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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...

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Pascal Bornet

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114,059 Aufrufe • vor 6 Monaten •via X (Twitter)

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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.
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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.

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The highest leverage thing you have to optimize for AI agents is their context. If you’re not getting enough out of AI agents, you probably have a context problem. By default the AI model knows absolutely nothing relevant to perform the task successfully. The whole job of context engineering is to prioritize what the model has as information to work with. Small deviations in the goals, instructions, domain understanding, or underlying data you supply the model will lead to wildly different outcomes. This is where you should spend almost all your time and energy in a workflow. We will start to religiously document more of our workflows and business processes, create clearer goals for people and agents, and ensure our tech infra is setup to get AI agents the right information. The lessons are currently being learned the fastest with coding agents, but the same lessons will apply to every field of knowledge work going forward. dex has a great talk on this that’s absolutely worth your time to watch.
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The highest leverage thing you have to optimize for AI agents is their context. If you’re not getting enough out of AI agents, you probably have a context problem. By default the AI model knows absolutely nothing relevant to perform the task successfully. The whole job of context engineering is to prioritize what the model has as information to work with. Small deviations in the goals, instructions, domain understanding, or underlying data you supply the model will lead to wildly different outcomes. This is where you should spend almost all your time and energy in a workflow. We will start to religiously document more of our workflows and business processes, create clearer goals for people and agents, and ensure our tech infra is setup to get AI agents the right information. The lessons are currently being learned the fastest with coding agents, but the same lessons will apply to every field of knowledge work going forward. dex has a great talk on this that’s absolutely worth your time to watch.

Aaron Levie

87,086 Aufrufe • vor 10 Monaten

THIS GUY SPENT 5 MINUTES SETTING UP A $20 CLAUDE WORKSPACE AND TURNED 30 MINUTES OF DAILY PROMPT CHAOS INTO A SYSTEM Most people open Claude, type 1 messy prompt, rewrite it 10 times, get the same generic answer and call the model “bad.” He opens Claude Cowork, adds the project rules, past context, working files, examples, decisions and a clean place for the model to remember what it is doing. Same Claude. Same $20 tool. Completely different output. The funny part is there is no magic prompt here. No secret template. No “10x Claude” trick. Just the boring layer Karpathy keeps pointing at: context, memory and structure before output. A setup like this can save 30 minutes a day, which is 15 hours a month. If you use Claude for client work at $50 to $100/hour, that is $750 to $1,500/month in time you stop burning on repeated prompts. The model is real. The output is real. The edge is in not making Claude start from zero every single time.
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THIS GUY SPENT 5 MINUTES SETTING UP A $20 CLAUDE WORKSPACE AND TURNED 30 MINUTES OF DAILY PROMPT CHAOS INTO A SYSTEM Most people open Claude, type 1 messy prompt, rewrite it 10 times, get the same generic answer and call the model “bad.” He opens Claude Cowork, adds the project rules, past context, working files, examples, decisions and a clean place for the model to remember what it is doing. Same Claude. Same $20 tool. Completely different output. The funny part is there is no magic prompt here. No secret template. No “10x Claude” trick. Just the boring layer Karpathy keeps pointing at: context, memory and structure before output. A setup like this can save 30 minutes a day, which is 15 hours a month. If you use Claude for client work at $50 to $100/hour, that is $750 to $1,500/month in time you stop burning on repeated prompts. The model is real. The output is real. The edge is in not making Claude start from zero every single time.

Gipp 🦅

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Today we're launching Airbyte Agents. It’s a platform meant to solve what we call the discovery gap. Agents in demos are successful because they know the fields and sources they have to call to solve their task. Agents in production fail because they don’t have that knowledge assembled beforehand. Airbyte Agents, powered by the Context Store, fixes that with one unified layer of pre-materialized context across all of your business systems. Real results. 80% fewer tokens. 40% fewer tool calls. Get more out of your AI tool’s plan limit. Three ways to build: MCP, SDK, or no-code Automations 💡 We're early, and we want to build with our users. Try Airbyte Agents today.
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Today we're launching Airbyte Agents. It’s a platform meant to solve what we call the discovery gap. Agents in demos are successful because they know the fields and sources they have to call to solve their task. Agents in production fail because they don’t have that knowledge assembled beforehand. Airbyte Agents, powered by the Context Store, fixes that with one unified layer of pre-materialized context across all of your business systems. Real results. 80% fewer tokens. 40% fewer tool calls. Get more out of your AI tool’s plan limit. Three ways to build: MCP, SDK, or no-code Automations 💡 We're early, and we want to build with our users. Try Airbyte Agents today.

