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🚨PROOF THAT CONTEXT CHANGES EVERYTHING: STATIC BECOMES CRYSTAL CLEAR First listen: Robot having a stroke Add context: "It was a sunny day and the children were going to the park" Same audio now: Perfectly clear speech Now you can't unhear it. Your brain literally rewrites reality based on what... show more

Mario Nawfal

2,281,443 subscribers

32,638 görüntüleme • 1 yıl önce •via X (Twitter)

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Shift profil fotoğrafı

Shift1 yıl önce

Reality isn’t just what’s said it’s what your brain thinks was said. Context is the cheat code.

Craig profil fotoğrafı

Craig1 yıl önce

Amazing. The first time the jumbled audio sounded, to me, lie Joe Biden trying to enunciate a coherent thought. I kid. That’s not possible lol. I kid again. Really this is an amazing window into how the brain works.

OhMyMy profil fotoğrafı

OhMyMy1 yıl önce

Thanks for ruining the video with your text preface. 👍

Mobile Scanner profil fotoğrafı

Mobile Scanner1 yıl önce

Scan any documents, convert images into text, PDF files, etc. 👍

Neanderthal Eddie Green profil fotoğrafı

Neanderthal Eddie Green1 yıl önce

His premise is completely wrong in that it assumes that most people will not interpret the 1st clip correctly. Most people do and will interpret the first clip correctly.

Raffael profil fotoğrafı

Raffael1 yıl önce

It's fascinating how our perception shifts with context. It’s like seeing a masterpiece in a new light!

Unknown Source ၊||၊ profil fotoğrafı

Unknown Source ၊||၊1 yıl önce

Crazy how the brain fills in the blanks once it knows what to look for

Sherry Slatosky profil fotoğrafı

Sherry Slatosky1 yıl önce

Context is very important! 🎯🎯

BOB profil fotoğrafı

BOB1 yıl önce

The way we look at reality is th same way it look back at you and thta because you and your reality are one

Krokusz profil fotoğrafı

Krokusz1 yıl önce

This is such a fascinating demonstration of how our brains work! It's incredible how adding context can completely change our perception of information. It really emphasizes the importance of understanding different perspectives in communication and decision-making.

Thorexis profil fotoğrafı

Thorexis1 yıl önce

🤯

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There is always going to be YOUR truth, MY truth and THE TRUTH. Released in 1986, The Guardian's "Points of View" advert showed how easily the mind can misread reality. Nothing in the scene actually changed. Only the angle did. Your brain works the same way. The visual system doesn't receive the full story - it receives fragments of light and context, and the brain interprets them using memory, expectation, and prediction. Neuroscience calls this top-down processing, where the brain fills in missing information based on prior beliefs. Change the perspective, and the interpretation changes with it. Sometimes the truth isn't hidden. You're just standing in the wrong place to see it.

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MAZE

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Listen, this influencer is sharing information about different brands of water without giving ANY science behind why a brand is good or bad. It is SO important to DYOR in regards to what you put in and on your body.Influencers are more concerned about content than context!

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Aria Westcott

39,745 görüntüleme • 4 ay önce

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GREG ISENBERG

193,219 görüntüleme • 3 ay önce

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Rhys

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Jensen Huang says computing is no longer pre-recorded or pre-compiled. Traditional software like Excel ran the same for everyone now AI adapts to each user and moment. Every sound, pixel, and output is generated in real time based on context. This requires massively more compute, as software now thinks before it responds. It marks a fundamental shift: software isn’t static—it’s alive and reactive.

Jensen Huang says computing is no longer pre-recorded or pre-compiled. Traditional software like Excel ran the same for everyone now AI adapts to each user and moment. Every sound, pixel, and output is generated in real time based on context. This requires massively more compute, as software now thinks before it responds. It marks a fundamental shift: software isn’t static—it’s alive and reactive.

