Article de Felienne Hermans accompagnant un épisode récent de son podcast.
Felienne et Hanna discutent du livre “Breaking things at work: The Luddites are right about why you hate your job” – Mueller, G. (2021).
La bulle spéculative de l’IA expliquée simplement 😅(Je reste une fan inconditionnelle de cette séquence télé) | Youna Bentabed
AI adoption strategies are overwhelmingly framed around productivity and efficiency. But that lens misses a critical constraint: the psychological cost of working with AI.
New research shows that “psychological debt”—a cluster of six negative effects including cognitive offloading, reduced autonomy, diminished competence, weakened social connection, credibility loss, and identity threat—can materially suppress adoption and erode ROI. In a survey of 1,200 employees across sectors, higher psychological debt was strongly associated with lower AI usage, less sophisticated application, and greater avoidance—even when employees acknowledged AI’s value.
Early-career workers were especially affected, suggesting that AI may be undermining skill development at precisely the stage when it matters most.
The implication is clear: AI adoption is not just a technology deployment challenge but a human one. Companies that ignore motivation risk investing in tools employees won’t fully use. The solution is to deliberately design human-AI interactions—introducing friction to preserve thinking, ensuring explainability and autonomy, reinforcing human expertise, and normalizing AI use socially and culturally.
Scrambler font to trick AI
- Can I slow down and enjoy this ?
- Can I relax for the same result ?
- … or a better one ?
The fleet can build anything and remembers nothing. Someone has to carry what the machines cannot.
Watch a good Fleet Lead work for an hour. Six agents in flight at once. Two are drafting against different halves of the spec, one is writing harness checks from the criteria, one is grinding through a migration, and two are parked, waiting on a judgment call their operator will make between other judgment calls. Work merges, checks run, something lands. A refactor that would have been a two-week argument goes in quietly before lunch. It looks less like programming than like conducting, and the throughput coming off one desk is, frankly, a little absurd. The six is a soft count, too. Agents spawn agents when the work splits, the pattern production orchestrator systems already run [1], and the formation swells and thins by the hour. In the work we run, the spikes reach scores of agents under one orchestrator when the day calls for it.
Unfair advantages
- importing ideas
- immunity of entrenchment
- Owning your intersections
Pour ne pas être juste "un collecteur"
- finir quelque chose de signitificatif
- rotation : suivre un intérêt par saison plutôt que tout en même temps
- construire les ponts de manière délibérée entre mes intérêts. Quand j'apprends qqchose dans un domaine, me poser délibérément la question : "Ou est-ce que ça s'applique aussi?"
Research referenced:
- Robert & Michele Root-Bernstein — biographical study of Nobel laureates' engagement in serious outside pursuits (art, music, writing, crafts)
- Erik Dane (Academy of Management Review) — cognitive entrenchment, how deep expertise hardens into rigidity
- Arne Güllich, Brooke Macnamara & David Hambrick (2022) — meta-analysis of 6,000+ athletes on sampling vs. early specialization
- Far transfer — the psychological mechanism of applying patterns learned in one domain to solve problems in another
Jeu où il faut trouver comment aller d'un département à un autre
Depuis quelques années, les métiers du design traversent une zone de turbulence.
L’arrivée massive de l’intelligence artificielle, la pression croissante sur la rentabilité des produits numériques et la difficulté à faire reconnaître la valeur du design dans les organisations ont profondément transformé les conditions de travail.Beaucoup de designers et designeuses expriment aujourd’hui un malaise diffus.
Une fatigue. Parfois même une crise de sens.
Et si cette crise révélait quelque chose de plus profond ?
Et si nous étions, nous aussi, pris dans un modèle qui nous rend plus performants… mais moins robustes ?
Pour explorer cette question, nous avons croisé trois regards :
Olivier Hamant, biologiste et directeur de l’Institut Michel Serres
Audrey Coudrin, designeuse et fondatrice du collectif territorial MARÉES
Chloë Breuillé, UX designeuse et cofondatrice de l’écolieu la Ferme des Coudreaux
Au sommaire de cet épisode
Chapitre 1 : nous définirons ce que recouvre le mot réemploi et comment il a pris place dans le parcours professionnel de nos invité•es.
Chapitre 2 : nous quittons les définitions pour entrer dans l’atelier du réel et comprendre comment un projet bascule vers le réemploi et comment il se frotte aux normes ou au budget.
Chapitre 3 : nous passons au politique: modèles économiques, plateformes, gouvernance, et preuves d’usage et montée en compétences pour faire échelle.
Chapitre 1 : pourquoi les systèmes naturels ne sont pas optimisés mais robustes, et ce que cela change pour penser nos organisations.
Chapitre 2 : comment la robustesse transforme les manières de travailler : collectifs, territoires, coopération et projets ancrés dans le réel.
Chapitre 3 : ralentir, diversifier ses compétences, sortir de l’hyper spécialisation et redéfinir ce que signifie concevoir aujourd’hui.
