LLMs have been trained on decades of freely available Java specifications, including JSRs, JEPs, MicroProfile and Jakarta EE. They know the patterns. They know the standards. Now let's put them to work. In this live coding session, we'll use LLM agents to build production-ready Java applications quickly. No slides, no theory - just real code, real prompts, and real results. We'll start with a typical enterprise requirement and demonstrate how to guide LLMs to generate clean, maintainable Java code following BCE/ECB architecture patterns that actually work in production. You will learn how to effectively access the LLM's in-depth knowledge of Java specifications, how to continuously improve the generated code with each iteration, and how to maintain a high velocity without creating a mess. Expect live coding, real-world scenarios from actual projects, and honest discussion about where LLMs excel and where human expertise remains crucial. Bring your questions - we'll solve them with code.
Guidelines for Agentic development.
"Put your AI on rails."
BCE architecture · Java · Web standards
Adam Bien
AI coding agents frequently ignore long rule documents. Asking them to hold on to an entire book's worth of coding advice is at best futile, at worst makes the agent's performance worse by polluting the context window.
Humans don't need to hold the same information in their head because humans can form habits through repetition. However, AI agents can't do this.
Human habits form when an easy-to-detect cue triggers a complex sequence of actions with the desired effect. This is the inspiration for habit hooks.
Linters provide a deterministic metric, but Goodhart's law postulates that a metric ceases to be a good metric if it becomes a target. AI agents are very good at gaming these metrics when they are only provided the metric.
Habit hooks wraps your linter to create the trigger, but instead of providing only the metric, it gives actionable advice on how to fix the issue. This creates AI behaviour that looks like human habits, and has similar effects.
The use of habit hooks:
- Increases code quality
- Improves AI performance ensuring that the AI always starts with good code quality
- Reduces token usage, since good quality code also means the AI doesn't need to read as much context to complete the task.
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
Présentation par : Sandrine BOITEAU (WEnvision)
📕 Résumé :
Pendant 15 ans, j'ai consommé une quantité astronomique de contenu sans jamais pouvoir le retrouver. Ce n'était pas un problème de volume, mais de VOLATILITÉ : l'information consommée devenait immédiatement du savoir mort.
Et si l'IA servait au contraire à arrêter l'hémorragie pour transformer votre veille en une véritable infrastructure de connaissance ?
Dans cette session, je ne vous vendrai pas un nouvel outil SaaS. Je vous ouvrirai le capot de mon propre second cerveau : une architecture locale souveraine (Markdown, Git) où l'IA ne sert pas à répondre à ma place, mais à devenir mon "Contrôleur Aérien" mental.
Découvrez comment j'ai automatisé avec Antigravity ma digestion de savoir en 12 étapes pour passer de la simple synthèse à la critique stratégique. Ne soyez plus l'utilisateur d'un outil, devenez l'architecte de votre propre pensée.
Enregistré en avril 2026 à Paris, Palais des Congrès, Porte Maillot.
Agents fait pour accompagner le développement de produits accessibles.
Fait par des gens avec déficience visuelle.
For decades, ADHD has been framed as a disadvantage—a condition that makes it harder to focus, stay organized, and succeed in a world built on routine.
But what if we've been looking at it all wrong?
As artificial intelligence transforms the way we work, create, and solve problems, the very traits once considered weaknesses—curiosity, rapid idea generation, pattern recognition, hyperfocus, and unconventional thinking—may become some of the most valuable skills of the future.
In this video, we explore why the rise of AI could fundamentally change the way we think about ADHD. We'll examine the science behind divergent thinking, the entrepreneurs who turned their neurodivergence into an advantage, and how AI is becoming a "second brain" that helps bridge the gap between imagination and execution.
Could the ADHD brain be uniquely suited for the AI age?
Maybe the problem was never ADHD.
Maybe the world just hadn't caught up yet.
In This Video:
• Why traditional work environments disadvantage ADHD minds
• How AI changes the rules of the game
• The hidden strengths of the ADHD brain
• Why curiosity may become more valuable than discipline
• Entrepreneurs who embraced their neurodivergent thinking
• How people with ADHD can use AI as a competitive advantage
💬 What do you think?
Do you believe ADHD will become an advantage in the age of AI, or will the future create even greater challenges for neurodivergent people?
Share your thoughts in the comments. I'd love to hear your perspective.
If you enjoy exploring the future of AI, emerging technologies, and the ideas shaping humanity's next chapter, consider subscribing to DeadLock.
Because the future isn't just happening to us.
It's being built right now.
Small utility to display environmental impact of claude - based on ecologits.ai
Tech sector says only carbon-emitting gas plants are reliable enough today to power the EU’s AI goals.
Chaine youtube qui parle du fonctionnement des algorithmes en programmation, et des technologies sous-jacentes.
What follows is a version of an email I sent our entire R&D team about an explicit goal and deliberate action we’ll take to become twice as productive through our embrace of AI.
Darragh Curran
Jun 13, 2025
Both sides are grappling with a real existential threat, and both sides feel like they are screaming into the void. There is a way to close the gap and get everyone pulling in the same direction.
La semaine dernière, j'ai écrit « Delete your backlog. » L'article a été bien reçu et beaucoup tourné, et une question est revenue plus souvent en MP presque toujours formulée de la même manière : « Oui, mais avec l'IA, alors ? »
Et c'est une vraie question. Parce qu'au moment exact où je plaidais pour supprimer, purger, faire de l'espace, une bonne partie des équipes Produit découvraient l'inverse : qu'il était devenu trivial de produire. De générer des epics, des features, des user stories, des critères d'acceptation, des variantes, en quelques secondes, presque gratuitement*.
Install and configure your own second brain: a private, personal, and robust assistant inside Claude that forgets nothing, helps you connect the dots, and makes you far more effective on your day-to-day tasks (semantic search via RAG included).
Ten takeaways from the Acceleration Whiplash report
Two years of telemetry. 22,000 developers. More than 4,000 teams.
The AI Engineering Report 2026 is not a survey of how developers feel about AI. It is a measurement of what AI is actually producing across the full software development lifecycle, tracking metric change between periods of lowest and highest AI adoption within each organization.
What it found has a name: the Acceleration Whiplash. AI has flooded a system built around human-paced development and human-quality code with output it was never designed to absorb.
Throughput is up. So are bugs, incidents, and the hidden costs accumulating at every stage downstream.
This report examines seven areas where that tension is visible: adoption, throughput, context switching, code complexity, pre-merge quality, workflow efficiency, and production quality. Here are ten takeaways from the data.