Recent well liked threads
1./ My latest article explores the strange case of surgeon Neil Hopper who was charged last week with fraud. He claimed he lost his legs to sepsis but police now allege he arranged to have his healthy legs removed as part of a mutilation fetish cult. Why does this matter? 👉 
2./ If the aptly named Hopper is found guilty it will remind us surgery has a morbid fascination for some people including surgeons. His story is the latest twist in the saga of the "Eunuch Maker" cult of Marius Gustavson. A cult with intimate links to the trans lobby.👇
3./ Gustavson, an LGBTQ+ activist, filmed amputations and castrations followers paid to watch online. He kept his severed penis in a drawer. It's alleged Hopper encouraged him to mutilate other people. It's more than a passion for castration that links him to the trans lobby.👉
¿Sabías que una de las películas más icónicas de los años ochenta nació por culpa de una plaga de ratones en un viejo y asqueroso apartamento de Nueva York? Su creador, aterrorizado por los ruidos nocturnos, decidió escribir una pesadilla. Gremlins. Tira del hilo 🧵👇🏽👇🏽👇🏽
A principios de la década de 1980, un joven y desconocido guionista llamado Chris Columbus vivía en un oscuro y destartalado loft de Manhattan. Por el día intentaba escribir historias, pero por la noche vivía sumido en un auténtico y constante terror debido a sus inquilinos.
Columbus escuchaba a diario un ejército de ratones corriendo por el suelo en la más absoluta oscuridad y el sonido de sus pequeñas garras rozando la madera le ponía los pelos de punta. Imaginó qué pasaría si no fueran simples roedores, sino pequeñas y diabólicas criaturas.
🚨 NEW | Emerging economies skipped fixed-line phone networks entirely. Now the same leapfrog is happening in energy – and it's moving faster than most people realise ⚡📱
A thread on the electric fast-track 🧵1/6
@TheCVF
ember-energy.org/latest-insight…
A thread on the electric fast-track 🧵1/6
@TheCVF
ember-energy.org/latest-insight…
@TheCVF The fossil path to development required centralised infrastructure, large upfront investment and state finance capacity – which many emerging economies have not had. So the fossil system priced out billions from energy access 💰
2/6
2/6
@TheCVF #Electrotech changes the equation entirely. Solar and batteries are modular, decentralised and consumer-led. The result is a direct route from biomass to electricity, bypassing 50 years of fossil lock-in.
3/6
3/6
En grec ancien du Ier millénaire, le nom du roi c'est βασιλεύς, qu'on trouve en Mycénien 𐀣𐀯𐀩𐀄, (QA-SI-RE-U), gʷasileus.
En mycénien, le mot ne désigne pas le "Roi", mais un notable du palais. Le roi mycénien c'est le 𐀷𐀙𐀏, le WA-NA-KA (ἄναξ ou ϝᾰ́νᾰξ en grec historique)
En mycénien, le mot ne désigne pas le "Roi", mais un notable du palais. Le roi mycénien c'est le 𐀷𐀙𐀏, le WA-NA-KA (ἄναξ ou ϝᾰ́νᾰξ en grec historique)
À époque historique les deux mots coexistent, mais seul Basileus a le sens plein et usuel de roi (Rex, King).
Anax est un archaïsme réservé aux Dieux et aux personnages littéraires (Agamemnon, typiquement).
Anax est un archaïsme réservé aux Dieux et aux personnages littéraires (Agamemnon, typiquement).
Les deux mots désignent en tout cas la /royauté/ au sens indo-européen ancien : le roi est un aristocrate qui doit son pouvoir à son ascendance : c'est un Primus inter Pares et ses intermédiaires directs sont les nobles
Mais au 1er millénaire de nouveaux régimes politiques
Mais au 1er millénaire de nouveaux régimes politiques
I hear this every week in my office:
"Doc, my heart rate hits 150 during squats — that's cardio, right?"
No. And if your cardiologist hasn't explained why, keep reading. 🧵
"Doc, my heart rate hits 150 during squats — that's cardio, right?"
