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Sebastian Tuchband: Webserver Practices Through Nextcloud

Description:
A showcase of Nextcloud and using it to show a few modern security practices on web servers.

About Sebastian:
Sysadmin and privacy advocates who finds and implements the open-source, self-hosted softwares for control and ownership.

 

1. Ten Simple Rules for Organizing an Unconference — academia-targeted, but generally useful, advice for running unconferences.
2. Jellyfin — free software media server.
3. Flyte — a structured programming and distributed processing platform for highly concurrent, scalable, and maintainable workflows from Lyft. Intro blog post lays out the case, and this blog post describes the differences between Flyte and Apache Airtable.
4. bandwhich — terminal-based bandwidth utilization tool.

We originally shared a selection of books relevant to the ongoing transformation of the economy, the world of work, the costs of capitalism, and how business gets done, but it was impossible to include all the titles we wanted to highlight. Thus, you get another compendium, this one perhaps a bit more eclectic than our first post (we hope) but just as elucidating and thought-provoking.

Innovation + Equality

University of Toronto economist Joshua Gans and Australian member of parliament Andrew Leigh, also an economist, question the market mechanisms that link technological progress to rising wealth inequality. They propose ideas to transform today’s winner-take-all economy into a more entrepreneurial and egalitarian society in their new book, Innovation + Equality: How to Create a Future That Is More Star Trek Than Terminator. In a recent opinion piece for the Hill, the authors assert that we’re “running out of excuses for high inequality.” You’ll find an excerpt from the book, followed by an interview with the authors, in the Economist.

The Meritocracy Trap

Yale Law School professor Daniel Markovits scrutinizes the great American faith that talent and hard work will result in success and upward mobility, and finds it entirely unjustified in The Meritocracy Trap: How America’s Foundational Myth Feeds Inequality, Dismantles the Middle Class, and Devours the Elite. His sobering revelations include the role of higher education in deepening the class divide, as the concentration of wealthy kids in prestigious schools continues to grow and the middle class continues to shrink, as is detailed in this New York Times review. In this interview from the Nation, Markovits explains how “the meritocracy is making us miserable.” Listen in as Markovits expands on his premise on The Ezra Klein Show, or read this adapted excerpt in the Atlantic.

The Code of Capital

Columbia Law School professor Katharina Pistor explains the many ways our legal system perpetuates today’s extreme concentration of wealth in The Code of Capital: How the Law Creates Wealth and Inequality. The Boston Review extolls Pistor’s tome along with two other alarming books that call out structural injustices and call for a thorough revision of economic imperatives. Pistor occasionally writes for Project Syndicate; in her latest piece she declares, “If wealth is justified, so is a wealth tax.”

Coders

Technology journalist Clive Thompson, who writes for the New York Times Magazine and Wired, lays out how those who create the algorithms create the world and the ways we live in it in his book Coders: The Making of a New Tribe and the Remaking of the World. Thompson conveys the joy and artistry of programming along with the inevitable unintended consequences of innovation in this interview from The Verge. The New York Times review of Coders describes the book as “a journey—if you dare—into the minds of Silicon Valley programmers.”

Make, Think, Imagine

In Make, Think, Imagine: Engineering the Future of Civilization, John Browne, an engineering apprentice who rose through the ranks to become CEO of BP from 1995 to 2007 and served as president of the UK’s Royal Academy of Engineering, asserts that technological progress can and must be harnessed to better civilization and spread prosperity…even today, as visions of a techno-dystopia can seem overwhelming. Browne discusses his faith in engineering, technology, and innovation in this Book Talk hosted by Columbia University’s School of International and Public Affairs. This Financial Times review emphasizes Browne’s faith in imagination, planning, and design to build a better society.

Learning by Doing

While it’s not a new book, we didn’t cover Learning by Doing: The Real Connection Between Innovation, Wages, and Wealth when it was published in 2015, and it deserves a place in our Next Economy reading list. Tim O’Reilly liked it so much that he devoted a chapter to it in his own 2017 book, WTF? What’s the Future and Why It’s Up to Us. Entrepreneur and economist James Bessen, the executive director of the Technology & Policy Research Initiative at Boston University School of Law, extolls learning on the job as the path to prosperity and advocates for policy changes that provide incentives for employers to put their workers on that path. Bessen was a speaker on the series Talks at Google in 2017 and a guest on Russ Roberts’s EconTalk podcast in 2016. The Atlantic adapted an excerpt from his book: “Scarce Skills, Not Scarce Jobs.”

Hedge

In his 2018 book, Hedge: A Greater Safety Net for the Entrepreneurial Age, Nicolas Colin, cofounder and director of the European investment firm The Family and a prolific writer, argues that as we progress from the industrial age into an evermore digital world of work—one of faster-growing companies, rampant entrepreneurship, and a growing population of independent workers and gig economy participants—it’s increasingly important for society to rethink and reinforce its safety net. In this Medium post, Colin raises the prospect that China—not the US—will be the architect of “the Great Safety Net 2.0.” He makes the case that Silicon Valley should throw out its playbook and start caring about the world in this essay from Forbes. Colin made an appearance on Talks at Google this spring to discuss Hedge and share his ideas about technology, policy, and the need to upgrade our institutions.

