The past 3 years of work in NLP have been characterized by the development and deployment of ever larger language models, especially for English. BERT, its variants, GPT-2/3, and others, most recently Switch-C, have pushed the boundaries of the possible both through architectural innovations and through sheer size. Using these pretrained models and the methodology of fine-tuning them for specific tasks, researchers have extended the state of the art on a wide array of tasks as measured by leaderboards on specific benchmarks for English. In this paper, we take a step back and ask: How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks? We provide recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research directions beyond ever larger language models.
Clara explores whether learning to code is still worthwhile, offering crucial insights into the learning process and the future of programming.
Clara explains how to use AI effectively when learning to code or improving your programming skills, discusses the potential drawbacks of the concept “AI as a tutor” and its impact on the learning experience, both practically and emotionally. 📃 Read the research this video is based on about the "widening gap" here: https://dl.acm.org/doi/10.1145/363262...
- 0:00 what’s up with AI and learning?
- 0:32 the experiment
- 0:58 pssst... check out our student pack
- 1:13 student A (struggling)
- 2:11 unawareness of unawareness
- 2:26 student B (non-struggling)
- 3:36 the widening gap
- 4:23 a rule of thumb 👍
- 4:39 doing things slowly is better for learning
- 4:48 imposter syndrome
- 4:56 meh.
- 5:43 🤑getting things done
- 6:09 the good old days
- 6:36 how to use LLMs in learning
- 6:54 what have we learned?
- 7:13 the psychology of learning to code
- 7:20 please… please... the student pack
- 7:32 begging and conclusion
Here is a visualization of a Java program. Each big circle represents a Class. The initial size (mass) of the circle is proportional to the number of fields and methods in the class. Each time the class calls a method of another class, the mass of the class increases, and a small circle will spawn. If class A calls B.method(), the small circle will have the same color (color B) as the circle that represents class B. If there are lots of small circles with color B following circle A, then the force of attraction on circle B exerted by circle A will be big. (Circle B does not attract Circle A if there are no small circles with color A following Circle B).
Question: Can you tell by looking at the visualization as to how the methods in classes are interacting with each other?
would you be able to describe it?
Is it a good way to visualize coupling between classes?
Is it good for anything at all?
Thank you so much
Un e-book interactif bien fait pour apprendre les transformées de Fourier de façon beaucoup plus claire que sur Wikipedia.
2026-06-03
Je rassemble ici en trois paragraphes et neuf liens tout ce que j’ai écrit sur l’IA générative (IAG) dans la partie Blog de mon site depuis deux ans. Ceci donne un bon aperçu de ma position concernant l’IAG à la date de publication de ce billet. Je mets également ce texte dans ma page sur l’IA dans la partie Cours du site, et c’est là-bas que je ferai des mises à jour si besoin.
idée de format de kata : "Skill issue"
Basée sur l'idée de "mauvaise foi dans le code, bonne foi dans les tests". (poke @romeu )
Driver joue le rôle d'un agent IA de mauvaise foi.
Driver dispose de quelques fichiers skill.md initialement un peu ambigües.
Driver les interprète de la pire façon.
Ensemble du groupe doit changer ses instructions et/ou les skills pour que Driver fait ce qui est attendu.
Le kata se fait sans L'utilisation de vrai LLM (indeed completion AI)
Accessibilité des ressources pédagogiques.
Ils organisent les premières "Forgéales" : des soirées de partage en visio les 24, 25 et 26 juin.
Je découvre une initiative qui s'appelle "La forge des communs numériques éducatifs".
La Forge des communs numériques éducatifs ?
- C’est une communauté d’enseignantes et d’enseignants qui créent et partagent des logiciels et des ressources éducatives libres.
- Ces outils sont conçus pour être utiles à leurs collègues et à leurs élèves, dans leur pratique quotidienne.
- Tout cela se passe dans un espace de travail collaboratif en ligne, qui rassemble plusieurs milliers de projets.
- Chacun peut utiliser ces ressources, les adapter à ses besoins, et surtout, y contribuer librement.
- Un lieu ouvert, vivant, fait par et pour la communauté éducative !
Ils organisent les premières "Forgéales" : des soirées de partage en visio les 24, 25 et 26 juin.
Diátaxis
A systematic approach to technical documentation authoring.
Diátaxis is a way of thinking about and doing documentation.
It prescribes approaches to content, architecture and form that emerge from a systematic approach to understanding the needs of documentation users.
Diátaxis identifies four distinct needs, and four corresponding forms of documentation - tutorials, how-to guides, technical reference and explanation. It places them in a systematic relationship, and proposes that documentation should itself be organised around the structures of those needs.
Diátaxis solves problems related to documentation content (what to write), style (how to write it) and architecture (how to organise it).
As well as serving the users of documentation, Diátaxis has value for documentation creators and maintainers. It is light-weight, easy to grasp and straightforward to apply. It doesn’t impose implementation constraints. It brings an active principle of quality to documentation that helps maintainers think effectively about their own work.
tutorialsandhow-to guidesare concerned with what the user does (action)referenceandexplanationare about what the user knows (cognition)
On the other hand:
tutorialsandexplanationserve the acquistion of skill (the user’s study)how-to guidesandreferenceserve the application of skill (the user’s work)