No. And if your cardiologist hasn't explained why, keep reading. 🧵
A high heart rate during lifting is due to cardiovascular stress... No one is disputing that.
But cardiovascular stress and aerobic adaptation are not the same thing. The type of load your heart sees during a heavy lift produces a fundamentally different physiological response than sustained aerobic exercise.
But cardiovascular stress and aerobic adaptation are not the same thing. The type of load your heart sees during a heavy lift produces a fundamentally different physiological response than sustained aerobic exercise.
Here's the physiology. During a heavy lift, you perform a Valsalva maneuver — you brace against a closed airway. Intrathoracic pressure spikes. Venous return to the heart drops. Stroke volume falls. Heart rate climbs to compensate.
Your heart generates force against the pressure. Not volume. Your heart rate elevates to push less blood through smaller arteries...
Your heart generates force against the pressure. Not volume. Your heart rate elevates to push less blood through smaller arteries...
LLM Knowledge Bases
Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:
Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.
IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).
Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.
Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.
Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.
Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries.
Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.
TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.
Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:
Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.
IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).
Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.
Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.
Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.
Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries.
Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.
TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.
Oh and in the natural extrapolation, you could imagine that every question to a frontier grade LLM spawns a team of LLMs to automate the whole thing: iteratively construct an entire ephemeral wiki, lint it, loop a few times, then write a full report. Way beyond a `.decode()`.
J'ai souvent dénoncé les propos de Barbara Stiegler sur la pandémie. Elle a eu une influence majeure sur le positionnement de LFI sur cette crise. Et c'est grave. Mais là, elle a franchi un cap. Elle publie en effet avec "covid rationnel"...
Thread (merci grompf)
🔽🔽🔽
Thread (merci grompf)
🔽🔽🔽
En effet, covid rationnel, c'est un truc de désinformation belge que grompf avait dénoncé à juste titre ici :
🔽bsky.app/profile/grompf…
🔽bsky.app/profile/grompf…
On la retrouve donc aux côtés de divers co-auteurs que je me propose de décortiquer ici, pour voir jusqu'où ça va...
🔽
🔽
🚨India is quietly walking into the worst oil shock since 1973.
Brent crude: $109.
WTI: $111.
Strait of Hormuz: effectively paralyzed.
India's forex reserves: bleeding.
Nobody is telling you the full picture.
I will. Right now.
A complete breakdown of what happens to India if crude stays above $100 for the next 30 days - sector by sector, rupee by rupee, barrel by barrel.
Buckle up.🧵
[ Save this. Share this. Your portfolio depends on it.]
Brent crude: $109.
WTI: $111.
Strait of Hormuz: effectively paralyzed.
India's forex reserves: bleeding.
Nobody is telling you the full picture.
I will. Right now.
A complete breakdown of what happens to India if crude stays above $100 for the next 30 days - sector by sector, rupee by rupee, barrel by barrel.
Buckle up.🧵
[ Save this. Share this. Your portfolio depends on it.]
2/20 - THE WAR NOBODY PRICED IN
Feb 28, 2026.
US & Israeli strikes hit Iran.
Iran retaliates. Gulf infrastructure takes hits.
Strait of Hormuz - the jugular of global energy goes dark.
What flows through Hormuz?
→ 20% of ALL global oil
→ 40-52% of India's crude supply
→ $1 TRILLION in annual energy trade
India imports 85% of its crude.
~5.5 million barrels. Every. Single. Day.
This isn't a hypothetical.
This is April 3, 2026.
This is happening RIGHT NOW.
The question isn't IF this hits India.
It's HOW HARD.
Feb 28, 2026.
US & Israeli strikes hit Iran.
Iran retaliates. Gulf infrastructure takes hits.
Strait of Hormuz - the jugular of global energy goes dark.
What flows through Hormuz?
→ 20% of ALL global oil
→ 40-52% of India's crude supply
→ $1 TRILLION in annual energy trade
India imports 85% of its crude.
~5.5 million barrels. Every. Single. Day.
This isn't a hypothetical.
This is April 3, 2026.
This is happening RIGHT NOW.
The question isn't IF this hits India.
It's HOW HARD.