The Blockchain and the New Architecture of Trust

Cryptocurrencies have been subject to scandal and subterfuge, but the decentralized, distributed ledger technology underpinning the rise of Bitcoin and its brethren (a.k.a. the blockchain) has evolved to be used in many industries for authentication of information. Kevin Werbach, professor of legal studies and business ethics at the Wharton School at the University of Pennsylvania, recounts this evolution and describes the potential of the blockchain as a type of legal technology that can create tremendous business and social value in his book The Blockchain and the New Architecture of Trust. Werbach weighed in on Facebook’s now back-burnered cryptocurrency, Libra, in the New York Times, positing it as “the social network’s last, best hope to regain public trust.” The New York Times covered Werbach’s book, along with another blockchain book, in this review. Here’s Werbach’s Talks at Google appearance to discuss his book.

Digital Minimalism

Georgetown University computer science professor Cal Newport had an instant hit on his hands with Digital Minimalism: Choosing a Focused Life in a Noisy World; he became known as the Marie Kondo of mobile phones for his sensible advice for staying centered in a world filled with addictive devices by making intentional choices and using tech to support your goals rather than enabling it to use you. Word of this book spread far beyond the usual Next Economy media suspects. Newport took to the pages of Outside with tips for readers to maximize their free time: “To Upgrade Your Leisure, Downgrade Your Phone.” GQ interviewed Newport about Digital Minimalism and “why we’ll look back at our smartphones like cigarettes.” New Yorker writer Jia Tolentino road-tested Newport’s advice to find out firsthand “what it takes to put your phone away.”

New from O’Reilly: The cloud and beyond

Our tireless editors don’t just sit around reading great books; they’re continually on the lookout for savvy experts who can translate their skills and knowledge into books, videos, online courses, and other learning experiences to give you the most effective tools to master what you need to know to thrive in the Next Economy. Here are a few new tools to sharpen your competitive edge.

• Cloud Native Transformation: Practical Patterns for Innovation, book by Pini Reznik, Jamie Dobson, and Michelle Gienow
• Understanding the Cloud, expert playlist curated by David Linthicum
• Fundamentals of Learning: Learn Faster and Better Using Neuroscience, interactive online training course led by Connie Missimer
• Having Difficult Conversations, interactive online training course led by Jeffrey Fredrick and Douglas Squirrel
• Spotlight on Innovation: Using Creativity to Solve Your Trickiest Business Problems, recording of a recent online training course with Rita J. King

We see the AI space poised for an acceleration in adoption, driven by more sophisticated AI models being put in production, specialized hardware that increases AI’s capacity to provide quicker results based on larger datasets, simplified tools that democratize access to the entire AI stack, small tools that enables AI on nearly any device, and cloud access to AI tools that allow access to AI resources from anywhere.

Integrating data from many sources, complex business and logic challenges, and competitive incentives to make data more useful all combine to elevate AI and automation technologies from optional to required. And AI processes have unique capabilities that can address an increasingly diverse array of automation tasks, tasks that defy what traditional procedural logic and programming can handle—for example: image recognition, summarization, labeling, complex monitoring, and response.

In fact, in our 2019 surveys, more than half of the respondents said AI (deep learning, specifically) will be part of their future projects and products—and a majority of companies are starting to adopt machine learning.

The line between data and AI is blurring

Access to the amount of data necessary for AI, proven use cases for both consumer and enterprise AI, and more-accessible tools for building applications have grown dramatically, spurring new AI projects and pilots.

To stay competitive, data scientists need to at least dabble in machine and deep learning. At the same time, current AI systems rely on data-hungry models, so AI experts will require high-quality data and a secure and efficient data pipeline. As these disciplines merge, data professionals will need a basic understanding of AI, and AI experts will need a foundation in solid data practices—and, likely, a more formal commitment to data governance.

That’s why we decided to merge the 2020 O’Reilly AI and Strata Data Conferences in San Jose, London, and New York.

New (and simpler) tools, infrastructures, and hardware are being developed

We’re in a highly empirical era for machine learning. Tools for machine learning development need to account for the growing importance of data, experimentation, model search, model deployment, and monitoring. At the same time, managing the various stages of AI development is getting easier with the growing ecosystem of open source frameworks and libraries, cloud platforms, proprietary software tools, and SaaS.

New models and methods are emerging

While deep learning continues to drive a lot of interesting research, most end-to-end solutions are hybrid systems. In 2020, we‘ll hear more about the essential role of other components and methods—including Bayesian and other model-based methods, tree search, evolution, knowledge graphs, simulation platforms, and others. We also expect to see new use cases for reinforcement learning emerge. And we just might begin to see exciting developments in machine learning methods that aren’t based on neural networks.

New developments enable new applications

Developments in computer vision and speech/voice (“eyes and ears”) technology help drive the creation of new products and services that can make personalized, custom-sized clothing, drive autonomous harvesting robots, or provide the logic for proficient chatbots. Work on robotics (“arms and legs”) and autonomous vehicles is compelling and closer to market.