Airbyte

6,761,845 Aufrufe • vor 2 Monaten

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.
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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

20,271 Aufrufe • vor 6 Monaten

Palantir CEO Alex Karp on why the model is not the moat. The application layer is: "To make them valuable in an enterprise, like a battlefield, regulated, or manufacturing context, you have to have what's called an application layer." "We have this thing called ontology that now everyone's copying. It takes a large language model and makes it safe and useful and precise." "Safe because it doesn't touch your underlying data." "Safe because it prevents the large language model from caching your data and replicating your business." The tell for any enterprise AI pitch: ask what sits between the model and your data. If the answer is nothing, you are not buying leverage. You are renting a fast way to hand your business to whoever owns the weights. Alex Karp, CEO of Palantir, on CNBC.
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Palantir CEO Alex Karp on why the model is not the moat. The application layer is: "To make them valuable in an enterprise, like a battlefield, regulated, or manufacturing context, you have to have what's called an application layer." "We have this thing called ontology that now everyone's copying. It takes a large language model and makes it safe and useful and precise." "Safe because it doesn't touch your underlying data." "Safe because it prevents the large language model from caching your data and replicating your business." The tell for any enterprise AI pitch: ask what sits between the model and your data. If the answer is nothing, you are not buying leverage. You are renting a fast way to hand your business to whoever owns the weights. Alex Karp, CEO of Palantir, on CNBC.

Karl Mehta

31,447 Aufrufe • vor 10 Tagen

Most people think AI means we don’t need to learn anymore. But that idea crumbled last month when I sat across from Stanford Graduate School of Education Dean, Dan Schwartz. Dan read this to the audience: “The procedure is actually quite simple. First you arrange things into different groups. Of course, one pile may be sufficient depending on how much there is. If you have to go somewhere else due to lack of facilities that is the next step; otherwise you are pretty well set. It is important not to overdo things. That is, it is better to do too few things at once than too many…” No one really understood it. Then he gave them one line of context: “This is about doing laundry.” And suddenly it all made sense. Same paragraph. Same words. But once you *knew* what it was about, your brain could organize the information. Dan looked at the audience and said, “If you don’t know enough, the AI’s output is just words and sentences. You might *think* you understand it, but you don’t.” And he’s right. AI doesn’t eliminate the need to learn—it makes real knowledge more important than ever. This is a conversation we’re constantly having at Alpha. Because when a kid asks ChatGPT a question, how do they know if the answer’s even good? How do they know when to push back or ask more? It all comes back to this: you need knowledge to interpret the output. That’s why we still teach writing. That’s why we teach proportionality. That’s why we want kids to understand germ theory, gravity, and the Bill of Rights. Not because they’ll regurgitate it on a test, but because they’ll need that knowledge to make sense of the world (and the tools) around them. AI will make learning faster, more playful, and more personalized. But it won’t replace the need to learn. It’ll just expose when we haven’t. And as Dan reminded us on stage, the real purpose of education isn’t just facts or grades. It’s helping kids build meaning, curiosity, and the wisdom to use their tools well. Deep thanks to Dean Dan Schwartz for sharing his time and expertise with Alpha and our community.
1:18

Most people think AI means we don’t need to learn anymore. But that idea crumbled last month when I sat across from Stanford Graduate School of Education Dean, Dan Schwartz. Dan read this to the audience: “The procedure is actually quite simple. First you arrange things into different groups. Of course, one pile may be sufficient depending on how much there is. If you have to go somewhere else due to lack of facilities that is the next step; otherwise you are pretty well set. It is important not to overdo things. That is, it is better to do too few things at once than too many…” No one really understood it. Then he gave them one line of context: “This is about doing laundry.” And suddenly it all made sense. Same paragraph. Same words. But once you *knew* what it was about, your brain could organize the information. Dan looked at the audience and said, “If you don’t know enough, the AI’s output is just words and sentences. You might *think* you understand it, but you don’t.” And he’s right. AI doesn’t eliminate the need to learn—it makes real knowledge more important than ever. This is a conversation we’re constantly having at Alpha. Because when a kid asks ChatGPT a question, how do they know if the answer’s even good? How do they know when to push back or ask more? It all comes back to this: you need knowledge to interpret the output. That’s why we still teach writing. That’s why we teach proportionality. That’s why we want kids to understand germ theory, gravity, and the Bill of Rights. Not because they’ll regurgitate it on a test, but because they’ll need that knowledge to make sense of the world (and the tools) around them. AI will make learning faster, more playful, and more personalized. But it won’t replace the need to learn. It’ll just expose when we haven’t. And as Dan reminded us on stage, the real purpose of education isn’t just facts or grades. It’s helping kids build meaning, curiosity, and the wisdom to use their tools well. Deep thanks to Dean Dan Schwartz for sharing his time and expertise with Alpha and our community.

