Photogimp fait ressembler Gimp a photoshop et facilite la transition.
Be sure to use AI when making
your next, I don’t know, meal plan,
for example. Definitely do not call
your friend who loves to cook and ask her
for her favorite recipes or tips or ways
to save time making meals,
because you will end
up talking for longer than you had hoped,
hearing, perhaps, about her father’s cancer
diagnosis or how lonely she’s been or even
what she’s planted in her spring
garden and then lost with the early frost.
...
Automated tests are important. Without them, programmers waste a huge amount of time manually checking and fixing their code.
Unfortunately, many automated tests also waste a huge amount of time. The easy, obvious way to write tests is to make broad tests that are automated versions of manual tests. But they’re flaky and slow.
Folks in the know use mocks and spies (I say “mocks” for short in this article) to write isolated interaction-based tests. Their tests are reliable and fast, but they tend to “lock in” implementation, making refactoring difficult, and they have to be supplemented with broad tests. It’s also easy to make poor-quality tests that are hard to read, or end up only testing themselves.
Bad tests are a sign of bad design, so some people use techniques such as Hexagonal Architecture and functional core, imperative shell to separate logic from infrastructure. (Infrastructure is code that involves external systems or state.) It fixes the problem... for logic. But infrastructure is often left untested, and it requires architectural changes that are out of reach for people with existing code.
This pattern language1 describes a fourth option. It avoids all the above problems: it doesn’t use broad tests, doesn’t use mocks, doesn’t ignore infrastructure, and doesn’t require architectural changes. It has the speed, reliability, and maintainability of unit tests and the power of broad tests. But it’s not without tradeoffs of its own.
1The structure of this article was inspired by Ward Cunningham’s CHECKS Pattern Language of Information Integrity, which is a model of clarity and usefulness.
The patterns combine sociable, state-based tests with a novel infrastructure technique called “Nullables.” At first glance, Nullables look like test doubles, but they're actually production code with an “off” switch. And that’s the tradeoff: do you want that in your production code? Your answer determines whether this pattern language is for you.
The rest of the article goes into detail. Don’t be intimidated by its size. It’s broken up into bite-sized pieces with lots of code examples.
- TTY
- tmux
- kmscon
- syncthing
- nmtui
- neovim
- Size font
- more than 16 colors
- Alternate typeface
- rudimentary mous support
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)
images of ai
to help understand bette
CC
Accessibilité des ressources pédagogiques.
Sophie Drouvroy
2005, une année qui aurait dû bouleverser la France, rendre le numérique plus accessible aux personnes handicapées. Le titre me laissait espérer pour un monde plus inclusif : Loi du 11 février 2005 pour l’égalité des droits et des chances, la participation et la citoyenneté des personnes handicapées.
À l’aube de 2025, le numérique n’est pas accessible à toutes et tous. La promesse d’égalité des droits et des chances, la participation et la citoyenneté des personnes handicapées ne sont qu’un écran de fumée.
Aujourd’hui, le numérique responsable et l’intelligence artificielle (IA) ont le vent en poupe.
Je me sens devant un effroyable paradoxe : choisir entre le numérique responsable et l’intelligence artificielle qui pourrait enfin rendre accessible ce que les humains n’ont pas réussi à faire jusqu’à présent ?
The longevity dose for sleep is 6.4 - 7.8 hours.
23 biological aging clocks
multi-omics: 11 proteomic, 5 metabolomic, 7 MRI
500,000 people
Interesting findings:
-
Brain proteins notice sleep loss before brain anatomy does. When you measure brain aging by plasma proteins, the brain looks biologically youngest at 7.82 hours of sleep in women and 7.70 hours in men. When you measure brain aging by MRI of brain anatomy, it looks youngest at 6.48 hours in women and 6.42 hours in men.
-
The brain and the metabolic organs share the same U-shape but hit their optimum at different hours. Fat tissue and the pancreas both bottomed near 6 hours. The brain bottomed higher, between 6.4 and 7.8 hours depending on whether you measure by MRI or plasma proteins. Sleep less or more than the organ-specific optimum and aging accelerates.
-
Short sleepers vs long sleepers DNA.
Short sleepers' DNA matched the DNA of people whose bodies are breaking down all over.
back pain 40%
depression 37%
substance use disorders 37%
anxiety 32%
heart failure 31%
lung disease 28%
type 2 diabetes 18%.
Looking at genes only, chronically too little sleep makes the body look like it's breaking down everywhere.
