Self portrait 01 big
Daniel Puglisi @daniel

Hi, I'm Daniel. I make Shimatta, Desk Hunt and cofounded Codegestalt. This is where I dump my brain.

Another pixel art piece I've made inspired by the style of Dave Grey:

Me typing along on a Macbook

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I started playing around with pixel art today and created my first piece:

That's me

Here are some resources I stumbled upon:

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Well, I'm looking into adding a recurring payment integration with Stripe for the third time. Already tried it twice on two other projects with no/part success. I'm always stumbling upon something new while developing and I haven't found a guide which shows a nice and clean complete overview of what's required for building a subscription service. It's not that I couldn't build a working prototype with the information available. More that I want to have a clear vision of how the system has to look instead of building the walls and realizing that some parts of the foundations are missing and wasting time refactoring everything five times. I did some research today and combined with my experiences from the last two trials I think I got a pretty good overview together (for now). I've thrown everything into a list and split it into different iteration steps which I'll tackle one after another. If everything works out as imagined I'll write up a complete guide on how to integrate Stripe subscriptions into a Rails application. So others can benefit from my struggle. So long.

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Oh, btw: Shimatta know supports public and private posts.

Where ma high fives at?

Hint: You can save posts as "drafts" by making them private, updating them and publishing them later on.

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The current plan is to add a simple "keyword" search to Shimatta which returns posts that contain the search query in their content. On a later stage (which means when we have more content from different users) it might be interesting to make search more "content aware". One possible solution could be by using a bayesian classifier.

It was introduced under a different name into the text retrieval community in the early 1960s and remains a popular (baseline) method for text categorization, the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, etc.) with word frequencies as the features.

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  • Artificial Narrow Intelligence (ANI): Weak AI, only specializes in one area
  • Artificial General Intelligence (AGI): Human Level AI, smart as a human across the board
  • Artificial Superintelligence (ASI): “an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills.”
  • ANI is everywhere in todays world (cars, phones, email spam filters, recommendations (ads, friends, ...), google translate / search).
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Chapter 6 (The geography of future markets) notes:

So far everyone tried it. But no one succeeded in copying Silicon Valley. Mostly because they have such a great density of domain based deep knowledge that it is almost impossible to keep up with them. Other countries should focus on their own domain knowledge and build it around an equal ecosystem. Instead of having the next Silicon Valley. We should be focusing on having 50 different Silicon Valleys all with another domain expertise.

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Chapter 5 (Data: The raw material of the information age) notes:

  • land was the raw material of the agriculture age. Iron wad the raw material of the industrial age. Data is the raw material of the information age.
  • In 2000. Only 25% of data was stored jn digital form. In 2007 that percentage skyrocketed to 94%.
  • 90% of the worlds digital data has been generated over the last two years.
  • every year the amount of data grows by 50%
  • Big data is the phrase used to describe how large amounts of data can be understand, analyzed and forecast trends jn real time. Also called data analytics, analytics or deep analytics.
  • "If I have this much budget to do something, I'm going to dedicate a certain portion of that budget toward figuring out wether the other portion of that budget is doing a damn thing."
  • One example of what big data will do is for example make the jobs of translaters obsolete. Big data could produce real time solutions for translating languages and mimicking the speakers voice. This opens the gateway for non english speakers to play in the global economy. A downside can be fraud and you can't trust what other people say if you don't look them in the eyes.
  • Other examples can be found in farming and fintech.
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Chapter 4 (The weaponization of code) notes:

  • Cybersecurity is going to be an industry of the future.
  • If one new person comes online, one new customer who needs cybersecurity enters the market.
  • The growth will be steep, the need will be sustained, and this ever growing need currently comes up against a major talent shortage. The qualifies job candidates are too few.
  • Cybersecurity is everyones problem. Not just governments or big companies. Also small companies and individuals are part of it.
  • Liberty without security is fragile, and security without liberty is oppressive. The years ahead will force us to balance these two as we have had not before.
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Chapter 3 (The code-ification of money, markets and trust) notes:

  • Code-ification of money, markets, payments, trust is the next big inflection point in the history of financial services.
  • By 2017 over 1 billion people will use mobile banking
  • Alibaba payments performs 2.85 million transactions per minute (more than PayPal or Square). I'm not sure but I think it is not banned in places like West Bank, Pakistan, Lebanon and Afghanistan (PayPal is), making it more global.
  • Congo is one of the world's poorest states. 75% live on less than 1 dollar. A third is illiterate. Life expectancy is 46 years.
  • Mobile phones in Congo are not just used to place calls. They help people reunite after displacement and to store money.
  • People/Entrepreneurs start getting interested in Africa from a purely business perspective (instead of doing charitable work or peacekeeping)
  • Kenya's M-Pesa stands for mobile money. It lets you create and load up an account at most gas stations, markets and stores and lets you transfer money via text messages. The money is stored on a commercial bank in Nairobi.
  • Estimated Sharing economy growth is 20 times the current value of $ 26billion by 2025.
  • Sharing in the sharing economy isn't free. Airbnb doesn't rent out accommodations for zero dollars. They charge for it. Similar is Uber. Uber doesn't share it's cars.
  • Uber might be expand it's service by letting you order a driver that picks up and delivers your package.
  • Bitcoin is based on digital trust. All transactions get logged in a blockchain which is distributed across all bitcoin users. The blockchain contains the whole history of every transaction ever made. Which makes it pretty hard to hack.
  • The blockchain will be to banking, law and accountancy as the internet was to media, commerce and advertising.
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