

(2) It is true that Python had some issues historically with the 2-to-3 transition, but it's not such a big deal these days. On the flip side, I have found interesting R code that doesn't run in modern R interpreters because of changes in core operations (e.g. assignment syntax).

(3) "Most of the time we only need a latest, working interpreter with the latest packages to run the code" -- this is where things get real and reveal some things that hurt data scientists. If this sentence is true, it's likely because you don't share code with coworkers.

(3) Really is a broader issue in data science: people only think of what they need to do their work if no one else existed and code was never maintained. Junior data scientists almost always operate on projects they start from scratch and don't have to maintain for long.