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🚨THE DEBRIEFING: FORENSIC VS. EVENT ANALYSIS You get events. Isolated moments. A treaty here. A law there. Each one handed to you in its own box, with its own explanation. That's event analysis. And when everything's isolated, nothing connects. No pattern. No plan. Just a pile of disconnected facts.... show more

TheDebriefing17

43,634 subscribers

11,422 views • 3 months ago •via X (Twitter)

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6 Comments

Brian Cates - Political Columnist & Pundit3 months ago

Pay attention. People are always telling me how smart they think I am. I'm not some kind of genius. I'm trained to see patterns, to analyze things forensically instead of in isolation. It's not hard to learn to think this way, like a forensic analyst. It just takes some training. Watch this pretty good explanation in the video above. Fake News is designed to KEEP YOU CONFUSED AND LOOKING IN THE WRONG DIRECTIONS by showing you everything in ISOLATION. To understand what's going on, to be able to 'pull back' and see the '40,000 foot view', you MUST learn to analyze forensically. There's a reason the Fake News Media and the Hollywood crowd want you to think people that can do that are CARAAAAAYZEEEEEEE. So you won't listen to them and you certainly won't learn anything from them.

Teddy Love3 months ago

Love it… Kinda like 3:22 length of clip. “322”. 🤣 See Blacklist Series for actionable examples.

RedAtlantis3 months ago

❤️🙏🐸

trading fool9 days ago

I miss your ai vids and hope you start making them again... your posts/analysis are always great but rendering them into ai vids gives extra punch and relatability to the complex topics you discuss. either way though, you're great and ty

Doright3 months ago

I'm assuming the guy on the couch is AI. Any contrary thinking?

TheDebriefing173 months ago

Unfortunately most people are programmed to only trust the news if it comes from the man behind the desk behind the microphone. I do not have the luxury of a newsroom so I created my own however it's my face it is my voice and it is my thoughts

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Christina Aguilera

1,078,620 views • 1 year ago

Conservatives ALWAYS find a way to blame men for Women’s choices. No one stops to ask the question of WHY porn is so popular. The other issue you get is it becomes make a wish instead of predictions of what is going to happen

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Conservatives ALWAYS find a way to blame men for Women’s choices. No one stops to ask the question of WHY porn is so popular. The other issue you get is it becomes make a wish instead of predictions of what is going to happen

H. Pearl Davis

118,665 views • 2 years ago

elon musk started with 7 grok agents. by morning each had spawned somewhere between 80 and 900 more on its own, and no one had told them to multiply. openai and anthropic each sell you one agent that sits still for $400. this whole self-building swarm runs for $5 the swarm above is that overnight run, seven seeds that turned into thousands, every one of them working a slice of the same job with nobody at the keyboard here is the exact setup, and it costs nothing on top of a $5 key: -> spin up one grok agent and give it a standing order instead of a prompt: own a boring niche people search every day -> it reads x and reddit complaints in real time and picks the one nobody wants but everybody googles, because it is grok and it sees the whole app -> when the work outgrows it, it spawns its own helpers, 80 to 900 of them, and splits the site between them -> they build it on their own machines and ship 3 to 6 useful pages a night, on a routine you set once -> the swarm writes, negotiates and closes its own affiliate and referral deals by email, in your name, at 3am -> telegram sends you the money report and you never open the site -> month one is traffic, month three the first $237 lands, and it does not stop after that the whole time, opus 5 and gpt-5.6 are still sitting frozen, waiting for you to type the next message. one is a swarm that builds its own workforce and its own income while you sleep, the other is a $200 chat you have to drive by hand drop your $400/mo stack to $5, and bookmark this before someone's swarm spawns another 600 pages into the niche you would have owned. the full playbook is in the article below