MacKenzie Price

14,491 Aufrufe • vor 1 Jahr

A few days ago Steven Kuhn, whose wife is Ukrainian, put out a video speaking the truth about Ukraine but as usual the haters came out calling him a liar, that he doesn’t know what he’s talking about, & demanded to know what his wife thinks about all of this, so here is his wife: “I am Ukrainian. I was born and raised in Kharkov. I grew up speaking Russian like many people in the east. And what my husband say in the last video is not just his opinion, that is our reality. Everyone has a different reality. My parents lost everything. People they know are either gone or living in fear. This isn’t politics just to us, this is OUR life.” Steven: “Her family lost everything. Their home, it’s gone. Their pensions cut because they left the country. Their friends - some of them taken off the streets forced into war never to be seen again and yet STILL some of you think questioning this is wrong. That speaking out somehow makes us the enemy.” His wife continues, “The people screaming the loudest online - they don’t live in Ukraine, they don’t know Ukraine but they think they can tell Ukrainians what it means to be Ukrainian. So let me make something very clear: most Ukrainians I know want peace, we want this war to end. However that happens we just want to go home, that’s it.” Steven: “And that’s why we speak out because millions of Ukrainians CAN’T because the media won’t tell you this, because politicians NEED you to believe a fairy tale while real people like my wife’s family are left with nothing at all. If you don’t agree that’s fine but don’t pretend you care more about Ukraine than we do. (Link to original video in the comments)
1:38

A few days ago Steven Kuhn, whose wife is Ukrainian, put out a video speaking the truth about Ukraine but as usual the haters came out calling him a liar, that he doesn’t know what he’s talking about, & demanded to know what his wife thinks about all of this, so here is his wife: “I am Ukrainian. I was born and raised in Kharkov. I grew up speaking Russian like many people in the east. And what my husband say in the last video is not just his opinion, that is our reality. Everyone has a different reality. My parents lost everything. People they know are either gone or living in fear. This isn’t politics just to us, this is OUR life.” Steven: “Her family lost everything. Their home, it’s gone. Their pensions cut because they left the country. Their friends - some of them taken off the streets forced into war never to be seen again and yet STILL some of you think questioning this is wrong. That speaking out somehow makes us the enemy.” His wife continues, “The people screaming the loudest online - they don’t live in Ukraine, they don’t know Ukraine but they think they can tell Ukrainians what it means to be Ukrainian. So let me make something very clear: most Ukrainians I know want peace, we want this war to end. However that happens we just want to go home, that’s it.” Steven: “And that’s why we speak out because millions of Ukrainians CAN’T because the media won’t tell you this, because politicians NEED you to believe a fairy tale while real people like my wife’s family are left with nothing at all. If you don’t agree that’s fine but don’t pretend you care more about Ukraine than we do. (Link to original video in the comments)

Sequencer

33,436 Aufrufe • vor 1 Jahr

IN NEWS: Attio raises $52M series B led by GV. Nicolas Sharp on AI in CRM: "LLMs work great when they have lots of context and you need to feed them that. One of the first things we worked on was this data model and being able to instantly ingest emails, calendars, product data, etc." "We have a thesis that code is becoming the new no-code. Software becomes so much more malleable because code is so much easier to generate."
2:09

IN NEWS: Attio raises $52M series B led by GV. Nicolas Sharp on AI in CRM: "LLMs work great when they have lots of context and you need to feed them that. One of the first things we worked on was this data model and being able to instantly ingest emails, calendars, product data, etc." "We have a thesis that code is becoming the new no-code. Software becomes so much more malleable because code is so much easier to generate."