Long sleepers' DNA matched the DNA of people with brain conditions versus whole body breakdown.
major depression 29%
schizophrenia 28%
ADHD 28%,
migraine 28%
bipolar disorder 21%
Short sleep gets you through the body directly: the nervous system get's aggravated, the immune system gets confused and stress hormones flood the bloodstream.
Long sleep get's you through the brain, but it's the result and not the cause. By the time someone is sleeping too long, the damage is already happening inside their organs.
Summary:
Less than 6.4 hours is a stressor. Your body is wearing down because it never gets enough time to recover. The short sleep is what is causing the damage.
More than 7.8 hours is a warning sign, signaling that something is already going wrong in your brain or your metabolic organs.
- Amendement DMA et interopérabilité : https://drive.proton.me/urls/PK7DANAP...
- Quitter les big techs : https://tournesol.app/entities/yt:P9E...
- Tournesol et Climat : https://tournesol.app/search?language...
- Veche : https://tournesol.app/entities/yt:bNy...
- Baromètre OFF February : https://accbf1a8-f033-496f-88b7-a912f...
- Les 4 piliers d'un numérique (vraiment) démocratique : https://tournesol.app/entities/yt:kt6...
For now I see two ways to use LLMs-that-do-my-work (as opposed to LLMs-that-search, LLMs-that-ask-me-questions or LLMs-that-build-tools-I-use for instance)
1️⃣ Generating generic things
Given LLMs approximate language, and language approximates intent, it can be argued that the highly over-marketed "artificial intelligence" aspect of LLMs (aka "do something for me that works plz") is most useful when we don't know precisely what we want, and become less valuable the more precise you get (applying semantic anchoring on too many items, typically)
2️⃣ Helping with producing precise things
This includes being technical, having a technical approach, and using the tool positively for the technical steps (for instance, Test Driven Development and Domain Driven Design)
✅ Pros:
- Feeling more productive [1]
- Maybe more productivity (all the studies [1] I've read seem to point it's not the case but this is still early and moving)
❌ Cons (some happen systematically, others conditionally) :
- Dependency on the tool (breaking changes, availability issues, pricing policy changes, data use, state of the relationship between your country and the seller's country, etc)
- Token saving management [2]
- Cognitive surrender [3]
- A LOT more bugs in production [23]
- Senior developer time mostly spent on reviewing code that should not have reached them in that state [23]
- Lack of friction causing drops in memory, learning, engagement, and motivation [4][27]
- Value given to Contributions rather than to Contributors : why should lead devs hire you instead of firing up their own LLM? [28]
- Addiction to Control and/or Validation and/or Slot machine loop ("Replay button") [5]
- Automation of what brought you Joy at work [6]
- More expectations/stress [22] while being paid the same
- Critical thinking presence needs to be maintained (through discipline for instance, which does not work) and/or delegated into automated tests (back to Control replacing Joy)
- Critical thinking quality needs to be maintained by regularly doing things manually despite the tool acting actively against it ("Claude would do it faster" is such a pernicious thought)
- Loss of diversity in collaborative conversations ("my Claude's arguments versus your Claude's arguments") [7]
- How do you mentor Juniors?
1️⃣ Pieces of data gathered about the impact of LLMs on productivity
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_ai-for-developer-productivity-what-now-activity-7452020616016195584-EX1V?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
2️⃣ Multilingual prompting saves 20-40% tokens
https://arxiv.org/pdf/2507.00246
3️⃣ Study from January 2026 about cognitive surrender
https://www.linkedin.com/posts/mehdi-moussaid-160ba916a_labandon-cognitif-cest-le-nom-donn%C3%A9-par-activity-7447901614989934592-HF6l?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
4️⃣ Friction : it matters (memory, learning, engagement, motivation)
https://youtu.be/rf642RFALDU?is=wl04yfCcEyU7A2d0
6️⃣ A study from March 2026 about LLMs automating the joy out of work
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_are-we-automating-the-joy-out-of-work-designing-activity-7451939177014841344-qmGA?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
7️⃣ LLM use reduces the number of fields explored by Research
https://www.linkedin.com/posts/mehdi-moussaid-160ba916a_encore-un-article-incroyable-publi%C3%A9-la-semaine-activity-7422549300930310144-Qv6v?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
8️⃣ study from April 2026 about LLMs corrupting your documents when you delegate, via sparse but severe errors
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_llms-corrupt-your-documents-when-you-delegate-activity-7453400644373303296-TyT4?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
9️⃣ A study from June 2025 about cognitive debt when using chatgpt as an assistant for essay writing
