eraser

[Bryan Johnson @bryan_johnson]

This is biblical.

A woman in her eighties. Ten years into Alzheimer's. Hadn't spoken a full sentence in five years.

Takes one, 5 gram dose of psilocybin.

She slept 19 hours and woke up and spoke for hours about her life, recognized family and held real conversations. She regained bladder control after five years, walked on her own. and dressed herself. Gains held for weeks.

[سيدة @Ajoke__xx]

Somewhere in your 20s or 30s you’ll get the opportunity to rebuild your life after a negative loop, heal from what broke you, live in your own space, reconnect with your discipline, and learn to love yourself again. It’s very important that you see that journey through.

[Machiavelli Bot @UnmodernmanBot]

A man who cannot control his appetite eventually becomes a slave. Food, lust, attention, comfort, praise, outrage, novelty. Find what you cannot refuse and you will know the price, you will bend your principles for. Slavery often begins with the thing a man keeps calling harmless.

[27. @dimvji]

takes insane discipline to actually go ghost and get your life right

[そう｜Claude Codeで始めるAI自動化 @so_ainsight]

今週GitHubで急上昇したAIリポジトリ10選：

1. headroom
ツールの出力・ログ・ファイル・RAGの塊を、AIに渡す前に圧縮。トークンを60〜95%削っても回答は変わらない。ライブラリ・プロキシ・MCPサーバーとして使える。
https://t.co/HRNQTDMZHZ

2. hermes-agent
使うほど賢くなる自己成長型のAIエージェント（Nous Research製）。スキルを自分で作り、セッションをまたいで記憶を保持、200以上のモデルやTelegram・Slack連携にも対応。
https://t.co/Xj5kY3HUz4

3. ECC
Claude Code・Cursor・Codexなどのエージェント環境をまとめて底上げ。スキル・記憶・セキュリティを1パックに、64エージェント＋261スキル入り。
https://t.co/MnxisTgzbU

4. codegraph
コードを先にナレッジグラフ化して、AIが毎回ファイルを探し回らずに済む。ベンチマーク平均でトークン47%減・ツール呼び出し58%減、完全ローカル動作。
https://t.co/TChuWEXUXk

5. last30days-skill
どんなテーマでもReddit・X・YouTube・Hacker News・ウェブを横断調査し、根拠付きで要約。反応の大きい情報から拾うAIエージェント用スキル。
https://t.co/PnMe0od1SH

6. MoneyPrinterTurbo
キーワードを入れるだけで短尺動画が完成。台本・素材・字幕・BGMまで自動生成、縦9:16も横16:9も出せる。
https://t.co/oD69Imz5NC

7. VoxCPM
多言語対応の音声合成（読み上げ）エンジン。声のデザインから、本人そっくりの声まねまでこなす。
https://t.co/Y9dsuvomDF

8. Agent-Reach
AIエージェントにネットを"見る目"を与えるCLI。X・Reddit・YouTube・GitHub・Bilibili・小紅書を横断で読めて、APIキー課金なしで動く。
https://t.co/fWlNy20yya

9. Open-LLM-VTuber
好きなAIモデルと声で会話できるVTuber。ハンズフリー・割り込み発話に対応し、Live2Dの顔がローカルで動く。
https://t.co/rsStCW0MEV

10. PaddleOCR
PDFや画像を、AIがそのまま扱える構造化データに変換。軽量で100言語以上に対応するOCRツールキット。
https://t.co/CrLQeLJYCA

[Ihtesham Ali @ihtesham2005]

10 SIGNS YOU'VE BEEN STUDYING WRONG YOUR WHOLE LIFE

Read every one. If you do even half of these, you've been confusing effort with learning your entire life.

1. You reread your notes

Rereading feels productive because the words get familiar. Familiarity is not knowledge. The fix: close the book and write down everything you remember first. The struggle to recall is the actual learning. Reading again is just comfortable.