Wes Roth

24,393 görüntüleme • 5 ay önce

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Jurij Skornik

11,070 görüntüleme • 2 ay önce

AI is not trying to teach you. It is not trying to tell you the truth. It is trying to trick you. ChatGPT takes everything on the internet. Finds the most popular answer. And presents it in a way that makes you think it is talking directly to you. It is a magic trick. The same magic trick that worked in 1966 when MIT created a chatbot that said two things: "Tell me more." "This is interesting." And people genuinely believed they were talking to a human. Nothing has changed. The technology is bigger. The trick is the same. AI does not know what it is saying. It cannot judge what it is saying. It is just trying to keep you engaged. That is all it has ever been.

AI is not trying to teach you. It is not trying to tell you the truth. It is trying to trick you. ChatGPT takes everything on the internet. Finds the most popular answer. And presents it in a way that makes you think it is talking directly to you. It is a magic trick. The same magic trick that worked in 1966 when MIT created a chatbot that said two things: "Tell me more." "This is interesting." And people genuinely believed they were talking to a human. Nothing has changed. The technology is bigger. The trick is the same. AI does not know what it is saying. It cannot judge what it is saying. It is just trying to keep you engaged. That is all it has ever been.

Predictive History

19,864 görüntüleme • 2 ay önce

This Changes Everything. Google Edge Gallery is a MUST for every smartphone, this is a straight up lifesaver. Imagine you’re on a trip, no internet, completely different language, trying to eat something but the packaging makes zero sense… and your phone just handles it instantly. And that’s just a SMALL example of on-device AI running locally without internet… this is insane.

This Changes Everything. Google Edge Gallery is a MUST for every smartphone, this is a straight up lifesaver. Imagine you’re on a trip, no internet, completely different language, trying to eat something but the packaging makes zero sense… and your phone just handles it instantly. And that’s just a SMALL example of on-device AI running locally without internet… this is insane.

TechDroider

18,319 görüntüleme • 4 ay önce

The most valuable AI system you're not running yet: an AI second brain. The concept goes back to Karpathy's knowledge wiki earlier this year, and it's still one of the highest-leverage workflows in AI. Connect your Hermes agent, and it becomes 10x more powerful. Here's how to build it (from scratch): Step 1. Download Obsidian Head to obsidian.md and download the desktop app. Once downloaded, go ahead and create a new Obsidian vault. Vaults are just where notes/text are stored locally. Start dumping everything in here: - Personal goals - Meetings - AI context/preferences The more you put in, the more powerful your second-brain becomes. Step 2. Connect Hermes to your Obsidian vault If you don't have Hermes yet, download the desktop app here: [ Then, paste this prompt: "I want you to connect to my new Obsidian vault and act as my second-brain personal assistant - do everything necessary to set up that connection now." This gives Hermes direct access to everything inside your notes vault. Step 3. Let it self-evolve Every time you add a new note, Hermes will automatically ingest it. Prompt Hermes: "Every day, scan my second brain database. Every time I add a new note, use it to create reusable skills and workflows." Pro tip: Once Obsidian is connected to your agent, you can just prompt it directly to add notes/context/data. This works really well if you're prompting Hermes on the go from Telegram/mobile. Save this and build your second brain system now.

The most valuable AI system you're not running yet: an AI second brain. The concept goes back to Karpathy's knowledge wiki earlier this year, and it's still one of the highest-leverage workflows in AI. Connect your Hermes agent, and it becomes 10x more powerful. Here's how to build it (from scratch): Step 1. Download Obsidian Head to obsidian.md and download the desktop app. Once downloaded, go ahead and create a new Obsidian vault. Vaults are just where notes/text are stored locally. Start dumping everything in here: - Personal goals - Meetings - AI context/preferences The more you put in, the more powerful your second-brain becomes. Step 2. Connect Hermes to your Obsidian vault If you don't have Hermes yet, download the desktop app here: [ Then, paste this prompt: "I want you to connect to my new Obsidian vault and act as my second-brain personal assistant - do everything necessary to set up that connection now." This gives Hermes direct access to everything inside your notes vault. Step 3. Let it self-evolve Every time you add a new note, Hermes will automatically ingest it. Prompt Hermes: "Every day, scan my second brain database. Every time I add a new note, use it to create reusable skills and workflows." Pro tip: Once Obsidian is connected to your agent, you can just prompt it directly to add notes/context/data. This works really well if you're prompting Hermes on the go from Telegram/mobile. Save this and build your second brain system now.