Cet épisode a été réalisé par Marie Grataloup et Nicolas Guerin. La post-production est réalisée par Anthony Adam. Les voix off sont de Zoé Pasquier.
Présentation par : Cyrille MARTRAIRE (Arolla)
📕 Résumé :
Je lis des publications scientifiques tout le temps, pour n'importe quel sujet que je veux apprendre sérieusement. Pourquoi perdre son temps à lire des opinions et des résumés superficiels quand vous pouvez trouver des réponses fiables et précises directement dans des publications scientifiques ? Mais on ne va pas se mentir, la littérature académique peut paraître intimidante si vous ne savez pas comment l'aborder. Et même avec l'IA, la lecture d'articles reste un ingrédient clé.
Si vous n'êtes pas familier avec l'univers des publications scientifiques, rejoignez-moi pour découvrir comment explorer ces contenus austères mais fascinants propulsera votre apprentissage, quel que soit le sujet. Nous illustrerons cela à la fois pour modéliser un domaine métier (à la DDD) et pour comprendre le groove en musique (pour le fun).
Enregistré en avril 2026 à Paris, Palais des Congrès, Porte Maillot.
Une liste de papiers sélectionnés par Cyrille dans le domaine de l'informatique pour commencer https://gist.github.com/cyriux/ee0d382bda9853e3fe86189f4f95658f
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Plain Pattern Language: a book review on the origin of Design Patterns - Lorie den Os
Have you actually read the GoF's "Design Patterns"? Lorie den Os hasn't! Instead, she read "A Pattern Language", a 1200-page book on physical architecture that deeply inspired the Gang of Four.
Initially picking it up with the sole intention of building better houses in The Sims 🎮, she shares what she learned from it as both a developer and a linguist. A refreshing book review that gets to the heart of a fundamental question: why do we code?
#designpatternsinjava #architecture #softwareengineering #sims4 #ForkIt2026 #bookreview
Accéder en anonyme
Publications scientifiques utilisées :
Kardas, M., & O'Brien, E. (2018). Easier Seen Than Done: Merely Watching Others Perform Can Foster an Illusion of Skill Acquisition.
Toujours le même refrain : dès qu'une mesure #écologique est mise en place (oui ça peut arriver), on entend s'élever certaines voix hurler à "L'écologie punitive". Alors, l'action #climatique est elle être liberticide ?
On a demandé un avis scientifique éclairé à Mélusine Boon-Falleur, chercheuse et autrice de "Les pingouins ne sauveront pas la banquise" paru aux éditions JC Lattès
More people are learning to code than ever, but most learning opportunities do not explicitly teach the problem solving skills necessary to succeed at open-ended programming problems.
In this paper, we present a new approach to impart these skills, consisting of:
1) explicit instruction on programming problem solving, which frames coding as a process of translating mental representations of problems and solutions into source code,
2) a method of visualizing and monitoring progression through six problem solving stages,
3) explicit, on-demand prompts for learners to reflect on their strategies when seeking help from instructors, and
4) context-sensitive help embedded in a code editor that reinforces the problem solving instruction.
We experimentally evaluated the effects of our intervention across two 2-week web development summer camps with 48 high school students, finding that the intervention increased productivity, independence, programming self-efficacy, metacognitive awareness, and growth mindset.
We discuss the implications of these results on learning technologies and classroom instruction
James Prather, Brent Reeves, Juho Leinonen, Stephen MacNeil, Arisoa S. Randrianasolo, Brett Becker, Bailey Kimmel, Jared Wright, Ben Briggs
Novice programmers often struggle through programming problem solving due to a lack of metacognitive awareness and strategies. Previous research has shown that novices can encounter multiple metacognitive difficulties while programming. Novices are typically unaware of how these difficulties are hindering their progress.
Meanwhile, many novices are now programming with generative AI (GenAI), which can provide complete solutions to most introductory programming problems, code suggestions, hints for next steps when stuck, and explain cryptic error messages. Its impact on novice metacognition has only started to be explored.
Here we replicate a previous study that examined novice programming problem solving behavior and extend it by incorporating GenAI tools. Through 21 lab sessions consisting of participant observation, interview, and eye tracking, we explore how novices are coding with GenAI tools.
Although 20 of 21 students completed the assigned programming problem, our findings show an unfortunate divide in the use of GenAI tools between students who accelerated and students who struggled.
Students who accelerated were able to use GenAI to create code they already intended to make and were able to ignore unhelpful or incorrect inline code suggestions.
But for students who struggled, our findings indicate that previously known metacognitive difficulties persist, and that GenAI unfortunately can compound them and even introduce new metacognitive difficulties.
Furthermore, struggling students often expressed cognitive dissonance about their problem solving ability, thought they performed better than they did, and finished with an illusion of competence. Based on our observations from both groups, we propose ways to scaffold the novice GenAI experience and make suggestions for future work.
Clara explores the concept of metacognitive awareness, the role it plays in learning, common traps in your thinking, and how using LLMs affects it in surprising ways.
Papiers de recherche :