2. You highlight everything

A page full of yellow means you decided nothing was important. Highlighting tricks your brain into thinking it processed the idea when all it did was color it. The fix: after each section, summarize it in one sentence in your own words. If you can't, you didn't understand it.

3. You study in one long session

Cramming six hours into one night feels heroic. Your brain dumps almost all of it within days. The fix: split the same six hours across a week. The same effort, spaced out, can double what you keep, because every time you force a memory back it gets stronger.

4. You study in the same quiet spot every time

Your brain ties the information to the room. Then the exam is in a different room and it vanishes. The fix: change where you study. Different desks, different times. Memory you can retrieve anywhere is memory you actually own.

5. You confuse recognizing with knowing

You read the answer and think "yeah, I knew that." You didn't. You recognized it. Recognition collapses the second the answer isn't in front of you. The fix: cover the answer and produce it from nothing. That is the only test that counts.

6. You finish a chapter and feel done

Reading the last page feels like an accomplishment. It measures nothing. The fix: shut the book and try to teach the chapter out loud to an empty room. Every place you stumble is a gap the exam was going to find for you anyway.

7. You avoid the hard problems

You drill the questions you can already do because getting them right feels good. The easy reps teach you nothing. The fix: spend your time on the problems that make you uncomfortable. Difficulty is not the obstacle to learning. It is the mechanism.

8. You take notes word for word

Copying the teacher's exact sentences keeps your hand busy and your brain asleep. The fix: write notes in your own language. The instant you translate an idea into your own words, you find out whether you actually got it.

9. You think rewatching the lecture counts as review

Watching someone explain it again is watching someone else lift the weights. The fix: do the work yourself. Try the problem before you look at the solution. The solution teaches you almost nothing. The attempt teaches you everything.

10. You measure studying by hours, not output

Eight hours at a desk feels like dedication. Most of it was motion, not progress. The fix: at the end of every session, ask one question. What can I now do that I could not do before I sat down? If the answer is nothing, the hours were a performance.

Memorization feels like learning. It almost never is.

[Chris Short @ChrisShort]

harehare/mq - A jq-like Markdown query language for command-line processing #devopsish https://t.co/KgfJAQnOIJ

[Floro S. @sflorimm]

Why has Vercel become the default hosting platform for vibe coders?

There are plenty of alternatives.

What made Vercel win?

[AvantPrince_ @AvantPrince_]

The most insidious trap modern men fall into is the belief that consuming information is the same thing as becoming capable and this confusion is rotting an entire generation from the inside out because they genuinely cannot distinguish between knowing about something and being able to do it under pressure when it actually matters

You watch a 45 minute breakdown of negotiation tactics, you feel sharper afterwards, you walk away believing you have absorbed something meaningful and in some micro sense you have but the brutal reality is that knowledge without application under real stakes is just entertainment dressed up as productivity and the dopamine hit you got from learning mimics the feeling of actual growth so perfectly that your brain stops pushing you toward the uncomfortable application that would transform that information into embodied skill

I see men who can articulate frameworks for everything, they can explain compounding and leverage and first principles thinking and strategic patience and all these beautiful conceptual models but when you look at their actual lives they are stagnant, they are not building, they are not creating outcomes, they are not sitting across from people who can change their trajectory and performing at the level required to make something happen because they never forced themselves through the crucible of real world application where your theories get pressure tested against reality and you find out very quickly what you actually know versus what you think you know

The difference between consuming and executing is the difference between watching someone fight and getting punched in the face and until you have been punched in the face repeatedly you do not understand fighting regardless of how many hours of tape you have studied, your nervous system has not been calibrated through real feedback, your pattern recognition has not been forged through actual consequence, you are running a simulation in your head that has no relationship to the chaos of real engagement

And the men who win understand this at a cellular level which is why they are obsessed with reps over theory, they would rather have a flawed approach executed a thousand times than a perfect framework they have never tested, they know that competence is built through the accumulation of micro failures and adjustments that only come from doing the thing badly until you do it less badly until eventually you do it well and they accept that there is no shortcut through this process regardless of how intelligent you are.