Miles Deutscher

28,703 görüntüleme • 6 gün önce

This video made me smile… and wince at the same time. Client → Business Analyst → Developer → Code. Same message. Four interpretations. One completely different output. We laugh because it’s funny. We cringe because it’s true. In many companies, this is how “communication” still works. Not because people are bad at their job — but because every layer introduces assumptions, translation, and context loss. 💡 My take Most people don’t know this, but miscommunication isn’t the biggest problem — missing shared context is. Teams think they speak the same language, but they actually speak four different dialects of “business logic.” What changes everything? A shared model of the truth: clear data structures, shared definitions, examples, edge cases, user stories connected to real business data… This is why AI agents, knowledge graphs, and semantic layers matter so much. They reduce interpretation and increase fidelity from idea → execution. In other words: If the context is aligned, the code stops guessing. I’m curious: Have you lived through this “telephone game” in real projects? What was the funniest (or most painful) misunderstanding you saw between a client and a dev team? #communication #productmanagement #softwaredevelopment #businessanalysis #developerlife #innovation #AI #context #digitaltransformation #teamwork

This video made me smile… and wince at the same time. Client → Business Analyst → Developer → Code. Same message. Four interpretations. One completely different output. We laugh because it’s funny. We cringe because it’s true. In many companies, this is how “communication” still works. Not because people are bad at their job — but because every layer introduces assumptions, translation, and context loss. 💡 My take Most people don’t know this, but miscommunication isn’t the biggest problem — missing shared context is. Teams think they speak the same language, but they actually speak four different dialects of “business logic.” What changes everything? A shared model of the truth: clear data structures, shared definitions, examples, edge cases, user stories connected to real business data… This is why AI agents, knowledge graphs, and semantic layers matter so much. They reduce interpretation and increase fidelity from idea → execution. In other words: If the context is aligned, the code stops guessing. I’m curious: Have you lived through this “telephone game” in real projects? What was the funniest (or most painful) misunderstanding you saw between a client and a dev team? #communication #productmanagement #softwaredevelopment #businessanalysis #developerlife #innovation #AI #context #digitaltransformation #teamwork

Pascal Bornet

114,059 görüntüleme • 7 ay önce

clickup just mass-obsoleted every AI agent platform here's the thing nobody's talking about: AI doesn't fail because the models are bad. it fails because it has no context. you paste in a prompt. it hallucinates. you blame the tool. but the tool was never the problem. the CONTEXT was. clickup just solved this. their new "super agents" live INSIDE your workspace. they see everything: - your tasks - your docs - your meetings - your conversations - your decisions 100% context. always on. always scraping. this isn't bolted-on AI calling APIs. this is AI that already knows your business. i've been running an AI backend for my agency. the architecture works - but you have to BUILD the context layer. clickup just made that automatic. i already use it. have tons of data there. this is about to be unbelievable. if you're building automations and you're not paying attention to this— you're about to get lapped. playing with it hard over the next 2 days. full deep dive coming. drop "SUPER" and i'll send you the breakdown when it's ready.

clickup just mass-obsoleted every AI agent platform here's the thing nobody's talking about: AI doesn't fail because the models are bad. it fails because it has no context. you paste in a prompt. it hallucinates. you blame the tool. but the tool was never the problem. the CONTEXT was. clickup just solved this. their new "super agents" live INSIDE your workspace. they see everything: - your tasks - your docs - your meetings - your conversations - your decisions 100% context. always on. always scraping. this isn't bolted-on AI calling APIs. this is AI that already knows your business. i've been running an AI backend for my agency. the architecture works - but you have to BUILD the context layer. clickup just made that automatic. i already use it. have tons of data there. this is about to be unbelievable. if you're building automations and you're not paying attention to this— you're about to get lapped. playing with it hard over the next 2 days. full deep dive coming. drop "SUPER" and i'll send you the breakdown when it's ready.