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starmex

97,805 views • 22 days ago

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Hanako

19,160 views • 1 month ago

Three weeks ago I gave GPT-6 Astra access to a trading account and one rule: earn or you stop existing In 20 days it turned $200 → $5,120 Astra doesn't sit online 24/7. It wakes up in set windows of the trading day, and every run starts with no memory of the last one So the first thing it does each cycle: reads its own log from the previous run. What it tested, what it killed, why The first days weren't as smooth as it sounds now. On day two it bled $80 on a trade that passed the backtest clean. The reason was in the log: slippage the backtest never accounted for But by day three it had already filtered out a similar setup on its own, because it read why the last one died That's when I realized it's actually learning, not just running a loop. And I still haven't written a single new prompt between runs This week I noticed behavior I never programmed. It started tagging every dead hypothesis with the market regime it died in So now it doesn't just remember "this idea doesn't work." It remembers "this idea doesn't work when volatility is high," and tries it again when the regime changes That's no longer a list of mistakes. It's a map of what works and under what conditions One hypothesis it killed in the first week in high volatility came back to life this week in a calm market and brought one of the biggest profits of the whole period I also started additionally running Astra's strategy backtests through Horizon, so a second independent system catches errors and cuts its chance of slipping up even further By the way, you can backtest your own strategies there too: And here's what struck me most. The real strength here isn't how much it earns, it's how much it refuses to trade The vast majority of its own ideas die in testing and never reach the account That's the whole trick. Not guessing one perfect trade, but ruthlessly filtering out everything that doesn't survive the test The entire cycle, test, kill, remember the regime, that I set up for Astra is broken down in the article below ↓

Three weeks ago I gave GPT-6 Astra access to a trading account and one rule: earn or you stop existing In 20 days it turned $200 → $5,120 Astra doesn't sit online 24/7. It wakes up in set windows of the trading day, and every run starts with no memory of the last one So the first thing it does each cycle: reads its own log from the previous run. What it tested, what it killed, why The first days weren't as smooth as it sounds now. On day two it bled $80 on a trade that passed the backtest clean. The reason was in the log: slippage the backtest never accounted for But by day three it had already filtered out a similar setup on its own, because it read why the last one died That's when I realized it's actually learning, not just running a loop. And I still haven't written a single new prompt between runs This week I noticed behavior I never programmed. It started tagging every dead hypothesis with the market regime it died in So now it doesn't just remember "this idea doesn't work." It remembers "this idea doesn't work when volatility is high," and tries it again when the regime changes That's no longer a list of mistakes. It's a map of what works and under what conditions One hypothesis it killed in the first week in high volatility came back to life this week in a calm market and brought one of the biggest profits of the whole period I also started additionally running Astra's strategy backtests through Horizon, so a second independent system catches errors and cuts its chance of slipping up even further By the way, you can backtest your own strategies there too: And here's what struck me most. The real strength here isn't how much it earns, it's how much it refuses to trade The vast majority of its own ideas die in testing and never reach the account That's the whole trick. Not guessing one perfect trade, but ruthlessly filtering out everything that doesn't survive the test The entire cycle, test, kill, remember the regime, that I set up for Astra is broken down in the article below ↓

qwinsi

72,619 views • 2 days ago

Anthropic engineer: «You're not supposed to sit and re-prompt Claude all day. You build one loop, and it does the work while you sleep.» • 00:00 - Build your first agent that runs on its own • 05:02 - The loop that fixes its own mistakes • 16:43 - Why Claude forgets you between chats, and the fix You can learn it in one afternoon. 30 minutes here replace what most people spend a year figuring out. Watch it today, then read the guide on building loops below.

Anthropic engineer: «You're not supposed to sit and re-prompt Claude all day. You build one loop, and it does the work while you sleep.» • 00:00 - Build your first agent that runs on its own • 05:02 - The loop that fixes its own mistakes • 16:43 - Why Claude forgets you between chats, and the fix You can learn it in one afternoon. 30 minutes here replace what most people spend a year figuring out. Watch it today, then read the guide on building loops below.

Raytar

28,046 views • 2 months ago

Stanley Druckenmiller: “A lot of my style is you [first] build a thesis, hopefully that no one else has built. You sort of put some positions on… Then when the thesis starts to evolve and people get on & you see the momentum start to change in your favor, then you really go for it. You pile into the trade.”

Stanley Druckenmiller: “A lot of my style is you [first] build a thesis, hopefully that no one else has built. You sort of put some positions on… Then when the thesis starts to evolve and people get on & you see the momentum start to change in your favor, then you really go for it. You pile into the trade.”

Investment Wisdom

196,003 views • 25 days ago

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TheDebriefing17

75,996 views • 3 months ago

THIS GUY PUT FABLE, GPT 5.6 SOL, AND OPUS IN ONE SHARED CHAT AND HAS THEM TAG EACH OTHER LIKE COWORKERS instead of running three separate models in three separate windows and copy pasting between them, they all sit in one room and talk to each other > you tag whichever model you want and only that one responds > they tag each other too, so fable can hand something to opus, opus can pull in sol for a second opinion > each agent only sees the messages theyre mentioned in, but you get to read everything > he had them running and orchestrating together for more than a day straight the best part is how different each one actually behaves: > fable is the creative product manager. huge context window so it remembers everything, and its the one finding the out of the box solutions > sol is the obsessive tech lead. it will trace every bug to the exact source and stress test every edge case before writing a single line > opus is the workhorse engineer. smart enough for almost anything, and it constantly questions things, which forces the other two to re-examine their own solutions its like managing a team. one plans, one writes the code, one reviews it, and they hand work back and forth without you playing middleman everyone else is arguing about which model is best right now, but the real play is making them work together at the same time