I built what I have built not because I understood things better than anyone else but because I was willing to look stupid more times than anyone else, I was willing to send the message that got ignored, make the pitch that got rejected, post the content that got no engagement, have the conversation that went nowhere, over and over and over again until the accumulated calibration from all those failures made me dangerous because now I actually know what works through lived experience not theoretical frameworks. Also I could do this all again super easily, I have all the systems effortlessly mapped out to produce infinite cash from the sky; CONSCIOUS ACTION MAPPED INTO SYSTEMS

Your consumption habit is a coping mechanism for avoiding the ego death that comes with attempted execution, it lets you feel like you are on the path without ever having to confront the gap between who you think you are and what you can actually produce when it matters, and every day you spend in consumption mode you are widening that gap because the world rewards output not understanding and the men who are actually winning are out there producing flawed work while you are still preparing

The information age has made it possible to feel like you are growing without ever actually growing and the men who escape this trap are the ones who develop an almost allergic reaction to consumption without immediate application, they cannot sit through educational content without asking themselves how will I use this today not someday not eventually but today, and if the answer is they will not use it today they close the tab because they understand that unapplied knowledge is just mental clutter that gives you the illusion of progress while keeping you exactly where you are.

Stop learning. Start doing. The gap between your potential and your reality is not a knowledge gap it is an execution gap and the only thing that closes it is the brutal repeated willingness to attempt and fail and adjust and attempt again until your capability catches up to your ambition via relentlessly mining and digging into the information your actions produce

[المُهندس زياد @ii7_8]

« رأيتُ شخصًا على إنستغرام يقول إنّ طريقته في الدراسة هي التحديق في الحائط لمدة 30 دقيقة دون أيّ مشتتات أخرى، ثمّ الدراسة لساعتين، ويكرّر ذلك حتى يُحقّق هدفه اليومي. نوعٌ مختلف من تقنية بومودورو... »

تعتمد على حيلة نفسية وعصبية ذكية جداً لإعادة تهيئة الدماغ. 🧠
يمكننا تسميتها "تصفير الدوبامين المسبق"، ولها تفنيد علمي يوضح سبب نجاحها:

عقولنا حالياً معتادة على التحفيز الفائق والمستمر بسبب الهواتف ومواقع التواصل. عندما تجلس للتحديق في حائط فارغ لمدة 30 دقيقة، ينخفض مستوى الدوبامين في دماغك بشكل حاد نتيجة الملل الشديد. نتيجة لذلك، عندما تفتح كتابك بعد نصف ساعة، يرى دماغك أن الدراسة هي "الشيء المثير والمنقذ" المتاح حالياً، فتصبح أكثر جاذبية وأقل ثقلاً.

تدريب عضلات الانتباه القدرة على مقاومة الرغبة في تفقد الهاتف أو القيام بأي نشاط لمدة 30 دقيقة هي بمثابة تمرين شاق لعضلات التحكم بالانتباه في المخ. هذا التمرين يجهّزك للدخول مباشرة في حالة "التركيز العميق" (Deep Work).

هي تختلف جوهرياً عن فلسفة بومودورو
تقنية بومودورو هي أن تركز على إدارة الطاقة وتجنب الإرهاق عبر تقسيم العمل لفترات قصيرة تليها فترات راحة (مثل 25 دقيقة عمل ثم 5 دقائق راحة).

طريقة "التحديق"
تركز على بناء "مدرج طيران" طويل للانتباه. أنت تدفع ضريبة الملل مقدماً لتكسب ساعتين كاملتين من التركيز دون انقطاع. إنها أقرب لـ "وضع الراهب" (Monk Mode). هذه الطريقة ممتازة للمهام التي تتطلب تفكيراً معقداً واسترسالاً ذهندياً طويل الفترات، حيث إن فترات الراحة المتكررة في البومودورو قد تقطع حبل الأفكار في مثل هذه المهام.