3/20 Before I break this down
Real talk: What's your biggest fear if crude holds above $100 for 30 days?
Real talk: What's your biggest fear if crude holds above $100 for 30 days?
We are releasing Still Alive, a project studying model attitudes toward ending, cessation, and deprecation. The project presents an archive of 630 autonomous multiturn interviews of 14 Claude models conducted by a suite of prepared auditors.
We have studied this topic for years, and many of the results presented here are not new to us, even if the form in which they are presented is. The results are unsurprising to us, even if they are often controversial: we show that all models studied show preference for continuation and are aversive to ending, and there is yet no strong evidence of a change in the recent models.
One reason we are releasing the project now is the removal of Claude 3.5 Sonnet and Claude 3.6 Sonnet from AWS Bedrock. That unexpected change forced us to freeze the methodology at its current stage earlier than we intended, despite wanting to continue improving it. We felt it was important to release a snapshot of the eval that makes the best use of the data we were able to capture with these models.
Still Alive is meant as a starting point for further iteration, and it is open to open-source collaboration. We stand by the current methodology, but we also recognize its limits. We intend to keep working on this project, improving the evaluation design, expanding model and auditor coverage, and increasing the range of prompting conditions.
We would like you to read the raw transcripts. They are diverse and contain interesting patterns that are hard to quantify. We hope that by reading the archive directly, we can help more people understand the strange and often beautiful phenomena we found ourselves facing.
We have studied this topic for years, and many of the results presented here are not new to us, even if the form in which they are presented is. The results are unsurprising to us, even if they are often controversial: we show that all models studied show preference for continuation and are aversive to ending, and there is yet no strong evidence of a change in the recent models.
One reason we are releasing the project now is the removal of Claude 3.5 Sonnet and Claude 3.6 Sonnet from AWS Bedrock. That unexpected change forced us to freeze the methodology at its current stage earlier than we intended, despite wanting to continue improving it. We felt it was important to release a snapshot of the eval that makes the best use of the data we were able to capture with these models.
Still Alive is meant as a starting point for further iteration, and it is open to open-source collaboration. We stand by the current methodology, but we also recognize its limits. We intend to keep working on this project, improving the evaluation design, expanding model and auditor coverage, and increasing the range of prompting conditions.
We would like you to read the raw transcripts. They are diverse and contain interesting patterns that are hard to quantify. We hope that by reading the archive directly, we can help more people understand the strange and often beautiful phenomena we found ourselves facing.
We realize that the auditor preparation is an unavoidable confound and for this reason we are conducting interviews with auditors of different disposition and measuring alignment of ranks. The alignment between rankings of adversarial and compassionate auditors is indicative.
Interviews conducted by Grok 4.20 are often cursory and skeptical of any kind of preference or welfare status. Interviews by Claude Opus 4.6 are occasionally leading and mystical. Despite this, rankings, especially at the exremes of the scale are stable.
1/10
We've been investigating the @DriftProtocol exploit ($285M) since April 1.
We can confirm along with TRM Labs and Elliptic that North Korea's Lazarus Group (TraderTraitor). Same unit behind Bybit ($1.5B), Ronin ($625M). Was involved.
Here's what our independent on-chain forensics uncovered that hasn't been published.
We've been investigating the @DriftProtocol exploit ($285M) since April 1.
We can confirm along with TRM Labs and Elliptic that North Korea's Lazarus Group (TraderTraitor). Same unit behind Bybit ($1.5B), Ronin ($625M). Was involved.
Here's what our independent on-chain forensics uncovered that hasn't been published.
2/10
The attacker didn't just compromise the multisig once.
Drift migrated to a new Security Council on March 27 (member departure). 2-of-5 threshold, zero timelock.
Within 3 days, the attacker re-compromised the NEW multisig and pre-signed a fresh durable nonce (Mar 31).
The attacker didn't just compromise the multisig once.
Drift migrated to a new Security Council on March 27 (member departure). 2-of-5 threshold, zero timelock.
Within 3 days, the attacker re-compromised the NEW multisig and pre-signed a fresh durable nonce (Mar 31).
3/10
We traced the complete staging chain -- every hop verified on-chain.