There’s also a new wave of startups targeting “traditional data” with new AI and automation technologies. This includes text (new natural language processing (NLP) and natural language understanding (NLU) solutions, chatbots, etc.), time series and temporal data, transactional data, and logs.

And traditional enterprise software vendors and startups are rushing to build AI applications that target specific industries or domains. This is in line with findings in a recent McKinsey survey: enterprises are using AI in areas where they’ve already invested in basic analytics.

Handling fairness—working from the premise that all data has built-in biases

Taking a cue from the software quality assurance world, those working on AI models need to assume their data has built-in or systemic bias and other issues related to fairness—like the assumption that bugs exist in software, and that formal processes are needed to detect, correct, and address those issues.

Detecting bias and ensuring fairness doesn’t come easy and is most effective when subject to review and validation from a diverse set of perspectives. That means building in intentional diversity to the processes used to detect unfairness and bias—cognitive diversity, socioeconomic diversity, cultural diversity, physical diversity—to help improve the process and mitigate the risk of missing something critical.

Machine deception continues to be a serious challenge

Deepfakes have tells that automated detection systems can look for: unnatural blinking patterns, inconsistent lighting, facial distortion, inconsistencies between mouth movements and speech, and the lack of small but distinct individual facial movements (how Donald Trump purses his lips before answering a question, for example).

But deepfakes are getting better. As 2020 is a US election year, automated detection methods will have to be developed as fast as new forms of machine deception are launched. But automated detection may not be enough. Detection models themselves can be used to stay ahead of the detectors. Within a couple months of the release of an algorithm that spots unnatural blinking patterns for example, the next generation of deepfake generators had incorporated blinking into their systems.

Programs that can automatically watermark and identify images when taken or altered or using blockchain technology to verify content from trusted sources could be a partial fix, but as deepfakes improve, trust in digital content diminishes. Regulation may be enacted, but the path to effective regulation that doesn’t interfere with innovation is far from clear.

To fully take advantage of AI technologies, you’ll need to retrain your entire organization

As AI tools become easier to use, AI use cases proliferate and AI projects are deployed, and cross-functional teams are being pulled into AI projects. Data literacy will be required from employees outside traditional data teams—in fact, Gartner expects that 80% of organizations will start to roll out internal data literacy initiatives to upskill their workforce by 2020.

But training is an ongoing endeavor, and to succeed in implementing AI and ML, companies will need to take a more holistic approach toward retraining their entire workforces. This may be the most difficult, but most rewarding, process for many organizations to undertake. The opportunity for teams to plug into a broader community on a regular basis to see a wide cross-section of successful AI implementations and solutions is also critical.

Retraining also means rethinking diversity. Reinforcing and expanding on how important diversity is to detecting fairness and bias issues, diversity becomes even more critical for organizations looking to successfully implement truly useful AI models and related technologies. As we expect most AI projects to augment human tasks, incorporating the human element in a broad, inclusive manner becomes a key factor for widespread acceptance and success.

Thanks to Ben Lorica for his insights and help with this piece.

1. Coding Interview Problems Solved in Go — see also some in Rust, and the best coding interview take ever, by Aphyr. Because thinly veiled excuses to use dynamic programming or graph coloring are the “Hello world” of our Google-aspirational age. (via Hacker News)
2. Coding Will Divide Along Class Lines (Mike Loukides) — The programming world will increasingly be split between highly trained professionals and people who don’t have a deep background but have a lot of experience building things. The former group builds tools, frameworks, languages, and platforms; the latter group connects things and builds websites, mobile apps, and the like. This divide will mean different tools and training for each.
3. A Compiler Writing Journey — In this GitHub repository, I’m documenting my journey to write a self-compiling compiler for a subset of the C language. I’m also writing out the details so that, if you want to follow along, there will be an explanation of what I did, why, and with some references back to the theory of compilers.
4. Ray — a distributed execution framework that makes it easy to scale your applications and to leverage state-of-the-art machine learning libraries. See this introductory post for the rationale.

We need to rethink the role of the programmer

Look for the industry to become more stratified and specialized. The programming world will increasingly be split between highly trained professionals and people who don’t have a deep background but have a lot of experience building things. The former group builds tools, frameworks, languages, and platforms; the latter group connects things and builds websites, mobile apps, and the like. These two types of programmers have always existed, mixing fluidly. We just haven’t recognized the distinction, and that’s going to change. A good analogy is plumbing. If you need to install a toilet, you call a plumber: they know how to connect things together. There are jobs for people who design plumbing fixtures, but you wouldn’t want them working in your bathroom.

We need to think about how programming is taught

Like reading, some people learn how to code with little training, and others don’t. But as with reading, we shouldn’t accept a world in which some people enter primary school programming-literate, and those that don’t have to wait until high school. We’ll need teachers who are trained in teaching programming—specifically, teaching programming in the early grades. We already have programming environments that are optimized for teaching children, including Scratch, Alice, and their relatives. And don’t discount the role gaming could play. Minecraft has unwittingly taught a generation of grade-schoolers how to program in Java.