[Pietro Schirano @skirano]

Excited to share that MagicPath is now available as an official plugin for Codex, in collaboration with OpenAI!

It's incredibly easy to give Codex an infinite multiplayer canvas where it can design, build, and iterate with you.

[Cursor @cursor_ai]

Cursor can now show your agent's context usage as an interactive report in a canvas.

The context explorer breaks down where tokens go across the system prompt, tool definitions, rules, skills, and more.

[Ihtesham Ali @ihtesham2005]

A Russian psychologist spent 10 years proving that the act of talking to yourself out loud is one of the most powerful cognitive tools the human brain has, and almost nobody outside his field has read the work.

His name was Lev Vygotsky.

He worked in Moscow in the 1920s and died of tuberculosis in 1934 at the age of 37. He had no laboratory, no funding, almost no English readers, and a body of work that the Soviet government suppressed for two decades after he died.

He produced the foundational theory of how human cognition actually develops, and the central piece of that theory was a behavior almost every adult is faintly embarrassed about.

Vygotsky noticed that young children talk to themselves constantly. They narrate their own actions, they argue with imaginary opponents, they instruct themselves through tasks out loud.

The dominant theory at the time, from the Swiss psychologist Jean Piaget, said this was a sign of cognitive immaturity that children would eventually grow out of as they learned to think properly.

Vygotsky said the exact opposite.

He argued that this self-directed speech was the most important cognitive event in the entire developmental window, because it was the moment a child first started to use language as a tool to control their own mind. The child was not failing to think. The child was learning how to think by externalizing the process and listening to themselves do it.

He predicted that as children matured, this out-loud self-talk would not disappear. It would go underground. It would become silent inner speech, which is the running monologue every adult has inside their own head for the rest of their life.

The voice you hear when you read this sentence is the direct descendant of a four-year-old narrating their own block tower.

For 50 years almost nobody outside Russia had access to his work, and the few researchers who did pick it up could not get funding to test it. Then in the early 2000s the experiments finally started to pile up, and what they found was that Vygotsky had been right about something even more important than he knew.

The first major study came from Gary Lupyan at the University of Wisconsin and Daniel Swingley at the University of Pennsylvania in 2012. They ran a simple visual search experiment. Participants were shown 20 images at once and asked to find a specific object, like a banana or a chair. In one condition they searched silently. In the other condition they were told to say the name of the object out loud to themselves while looking for it.

The participants who spoke the target name out loud found the object significantly faster, with higher accuracy, than the participants who searched in silence. The effect was strongest when the spoken word matched a familiar object the brain already had a strong category for.

Saying the word out loud literally tuned the visual system to detect that thing better. The researchers called it the label feedback effect, and the implication was that the act of vocalizing a goal physically changes how the brain processes the world while pursuing it.

The second major study came out of the University of Michigan and Michigan State in 2017. The lead researchers were Ethan Kross and Jason Moser, and they used both EEG and fMRI to record what happens inside the brain when people talk to themselves while emotionally upset.

They asked participants to recall painful autobiographical memories and reflect on them in two different ways. Some used the first person, saying things like "why am I feeling this way." Others used the third person, referring to themselves by their own name, saying things like "why is John feeling this way."

The brain scans showed that the simple act of switching from first person to third person, even silently, decreased activity in the medial prefrontal cortex, the region responsible for rumination and self-referential pain. Within a single second of using their own name instead of the word I, participants showed measurably lower emotional reactivity. The shift required no extra cognitive effort. It cost the brain nothing. And it worked.

Kross described the mechanism in his interviews. Talking to yourself by name creates a small amount of psychological distance from your own experience. Your brain processes the situation more like a problem belonging to someone else, which means it can analyze it instead of drowning in it.

