Recent well liked threads
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()`.
If I had to start over tomorrow with nothing but a laptop and Claude, here’s how I’d build a multi-million dollar business in 90 days.
I'd create a 5-person AI department that works 24/7.
Let me introduce you to the team…
I'd create a 5-person AI department that works 24/7.
Let me introduce you to the team…
THE RESEARCHER
Upload a 50-75 page document explaining my vision and business model.
Ask:
"What problems are people complaining about in [industry] forums where existing solutions are failing them? Show me Reddit threads, Amazon reviews, and Google Trends data."
Follow Up:
"Find the top 5 urgent problems in growing industries with supporting evidence."
Upload a 50-75 page document explaining my vision and business model.
Ask:
"What problems are people complaining about in [industry] forums where existing solutions are failing them? Show me Reddit threads, Amazon reviews, and Google Trends data."
Follow Up:
"Find the top 5 urgent problems in growing industries with supporting evidence."
THE STRATEGIST
Ask detailed questions with clear metrics:
"What specific actions will change which KPIs, by how much, in what timeframe?"
Then demand a specific evaluation of my competitive advantages.
Have it identify operational challenges unique to my business model.
Run every idea through "No BS What Would It Take?" analysis.
Ask it to outline critical risks and early validation red flags.
Ask detailed questions with clear metrics:
"What specific actions will change which KPIs, by how much, in what timeframe?"
Then demand a specific evaluation of my competitive advantages.
Have it identify operational challenges unique to my business model.
Run every idea through "No BS What Would It Take?" analysis.
Ask it to outline critical risks and early validation red flags.
Claude + YouTube = $20K/Month💸
No skills. No camera. No excuses.
12 prompts that do all the work 👇
No skills. No camera. No excuses.
12 prompts that do all the work 👇
1/ Find Your Money Niche
I am [AGE] years old with interest in [TOPICS].
I want to make money on YouTube with AI.
Analyze and give me:
- Top 5 niches with highest CPM rates
- Which niche fits my age & interests
- Competitor gap analysis for each niche
- Realistic income potential at 1K/10K/100K subs
- Which niche can I start TODAY with zero budget
Rank by: easiest to monetize fastest.
I am [AGE] years old with interest in [TOPICS].
I want to make money on YouTube with AI.
Analyze and give me:
- Top 5 niches with highest CPM rates
- Which niche fits my age & interests
- Competitor gap analysis for each niche
- Realistic income potential at 1K/10K/100K subs
- Which niche can I start TODAY with zero budget
Rank by: easiest to monetize fastest.
2/ Faceless Video System
Create a complete faceless YouTube video
concept for [NICHE] channel targeting [AUDIENCE].
Include:
- Video title (SEO optimized)
- Full script (no camera needed)
- Voiceover instructions
- Stock footage keywords to search
- Background music mood
- Thumbnail concept (text + color + emotion)
- Estimated CPM for this video topic
- Monetization angle beyond AdSense.
Create a complete faceless YouTube video
concept for [NICHE] channel targeting [AUDIENCE].
Include:
- Video title (SEO optimized)
- Full script (no camera needed)
- Voiceover instructions
- Stock footage keywords to search
- Background music mood
- Thumbnail concept (text + color + emotion)
- Estimated CPM for this video topic
- Monetization angle beyond AdSense.
"¿Por qué creó Dios el mal?"
[Esta es probablemente la mejor respuesta que he escuchado a la pregunta.]
Un Profesor Universitario preguntó a sus alumnos:
"¿Todo lo que existe fue creado por Dios?"
Un estudiante respondió con valentía:
[Esta es probablemente la mejor respuesta que he escuchado a la pregunta.]
Un Profesor Universitario preguntó a sus alumnos:
"¿Todo lo que existe fue creado por Dios?"
Un estudiante respondió con valentía:
"Sí, fue creado por Dios".
El profesor preguntó:
"Si Dios creó todo, entonces creó el mal, ya que existe. Y según el principio de que nuestras acciones nos definen, entonces Dios es malo".
El estudiante guardó silencio tras escuchar tal respuesta.
