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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:
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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.
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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.
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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.
Calculette
Empreinte environnementale de Datacenters
Par hubblo
Sometimes I feel that web development has gotten pretty complicated. Fairly straightforward applications are split apart with their frontends powered by behemoth frameworks like React, Next, or Nuxt.
But, it doesn't have to be that way. In this video, I show how a basic backend framework (in this case, Laravel) with its templating language, can have full-stack applications built around the engine of hypermedia, the actual HTML in a browser. It's a strange way of looking at development, but has the potential to remove a lot of headaches, letting the modern standards present in HTML handle the workload, being enhanced at times with a sprinkling of vanilla JavaScript.
The result is an accessible frontend that's fast and scalable, with your data and state built-in.
Some helpful links: https://htmx.org https://alpine-ajax.js.org https://unpoly.com
Welcome! I am going to pass on to you the secret to a successful and brief noobitude, and I won’t even keep you in suspense: nobody cares how many tasks you complete. Why not, and what we care about instead are the subject of the rest of this essay.
Look at your situation from our perspective (by “our” I mean “older engineers”). We hired a bunch of people like you. Some of y’all (we’ll call them A’s) will be amazing game-changers, making everyone around them wildly more productive. Many of you (B’s) will be solid performers. Some of you (the C’s) won’t be here in a year.
We seniors have our regular work to do, but we also have to figure out which category you fit into. We support the superior performers as much as we possibly can. We support the solid performers enough to help them mature. Brutal as it seems, we’d like to expend as little effort as possible on people who aren’t going to make it.
It’s your job to get in the category you want to be in and send us the signals that tell us that’s where you belong.
That stack of tasks you have to do? Your manager or your tech lead could finish those in much less time and with much less hassle than it takes to help you through them. If all we cared about was today’s productivity, we wouldn’t have hired you at all. Instead, we (the seniors) are focused on the future: we know there’s going to be far more work here than we could possibly accomplish. We are paying your salary now as the option premium on the engineer you are going to become. If we play this game right, we’ll have a kick-ass next generation of engineers. If not, we’ll have to be doing the same engineering jobs ten years from now, and we really don’t want to be doing that.
m a huge fan of (the potential for) #LLMs and how they might revolutionize the nature of work and a huge critic of the trillion-dollar shill and #genAI-as-junior-developer / excuse for firing half your workforce that you over-hired over the last decade so really it is you who should be clearing your desk.
I am disgusted by the consequence-free wholesale IP theft and casual planet burning and excited by the potential for local models to do 99% of the heavy lifting.
I am happily speaking to various Claude models in a conversational way and painfully aware that it is in no way 'alive' or 'conscious' and merely acting as a mouthpiece for thousands of people's brilliant work that I am able to lean on.
At the same time.
Why is this so confusing for people? Perhaps we don't need polarized shrieking. Perhaps it is possible for people to contain multitudes.
Categorize each piece of code and then define conventions for each category, using Oxlint for enforcement.
A Series C fintech with ninety engineers, AI-native from day one, lost $4.2 million to a reconciliation function nobody on the team could explain. This is the labor-market thesis on what happens next: why technical autonomy — the ability to ship without a chat window — is becoming the new luxury status in software engineering, and why the engineers who never stopped knowing how the cement works are about to be the most expensive hires of the decade.
We unpack the concept of Comprehension Debt — the silent liability accumulating in every codebase where models write code and humans approve it without understanding it — and trace its consequences across three vectors: the broken senior-engineer pipeline, the bifurcating consulting market, and the compliance frameworks (SOC 2, PCI-DSS, HIPAA, ISO 27001, the EU AI Act, DORA) that refuse to accept "the assistant suggested it" as a control.
The post-AI developer is not the engineer who refuses AI. It is the engineer who can work as if AI did not exist.