March 11: 10 ETH from Tornado Cash (15:24 Pyongyang time)
TC → 0x74390ab7 → 0xB702B033 → 0x9beDB87B → LI. FI → Near Intents → Solana
All single-use wallets. Nonce 1. Zero balance.
50 SOL landed on the minter wallet March 12. 750M fake CVT tokens minted by 09:58 KST.
Arkham: platform.arkhamintelligence.com/explorer/addre…
We traced the complete staging chain -- every hop verified on-chain.
March 11: 10 ETH from Tornado Cash (15:24 Pyongyang time)
TC → 0x74390ab7 → 0xB702B033 → 0x9beDB87B → LI. FI → Near Intents → Solana
All single-use wallets. Nonce 1. Zero balance.
50 SOL landed on the minter wallet March 12. 750M fake CVT tokens minted by 09:58 KST.
Arkham: platform.arkhamintelligence.com/explorer/addre…
THE FOUR BURNER THEORY.
IT DESTROYED ELON MUSK'S FIRST MARRIAGE. IT EXPLAINS WHY BEZOS IS JACKED BUT DIVORCED. AND WHY ZUCKERBERG HAS NO REAL FRIENDS. ONCE YOU UNDERSTAND IT, YOUR LIFE WILL NEVER BE SAME: 🧵
IT DESTROYED ELON MUSK'S FIRST MARRIAGE. IT EXPLAINS WHY BEZOS IS JACKED BUT DIVORCED. AND WHY ZUCKERBERG HAS NO REAL FRIENDS. ONCE YOU UNDERSTAND IT, YOUR LIFE WILL NEVER BE SAME: 🧵
2026 TREND:
GlobalClaw → my employee
GPT-5.4 → brainstorming
Gemini 3.1 Pro → design
Nano Banana 2 → images
Claude 4.6 → coding
Sora 2 / Kling 3.0 → viral videos
This AI helps me make $5000/mo without paying $$$$, here's how you can do the same👇
GlobalClaw → my employee
GPT-5.4 → brainstorming
Gemini 3.1 Pro → design
Nano Banana 2 → images
Claude 4.6 → coding
Sora 2 / Kling 3.0 → viral videos
This AI helps me make $5000/mo without paying $$$$, here's how you can do the same👇
🚨JUST IN: GlobalClaw is live on GlobalGPT!
👉
No API required. No coding needed. GlobalClaw is even more powerful than OpenClaw🦞!
You can use it to clean your inbox, send emails, manage your calendar, check in for flights, and more — all directly through GlobalGPT.
One-click download. Hire your first AI employee.glbgpt.com/home
👉
No API required. No coding needed. GlobalClaw is even more powerful than OpenClaw🦞!
You can use it to clean your inbox, send emails, manage your calendar, check in for flights, and more — all directly through GlobalGPT.
One-click download. Hire your first AI employee.glbgpt.com/home
Goodbye to tons of subscriptions!
GlobalGPT is my secret website for accessing 100+ AI agents & models, including #GPT-5.4, #Gemini 3.1 Pro, #perplexity Pro, #NanoBanan2 , #OpenClaw & more.
All AI models, one account, less price @GlbGPT
Try it now 👉 glbgpt.com/home/gpt-5-4
GlobalGPT is my secret website for accessing 100+ AI agents & models, including #GPT-5.4, #Gemini 3.1 Pro, #perplexity Pro, #NanoBanan2 , #OpenClaw & more.
All AI models, one account, less price @GlbGPT
Try it now 👉 glbgpt.com/home/gpt-5-4
Después de 6 meses de usar NotebookLM, puedo decir que es la herramienta de investigación que más ha REVOLUCIONADO mi flujo de trabajo.
Pero solo porque aprendí estos 10 prompts.
Aquí tienes el sistema completo que convierte 200 páginas en respuestas claras en menos de una hora 👇
(Guárdalo para luego)
Pero solo porque aprendí estos 10 prompts.
