New iPad Pro has Face ID, thinner bezels, squared off edges. A new touch sensitive Pencil that magnetically attaches to the edges. Thin enough to have a camera bump. Uses the XR's LCD cornering techniques.
Apple's silicon team is just dropping bombs every event.
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"I really want to break into Product Management"
make products.
"If only someone would tell me how I can get a startup to notice me."
Make Products.
"I guess it's impossible and I'll never break into the industry."
MAKE PRODUCTS.
Courtesy of @edbrisson's wonderful thread on breaking into comics – https://t.co/TgNblNSCBj – here is why the same applies to Product Management, too.
There is no better way of learning the craft of product, or proving your potential to employers, than just doing it.
You do not need anybody's permission. We don't have diplomas, nor doctorates. We can barely agree on a single standard of what a Product Manager is supposed to do.
But – there is at least one blindingly obvious industry consensus – a Product Manager makes Products.
And they don't need to be kept at the exact right temperature, given endless resource, or carefully protected in order to do this.
They find their own way.
make products.
"If only someone would tell me how I can get a startup to notice me."
Make Products.
"I guess it's impossible and I'll never break into the industry."
MAKE PRODUCTS.
Courtesy of @edbrisson's wonderful thread on breaking into comics – https://t.co/TgNblNSCBj – here is why the same applies to Product Management, too.
"I really want to break into comics"
— Ed Brisson (@edbrisson) December 4, 2018
make comics.
"If only someone would tell me how I can get an editor to notice me."
Make Comics.
"I guess it's impossible and I'll never break into the industry."
MAKE COMICS.
There is no better way of learning the craft of product, or proving your potential to employers, than just doing it.
You do not need anybody's permission. We don't have diplomas, nor doctorates. We can barely agree on a single standard of what a Product Manager is supposed to do.
But – there is at least one blindingly obvious industry consensus – a Product Manager makes Products.
And they don't need to be kept at the exact right temperature, given endless resource, or carefully protected in order to do this.
They find their own way.
Recently, the @CNIL issued a decision regarding the GDPR compliance of an unknown French adtech company named "Vectaury". It may seem like small fry, but the decision has potential wide-ranging impacts for Google, the IAB framework, and today's adtech. It's thread time! 👇
It's all in French, but if you're up for it you can read:
• Their blog post (lacks the most interesting details): https://t.co/PHkDcOT1hy
• Their high-level legal decision: https://t.co/hwpiEvjodt
• The full notification: https://t.co/QQB7rfynha
I've read it so you needn't!
Vectaury was collecting geolocation data in order to create profiles (eg. people who often go to this or that type of shop) so as to power ad targeting. They operate through embedded SDKs and ad bidding, making them invisible to users.
The @CNIL notes that profiling based off of geolocation presents particular risks since it reveals people's movements and habits. As risky, the processing requires consent — this will be the heart of their assessment.
Interesting point: they justify the decision in part because of how many people COULD be targeted in this way (rather than how many have — though they note that too). Because it's on a phone, and many have phones, it is considered large-scale processing no matter what.
It's all in French, but if you're up for it you can read:
• Their blog post (lacks the most interesting details): https://t.co/PHkDcOT1hy
• Their high-level legal decision: https://t.co/hwpiEvjodt
• The full notification: https://t.co/QQB7rfynha
I've read it so you needn't!
Vectaury was collecting geolocation data in order to create profiles (eg. people who often go to this or that type of shop) so as to power ad targeting. They operate through embedded SDKs and ad bidding, making them invisible to users.
The @CNIL notes that profiling based off of geolocation presents particular risks since it reveals people's movements and habits. As risky, the processing requires consent — this will be the heart of their assessment.
Interesting point: they justify the decision in part because of how many people COULD be targeted in this way (rather than how many have — though they note that too). Because it's on a phone, and many have phones, it is considered large-scale processing no matter what.
Machine translation can be a wonderful translation tool, but its uses are widely misunderstood.
Let's talk about Google Translate, its current state in the professional translation industry, and why robots are terrible at interpreting culture and context.
Straight to the point: machine translation (MT) is an incredibly helpful tool for translation! But just like any tool, there are specific times and places for it.
You wouldn't use a jackhammer to nail a painting to the wall.
Two factors are at play when determining how useful MT is: language pair and context.
Certain language pairs are better suited for MT. Typically, the more similar the grammar structure, the better the MT will be. Think Spanish <> Portuguese vs. Spanish <> Japanese.
No two MT engines are the same, though! Check out how human professionals ranked their choice of MT engine in a Phrase survey:
https://t.co/yiVPmHnjKv
When it comes to context, the first thing to look at is the type of text you want to translate. Typically, the more technical and straightforward the text, the better a machine will be at working on it.
Let's talk about Google Translate, its current state in the professional translation industry, and why robots are terrible at interpreting culture and context.
Straight to the point: machine translation (MT) is an incredibly helpful tool for translation! But just like any tool, there are specific times and places for it.
You wouldn't use a jackhammer to nail a painting to the wall.
Two factors are at play when determining how useful MT is: language pair and context.
Certain language pairs are better suited for MT. Typically, the more similar the grammar structure, the better the MT will be. Think Spanish <> Portuguese vs. Spanish <> Japanese.
No two MT engines are the same, though! Check out how human professionals ranked their choice of MT engine in a Phrase survey:
https://t.co/yiVPmHnjKv
When it comes to context, the first thing to look at is the type of text you want to translate. Typically, the more technical and straightforward the text, the better a machine will be at working on it.
What an amazing presentation! Loved how @ravidharamshi77 brilliantly started off with global macros & capital markets, and then gradually migrated to Indian equities, summing up his thesis for a bull market case!
@MadhusudanKela @VQIndia @sameervq
My key learnings: ⬇️⬇️⬇️
First, the BEAR case:
1. Bitcoin has surpassed all the bubbles of the last 45 years in extent that includes Gold, Nikkei, dotcom bubble.
2. Cyclically adjusted PE ratio for S&P 500 almost at 1929 (The Great Depression) peaks, at highest levels except the dotcom crisis in 2000.
3. World market cap to GDP ratio presently at 124% vs last 5 years average of 92% & last 10 years average of 85%.
US market cap to GDP nearing 200%.
4. Bitcoin (as an asset class) has moved to the 3rd place in terms of price gains in preceding 3 years before peak (900%); 1st was Tulip bubble in 17th century (rising 2200%).
@MadhusudanKela @VQIndia @sameervq
My key learnings: ⬇️⬇️⬇️
Bubble or Bull Market? Join us for a short presentation and candid one on one on 27th Jan, 4pm with Shri \u2066@MadhusudanKela\u2069. \u2066@VQIndia\u2069 \u2066@sameervq\u2069 #bubbleorbullmarket pic.twitter.com/LBvlBrz6mS
— Ravi Dharamshi (@ravidharamshi77) January 24, 2021
First, the BEAR case:
1. Bitcoin has surpassed all the bubbles of the last 45 years in extent that includes Gold, Nikkei, dotcom bubble.
2. Cyclically adjusted PE ratio for S&P 500 almost at 1929 (The Great Depression) peaks, at highest levels except the dotcom crisis in 2000.
3. World market cap to GDP ratio presently at 124% vs last 5 years average of 92% & last 10 years average of 85%.
US market cap to GDP nearing 200%.
4. Bitcoin (as an asset class) has moved to the 3rd place in terms of price gains in preceding 3 years before peak (900%); 1st was Tulip bubble in 17th century (rising 2200%).