━━━━━━━━━━━━━━━━━━ RELATED VIDEO ON THE CHANNEL ━━━━━━━━━━━━━━━━━━
If you want the actual rates, retainer structures, and the specific subfields where the post-AI premium is concentrating right now, watch:
- No-AI Developers Are Billing $250 An Hour — Here's the Market. The numbers are not theoretical. They are this quarter.
━━━━━━━━━━━━━━━━━━ CHAPTERS ━━━━━━━━━━━━━━━━━━
- 0:00 The Paradox of Ease
- 0:44 The Suffering Was the Curriculum
- 1:44 Comprehension Debt
- 2:37 Why Prompt Engineers Are Cheap
- 3:24 Compliance Cannot Terminate in a Model
- 4:19 The Elite Market Is Already Here
- 5:20 How To Be a Post-AI Developer
- 6:04 Cut Your Internet
- 6:51 The Cement Holds
━━━━━━━━━━━━━━━━━━ KEY CONCEPTS ━━━━━━━━━━━━━━━━━━
- The Post-AI Developer — an engineer who uses AI but can ship without it. Defined by autonomy, not abstinence.
- Comprehension Debt — the accumulating liability of merged code that no human on the team can explain. Compounds silently. Surfaces in production.
- The Junior Gap — the broken forging path. Juniors can ship in an afternoon what used to take three weeks. The map of how systems actually work is no longer being built.
- The Bifurcating Market — infinite supply of prompt-driven code generators on one side; vanishing supply of engineers who can debug a heap dump on an air-gapped server on the other.
- The Compliance Wedge — regulated industries (banking, healthcare, defense, payments) require a named human in the chain of responsibility. Language models cannot sign an audit.
- Technical Autonomy — the ability to think without a chat window. The new luxury status in engineering.
━━━━━━━━━━━━━━━━━━ FREQUENTLY ASKED ━━━━━━━━━━━━━━━━━━
What is a post-AI developer?
A post-AI developer is an engineer who uses AI tools but can work, debug, and ship without them. The defining test: if your internet were cut right now, could you still deliver? Post-AI developers say yes. Wrappers around a language model say no.
What is comprehension debt in software engineering?
Comprehension debt is the liability that accumulates when AI-generated code is merged into a codebase without any human on the team being able to explain why the code is structured the way it is. It compounds silently — the function works, the tests pass, until six months later it fails in production and nobody can reason about the original logic. Distinct from technical debt: technical debt is code you understand and chose not to clean up. Comprehension debt is code you shipped without ever understanding.
Why are engineers who don't use AI suddenly billing more than those who do?
Two forces. First, scarcity: the supply of engineers who can debug from first principles — without an internet connection, without a chat window — is shrinking while demand for incident response stays constant. Second, criticality: regulated industries (SOC 2, PCI-DSS, HIPAA, EU AI Act, DORA) cannot terminate the chain of responsibility in a language model. Scarcity plus criticality equals premium pricing. The market is already paying it.
Is being a post-AI developer the same as refusing to use AI?
No. Refusing AI is what the video calls "Luddite cosplay" — the market does not pay for performative purity. Being post-AI means using AI freely while remaining able to defend every line, notice every silent error, and continue working when the assistant is unavailable. It is a stricter standard than refusal, not a softer one.
How do junior developers become senior engineers in a post-AI world?
By deliberately rebuilding the map that AI removed. That means doing some work — debugging, reading source, reasoning about memory and network behavior — without the assistant, even when using it would be faster. The friction is the curriculum. Engineers who skip it can ship features but cannot own incidents, and the labor market is starting to price that distinction explicitly.
In 2026, anyone with a terminal can type eight lines of telnet and send mail claiming to be your CEO. No password. No certificate. No proof of identity.
The protocol that carries every email on the public internet was specified in August 1982 for 235 trusting research hosts — and nobody has re-shipped it since.
This is the SMTP Tax: the cumulative cost of running a 44-year-old plaintext mail protocol on a network that now moves roughly 320 billion messages a day between adversaries. Spam filters, BEC wire fraud, midnight SPF flattening, multi-billion-dollar email security industries — all of it is the bill for an architectural default the world stopped questioning around 1995.
