Will Chen

Like most developers, I always get excited about new, up and coming open source technologies. In the spirit of Thoughtworks Radar, albeit much more casually, I'd like to list some technologies that I'm think are quite interesting and worth monitoring.

Reputable technologies - Significant use in production and established communities

• Docker
• Kubernetes
• React
• Vagrant
• Node.js + Express
• Typescript

Rising star - Worth experimenting / prototyping with these technologies

• Meteor
• Relay + GraphQL
• Falcor.js
• Flow Type (by Facebook)
• Deis - Heroku-like open-source PaaS
• Angular 2
• React Native
• AWS Lambda
• Babel.js - transcompiles future JS into ES5
• ES7 - async, await
• Redux - new, but popular flux-like architecture and tiny footprint

Moonshots - These are new, unproven technologies but have a potentially large benefit.

• Black screen - terminal emulator built in Electron
• Otto - created by the makers of Vagrant, provides a simpler workflow through more "magic"
• Koa.js
• Elm - functional programming made easier (Haskell that transcompiles into Javascript)

I've heard a bit about Deis for a while but I never quite understood how it compared to other open source technologies like Kubernetes. This Stack Overflow answer was quite helpful: http://stackoverflow.com/questions/27242980/whats-the-difference-between-kubernetes-flynn-deis

Especially insightful was the comment by the founder of Deis, with a link to this technology stack diagram:

https://pbs.twimg.com/media/B33GFtNCUAE-vEX.png:large

• People overestimate the impact of key individuals on an outcome (e.g. entrepreneurs, CEOs, politicians) by failing to account for other factors such as organizational culture, industry trends, economic climate, and so on.
• External factors (to an individual) can be considered luck - and they are very important.
• Linear regression when used properly is a simple but powerful tool for isolating the effect of something - however it only measures correlation not causality.
• Causation is much harder to prove than correlation. Experiments (e.g. a/b testing) are ideal but not always practical, particularly for macro decisions (e.g. new product launch, M&A)
• Complex outcomes (e.g. 5-year company growth, civil war in the Middle East) oftentimes have multiple causes and defy simple explanations.
• Plans are too optimistic because people do not account for potential unknown unknowns.
• Improve estimates by using reference points from similar cases. Modify your estimate based on these references by acknowledging differences from your current case with previous cases.
• Consider reverse causality. Does good company culture lead to good financial performance? Or does good financial performance help foster a good company culture?
• Consider confounding factors. Is there a third variable that affects both the independent and dependent variables you are looking at?
• Look for probabilistic distributions. Things are rarely surefire and once in a while failure is inevitable.
• Is there a worst case scenario that you haven't thought of yet? How undesirable is it? Is it so bad (e.g. the Great Recession) that once is one too many times?
• Does the organization as a whole not take enough risks due to incentives for individuals to avoid taking risks?
• Check for survivor bias. Are there instances that should be considered but are not because they are not well-known / out of business / etc?
• Is the halo effect causing you to subconsciously use the company's good financial performance to attribute positive beliefs on other aspects of the company (e.g. strategy, culture, people, etc)?
• Am I considering a point in time (e.g. cross-sectional) vs over time (e.g. longitudinal)? Studies over long periods of time are ideal. Asking individuals to recall events after the fact is likely to be affected with hindsight bias (i.e. risky decisions are seen as foolish / prescient given how things unfolded).
• People overestimate their ability to assess other's skills. Quantitatively assessing interview scores with post-hiring job reviews allows you to improve the hiring process in a systematic way.
• When analyzing the root cause of a failure, consider whether it was the failing of an individual (e.g. that person was irresponsible and should be fired) or the failing of a system (e.g. that mistake could have happened to any of us).
• Self-evaluate what lens / filters affects your perspective.
• Make decisions based on expected value when possible - it will slowly add up
• Use multiple disciplines when possible - think quantitatively and qualitatively.

Thoughts from reading the books Thinking Fast and Slow by Daniel Kahneman and The Halo Effect by Phil Rosenzweig.

• Humans are prone to cognitive biases which can lead to suboptimal decision making. Many of these biases are driven by our desire to make sense of what is happening around us and to create stories that logically explain major events.
• Halo effect is the tendency to attribute traits (independent variables) based on outcome (dependent variable). For example, if a company has several years of growth, its CEO will be praised for being pioneering and aggressively entering an adjacent market. In the counterfactual situation where the company struggled, the same CEO might be criticized for taking wantonly taking risks and neglecting the core business.
  
• People, particularly experts and optimists, greatly overestimate their impact or understanding. Leaders of organizations, such as the President, CEOs, etc., are seen as responsible for the overall outcome of their organizations. Studies have shown that a better CEO is only likely to outperform his or her counterparts 6 out of 10 times (i.e. a 10% improvement from flipping the coin). CFOs of major companies were asked to predict the movement of the S&P 500 markets. Not only were they significantly off, they were overtly confident about their predictions.
• People go for simplistic explanations because it's easier to comprehend and follow. Complex explanations such as "growing a business requires a careful analysis of the competitive landscape, assessment of potential disruptive innovations, and managing risks through a balanced portfolio" is harder to follow than a simple explanation such as "building a great company is about focusing on customers and empowering employees". The reason why the first is more complex is not that there's more steps - it's that it focuses not on concrete actions (e.g. promote employees within your organization to groom leaders), but on a mindset (e.g. assess various scenarios in how 3D printing can affect this industry). 
• Survivor bias can lead us to idolizing risk-taking companies because we are not as aware about that failures.Rather than taking into account the risk inherent in big movies such as acquisitions or entering a new market, people focus on the successful stories. Michael Raynor wrote a book The Strategy Paradox: Why Committing to Success Leads to Failure that covers this issue.
  
• System 1 is the lizard brain - it feeds off emotions and generates our gut reactions. System 2 is the higher order thinking brain. It can carefully consider tradeoffs, but it takes significant energy to use it, so we conserve energy by not using it whenever possible.
• The framing of a choice makes a big impact in how people respond -- even professionals (such as doctors about medical decisions). People consistently are risk-averse when given a choice of a guaranteed win (100% to win $500) over a very likely win (80% chance to win $650). People are risk-taking when given two bad choices and have a chance to entirely avoid the bad outcome (although with a smaller expected value).The prospect theory helps explain these decisions by focusing on the gains and losses vs. the final state, which is what expected utility theory focused on. Prospect theory also emphasizes the importance of the reference point, which is typically the previous states.
• The two selves are the remembering self which is how you think about your memories and what you use to make future decisions and the experiencing self which is how you feel at a given moment.

Excellent talk by Rich Hickey. Even if you never use Clojure, it's an enlightening talk about the benefits of immutable data that's applicable to everyday programming: