Access to information matters. Information helps us make decisions and is a key part of being able to participate in society: from getting details about elections, learning about a global pandemic, and keeping track of personal health, we create interactive experiences with data in order to help people live fuller, more-informed lives.

But many of the visualizations, dashboards, charts, and graphs we create exclude people with disabilities from accessing information. In this session, I'll give a short introduction to accessibility, what it is and why it is important, and share helpful frameworks, tools, and guidelines for how to get started doing accessibility work for designing and building interactive data experiences. I'll teach practical, applicable skills and attendees will have exposure to examples that can help reinforce what they've learned.

This talk is intended to give everyone a sense of shared understanding of what accessibility is and how to practically apply it to data work.

TIMECODES

• 00:00 Intro
• 00:57 Understanding perception of language
• 04:07 Agenda
• 09:15 Why care about accessibility?
• 16:32 What is "disability"?
• 24:12 How?
• 27:02 Perceivable
• 31:11 Don't rely on color alone
• 33:31 Operable
• 37:04 Understandable
• 38:51 Outro

Download slides and read the full abstract here: https://yowcon.com/brisbane-2025/sess...

Une autre présentation du langage clair.

Des outils pour évaluer la clarté de texte sont mentionnés.

Description du langage clair par une agence de communication.

Au passage? Je trouve que c’est une page marketing bien construite.

AI is making your team ship faster. It's also filling your codebase with code nobody understands, security flaws nobody caught, and architecture debt that will cost you six months to untangle. This video breaks down exactly what's happening inside AI-assisted engineering teams - and what separates the developers who get replaced from the ones who govern the automation.

━━━━━━━━━━━━━━━━━━━━ What you'll learn: ━━━━━━━━━━━━━━━━━━━━

🧠 Why comprehension debt is more dangerous than technical debt - and why it's invisible until it's too late

⚠️ What "AI slop" actually is - code that compiles, passes review, and quietly breaks your system

🔐 Why 45% of AI-generated code contains security vulnerabilities - and why "the AI wrote it" is not a legal defense

📉 The senior developer productivity paradox - why AI speeds up juniors but drops senior output by 19%

🎓 What happens to junior developers who never struggle through the logic - and why this is a long-term engineering crisis

📋 Spec-Driven Development — how to shift the primary artifact from code to intent before AI touches anything

🏗️ How to architect AI like a slow, unreliable dependency - queues, circuit breakers, fallback paths, SLOs

🚫 Why "future AI will refactor the debt" is the most dangerous assumption in engineering right now

💼 Why the market value of expert verification is rising - and what the engineer who survives this transition actually does

━━━━━━━━━━━━━━━━━━━━ ⏱ CHAPTERS

• 0:00 — You're Shipping Code You Don't Own
• 0:24 — Speed Without Comprehension Is a Delayed Disaster
• 0:32 — Comprehension Debt: The Invisible Crisis
• 1:19 — AI Slop Is Already in Your Repo
• 2:20 — The Security Numbers Should Scare You
• 3:17 — The Senior Developer Squeeze Nobody Talks About
• 4:09 — Junior Developers Are Being Hollowed Out
• 5:02 — Spec-Driven Development: Take Back Control
• 6:08 — Architect AI Like an Unreliable Dependency
• 7:00 — The "Future AI Will Fix It" Trap
• 8:47 — The Engineer Who Survives the Transition

━━━━━━━━━━━━━━━━━━━━

If you can't explain what your AI wrote when the system fails - you're not an engineer anymore. You're a liability with a GitHub account.

Abstract

Generative Artificial Intelligence (GenAI) tools (e.g., ChatGPT, Calude) have rapidly become integral to software development.

These tools are especially attractive to students, as they can reduce cognitive load. However, their adoption also introduces a socio-cognitive risk: the accumulation of Comprehension Debt (CD).

CD refers to the growing gap between what a development team knows about its codebase and what it actually needs to understand in order to maintain and modify it effectively. This qualitative study investigate how GenAI tools contribute to CD in the context of an undergraduate software engineering project. Our study is based on 621 reflective diaries from 207 students over eight weeks. We identify four CD accumulation patterns and one mitigating pattern in students’ use of GenAI tools.

The four accumulation patterns include:

• (1) AI-as-black-box code acceptance,
• (2) context-mismatch debt,
• (3) dependency-induced atrophy, and
• (4) verification-bypass.

In contrast, the mitigating pattern involves students using GenAI as a comprehension scaffold, allowing them to build a deeper understanding of the code.

We argue that CD is distinct from traditional technical debt because it resides in the collective cognition of development teams rather than in the codebase itself. Our findings highlight the need for explicit pedagogical strategies to mitigate CD in software engineering education, emphasizing verification practices, structured retrospectives, and active learning assessments.

Comprehension Debt, Generative AI, Software Engineering Education, Agile, Cognitive Load, Technical Debt

Conclusion

This paper examined how GenAI tools influence understanding in student software engineering projects. We introduced CD as a socio-cognitive construct describing gap between codebase demands and collective team understanding. We identified four CD accumulating patterns (black-box acceptance, context-mismatch, dependency atrophy, and verification bypass) and one CD mitigating pattern (AI as comprehension scaffold). We also articulate conceptual model of linking epistemic orientation, germane cognitive load investment, and CD accumulation.

GenAI tools do not inherently undermine or enhance learning. Our study shows that they act as amplifiers of students existing orientation toward acceleration or exploration.

Additionally, shows that verification competence must be intentionally cultivated through courses or module design. Otherwise students may find themselves in competence trap, where they lack domain knowledge required to safely use the tools they rely on for code generation.

By integrating structured retrospectives, active learning assessments, and cognitive apprenticeship models, educators can promote comprehension oriented.

CD offers a vital lens to ensure that next generation of software engineers possesses not just speed to generate code, but the depth to sustain it.

Optimisations IA en compensation à moteur de jeu générant des images intrinsèquement instables.

Change la couleur des liens si hosting chez green web foundation

Simulation de problèmes d'accessibilité numérique.

trouvé via le podcast "Salut les designers" de l'agence Lunaweb

https://media.ccc.de/v/lgm25-upstream...

Images created with digital graphic techniques have played a central role in promoting the seemingly endless growth of a computing culture that many of us now recognize as extractive, polluting and exploitative. Beyond that, something that once promised a utopian future now feels like it has delivered a rather crappy, disappointing reality. FLOSS has undoubtedly played some role in this development, but it also holds a great potential as part of a shift in imagination to new practices and aesthetics that are more verdant, just and humane.

This presentation will propose the concept of "Permacomputing" as a useful lens to consider the role of FLOSS in developing alternative practices. Permacomputing is both a concept and a community of practice oriented around issues of resilience and regenerativity in computer and network technology inspired by permaculture.

We will start with a brief introduction to the concepts and emerging projects around these ideas. With these notions in mind, we can then examine how some of the existing practices of Libre Graphics might inspire or give relevant support to newer, more holistic ways of working. And finally, we will look at some specific examples of software development, education and image making using the generative flora of computing within limits.

Brendan Howell https://pretalx.c3voc.de/lgm25-upstre... #lgm2025

Licensed to the public under https://creativecommons.org/licenses/...

Contrairement à ce que certains titres racoleurs laissent entendre, Signal n’est pas en cause ici.
Le problème vient bien de iOS. Lorsque les aperçus de notifications sont activés (et ils le sont par défaut), le contenu des messages reçus est stocké en clair dans une base de données locale du système iOS, sur ton téléphone quoi.
Oui : en clair. Pour une marque qui met en avant le côté respect de la vie privée de ses utilisateurices, c’est quand même fort de kawa.

Ces données sont enregistrées dans une base SQLite interne liée aux notifications. Et surtout, elles restent stockées même si

Joular Code - Java is a lightweight and efficient Java agent for monitoring the energy consumption of methods and execution branches at the source code level.

This project is part of Joular Code, and is the successor of JoularJX.