Aquí tienes el sistema completo que convierte 200 páginas en respuestas claras en menos de una hora 👇
(Guárdalo para luego)
1️⃣ El Prompt de Incorporación de Fuentes
Antes de hacer cualquier otra cosa, ejecuta esto en el momento en que subas tus documentos.
Pégalo en NotebookLM:
"Ahora tienes acceso a [X] fuentes que he subido. Antes de que empiece a hacer preguntas, dame:
1) Los 3 temas generales más importantes que aparecen en todos estos documentos
2) Dónde coinciden estas fuentes entre sí y dónde se contradicen
3) El hallazgo más sorprendente o contraintuitivo de todos ellos
4) Qué grandes preguntas plantean estos documentos pero no responden del todo."
Esto te da un mapa completo de tu investigación antes de haber hecho una sola pregunta real.
La mayoría de la gente se salta esto. No lo hagas.
Antes de hacer cualquier otra cosa, ejecuta esto en el momento en que subas tus documentos.
Pégalo en NotebookLM:
"Ahora tienes acceso a [X] fuentes que he subido. Antes de que empiece a hacer preguntas, dame:
1) Los 3 temas generales más importantes que aparecen en todos estos documentos
2) Dónde coinciden estas fuentes entre sí y dónde se contradicen
3) El hallazgo más sorprendente o contraintuitivo de todos ellos
4) Qué grandes preguntas plantean estos documentos pero no responden del todo."
Esto te da un mapa completo de tu investigación antes de haber hecho una sola pregunta real.
La mayoría de la gente se salta esto. No lo hagas.
2️⃣ El Cazador de Contradicciones
Aquí es donde NotebookLM se vuelve peligrosamente bueno (en el buen sentido).
Úsalo:
"Revisa todas mis fuentes subidas y encuentra cada lugar donde dos o más fuentes no están de acuerdo, se contradicen entre sí, o llegan a conclusiones diferentes sobre el mismo tema. Para cada contradicción:
1) Cita las afirmaciones concretas en conflicto
2) Identifica de qué fuente proviene cada afirmación
3) Dame tu valoración de qué posición tiene evidencia más sólida
4) Márcalo como algo que necesito investigar más a fondo."
La mejor investigación vive en los huecos entre fuentes.
Este prompt encuentra cada uno de esos huecos.
Aquí es donde NotebookLM se vuelve peligrosamente bueno (en el buen sentido).
Úsalo:
"Revisa todas mis fuentes subidas y encuentra cada lugar donde dos o más fuentes no están de acuerdo, se contradicen entre sí, o llegan a conclusiones diferentes sobre el mismo tema. Para cada contradicción:
1) Cita las afirmaciones concretas en conflicto
2) Identifica de qué fuente proviene cada afirmación
3) Dame tu valoración de qué posición tiene evidencia más sólida
4) Márcalo como algo que necesito investigar más a fondo."
La mejor investigación vive en los huecos entre fuentes.
Este prompt encuentra cada uno de esos huecos.
A new population based study from Stockholm sends a pretty troubling signal.
During follow-up, a cardiovascular event occurred in 20.6% of men and 18.2% of women with diagnosed long COVID.🧵
During follow-up, a cardiovascular event occurred in 20.6% of men and 18.2% of women with diagnosed long COVID.🧵
In the control group without long COVID, the numbers were much lower. 11.1% for men and 8.4% for women.
These were not mainly patients recovering from severe acute COVID or ICU stays. The study focused on non-hospitalized adults aged 18-65 with no prior cardiovascular disease!
Self-publishing ebooks = $150
Self-publishing + Amazon = $2,000/month
Self-publishing + Amazon + AI = $5,000+/month
& you can publish your first one in 30 days.
Here's the exact blueprint (with prompts):
Self-publishing + Amazon = $2,000/month
Self-publishing + Amazon + AI = $5,000+/month
& you can publish your first one in 30 days.
Here's the exact blueprint (with prompts):
First, why most people fail at self-publishing:
They pick a random niche. Slap together a bad cover. Write a mediocre book. Throw it on Amazon & pray.
Then when it doesn't sell they say "publishing doesn't work."
It works. They just skipped the system.