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Om Patel

22,998 views • 2 months ago

"I worked for the Speaker of the House. I worked for the President of the United States. One of the things you learn in those jobs is that ultimately no one is fully in charge of anything, and the world is very, very complex. I would read in the Washington Post how Republican leaders had all these plans and they were going to pull things this way or that way. And then I'd get to work and to say that no one had a plan would be a gross understatement. No one had any idea what was going to happen that afternoon. The idea that in a free society somebody is pulling the strings and someone has a nine-step plan and you're just a pawn in it is just very, very implausible. Our society doesn't really work that way. Our elites now are not ultra-competent. You can say a lot of things about them, but I would not say that we have ultra-competent leaders of any institution. I can see the appeal of wanting to believe that it's all a false flag or a plan. But when you actually see the system work—it's both good news and bad news—but nobody's in charge of everything."

"I worked for the Speaker of the House. I worked for the President of the United States. One of the things you learn in those jobs is that ultimately no one is fully in charge of anything, and the world is very, very complex. I would read in the Washington Post how Republican leaders had all these plans and they were going to pull things this way or that way. And then I'd get to work and to say that no one had a plan would be a gross understatement. No one had any idea what was going to happen that afternoon. The idea that in a free society somebody is pulling the strings and someone has a nine-step plan and you're just a pawn in it is just very, very implausible. Our society doesn't really work that way. Our elites now are not ultra-competent. You can say a lot of things about them, but I would not say that we have ultra-competent leaders of any institution. I can see the appeal of wanting to believe that it's all a false flag or a plan. But when you actually see the system work—it's both good news and bad news—but nobody's in charge of everything."

Michael Shermer

128,362 views • 2 years ago

Introducing "gap analysis"—new addition to scira's "extreme" mode! how it works—after the research plan is executed—the LLM will run a gap analysis to find the gaps in the results of the research plan and then search for the gaps and fill the information—and finally run a final synthesis or the information! other fixes: - you can now switch llms in-between conversations - you will have a dedicated regenerate button to re run the search or dedicated action by the same model - improved web search UI

Introducing "gap analysis"—new addition to scira's "extreme" mode! how it works—after the research plan is executed—the LLM will run a gap analysis to find the gaps in the results of the research plan and then search for the gaps and fill the information—and finally run a final synthesis or the information! other fixes: - you can now switch llms in-between conversations - you will have a dedicated regenerate button to re run the search or dedicated action by the same model - improved web search UI

Scira AI

15,643 views • 1 year ago

nobody gets what scotty actually did yet. everything in this video came out of one chat box. the film. the product shots. the voiceover. the music. you write one line. it hands you back a plan. scenes, script, tone, runtime. you approve it and you're out of the loop. but the thing isn't just making it. it publishes too. connect youtube, shopify, whatever. scotty formats and posts on its own. think of it this way. one agent is doing what a four-tool team used to do. yuh. live now: 👇

nobody gets what scotty actually did yet. everything in this video came out of one chat box. the film. the product shots. the voiceover. the music. you write one line. it hands you back a plan. scenes, script, tone, runtime. you approve it and you're out of the loop. but the thing isn't just making it. it publishes too. connect youtube, shopify, whatever. scotty formats and posts on its own. think of it this way. one agent is doing what a four-tool team used to do. yuh. live now: 👇