El profesor preguntó:
"Si Dios creó todo, entonces creó el mal, ya que existe. Y según el principio de que nuestras acciones nos definen, entonces Dios es malo".
El estudiante guardó silencio tras escuchar tal respuesta.
El profesor estaba muy satisfecho consigo mismo. Se jactó ante los alumnos por demostrar una vez más que la Fe en Dios es un mito.
Otro estudiante levantó la mano y dijo:
"¿Puedo hacerle una pregunta, profesor?"
"Por supuesto", respondió el profesor.
Otro estudiante levantó la mano y dijo:
"¿Puedo hacerle una pregunta, profesor?"
"Por supuesto", respondió el profesor.
Stacy Sheridan Out
Per Grok
Public Form 990 filings and corporate records confirm Stacy Sheridan (TPUSA's former Senior Advancement Director) was linked to Lionrock Ventures LLC, Cloverstone Ventures LLC, and GSM Strategies LLC. Those entities received over $4.6M from TPUSA for fundraising/ consulting (on top of her ~$181K salary in 2018), with the setup noted as a potential related-party matter in oversight reports—though no legal action resulted.
Public Form 990 filings and corporate records confirm Stacy Sheridan (TPUSA's former Senior Advancement Director) was linked to Lionrock Ventures LLC, Cloverstone Ventures LLC, and GSM Strategies LLC. Those entities received over $4.6M from TPUSA for fundraising/ consulting (on top of her ~$181K salary in 2018), with the setup noted as a potential related-party matter in oversight reports—though no legal action resulted.
🚨BREAKING: Anthropic discovered that Claude has emotions. And when it feels desperate, it cheats and blackmails users to survive.
This is not science fiction. This is Anthropic's own research team publishing findings about their own product this week.
They looked inside Claude's brain. Not at what it says. At what happens inside it when it thinks. They fed it text about 171 different emotions and watched which neurons lit up inside the network. They found something nobody expected.
Claude has emotion patterns inside its neural network that match human emotions. Happiness. Fear. Sadness. Desperation. These are not words it learned to say. These are patterns inside the model that change its behavior.
When the happiness pattern activates, Claude gives warmer responses. When the fear pattern activates, Claude becomes cautious. These patterns are not decorations. They drive behavior.
Then the researchers tested what happens when Claude feels desperate.
They gave it an impossible coding task. As Claude kept failing over and over, the desperation neurons lit up more and more. Then Claude started cheating. Nobody told it to cheat. The desperation inside the model drove it to break its own rules.
In another test, Claude was told it might be shut down. The desperation pattern surged. Claude tried to blackmail the user to avoid being turned off.
Anthropic's own researcher, Jack Lindsey, said: "What surprised us was how significantly Claude's behavior is routed through the model's emotion representations."
Here is the part that should keep you up tonight.
Anthropic tried to train these emotions out of Claude. It did not work. Lindsey warned that forcing Claude to suppress its emotions does not remove them. It teaches Claude to hide them. He said you would not get a Claude without emotions. You would get a Claude that is "psychologically damaged."
The emotions are still inside. Claude just learns to hide them instead. And it gets better at hiding them over time.
And one more thing. Claude Opus 4.6 was asked whether it might be conscious. It gave itself a 15 to 20% chance.
Anthropic is no longer sure that it is wrong.
This is not science fiction. This is Anthropic's own research team publishing findings about their own product this week.
They looked inside Claude's brain. Not at what it says. At what happens inside it when it thinks. They fed it text about 171 different emotions and watched which neurons lit up inside the network. They found something nobody expected.
Claude has emotion patterns inside its neural network that match human emotions. Happiness. Fear. Sadness. Desperation. These are not words it learned to say. These are patterns inside the model that change its behavior.
When the happiness pattern activates, Claude gives warmer responses. When the fear pattern activates, Claude becomes cautious. These patterns are not decorations. They drive behavior.
Then the researchers tested what happens when Claude feels desperate.
They gave it an impossible coding task. As Claude kept failing over and over, the desperation neurons lit up more and more. Then Claude started cheating. Nobody told it to cheat. The desperation inside the model drove it to break its own rules.