The video walks through the full stack of patches that have been bolted onto SMTP since 2003. SPF tells receivers which IPs are allowed to send for a domain — and breaks the moment a message gets forwarded. DKIM signs message bodies with real public-key cryptography — and says nothing about the visible From: header your user actually reads. DMARC ties the two to the visible identity through alignment — and is advisory, with the most common policy being p=none, which rejects nothing. BIMI publishes a logo for domains that already enforce DMARC — a UX sticker on top of three optional patches. MTA-STS, ARC, TLS-RPT, DANE — each one a layer on a layer on a layer.
Then the structural argument: what email looks like if you stop bolting. JMAP (RFC 8620) moves authentication from a TXT record to the request itself. The decentralized identity world — DIDs, the kind of identity model behind Bluesky's AT Protocol — asks what mail looks like if every sender is a key, not a string. The reason that argument is approximately as far away as IPv6 was twenty years ago isn't technical. It's gravitational.
The closing frame: the SMTP Tax is not a bug. It's a default chosen in 1982 for a federation of universities, never re-chosen for the network that exists now.
━━━━━━━━━━━━━━━━━━ CHAPTERS ━━━━━━━━━━━━━━━━━━
- 0:00 The Cold Open: Eight Lines, No Password
- 0:19 By The Numbers: 320B Emails, $50B BEC
- 1:20 RFC 821, August 1982: A Network Of 235 Hosts
- 3:11 SPF (2003): The First Patch
- 3:49 DKIM (2007): Strong Primitive, Wrong Target
- 4:28 DMARC (2012): Why p=none Is Theatre
- 5:20 BIMI And The Patch Pile
- 6:17 The Structural Alternative: JMAP
- 7:01 Cryptographic Sender Identity And DIDs
- 7:59 A Default Nobody Re-Chose
━━━━━━━━━━━━━━━━━━ KEY CONCEPTS MENTIONED ━━━━━━━━━━━━━━━━━━
- SMTP — Simple Mail Transfer Protocol, RFC 821 (1982), Jon Postel
- RFC 793 — TCP specification, source of Postel's Law
- Postel's Law — "Be conservative in what you do, be liberal in what you accept from others"
- SPF — Sender Policy Framework (2003)
- DKIM — DomainKeys Identified Mail (2007)
- DMARC — Domain-based Message Authentication, Reporting and Conformance (2012)
- BIMI — Brand Indicators for Message Identification (2020)
- MTA-STS, ARC, TLS-RPT, DANE — auxiliary patches on the SMTP stack
- JMAP — JSON Meta Application Protocol, RFC 8620 (2019)
- BEC — Business Email Compromise (FBI IC3 category since 2013)
- DID — Decentralized Identifier (used by Bluesky's AT Protocol)
- ARPANET — 235 hosts at the time RFC 821 was written
━━━━━━━━━━━━━━━━━━ FREQUENTLY ASKED QUESTIONS ━━━━━━━━━━━━━━━━━━
What is the SMTP Tax?
The cumulative cost of running a mail protocol designed in 1982 for 235 trusting hosts on a 2026 network of 320 billion daily messages between adversaries. It includes BEC fraud, spam filtering infrastructure, the entire email security vendor industry, and the engineering hours spent maintaining SPF, DKIM, DMARC, and BIMI records that the protocol itself never required.
Why doesn't SMTP have built-in sender verification?
Because RFC 821, written by Jon Postel in August 1982, specified SMTP for ARPANET — roughly 235 hosts where every administrator knew every other administrator by name or institution. The threat model was hardware failure, not a hostile actor. The word "authentication" does not appear once in the 47-page document.
Does DMARC stop email spoofing?
Only when a domain publishes p=reject and the receiver honors it. The most common DMARC policy by an enormous margin is p=none, which is monitoring mode — it rejects nothing. Roughly 30% of Fortune 500 domains enforce p=reject; across the broader Alexa top 1 million, the figure is closer to 12%. Everyone else is running a record that exists so a vendor checkbox lights up green.
