PodQueue

Think of that feeling you get when you follow an online tutorial or documentation and the code works on the first run. Now think of all the hours spent wasted following broken, outdated, or incomplete documentation. From our favorite tutorials to bad product docs we all consume technical writing. Tutorials, blog posts, and product docs help developers learn new things, build projects, and debug issues. But what makes one tutorial better than another? In this talk I'll discuss how you can write the documentation that developers love and I’ll share 10 tips and tricks to improve your technical writing.

Slides: https://pycon-assets.s3.amazonaws.com/2022/media/presentation_slides/173/2022-04-28T16%3A12%3A34.131557/Write_Docs_Devs_Love.pdf

It's one thing to build a new application that meets the latest accessibility standards, but it's another thing to update an existing application to meet them. In this talk, we'll share how we're using automated accessibility scanning, preview-driven development, and an accessibility-first form builder to make GitHub's 15-year-old, 1,400-controller Ruby on Rails monolith accessible.

Interview with Georges Didi-Huberman about the problems regarding the way in which we see and interpret images, a problematic issue that stems from the definition...

We on the left find ourselves in a world that seems sullied in at every level — the air we breathe, the water we drink, the unequal social relations between people, and an exploitative relationship with the natural world.  Our answer, not surprisingly, is to struggle for more purity, both in our environment and in … Continued

In this episode of Code For Thought, SSI Fellow Peter Schmidt talks with data scientist and entrepreneur Nick Radcliffe about the importance of testing your data. Test driven development is a familiar concept to research software engineers. Test driven data analysis puts the same emphasis on validating and testing data for your AI app. They also dive into the Python library of the same name, tdda.

03/01/2023

Research Series Speaker Eytan Tepper

“Polycentric Multilateralism: Reimagining the Roles of International Institutions in Space Governance and Beyond”

Using the case study of space governance, this paper envisions a polycentric approach to the governance of critical transnational challenges. Reimagining the roles of long-standing multilateral international institutions that suffer from decades-long gridlock, this study explores the capacity of polycentricity to provide efficient responses to the governance of the global commons and global affairs more generally. Nobel Laureate Elinor Ostrom (Economic Sciences, 2009) found strong empirical proof favoring polycentric governance of local commons and suggested the conclusions might apply to global commons. In her work with leading international relations scholar Robert Keohane, they imagined the application of her findings to global affairs but have not gone all the way. This leaves, as Keohane observed, “unexploited opportunities” for investigators seeking to understand issues in global affairs. This paper aims to exploit this opportunity. It uses the Institutional Analysis and Development framework (IAD) and continues with the testing the hypothesis that Ostrom’s findings, and more generally the knowledge accumulated at the Ostrom Workshop, can be applied to global commons and global affairs more generally. Based on the research finding, this paper seeks to reimagine the role of existing international institutions as less of monocentric decision-making centers and more as connecting hubs that support and coordinate emerging polycentric networks. We call it ‘polycentric multilateralism’. This approach would reinvigorate the existing institutional system to better respond to contemporary and future challenges (e.g., space debris, space security, space resource exploitation). A polycentric structure would be better adapted to the reality of global politics, including of power shifts and power diffusion.

03/06/2023

Colloquium Series - Ben Green

Algorithmic Realism: Reimagining Data Science to Promote Social Justice

The field of data science faces an impasse. With an ability to uncover empirical patterns and predict future events, data science appears to be a valuable tool for improving society. Data scientists have therefore proposed using algorithms to inform decision-making in high-stakes settings such as courts, schools, and hospitals. However, humanists, social scientists, and affected communities have exposed how these same algorithms can discriminate against racial minorities, marginalize workers, and increase surveillance. In response, data scientists have developed new methods focused on ethics and fairness. Yet even these methods fail to prevent—and at times exacerbate— the most severe harms of algorithmic decision-making. Thus, as data scientists strive to responsibly contribute to the social good, the field confronts a fundamental question: how can data scientists develop algorithms that reliably improve society?

Algorithmic Realism breaks through this impasse by presenting a guide for using data science to promote social justice. I argue that this task requires developing a new methodology for algorithm design and evaluation. The current methodology—which I call “algorithmic formalism”—focuses on the formal mathematical attributes of algorithms. As a result, even algorithms that satisfy disciplinary standards for rigor can entrench unjust social conditions, impose narrow logics of efficiency across domains, and fail to achieve the desired real-world benefits. To remedy these issues, I propose an alternative approach. “Algorithmic realism” is a data science methodology that situates design and evaluation around the real-world impacts of algorithms. Algorithmic Realism describes how to transform data science from a methodology based on formal, mathematical models into a practical tool for addressing real-world problems. This approach reveals why many high-profile algorithms (such as pretrial risk assessments) are unnecessary or even detrimental tools for reform. However, algorithmic realism also suggests new strategies for how data scientists can contribute to a more equitable society.

Seize the Means of Computation: The Big Tech Disassembly Manual

40 years ago, we shot antitrust law in the guts, and we let companies led by mediocre idiots no better than their forebears establish monopolies. These donkeys were able to parlay their monopoly winnings into policies that prevented new technologies from supplanting their own. They got to decide who was allowed to compete with them, and how.

Notably, tech giants today are able to wield the law against interoperators: new technologies that plug into their services, systems and platforms. That’s a privilege that none of yesterday’s easily toppled tech giants had - if IBM wanted to prevent its competitors (the “seven dwarves” of the mainframe era) from making software, printers, keyboards, and storage for its mainframes, it had to figure out how to build a computer that no one else could reverse-engineer and improve on.

For complex reasons, this is impossible. The very bedrock of computer science - ideas named for midcentury computing demigods like “Turing completeness” and “Von Neumann machines” - dictates that the creation of noninteroperable computers is a fool’s errand. It’s fantasy, not science fiction, like a time machine or a faster-than-light drive.

Today’s tech giants have not invented an interop-proof computer. They’ve invented laws that make interoperability illegal unless they give permission for it. A new, complex thicket of copyright, patent, trade secret, noncompete, and other IP rights has conjured up a new offense we can think of as “Felony contempt of business model” - the right of large firms to dictate how their customers, competitors and even their critics must use their products.

As we build sites that are more heavily reliant on JavaScript, we sometimes pay for what we send down in ways that we can’t always easily see. In this talk, Addy explains how and why JavaScript is the most expensive resource your site uses today—especially on mobile and lower-end desktops.

Web developers should focus on optimizing JavaScript bundles by keeping them small to improve download speeds, especially on mobile devices. Small JavaScript bundles improve network transfer speeds, lower memory usage, and reduce CPU costs.This can be achieved by splitting bundles larger than 50-100 kB. Additionally, it's crucial to enhance execution time by avoiding tasks that overly engage the main thread and by not using large inline scripts over 1 kB. Avoid creating Long Tasks that can keep the main UI thread busy and can push out how soon pages are interactive by users. These steps are vital as parse and compile costs have become faster than before.

He will also share tips for fixing JavaScript performance issues so everything loads quicker. A little discipline can help if you want your site to load and be interactive as soon as possible on mobile.

A few things that will be covered:

- What the dominant costs of heavy JavaScript are

- How median hardware plus heavy JavaScript can impact user-experience

- Tips and tricks for reducing the cost of JavaScript, reducing long-tasks and improving the Core Web Vitals metrics

- Things frameworks and browsers are doing to try reducing the cost of JavaScript by default.

- How new performance metrics can help you reason about where to optimize JavaScript to improve user interactions

Target Audience: Intermediate web developers

Originally presented at Web Unleashed

#javascript #webdevelopers #programming #webperformance