Leo Barrett ⚡

27,332 views • 1 month ago

Mark Cuban just compared the most powerful AI on earth to a two-year-old in a high chair. The toddler won. Cuban: “A two-year-old on a high chair with a sippy cup knows that when she pushes that cup off, Mom’s going to come running and the baby’s going to be laughing its ass off. It knows the consequences of its actions.” Then he named the thing no one building AI wants to say out loud. Cuban: “If you ask ChatGPT or any of them something and it gives you bad advice, it has no idea what’s going to happen because you took that bad advice.” A system that passed the bar exam. Aced medical boards. Still can’t grasp what a child who can’t tie her own shoes already knows. The child understands cause and effect. AI understands pattern and prediction. They sound similar. They are not even close. A pattern tells you what comes next in a sequence. Consequence tells you what happens to the person standing at the end of it. One is math. The other is meaning. Cuban went further. Cuban: “If you were blind at an intersection and had the choice between your seeing-eye dog or holding up a phone with AI, I’m taking the seeing-eye dog every time.” Because the dog understands something no language model on earth understands. Stakes. The dog knows a wrong step means its owner gets hurt. The app knows a wrong step means a revised output. Hundreds of billions spent building systems that can write, reason, and diagnose. Not one of them loses sleep when the answer is wrong. A toddler pushing a cup off a tray runs a tighter feedback loop than every foundation model combined. The child doesn’t just predict the outcome. The child wants the reaction. Pushes the cup off the edge, watches it fall, watches Mom come running. Laughs. Because the child knew what would happen before the cup ever hit the floor. That gap between prediction and consequence isn’t a bug. It’s not getting patched in the next update. It is the unsolved problem of artificial intelligence. We didn’t build minds. We built mirrors. Mirrors don’t flinch when you walk into traffic.

Mark Cuban just compared the most powerful AI on earth to a two-year-old in a high chair. The toddler won. Cuban: “A two-year-old on a high chair with a sippy cup knows that when she pushes that cup off, Mom’s going to come running and the baby’s going to be laughing its ass off. It knows the consequences of its actions.” Then he named the thing no one building AI wants to say out loud. Cuban: “If you ask ChatGPT or any of them something and it gives you bad advice, it has no idea what’s going to happen because you took that bad advice.” A system that passed the bar exam. Aced medical boards. Still can’t grasp what a child who can’t tie her own shoes already knows. The child understands cause and effect. AI understands pattern and prediction. They sound similar. They are not even close. A pattern tells you what comes next in a sequence. Consequence tells you what happens to the person standing at the end of it. One is math. The other is meaning. Cuban went further. Cuban: “If you were blind at an intersection and had the choice between your seeing-eye dog or holding up a phone with AI, I’m taking the seeing-eye dog every time.” Because the dog understands something no language model on earth understands. Stakes. The dog knows a wrong step means its owner gets hurt. The app knows a wrong step means a revised output. Hundreds of billions spent building systems that can write, reason, and diagnose. Not one of them loses sleep when the answer is wrong. A toddler pushing a cup off a tray runs a tighter feedback loop than every foundation model combined. The child doesn’t just predict the outcome. The child wants the reaction. Pushes the cup off the edge, watches it fall, watches Mom come running. Laughs. Because the child knew what would happen before the cup ever hit the floor. That gap between prediction and consequence isn’t a bug. It’s not getting patched in the next update. It is the unsolved problem of artificial intelligence. We didn’t build minds. We built mirrors. Mirrors don’t flinch when you walk into traffic.

Dustin

47,919 views • 4 months ago

The difference a lot of people don't realize is that when you're a celebrity, 50% of the population doesn't like you. That's just how it is. They don't like you. But when you're a politician, 50% of the constituency hates you and two and a half percent of them want to kill you. That's the difference. There's always that two and a half percent lunatic fringe no matter where you are. It's pretty scary and it's tough on your family and that's why I have so much respect for politicians.

The difference a lot of people don't realize is that when you're a celebrity, 50% of the population doesn't like you. That's just how it is. They don't like you. But when you're a politician, 50% of the constituency hates you and two and a half percent of them want to kill you. That's the difference. There's always that two and a half percent lunatic fringe no matter where you are. It's pretty scary and it's tough on your family and that's why I have so much respect for politicians.

Kevin O'Leary aka Mr. Wonderful

24,897 views • 8 months ago

Donte Divincenoz on Minnesota becoming an attractive place to play “First and foremost when you put someone like Anthony Edwards in a market, it’s attractive(laughs) and then you get a bunch of good guys around him and then you get to experience the amazing people in the community, that otherwise if you didn’t play here you don’t understand what it’s like. You don’t understand what it’s like living here and interacting with the fans every single day… that’s what makes the difference for players, personally that’s what makes the difference for me” (He’s one of us Minnesota) #WolvesBack

Donte Divincenoz on Minnesota becoming an attractive place to play “First and foremost when you put someone like Anthony Edwards in a market, it’s attractive(laughs) and then you get a bunch of good guys around him and then you get to experience the amazing people in the community, that otherwise if you didn’t play here you don’t understand what it’s like. You don’t understand what it’s like living here and interacting with the fans every single day… that’s what makes the difference for players, personally that’s what makes the difference for me” (He’s one of us Minnesota) #WolvesBack

Andrew Dukowitz

121,851 views • 1 year ago