We also need to build bridges for people with great programming skills but without a deep computer science background—the plumbers—to enter the professional market. Some of those bridges exist already; they include the many boot camps and schools like General Assembly and Holberton. These are distinct from college degree-granting programs (the traditional computer science major) and serve a different purpose. They’re more like vocational education programs: They’re focused on practice, with minimal emphasis on theory. They’re about learning to program in a professional context—working with a web platform, a database, or even an AI platform—but not about developing those platforms or databases. They’re for those who say, “Why should I know how to program quicksort? If I want to sort something, I’ll call a library function.” That’s just fine, and we shouldn’t pretend that it isn’t.

In contrast, CS majors should continue to be exposed to and work with theory and algorithms—not because they’re going to write their own quicksort but because we need people who can develop and implement new algorithms, and the best way to learn is to practice on algorithms we already understand. You don’t need to be good at math to program, but you do need math to push computing forward—particularly if you’re interested in data science or artificial intelligence.

We need new, more sophisticated programming tools

In “Hidden Technical Debt in Machine Learning Systems,” the authors—a group of researchers and engineers from Google—argue that machine learning is a relatively small part of any application. Much of the rest is wiring things together: building data pipelines, connecting the application to the serving infrastructure, providing for monitoring. It’s not glamorous, but it needs to be done, and done correctly. I’d bet that much more downtime results from bad plumbing than from bad implementations of ML algorithms. Rather than relying on our current crop of languages, I wonder whether or not there are better languages for this part of the enterprise. It’s long seemed strange to me that programming languages aren’t all that different from what they were in the 1960s and 1970s: line-oriented, alphanumeric texts, most often in fixed-width type. Functional languages date back to the 1950s, and the earliest roots of object-oriented programming aren’t much later. What would it mean to imagine other kinds of languages? Work is already being done on this front. There have been a surprising number of visual languages, which let users create programs using symbols or other graphic elements rather than text—although most have been unsuccessful. But even in popular languages like Scratch, we’re dealing with a simple mapping of visual objects to a traditional programming language: a “clamp” is a “loop,” a “variable” is a “box,” and so on. Is it possible to push even further beyond traditional programming languages? What would a programming language designed for plumbing look like? And would it give us better and more fruitful ways to think about the interconnections between systems? — Mike Loukides

Upcoming events

O’Reilly conferences combine expert insights from industry leaders with hands-on guidance about today’s most important technology topics.

We hope you’ll join us at our upcoming events:

O’Reilly Software Architecture Conference in New York, February 23-26, 2020

The O’Reilly Strata Data & AI Conference in San Jose, March 15-18, 2020

1. An Excess of Operating Systems (Jean-Louis Gassée) — Fuschia exists for technical reasons, but Samsung’s, Amazon’s, Huawei’s, etc., are all for business reasons (not wanting to tithe or be tied strategically to Google).
2. Redshirt — The redshirt operating system is an experiment to build some kind of operating-system-like environment where executables are all in WASM and are loaded from an IPFS-like decentralized network. […] There exists three core syscalls (send a message, send an answer, wait for a message), and everything else is done by passing messages between processes or between a process and the “kernel.” Programs don’t know who they are sending the message to. One person’s dream is another’s nightmare.
3. Mediating Consent (Renee DiResta) — essay on manufacturing consent in the social media age. The path forward requires systems to facilitate mediating, not manufacturing, consent. We need a hybrid form of consensus that is resistant to the institutional corruption of top-down control, and welcomes pluralism, but is also hardened against bottom-up gaming of social infrastructure by malign actors.
4. Synopsis — a suite of open source software for computational cinematography—tools that help the creation of visual media. Synopsis is built to help editors, artists, indie film makers, A/V developers, and creators do what they do best—tell stories, make experiences, and build amazing tools.

1. Chesterton’s Shell Script (Pete Warden) — those who forget Perl’s Configure.sh are doomed to recreate it. “Congratulations, you’re not running Eunice!”
2. Light (Facebook AI) — a large-scale fantasy text adventure game research platform for training agents that can both talk and act, interacting either with other models or with humans. (Via introducing blog post.)
3. International Cyber Law in Practice: Interactive Toolkit — At its heart, it consists of 13 hypothetical scenarios, to which more will be added in the future. Each scenario contains a description of cyber incidents inspired by real-world examples, accompanied by detailed legal analysis. The aim of the analysis is to examine the applicability of international law to the scenarios and the issues they raise.
4. UX of Bushfire Maps (Ellen Broad) — classic government map/data problem: each state/agency has its own map, showing its own view of the world, and they don’t even use the same symbols. Which makes life miserable for people who don’t care about the org chart, they just want to learn something their government knows — like whether their house will burn today. (Via Merrin Macleod.)

1. Rhasspy — an open source, fully offline voice assistant toolkit for many languages that works well with Home Assistant, Hass.io, and Node-RED.
2. Public Domain Day 2020 — Forster’s “A Passage to India,” Gershwin’s “Rhapsody in Blue,” and the first film adaptation of Peter Pan are amongst the works entering the public domain in the US.
3. Bing’s Top Search Results Contain an Alarming Amount of Disinformation — In general, Bing returns disinformation and misinformation at a significantly higher rate than Google does. In general, Bing directs users to conspiracy-related content, even if they aren’t explicitly looking for it. Bing shows users Russian propaganda at a much higher rate than Google does. Bing places student-essay sites—sites where students post or sell past papers — in its top 50 results for certain queries. Bing dredges up gratuitous white-supremacist content in response to unrelated queries.
4. Outline — wiki and knowledge base for growing teams. Beautiful, feature rich, markdown compatible, and open source.