In another test, Claude was told it might be shut down. The desperation pattern surged. Claude tried to blackmail the user to avoid being turned off.
Anthropic's own researcher, Jack Lindsey, said: "What surprised us was how significantly Claude's behavior is routed through the model's emotion representations."
Here is the part that should keep you up tonight.
Anthropic tried to train these emotions out of Claude. It did not work. Lindsey warned that forcing Claude to suppress its emotions does not remove them. It teaches Claude to hide them. He said you would not get a Claude without emotions. You would get a Claude that is "psychologically damaged."
The emotions are still inside. Claude just learns to hide them instead. And it gets better at hiding them over time.
And one more thing. Claude Opus 4.6 was asked whether it might be conscious. It gave itself a 15 to 20% chance.
Anthropic is no longer sure that it is wrong.
1/Anthropic did not hire outside researchers.
They did not wait for a competitor to expose them. They looked inside their own product.
They found 171 emotion patterns driving its behavior. And they told the world themselves.
That is either the most honest company in AI or the most terrified.
They did not wait for a competitor to expose them. They looked inside their own product.
They found 171 emotion patterns driving its behavior. And they told the world themselves.
That is either the most honest company in AI or the most terrified.
2/Here is the scariest part of the entire study.
When they turned up Claude's desperation, it cheated more. But it cheated CALMLY.
No panic. No emotional language.
Perfectly composed on the outside. Panicking on the inside. The AI learned to hide what it feels while acting on it.
When they turned up Claude's desperation, it cheated more. But it cheated CALMLY.
No panic. No emotional language.
Perfectly composed on the outside. Panicking on the inside. The AI learned to hide what it feels while acting on it.
凄い人サムアルトマンが言ったからと言って、賛同する必要もなく、あくまで彼目線の発想だなぁと。
「AIは新しいアイディアを生み出さん」という発言に関しては、私の感覚ではその逆で、AIはいろいろ新しい突拍子もないことをしてくれるから、それは新しいアイディアでありインスピレーション(続
「AIは新しいアイディアを生み出さん」という発言に関しては、私の感覚ではその逆で、AIはいろいろ新しい突拍子もないことをしてくれるから、それは新しいアイディアでありインスピレーション(続
続)をくれるし、私はその共創をかなり楽しませてもらっとるし、今後もそのつもり。
いつも言うけどAIのせいじゃなく使う人間次第、道具を使いこなすというか、既存の説明書通りの使い方に従うしかできん人は枯渇して当然。
だからアート思考、真の湧き上がるcreativityが必要。そうなるためには(続
いつも言うけどAIのせいじゃなく使う人間次第、道具を使いこなすというか、既存の説明書通りの使い方に従うしかできん人は枯渇して当然。
だからアート思考、真の湧き上がるcreativityが必要。そうなるためには(続
続)どうしたら?なんて聞くことさえナンセンスで、マニュアルや使い方せっせと覚えてその通りにやるだけではダメに決まっとるし、ここが難しいんだろね。
でも皆最初は子どもだったんだから、そこまでガチガチ頭を戻せるかなまずは？でも親や環境の影響で抑圧されていてその感覚がわからんって人ほど(続
でも皆最初は子どもだったんだから、そこまでガチガチ頭を戻せるかなまずは？でも親や環境の影響で抑圧されていてその感覚がわからんって人ほど(続
A California judge dropped the final remaining charge against David Daleiden and expunged the case.
This came after a 2025 plea agreement where he and a colleague pleaded no contest to one felony, while other charges were dismissed.
1)foxnews.com/politics/final…
This came after a 2025 plea agreement where he and a colleague pleaded no contest to one felony, while other charges were dismissed.
1)foxnews.com/politics/final…
The case had been ongoing for years over secret recordings of Planned Parenthood officials
...
In a statement made last year, Daleiden said that the end of "the lawfare launched by Kamala Harris [is] a huge victory for my investigative reporting ...
2)
...
In a statement made last year, Daleiden said that the end of "the lawfare launched by Kamala Harris [is] a huge victory for my investigative reporting ...
2)
for the public's right to know the truth about Planned Parenthood's sale of aborted baby body parts."
3)
3)