🚀 Features

Monitor power consumption and energy of each method and execution branch at runtime
Works as a Java agent — no source code instrumentation or modification needed
Samples the JVM stack at high frequency (default: every 10 ms) and attributes energy every second
Supports three power data source backends from Joular Core:
Shared memory ring buffer (IPC) — lowest latency, recommended
CSV file — file-based polling
HTTP endpoint — remote or containerized setups
Generates CSV files with per-method and per-branch power (Watts) and energy (Joules)
Produces two output sets: one for all methods (including JDK internals), one filtered to your application packages
Configurable method filtering by package/class prefix to focus energy data on your code
Cross-platform: Windows, macOS, Linux, and Raspberry Pi

L'abandon cognitif, c'est le nom donné par les psychologues au fait de consulter une IA et d'adopter son avis, sans faire l'effort de se pencher sur le problème. Je vous explique.

Depuis les années 70, les psychologues distinguent deux modes de pensée :
➡️ Le système 1, rapide et automatique.
➡️ Le système 2, lent et analytique.

Un test psychologique, le CRT, a été conçu exprès pour mettre ces deux systèmes en tension. Il contient des questions où la première réponse qui vient à l'esprit est fausse (donc issue du système 1), tandis qu'en prenant le temps d'y réfléchir on trouve immédiatement la bonne réponse (avec le système 2).

Eh bien une équipe de chercheur a récemment refait l'expérience du test CRT, mais cette fois en laissant la possibilité aux participants de consulter un assistant IA s'ils le désiraient.

Résultats :
1️⃣ 50% des participants demandent immédiatement à l'IA
2️⃣ Parmi eux, 87% suivent l'avis de la machine.
3️⃣ Leur degré de confiance est de 77% s'ils ont utilisé l'IA contre 65% pour ceux qui ont répondu sans l'aide de la machine.

Autrement dit, la majorité des participants n'activent ni leur système 1 ni leur système 2. Ils s'en remettent immédiatement à l'IA – une sorte de système 0, si vous voulez. Les chercheurs appellent ça le "cognitive surrender", l'abandon cognitif.

C'est d'autant plus préoccupant que les participants qui ont utilisé l'IA affichent une confiance plus importante dans leur réponse, alors même que la subtilité de la question leur a échappée.

📚 Ref : Shaw et al (2026). Thinking—Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender.

Le livre de Geoffrey

After decades of gluing together software from a random set of libraries and frameworks, industry wide attacks on the software supply chain have proven this approach is unsustainable, and it's time to shift our thinking on how we write and ship software. In this session we will explore the various tools used to secure the software supply chain, and through a collection of live demos, learn how to put them to use in the real world.

Kelsey Hightower
Google, Inc.

As a curious and motivated self-learner, I gained an interest in computing at a young age, and started my IT career by opening a small consulting shop 20 years ago. From those beginnings my career progressed quickly, eventually passing through the halls of Google, Puppet Labs, New Relic and CoreOS. I am a system administrator by trade, a programmer by necessity, but a problem-solver at heart. With a passion for helping others, many successful speaking and teaching engagements under my belt, and a proven track record of getting things done and enabling others, I hope to solve the many problems facing IT culture by equipping people with the mental and computational software they need to succeed in the competitive world of technology.

Locality of Behaviour (LoB)

Carson Gross
May 29, 2020

“The primary feature for easy maintenance is locality: Locality is that characteristic of source code that enables a programmer to understand that source by looking at only a small portion of it.” – Richard Gabriel

The LoB Principle

Locality of Behaviour is the principle that:

The behaviour of a unit of code should be as obvious as possible by looking only at that unit of code

This an attempt in the same spirit as Morris Vanilla To Do to demonstrate how you can develop a PWA with only HTML, CSS and pure Javascript.

I have aggregated all the knowledge and edge cases I’ve experienced through many PWA development and in production

Want a demo? Go over her here: demo.purejs-pwa.alethgueguen.com
The Github repo: github.com/planeth44/pure-JS-PWA

• The constraints
• Technical choices
• What the app does
  • Things
  • Files
• HTML forms
• Auto save
  • alternate method
• Form validation
• Syncing and offline
  • Update
  • possible scenario
• Back-end
• Offline Multi Page, how you do that?
• Dependencies