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_your-brain-on-chatgpt-accumulation-of-cognitive-activity-7451938489916506112-mfOD?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
🔟 LLMs induce slavery
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_les-travailleurses-du-clic-activity-7450846472041975810-9ma6?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
1️⃣1️⃣ Lack of intelligence
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_vous-voulez-gagner-2-millions-de-dollars-activity-7450472933619253248-xMpe?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
1️⃣2️⃣ Deleted volume
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_claude-code-deleted-my-entire-archive-activity-7447698537464819712-UFoG?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
https://arxiv.org/abs/2306.08189 (An analysis of language models on negation benchmarks)
1️⃣3️⃣ A study from March 2026 about LLMs doing human work
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_crashing-waves-vs-rising-tides-preliminary-activity-7447605340403191808-ZsaI?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
1️⃣4️⃣ A study from March 2026 about LLMs answering correctly to visual questions, despite not being given any visual input
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_ia-santaez-hallucinations-activity-7446491905867247616-Q5uH?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
1️⃣5️⃣ A study from February 2026 on ai psychosis (aka delusional spiraling) caused by chatbots
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_ai-ai-activity-7445881612736712704-7NFv?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
1️⃣7️⃣ A study from March 2026 about LLM writing assistants shifting users' attitudes on societal issues through their bias
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_when-people-use-ai-for-writing-assistance-activity-7448643257019809792-_ccP?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
1️⃣8️⃣ Generated nudes from victims of Crans-Montana fire
https://www.lebigdata.fr/a-vomir-ils-utilisent-lia-grok-pour-denuder-les-victimes-de-crans-montana
1️⃣9️⃣ The Chinese government floods X search results with porn whenever there is political unrest
https://x.com/nikitabier/status/2017134769113542752
2️⃣0️⃣ Hallucination is feature, not a bug (in probabilistic tools)
https://www.linkedin.com/posts/matsanchez_on-na-jamais-%C3%A9t%C3%A9-aussi-pr%C3%A8s-de-lia-g%C3%A9n%C3%A9rale-activity-7455161889161949184-IcQ2?utm_source=share&utm_medium=member_desktop&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
2️⃣2️⃣ Compressed Cognition : Agentic coding is mentally expensive
https://www.linkedin.com/posts/adam-tornhill-71759b48_i-have-to-admit-that-i-havent-had-this-much-share-7458044119022616577-q6K4?utm_source=share&utm_medium=member_android&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
2️⃣3️⃣ The acceleration whiplash
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_the-ai-engineering-report-2026-the-ai-acceleration-activity-7459198398634733569-XTzE?utm_source=share&utm_medium=member_android&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
2️⃣4️⃣ LLMs return Trendslop for Strategic advice
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_researchers-asked-llms-for-strategic-advice-activity-7460645767389818880-FXaN?utm_source=share&utm_medium=member_android&rcm=ACoAABIeznoBdvfOCQc-Pz317B5HZYqHGwcBOgU
2️⃣5️⃣ PR with (+10k, -4million) lines of code
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_from-the-claudecode-community-on-reddit-share-7461460781025488896-7RMV
2️⃣6️⃣ Example of anthropomorphizing (work conditions of the agent)
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_une-%C3%A9tude-r%C3%A9cente-de-stanford-r%C3%A9v%C3%A8le-que-activity-7462070011168288768-v4Cp
2️⃣7️⃣ Software developers who abandoned LLMs to go back to traditional
https://youtu.be/iXG_b1K8GK8?is=OGvvC-40wdZiku_s
https://youtu.be/pzkwn3hu1Cc?is=kzL6Dw9v0YW2q4uh
2️⃣8️⃣ Software project with strict anti-LLM policy and the reasons why
https://www.linkedin.com/posts/minh-t%C3%A2m-tran_the-zig-projects-rationale-for-their-firm-activity-7462098611229638656-64xk
2️⃣9️⃣ Bun's migration from Zig to Rust : (+1million, -4k) lines of code in 11 days
https://www.linkedin.com/posts/fabricebernhard_1009257-lines-of-code-migrated-in-11-days-share-7460996434058805248-key-
In 2024, the World Bank estimated the internet to account for 1-4% of global greenhouse gas emissions, similar to aviation. “If the internet was a country, it would be the 13th largest emitter between Mexico and Brazil,” says the Sustainable Web Manifesto.
These numbers may be dazzling, but there is actually a silver lining here: If you work in tech, you have lots of opportunities to help improve the situation at work.
In this article, we'll look at the Web Sustainability Guidelines and how they apply to the components in your design system.
Patterns are best practice design solutions for specific user-focused tasks and page types.
All of the patterns in this section are supported by written guidance and contain coded examples where possible.
Patterns often use one or more components and explain how to adapt them to the context.