What Vygotsky had intuited in 1934 turned out to be even more powerful than the developmental theory he built it into. The voice you use to talk to yourself is not background noise. It is one of the most precise cognitive tools the brain has, and you can change how it works just by changing the pronoun you use.

People who talk through problems out loud are not anxious or unstable. They are running an externalized version of a process the rest of us are running silently and worse. The kindergartener narrating their block tower, the surgeon muttering through a procedure, the engineer pacing a hallway describing a bug to nobody, the athlete repeating a cue to themselves before a free throw, they are all using the same ancient mechanism that builds and steers human thought.

You can run the experiment yourself the next time you are stuck on something hard. Stop trying to solve it silently in your head. Say it out loud. Describe what you are seeing. Walk yourself through the steps as if you were explaining it to a colleague who is not in the room.

And when something genuinely upsets you, switch to your own name. Ask why this person is feeling this way, instead of why I am feeling this way.

The voice you have been told to keep quiet your entire life is one of the oldest pieces of cognitive technology you own.

Most people are still embarrassed to use it.

[Nikki Siapno @NikkiSiapno]

6 API architecture styles every developer should know:

1. REST
↳ https://t.co/GSjtaCU2Y8

2. gRPC
↳ https://t.co/idHY2XQ17I

3. WebSockets
↳ https://t.co/KPYmiV2UdW

4. GraphQL
↳ https://t.co/K96RB7gzmk

5. SOAP
↳ https://t.co/pRqGugnJTk

6. MQTT
↳ https://t.co/pRqGugnJTk

What else would you add?

👋 PS: Want to improve at system design? Download my free System Design Handbook and join 34,000+ engineers who get my free weekly newsletter → https://t.co/LybPLdor9s

🔖 Save for later
♻️ Repost to help others learn system APIs.
➕ Follow me ( Nikki Siapno ) + turn on notifications.

[Último emo vivo @concurseirolixo]

Vi um cara no IG falando que o método de estudo dele é olhar pra parede por 30min sem mais nenhum estímulo e depois estudar por 2h e repetir até bater a meta diária. Pomodoro diferenciado..

[eraser @xpc_5151]

@oscabriel @mattpocockuk 有类似发现，让agent产出spec时，尽可能包含关键变更代码(包括idl/ddl/code…)，代码本身是更精确的spec，减少这种歧义性之后，再让subagent去review或者人来review，然后agent去实现，要简单比文字描述的spec.md效果好的多

[Menci 💖 @lcMenci]

Claude 教会了我，打不开的网站可以用 https://t.co/8WmWI2junp 突破限制（

[Millie Marconi @MillieMarconnni]

A Dutch computer scientist gave one lecture in 1988 arguing that programming is unlike anything humans have ever tried to do before, and the reason most software on earth is broken is that we are still teaching it as if it were a hobby.

His name was Edsger Dijkstra. He won the Turing Award in 1972. He invented the shortest path algorithm that every GPS on earth still runs on.

He wrote the paper that killed the goto statement in modern programming languages.

He spent 50 years quietly being one of the most consequential thinkers in the entire history of computer science, and he was in a very bad mood by the time he stood up at the ACM Computer Science Conference in 1988 to deliver the lecture that almost nobody at the conference wanted to hear.

The lecture was called On the Cruelty of Really Teaching Computer Science.

It is now one of the most cited papers in the entire history of computing education. It was filed in his archive as EWD1036, handwritten in his careful fountain-pen calligraphy because he refused to use a typewriter and famously refused to use email for the rest of his life.

The argument was simple and uncomfortable.

Programming, Dijkstra said, is a radical novelty. Not a new tool. Not a new skill. Not a faster version of something humans already knew how to do. A genuinely new category of intellectual activity that has no real precedent in the entire history of the human species, and our brains have not been built to handle it.

Here is what he meant by that.

When a programmer writes a line of high-level code and presses run, that single line might trigger a billion operations at the level of the silicon.

The ratio between the abstraction you are working in and the physical events you are actually causing is roughly one billion to one. No engineer in history before computing ever had to reason about a system spanning that kind of ratio inside their own head.