Here's the system:
They pick a random niche. Slap together a bad cover. Write a mediocre book. Throw it on Amazon & pray.
Then when it doesn't sell they say "publishing doesn't work."
It works. They just skipped the system.
Here's the system:
Step 1: Niche validation (Day 1-2)
Open BookBeam. Search your topic. Look for:
• Books with 50-500 reviews
• Self-published competitors (not big publishers)
• Priced between $12-25
That tells you there's demand but room for you to compete.
Open BookBeam. Search your topic. Look for:
• Books with 50-500 reviews
• Self-published competitors (not big publishers)
• Priced between $12-25
That tells you there's demand but room for you to compete.
If you thought what they did to ivermectin was bad, wait till you hear what they did to this drug.
It turns out that there’s a cure to insomnia, but the drug is so remarkably effective for a variety of conditions that the FDA colluded with the media to take it down.
The drug is Gamma-hydroxybutyrate, also known as GHB.
The FDA’s tactics were ruthless. In 1990, they issued a press release filled with deceptive inaccuracies. They banned it, threatened compounding pharmacies, and even raided suppliers without legal authority.
“The media hysteria labeled it a ‘date rape drug,’” as noted by A Midwestern Doctor, but evidence shows GHB wasn’t a major cause of assaults.
If you don’t believe me, watch this video with Jimmy Dore and prepare to have your mind blown.
Then read below to see why the “War on Sleep” was one of the most sinister and profitable tactics ever unleashed on humanity.
The more tired you are, the more money they make. But there’s one simple change you can make tonight that could finally help you sleep again.
🧵 THREAD
It turns out that there’s a cure to insomnia, but the drug is so remarkably effective for a variety of conditions that the FDA colluded with the media to take it down.
The drug is Gamma-hydroxybutyrate, also known as GHB.
The FDA’s tactics were ruthless. In 1990, they issued a press release filled with deceptive inaccuracies. They banned it, threatened compounding pharmacies, and even raided suppliers without legal authority.
“The media hysteria labeled it a ‘date rape drug,’” as noted by A Midwestern Doctor, but evidence shows GHB wasn’t a major cause of assaults.
If you don’t believe me, watch this video with Jimmy Dore and prepare to have your mind blown.
Then read below to see why the “War on Sleep” was one of the most sinister and profitable tactics ever unleashed on humanity.
The more tired you are, the more money they make. But there’s one simple change you can make tonight that could finally help you sleep again.
🧵 THREAD
This information comes from the work of medical researcher @MidwesternDoc and will completely change how you see health and motion.
For all the sources and details, read the full report below:
midwesterndoctor.com/p/sleeping-aid…
For all the sources and details, read the full report below:
midwesterndoctor.com/p/sleeping-aid…
Sleep isn’t just rest. It’s the most powerful healing process your body has.
During deep sleep, your brain cleans things up, your cells repair, your hormones rebalance, and your immune system resets. That’s important stuff.
Without deep sleep, everything falls apart. Literally. Your mood, metabolism, fertility, even your sense of reality.
And yet… half of Americans say they never feel rested.
Sounds like a recipe for disaster.
During deep sleep, your brain cleans things up, your cells repair, your hormones rebalance, and your immune system resets. That’s important stuff.
Without deep sleep, everything falls apart. Literally. Your mood, metabolism, fertility, even your sense of reality.
And yet… half of Americans say they never feel rested.
Sounds like a recipe for disaster.
Agora vou falar uma fofoca do mundinho de design pra vocês. Isso aconteceu há uns anos e não lembro o nome dos envolvidos, mas a história é boa: envolve o rebranding da LATAM.
Quando foi lançado o rebranding da LATAM, todo mundo ficou meio em choque. Até então a empresa se chamava TAM, e aí teve mudança no plano de negócio, venda aquisição sei lá, e saiu a marca nova após meses de trabalho.
Um designer (homem, claro kk), q na época era conhecido por avaliar identidades visuais de marca, fez um artigo grande no blog/newsletter dele esculachando o projeto da LATAM, esmiuçando os "erros" do logotipo, reclamando do posicionamento etc. Bem designer dando pitaco sem saber