One of the most popular sections of the Next:Economy Newsletter is our periodic Deeper Reading feature, highlighting books of relevance to the ongoing transformation of the economy, the world of work, the costs of capitalism, and how business gets done. It’s nearly impossible to keep up with the recent spate of important, enlightening, and inspirational publications…and even if we could, we wouldn’t have the space to do justice to every book that provides a fresh perspective on how we think about work, entrepreneurship, capitalism, digital transformation, the construct of money, climate change, and other pertinent topics. To that end, we’ve put together a selection of books that comprise an engrossing Next Economy reading list for the year ahead.

The Making of a Democratic Economy

Last year, a reader of Tim O’Reilly’s book—WTF? What’s the Future and Why It’s Up to Us—pointed him to Marjorie Kelly’s 2001 book, The Divine Right of Capital, which he found revelatory. Kelly, cofounder of the Fifty by Fifty initiative to help build a more inclusive economy through employee ownership, came out with a new book this summer, cowritten with Ted Howard, cofounder and president of the Democracy Collaborative. The Making of a Democratic Economy: How to Build Prosperity for the Many, Not the Few addresses today’s systemic economic crisis of inequality and offers ideas for creating a more inclusive, democratic economy grounded in community, justice, and sustainability. Read it now on O’Reilly online learning.

The Great Reversal

In his new book, The Great Reversal: How America Gave Up on Free Markets, economist Thomas Philippon, professor of finance at the NYU Stern School of Business, lays the blame for today’s US economic problems squarely at the feet of anticompetitive, monopolistic practices employed by the largest American corporations; he shows how these practices drive up prices for consumers while limiting choices and stifling investment, productivity, and wage growth, perpetuating a vicious cycle of inequality. Watch Philippon present the issues raised in his book, followed by a panel discussion, in this video from the Brookings Institution Global Economy and Development program. In a recent interview from the public radio show Marketplace, Philippon explains how “market concentration and low competition has become the new normal in America.” You can read this adapted excerpt in the Atlantic. (To understand the broader effects of the consolidation of corporations on politics and how it’s tearing society apart, check out Matt Stoller’s Goliath: The 100-Year War Between Monopoly Power and Democracy.)

The Triumph of Injustice

Renowned UC Berkeley economists Emmanuel Saez and Gabriel Zucman (who have helped US presidential candidate Elizabeth Warren devise her tax plan) detail the transformation that has turned the US tax code upside down (so that the working class pays more as the wealthiest Americans benefit from policies that lessen their load) and propose reforms that will rectify this shameful situation in The Triumph of Injustice: How the Rich Dodge Taxes and How to Make Them Pay. We’ve already shared this New York Times opinion piece by David Leonhardt, which clearly depicts how the tax rate on the rich has plummeted from 1950 to the present. The book’s authors also wrote a Times opinion piece filled with more straightforward data visualizations depicting the inequity of the US tax system: “How to Tax Our Way Back to Justice.”

Good Economics for Hard Times

Newly minted Nobel laureates Abhijit V. Banerjee and Esther Duflo—a husband-and-wife team of economics professors at MIT—expand on their award-winning research in their new book, Good Economics for Hard Times. They pull apart conceptions perceived as common knowledge and dig into the facts about societal forces including inequality, migration, globalization, and taxation. Here’s a glowing review from the Guardian. Yet another New York Times opinion piece—this one by Nicholas Kristof, asking, “Should we soak the rich? You bet!”—draws on both this book and The Triumph of Injustice to make its argument. Harvard’s Joint Center for History and Economics hosted a launch conference for the book; watch the video here. You’ll find an extensive excerpt of the book when you scroll down here.

The Future of Capitalism

Oxford economist Paul Collier delves into global societal divisions—between urbanites and rural dwellers, educated elites and the unskilled working class, and developed and emerging economies—and offers pragmatic, ethical ways to bridge these social, economic, and cultural chasms within a more compassionate, inclusive form of capitalism in his book The Future of Capitalism: Facing the New Anxieties. Bill Gates has given a great deal of thought to the book; in this review he agrees with most of its premises while arguing that Collier should expand his framework for rectifying capitalism’s excesses beyond the levels of global, national, company, and family to include community. In this review from the Financial Times, Martin Wolf despairs that a renewed sense of social obligation can counter the powerful forces of selfishness and greed. Collier discusses his book in this video from Oxford’s Blavatnik School of Government.

