I saw the best minds of my generation contented by cash,
thriving for corporate initiative,
dragging themselves through the newsfeed all day looking
for that next notification fix...
Daniel Jeffries recent story, Motivating the Greatest Geniuses in AI to Change the World Instead of Destroy It, reminded me of Howl, but what I now can’t stop thinking about is the career decision we’re all faced with: compensation vs. purpose. Rarely is one fortunate enough to maximize both compensation and purpose in one role. To maximize for profit can often lead to work that disagrees with personal ethos. To maximize for purpose can often lead to a life that sleeps in an undesirable bed. It takes bravery to sacrifice compensation for purpose, even if done in small doses. It takes realism to sacrifice purpose for compensation, even if it may feel heartless at the time. Artificial Intelligence has powerful implications. Will that power be for good or evil? Great minds should be working to make the power of AI lead to less societal dangers and more societal benefits. I do believe it’s one of the most impactful fields to work in right now (and AI companies do pay pretty well…). Anyways, that’s my two cents. For a more thought out argument grounded by the specialization of labor, Ken Burns’ documentaries, and the evolution of the airline safety, read Motivating the Greatest Geniuses in AI to Change the World Instead of Destroy It.
Onto the rest of this week’s top 13 tech stories:
Zuck’s press release: This was a breach of trust between Kogan, Cambridge Analytica and Facebook. But it was also a breach of trust between Facebook and the people who share their data with us and expect us to protect it. We need to fix that. and his CNN interview [VIDEO]
Cambridge Analytica: What The Media Won’t Tell You by Trent Lapinski. It isn’t the machines we need to fear, it is the people who are in control of those machines and our data that we need to worry about. Machines are just tools, but people are fickle and with the right incentive easily corrupted.
50 Big Companies that Started with Little or No Money by Founder Collective. There’s a widespread belief among founders that venture capital is a precursor to success. It is true that VC is a common denominator of the most successful tech startups, but it isn’t a prerequisite, especially at the early stages.
How to build a SaaS with $0 by ipdata. My plan at the time was to throw as much up against the wall as I could and narrow my focus to what sticks. To brace myself for the expected long haul, I planned to spend as little time and money as possible on each idea. Only spending money on an idea once I had at least one paying customer. In doing this I’ve found numerous products whose free tiers alone made it viable to actually build entire products on them without spending a dollar.
10x Performance Increases: Optimizing a Static Site by JonLuca De Caro. The first step of the process was to profile the site. I wanted to figure out what was taking the longest, and how to best parallelize everything. I ran various tools to profile my site and test it from various locations around the world…
Python 3.7’s new builtin breakpoint — a quick tour by Anthony Shaw. Python 3.7 implements PEP553, a new way of inserting breakpoints in your code… OK, first breakpoint is a function and NOT a keyword. So just putting
breakpoint on the line of code you want to break on does nothing. You need to call it as a method, ie.
breakpoint()The default implementation of breakpoint will
import pdb and call
Building A Good Cryptocurrency Model Is Harder Than You Think by Daniel Chen. As cryptocurrencies have exploded in value, so too have the attempts tounderstand them. Even more exciting is a recent uptick in quantitativeanalysis. Spreadsheet models have risen in popularity as a tool for evaluating and predicting trends. The effort and analytic rigor behind these models is phenomenal. However, there are strong reasons why most top funds do not use this as part of their evaluation methodology. Often, there is no objective measure — what am I trying to predict, price in 1 week? 1 year? 10 years? And the feature set is often not very predictive (are we really so sure that the number of Telegram users is a strong indicator of future price?). In this space, it’s all too easy to fall into the trap of cargo cult analysis: building complex models that are not all that predictive once the assumptions and objectives are stated and the model is tested.
Cryptocurrency has 3 properties that make it so hard to categorize.
1. Commodity: People trade it like gold and silver.
2. Property: Sometimes people use cryptocurrency to transfer money to abroad. In this sense, cryptocurrency is treated as a currency or also known as property.
3. Asset/Security: Exchanges and ICO (equivalent of IPO in crypto world) are both categorized under “investment product”.
Cryptocurrencies’ numerous properties have caused lawmakers to scratch their heads about how to categorize and regulate crypto.
SXSW 2018: 10 Crypto & Blockchain Takeaways by Lolita M Taub. The 2018 Edelman Trust Barometer Global Report was cited over and over again. The phrase “trust is on the decline” was shared across the board. Trustlessness was identified as the problem and blockchain as a solution.
As a SXSW media partner, I heard the word “blockchain” a lot.
Until next time, don’t take the realities of the world for granted.