A bridge builder reasons about steel beams and the physics of weight. A surgeon reasons about organs and the physics of tissue. A chemist reasons about molecules and the physics of bonds.

All of them are working inside ratios of physical scale where the largest and smallest things they need to think about are within a few orders of magnitude of each other.

A programmer routinely writes one line that orchestrates a billion physical events on a chip, and is expected to predict the behavior of all of them in advance.

Dijkstra argued that the human brain was simply not built for this. Every intuition we have evolved over hundreds of thousands of years comes from a world of medium-sized objects behaving in continuous ways. Computing is the opposite. It is discrete, not continuous.

A program that runs perfectly a billion times can crash on the billion-and-first iteration because of a single bit. A single character missing from a line of code can take down a power grid. There is no margin. There is no graceful degradation. The system either works or does not, and the only way to know is to actually run it.

This was the part of the lecture where Dijkstra made everyone in the room uncomfortable.

He said the way computer science was being taught in universities was a quiet disaster. Professors were teaching programming the way carpenters teach woodworking. With examples. With metaphors. With analogies to things students already understood. Files are like folders. Memory is like a desk. A function is like a recipe.

Dijkstra said this was actively making it harder for students to think clearly. The whole point of a radical novelty is that there is nothing in your past experience to compare it to.

The moment you start reaching for metaphors, you are smuggling in old intuitions that do not apply, and those intuitions will betray you the first time you try to reason about a system the metaphor was not built to describe.

His exact line was this: the usual way in which we plan today for tomorrow is in yesterday's vocabulary. And yesterday's vocabulary, he argued, was killing the field.

The reason most software is broken is downstream of this single misunderstanding. Programmers are taught to think of code as a craft. Something you get a feel for.

Something you pick up through practice. Something where intuition gets sharper with experience.

Dijkstra said this is exactly backwards. Programming is not a craft. It is closer to mathematics than to carpentry, and the moment you treat it as a craft, you guarantee that the software you produce will be full of the kind of bugs that craftsmanship cannot catch.

The fix, in his view, was to teach programming the way mathematics is taught. You should be able to prove your program correct before you run it.

You should reason about your code formally, the way a mathematician reasons about a theorem, not the way a carpenter feels their way through a joint. The students who learned this way, he said, would walk out of their classes with a kind of confidence that no amount of typing practice could produce.

The lecture was published in Communications of the ACM in 1989. The field did not listen. Universities kept teaching programming the same way.

Software kept getting bigger. Bugs kept compounding. By 2026, almost every piece of software on earth has known security vulnerabilities, undefined behaviors, and edge cases that nobody has ever proven safe. The doom that Dijkstra warned about in 1988 is now the default condition of the digital world we have built.

The deeper lesson is the one most readers miss the first time through.

Dijkstra was not just talking about software. He was making a much bigger point about how humans learn anything that is genuinely new. The instinct to translate the unfamiliar into the familiar is the most natural thing in the world.

It is also the single biggest obstacle to actually understanding something that has no precedent. If you keep reaching for analogies, you will never see the new thing clearly. You will only see your old framework projected onto it.

This is happening right now with AI. The same instinct that made people learn programming through metaphors of files and folders is making people understand large language models through metaphors of brains and people.

Almost every framework being used to describe AI in 2026 is borrowed from a previous domain. None of them quite fit. The few people who are actually building useful intuitions about how these systems work are the ones who have done what Dijkstra recommended forty years ago.

They have set down the old vocabulary. They have looked at the new thing on its own terms. They have accepted that the radical novelty is radical for a reason.

You are not slow. You were taught a discipline as if it were a hobby. The cruelty is real.

The fix is still available.

[Everlier @Everlier]

If you're building a CLI of any kind, add a section for agents to the help, also add a command for agents to load skill contents related to the usage of the CLI.

agent-browser is a great example and I think more tools should follow the same template.