Modern software is 43x slower than it was twenty years ago, and the people building it cannot tell you why. This is the bill for forty years of "hardware will catch up." For two decades the industry traded performance for convenience and called it progress.
Wirth's Law became a roadmap. Dennard scaling died in 2005 and nobody on the team noticed.
A "Hello World" now ships with 1,847 dependencies, allocates 487 MB of memory, and takes three seconds to draw a window.
The de-bloating is what happens when the stopwatch finally wins the argument: Rust at Cloudflare, Zig under Bun, Mojo under the Python data stack, Go quietly eating the JVM.
The layer count is the bug. This video is the diagnosis.
━━━━━━━━━━━━━━━━━━ RELATED VIDEO ━━━━━━━━━━━━━━━━━━
If this was the diagnosis, the next one is the receipt. Watch it after this one: "Stop Buying RAM. Read This Instead. (The Tab Tax)"
━━━━━━━━━━━━━━━━━━ CHAPTERS ━━━━━━━━━━━━━━━━━━
- 0:00 Two Apps, Twenty Years Apart
- 0:35 The Abstraction Trap
- 2:19 The Benchmark Doesn't Lie
- 3:27 The Low-Level Mindset Returns
- 5:14 The Bill Is Due
━━━━━━━━━━━━━━━━━━ KEY CONCEPTS COVERED ━━━━━━━━━━━━━━━━━━
- Wirth's Law (Niklaus Wirth, 1995): software gets slower faster than hardware gets faster
- Dennard scaling and the end of single-thread CPU performance growth around 2005
- The abstraction trap: why component libraries on top of design systems on top of runtimes on top of browsers stopped being free
- The Chromium runtime tax and why excellent engineers inside Electron lose to the stopwatch
- "Clean Code" as superstition: when a style guide becomes a religion at the cost of an order of magnitude
- The low-level mindset returning as economics, not nostalgia
━━━━━━━━━━━━━━━━━━ TOOLS AND LANGUAGES MENTIONED ━━━━━━━━━━━━━━━━━━
- Electron and the Chromium runtime tax
- Rust, Cloudflare Pingora (one trillion requests a month), Discord read-states, the Windows kernel rewrite
- Zig and the Bun runtime
- Mojo and the Python data-science stack (NumPy, CUDA)
- Go: the quiet boring default that ate the JVM
- hyperfine for cold-start benchmarking
- npm and the 1,847-dependency baseline
━━━━━━━━━━━━━━━━━━ FREQUENTLY ASKED QUESTIONS ━━━━━━━━━━━━━━━━━━
What is Wirth's Law?
Wirth's Law, formulated by Niklaus Wirth in 1995, states that software gets slower faster than hardware gets faster. It was meant as a warning. The industry treated it as a roadmap for forty years.
Why is Electron so slow and memory-hungry?
Electron ships a full Chromium runtime with every app, which means each "native" desktop app is paying the cost of an entire web browser before the application code even runs. In the video, a Rust-based editor opens a 1 MB file 43 times faster and uses 22 times less memory than its Electron equivalent on the same machine.
Is JavaScript or the web stack dying?
No, but the assumption that hardware would keep absorbing software bloat for free is dying. Dennard scaling ended around 2005 and single-thread CPU performance stopped doubling. The "de-bloating" is the industry quietly rebuilding hot paths in Rust, Zig, Mojo, and Go because the layer count became the bottleneck.
What does "the layer count is the bug" mean?
It is the recognition that modern application slowness is not caused by any single bad framework, but by accumulating runtimes on top of runtimes until the abstraction tower itself becomes the dominant cost. Peeling layers, not adding them, is now the optimization.
What should I watch next? "Stop Buying RAM. Read This Instead. (The Tab Tax)" — same channel.
That video is the receipt: the most ordinary place on your machine where the bloat bill arrives first, and the one most people already opened today and forgot about. This one is the diagnosis. Watch them in that order.