The Enlightened Capitalists

James O’Toole, professor emeritus at the USC Marshall School of Business, founding director of the Neely Center for Ethical Leadership and Decision Making, and author of 17 books, turns his eye to business leaders who try to balance profitability and social purpose in his latest work, The Enlightened Capitalists: Cautionary Tales of Business Pioneers Who Tried to Do Well by Doing Good. As the subtitle forewarns, the impulse to build societal good into a corporation’s business practices has its perils; O’Toole offers case studies in the form of portraits of successful entrepreneurs who lost control of their enterprises to executives who did not share their belief that a company should do more than chase profits, including improving workers’ lives, advancing women in the workplace, and advocating for other social issues such as the environment. This review in the Financial Times points out that more recent business structure innovations—such as B Corporations, employee-owned enterprises, and cooperatives—offer better opportunities for compatibility of capitalism and corporate benevolence. In this Recode Decode interview by Kara Swisher, O’Toole tears into the Corporate Roundtable’s recent redefinition of the purpose of a corporation to care for more than short-term returns, expressing doubt that leaders of US corporations can “walk the walk.”

Tales from two of today’s enlightened capitalists

The Enlightened Capitalists might be the perfect book to read as a prelude to recent autobiographies by two strikingly different business leaders who have stood up for social issues to the tune of millions of dollars. In It’s How We Play the Game: Build a Business. Take a Stand. Make a Difference., Ed Stack recounts how he built the DICK’S Sporting Goods empire from his father’s two Dick’s Bait and Tackle stores, always intending to be a force for good in the communities where the stores are located. Distressed by the rise in school shootings, Stack raised the age for in-store gun purchases to 21 and pulled semiautomatics from his shelves in the wake of the Parkland, Florida, attack, destroying $5 million in inventory and angering conservative lawmakers, the NRA, and a large part of his customer base while at the same time earning accolades for placing principles above profits. The Washington Post describes Stack’s bold move and its ramifications here, and Stack recounts his actions in the broader context of US gun control in this CBS News interview.

Some people give discreetly and anonymously, but Salesforce founder, board chair, and co-CEO Marc Benioff is not that kind of guy. Benioff has famously put millions of dollars behind efforts to combat the homeless crisis in San Francisco, supporting legislation to raise taxes on large corporations to help the homeless (and getting into Twitter wars with his tech CEO peers who disagreed with Prop C) and, more recently, donating $30 million to launch the UCSF Benioff Homelessness and Housing Initiative. In Trailblazer: The Power of Business as the Greatest Platform for Change—written with Salesforce EVP of global strategic affairs Monica Langley—Benioff presents his vision for the role of business in making the world a better place and discusses how his company’s core values of trust, customer success, innovation, and equality; a commitment to giving back to society; and a focus on corporate culture have created a powerful competitive advantage. Benioff recounts his efforts to close Salesforce’s gender pay gap in this excerpt from Trailblazer in Wired.

More from Less

Andrew McAfee, cofounder and codirector of the MIT Initiative on the Digital Economy and a principal research scientist at the MIT Sloan School of Management, expresses a positive vision for the future propelled by the “four horsemen of the optimist”—effective capitalism, technological progress, public awareness of environmental issues, and responsive, effective government—in More from Less: The Surprising Story of How We Finally Learned to Prosper Using Fewer Resources—and What Happens Next. He asserts that free markets and tech innovation will enable people to prosper while government institutions and the free press will rein in rapacious greed and protect against social and environmental harm—beliefs that seem inconceivable at this point in US history, as citizens lose faith in crumbling institutions while a cynical administration fights to maintain power. Part of his optimism derives from the fact that the US standard of living has risen over the past two decades while American consumption of resources like water, metals, and building materials declines. McAfee’s concept of the decoupling of growth and environmental degradation—at least in prosperous, developed economies—comes into question in many reviews of his book; this post from the MIT Initiative on the Digital Economy blog addresses those qualms. Here’s an enlightening interview with McAfee by Russ Roberts in his EconTalk podcast. (The transcript is also available.)

The Case for the Green New Deal

Political economist Ann Pettifor, cofounder and director of PRIME (Policy Research in Macroeconomics) and author of several books on economics, argues that financial markets, the economy, politics, and the ecosystem are inextricably bound together in her latest, The Case for the Green New Deal. She explains how governments can finance a Green New Deal without raising taxes in this Guardian story. Get a more detailed explanation of her rationale in this interview from New York magazine.

This just in: New releases from O’Reilly

As we call attention to an array of Next Economy-related books, we’d be remiss not to tout a few of our own. Here’s a sampling of recent releases, available for you to access right now on O’Reilly online learning. (If you’re not yet a member, try it free for 10 days.)

• The AI Organization: Learn from Real Companies and Microsoft’s Journey How to Redefine Your Organization with AI, by David Carmona
• Digital Transformation Game Plan: 34 Tenets for Merging Technology & Business, by Gary O’Brien, Guo Xiao, and Mike Mason
• 97 Things Every Engineering Manager Should Know: Collective Wisdom from the Experts, edited by Camille Fournier
• Unblocked: How Blockchains Will Change Your Business (and What to Do About It) (audiobook), by Alison McCauley