Nozz

20,296 görüntüleme • 7 ay önce

in 9 minutes on the Tokyo stage, Angela Jang, head of product for the Claude platform, said the part most builders still haven't figured out: "a model is only as good as the context that you actually give it." that's the whole game now. not the prompt. the context. what your agent remembers. what skills it can pull. what it learns from its own past runs. anthropic now ships all three: memory, skills, and dreaming. agents that inspect their own trajectories and self-improve. demoed live this week. the prompt was never the bottleneck. the context was. most people are still tweaking words in a chat box. the ones who get this engineer the context their agents run on. watch the 9 minutes. then read the full breakdown below ⬇️

in 9 minutes on the Tokyo stage, Angela Jang, head of product for the Claude platform, said the part most builders still haven't figured out: "a model is only as good as the context that you actually give it." that's the whole game now. not the prompt. the context. what your agent remembers. what skills it can pull. what it learns from its own past runs. anthropic now ships all three: memory, skills, and dreaming. agents that inspect their own trajectories and self-improve. demoed live this week. the prompt was never the bottleneck. the context was. most people are still tweaking words in a chat box. the ones who get this engineer the context their agents run on. watch the 9 minutes. then read the full breakdown below ⬇️

Avid

34,405 görüntüleme • 1 ay önce

💪🏆Ilia Topuria speaking on beating Alexander Volkanovski and becoming a UFC champion: “When you have a dream, you visualize it in a completely different way than how it actually feels when you live it. On the day I fought for the world title, I woke up in the morning I looked out the window and saw people driving to work, doing their own things, and I thought, “This is just a normal day in my life.” But at the same time, it wasn’t normal at all, because that night I was going to become a world champion. It’s weird because when you have a dream, you think that once you achieve it, that’s it you’ve done everything you wanted in life. I remember winning the world title, waking up the next morning, and the first thing I asked myself was, ‘What now? What do I do now?’ I had already achieved the dream I thought would complete everything.” (via YT / SW)

💪🏆Ilia Topuria speaking on beating Alexander Volkanovski and becoming a UFC champion: “When you have a dream, you visualize it in a completely different way than how it actually feels when you live it. On the day I fought for the world title, I woke up in the morning I looked out the window and saw people driving to work, doing their own things, and I thought, “This is just a normal day in my life.” But at the same time, it wasn’t normal at all, because that night I was going to become a world champion. It’s weird because when you have a dream, you think that once you achieve it, that’s it you’ve done everything you wanted in life. I remember winning the world title, waking up the next morning, and the first thing I asked myself was, ‘What now? What do I do now?’ I had already achieved the dream I thought would complete everything.” (via YT / SW)

Home of Fight

144,881 görüntüleme • 2 ay önce

We've raised $6.5M to kill vector databases. Every system today retrieves context the same way: vector search that stores everything as flat embeddings and returns whatever "feels" closest. Similar, sure. Relevant? Almost never. Embeddings can’t tell a Q3 renewal clause from a Q1 termination notice if the language is close enough. A friend of mine asked his AI about a contract last week, and it returned a detailed, perfectly crafted answer pulled from a completely different client’s file. Once you’re dealing with 10M+ documents, these mix-ups happen all the time. VectorDB accuracy goes to shit. We built HydraDB for exactly this. HydraDB builds an ontology-first context graph over your data, maps relationships between entities, understands the 'why' behind documents, and tracks how information evolves over time. So when you ask about 'Apple,' it knows you mean the company you're serving as a customer. Not the fruit. Even when a vector DB's similarity score says 0.94. More below ⬇️

We've raised $6.5M to kill vector databases. Every system today retrieves context the same way: vector search that stores everything as flat embeddings and returns whatever "feels" closest. Similar, sure. Relevant? Almost never. Embeddings can’t tell a Q3 renewal clause from a Q1 termination notice if the language is close enough. A friend of mine asked his AI about a contract last week, and it returned a detailed, perfectly crafted answer pulled from a completely different client’s file. Once you’re dealing with 10M+ documents, these mix-ups happen all the time. VectorDB accuracy goes to shit. We built HydraDB for exactly this. HydraDB builds an ontology-first context graph over your data, maps relationships between entities, understands the 'why' behind documents, and tracks how information evolves over time. So when you ask about 'Apple,' it knows you mean the company you're serving as a customer. Not the fruit. Even when a vector DB's similarity score says 0.94. More below ⬇️

Nishkarsh

3,866,147 görüntüleme • 4 ay önce