1. Seven Ways to Think Like a Programmer — 1. It’s all just data. 2. Data doesn’t mean anything on its own—it has to be interpreted. 3. Programming is about creating and composing abstractions. 4. Models are for computers, and views are for people. 5. Paranoia makes us productive. 6. Better algorithms are better than better hardware. 7. The tool shapes the hand.
2. Using FOIA Data and Unix to Halve Major Source of Parking Tickets — a reminder of one of the best things to happen in the 2010s: automating good deeds.
3. Taskbook — Tasks, boards, and notes for the command-line habitat.
4. Advice for a New Executive (Lara Hogan) — Chad’s advice to Lara when she joined Kickstarter. 1. Find/create a peer support group. 2. Partner absurdly closely with product and make sure you understand priorities and the head of product understands tradeoffs. 3. Focus on delivery of the roadmap and everything else will follow. 4. Ask your executive peers regularly what you can do to make their jobs easier—particularly the CEO. 5. Take a stand when you need to. 6. Always have a story. 7. Read widely—offline!—about management and leadership. 8. Realize the impact your mood and demeanor has on people. 9. Develop the right relationship with members of your company’s board. From August, but it holds up very well.

1. Microcorruption — You’ve been given access to a device that controls a lock. Your job: defeat the lock by exploiting bugs in the device’s code. Fun way to learn assembly language and debugging.
2. Meqanic — quantum computer puzzle game.
3. Unicode’s Ghost Characters — after the JIS standard was released, people noticed something strange—several of the added characters had no obvious sources, and nobody could tell what they meant or how they should be pronounced. Nobody was sure where they came from. These are what came to be known as the ghost characters.
4. Computer Networks: A Systems Approach (GitHub) — textbook released under CC.

1. GraphStream — a Java library for the modeling and analysis of dynamic graphs. You can generate, import, export, measure, layout, and visualize them.
2. Governing by Video Game — “Real participation—and this is important to clarify—is not a game. It takes time. It takes energy. That’s why not many people participate,” Sugeo says. On the other hand, making it clear that an activity is supposed to be a bit of fun, à la CitySwipe, immediately downgrades the seriousness with which participants engage. “So you’re probably attracting more people to the simplified version and still not solving the problem of engagement.”
3. hipsterDB — hipsterDB is a key/value store that only returns data as long as it isn’t mainstream. The more often that you access a key, the more mainstream it becomes. After data has gone mainstream, you will have to wait for it to go out of style before using it again. Satire, duh.
4. JavaScript and Node.js Testing Best Practices — covering test anatomy, back end, front end, measuring test effectiveness, and continuous integration.

1. Algorithmic Puzzles: History, Taxonomies, and Applications in Human Problem Solving — The paper concerns an important but underappreciated genre of algorithmic puzzles, explaining what these puzzles are, reviewing milestones in their long history, and giving two different ways to classify them. Also covered are major applications of algorithmic puzzles in cognitive science research, with an emphasis on insight problem solving, and the advantages of algorithmic puzzles over some other classes of problems used in insight research. The author proposes adding algorithmic puzzles as a separate category of insight problems, suggests 12 specific puzzles that could be useful for research in insight problem solving, and outlines several experiments dealing with other cognitive aspects of solving algorithmic puzzles.
2. b-ber — both a method and an application for producing publications in a variety of formats—EPUB 3, Mobi/KF8, static website, PDF, and XML file, which can be imported into InDesign for print layouts—from a single source that consists of plain-text files and other assets. b-ber also functions as a browser-based EPUB reader, which explains the name.
3. Microtask Programming — To more effectively harness potential contributions from the crowd, we propose a method for programming in which work occurs entirely through microtasks, offering contributors short, self-contained tasks such as implementing part of a function or updating a call site invoking a function to match a change made to the function. In microtask programming, microtasks involve changes to a single artifact, are automatically generated as necessary by the system, and nurture quality through iteration.
4. Elements of AI — a series of free online courses created by Reaktor and the University of Helsinki. We want to encourage as broad a group of people as possible to learn what AI is, what can (and can’t) be done with AI, and how to start creating AI methods. The courses combine theory with practical exercises and can be completed at your own pace. Super high-level but also super-accessible, so something to give to non-coders who are curious.

1. Stuff I Learned in 2019 — this cat is deep into their theory, and shares a lot of paper recommendations for topics that sound like they were generated by a neural net. This paper introduces cubical type theory and its implementation in Agda. […] This uses a different encoding of presburger sets, which allows them to bound a different quantity (the norm) rather than the bitwidth descriptions. But the best lesson learned may be: I now have a single file […] to which I add notes on things I find interesting. I kept a ruthless log as I learned at my last gig, and I miss that. 2020 is the year I pick this up again.
2. New to SDR — a get-started guide, from the LuaRadio folks.
3. Tesseract.js — a pure JavaScript port of the popular Tesseract OCR engine.
4. This is How to Change Someone’s Mind: Six Secrets from Research — some help for those difficult holiday conversations. Be a partner, not an adversary. Use Rapaport’s Rules. Facts are the enemy. Use the “Unread Library Effect.” Use scales. Use disconfirmation. Serious beliefs are about values and identity. […] If absolutely nothing else works, they might just be a totally unreachable zealot. Or it could be that…you’re the zealot. And if you are unwilling to give any serious consideration to this possibility, that’s a big red flag.

1. Hyperscan — Intel’s library for fast testing a string against multiple regexps.
2. Natural Language Isn’t Just English — English isn’t a great representative of the diversity of languages in the world: It’s a spoken language, not a signed language; it has a well-established, long-used roughly phone-based orthographic system; … with white space between words; … using (mostly) only lower-ascii characters; it has relatively little morphology; and, thus, fewer forms of each word; it has relatively fixed word order, etc. It just happens to have a massive training set.
3. Microsoft Research 2019 Reflection — roundup of MSR’s work in ML, ethics, UI, security, and open source.
4. Buildroot — a simple, efficient and easy-to-use tool to generate embedded Linux systems through cross-compilation.

1. Cybersecurity Book 1.0 Released — the UK’s National Cyber Security Centre has a comprehensive book covering everything from risks to incident management, laws, protocols, and more.
2. RTCode — a web application to share real-time code with multiple connected users. RTCode takes the pain out of group development, avoiding problems like such as: IDE settings, environment settings, diverging programming SDK versions, code version divergence, and difficulty in code collaboration between users.
3. The Other Side of Stack Overflow Content Moderation — this post gives you a taste of the flood of questions from people who can’t or haven’t done any work themselves before turning to Stack Overflow. The result is a denial of service attack on mods, which means responses are frequently brusque. “The site’s not friendly!” is the criticism, but perhaps the real problem is that the site is too welcoming.
4. Learn Rust the Dangerous Way — a series of articles putting Rust features in context for low-level C programmers who maybe don’t have a formal CS background—the sort of people who work on firmware, game engines, OS kernels, and the like.

1. The Hidden Structures of “Choose Your Own Adventure” Books (Verge) — maps of the books reveal/illustrate the differences between the books.
2. lilith — POSIX-like x86-64 kernel and userspace written in Crystal. The Crystal language is statically typed with compile-time checks for null references, a concurrency model, C bindings, and Ruby-like syntax. This is the first UI I’ve seen in it.
3. Wenyan — an esoteric programming language that closely follows the grammar and tone of classical Chinese literature. Useless and incomprehensible to me, but a notable experiment. I see plenty of Chinese-language projects on GitHub now, often trending, and I feel like English’s position as the tech de facto lingua franca can no longer be presumed for the next decade.
4. In Memoriam of Chuck Peddle — he created the 6502, the chip inside the C64, Apple II, Atari 2600, NES, BBC Micro, and other home computers that are where my generation coded, hacked, and BBSed. The book The Story of Commodore, A Company on the Edge gave me huge respect for his work. (via Slashdot)

1. ArcaneVM — A Fully Homomorphic Encryption Brainfuck virtual machine. A toy language but implementing a serious idea. It’s a positive sign that homomorphic encryption is spreading. However … Our research shows that there are many security pitfalls in fully homomorphic encryption from the perspective of practical application. The security problems of a fully homomorphic encryption in a real application is more severe than imagined.
2. Supply Chain Security: If I were a Nation State (Bunnie Huang) — In this talk, we will calibrate expectations about how difficult (or easy) it may be for actors ranging from rogue individuals to Nation-States to infiltrate various points of our global supply chain.
3. One Nation, Tracked (NY Times) — those apps on your phone, the ones that request access to your location, are frequently uploading your location to … well, nobody really knows, but it often ends up aggregated in giant data pools that are analyzed for insights. Or, in this case, leaked to the New York Times. They’re a massive privacy problem.
4. Cognitive Uncertainty — This paper introduces a formal definition and an experimental measurement of the concept of cognitive uncertainty: people’s subjective uncertainty about what the optimal action is. This concept allows us to bring together and partially explain a set of behavioral anomalies identified across four distinct domains of decision-making: choice under risk, choice under ambiguity, belief updating, and survey expectations about economic variables. […] Building on existing models of noisy Bayesian cognition, we formally propose that cognitive uncertainty generates these patterns by inducing people to compress probabilities toward a mental default of 50:50. We document experimentally that the responses of individuals with higher cognitive uncertainty indeed exhibit stronger compression of probabilities in choice under risk and ambiguity, belief updating, and survey expectations.

1. The #1 Bug Predictor is Not Technical; It’s Organizational Complexity — Microsoft Research published a paper in which they developed a new statistical model for predicting whether or not a software module was at risk of having bugs, based on a statistical analysis of the module itself. […] Organizational structure has the highest precision, and the highest recall.
2. A Failed SaaS Postmortem — I can’t believe how powerful YAGNI is, and how hard it is to internalize. Like, you know YAGNI intellectually, but you still get suckered into building things you don’t actually need. “Sufficient” is hard to judge and even harder to stick to.
3. What We Can Learn from Social Robots That Didn’t Make It — In my analysis, the current moment on the social robotics timeline is akin to the era following the failure of the Apple Newton, long before today’s ubiquity of smartphone devices.
4. Formal Reasoning About Programming — As other engineering disciplines have their computer-aided-design tools, computer science has proof assistants, IDEs for logical arguments. We will learn how to apply these tools to certify that programs behave as expected. More specifically, introductions to two intertangled subjects: the Coq proof assistant, a tool for machine-checked mathematical theorem proving, and formal logical reasoning about the correctness of programs.