TBPN

16,200 Aufrufe • vor 10 Monaten

Most elections, only about 60% of people vote. That means 40% don’t. Not because they don’t care… but because they’ve given up on the system. But something different is happening right now in Alberta. I was at a canvasser recognition event in Innisfail, and I kept hearing the same thing over and over: “This is my first time ever getting involved.” People who have NEVER volunteered. NEVER canvassed. NEVER participated in politics. And now they’re all in. A lot of them come from that 40%. Think about what that means. If you can engage the people who’ve checked out… And combine them with the people who still vote but are frustrated… You don’t just grow support. You change everything. This is what a real grassroots movement looks like.
0:34

Most elections, only about 60% of people vote. That means 40% don’t. Not because they don’t care… but because they’ve given up on the system. But something different is happening right now in Alberta. I was at a canvasser recognition event in Innisfail, and I kept hearing the same thing over and over: “This is my first time ever getting involved.” People who have NEVER volunteered. NEVER canvassed. NEVER participated in politics. And now they’re all in. A lot of them come from that 40%. Think about what that means. If you can engage the people who’ve checked out… And combine them with the people who still vote but are frustrated… You don’t just grow support. You change everything. This is what a real grassroots movement looks like.

Jon Alberta Patriot

16,622 Aufrufe • vor 3 Monaten

I’m fully with Demis Hassabis on this. STEM still matters deeply, not because humans will outcode AI, but because we need to understand, steer, and define problems at a high level. At the same time, AI is blowing the doors open for creativity. When technical barriers fall, taste, judgment, and vision become the real edge. The future belongs to people who know what to build and why, not just how. Do you think schools are preparing kids for that reality or are they still training them for a world that’s already gone?
0:37

I’m fully with Demis Hassabis on this. STEM still matters deeply, not because humans will outcode AI, but because we need to understand, steer, and define problems at a high level. At the same time, AI is blowing the doors open for creativity. When technical barriers fall, taste, judgment, and vision become the real edge. The future belongs to people who know what to build and why, not just how. Do you think schools are preparing kids for that reality or are they still training them for a world that’s already gone?

VraserX e/acc

58,341 Aufrufe • vor 4 Monaten

"What we’ve done is solve hallucinations in general AI by anchoring the model to truth—your company’s real data, goals, people, and structure. With our context engine, the AI understands who you are, why you exist, and what you’re trying to do. So every answer is grounded in your company’s DNA. That’s how we built FOSTR." Today’s episode is with Co-Founder, Jason Baxter, to introduce our latest venture, FOSTR AI—a company built to solve one of the most pressing challenges facing businesses today: how to implement AI in your business in a meaningful, aligned, and scalable way. We unpack the fragmented state of AI adoption across small to mid-sized businesses and explain why most organizations, despite interest, are either stuck in experimentation or using disconnected tools that don’t move the business forward. FOSTR AI is the answer to that problem—an execution intelligence layer that creates a company's “digital twin,” aligning AI usage with team structure, goals, and strategy from day one. We discuss how FOSTR helps companies: - Onboard and operationalize AI in a matter of minutes - Centralize AI usage across teams while maintaining control, security, and context - Reduce risk from siloed tools and misaligned AI use Whether you're running a 50-person business or leading an enterprise team, this episode explores how to unlock the full potential of AI, without losing visibility or control. If you’re an owner, operator, investor, or builder curious about the future of AI in business, this episode offers a first look at what that future will look like. 0:52 - Introducing FOSTR 2:40 - The challenges businesses face with AI today 5:14 - How companies and employees are misusing AI. 10:29 - Additional Challenges 12:33 - How FOSTR is creating alignment between companies and AI Solutions. 25:57 - Where FOSTR is in its life cycle 30:39 - How to get in touch with, work at, or invest in FOSTR
31:27

"What we’ve done is solve hallucinations in general AI by anchoring the model to truth—your company’s real data, goals, people, and structure. With our context engine, the AI understands who you are, why you exist, and what you’re trying to do. So every answer is grounded in your company’s DNA. That’s how we built FOSTR." Today’s episode is with Co-Founder, Jason Baxter, to introduce our latest venture, FOSTR AI—a company built to solve one of the most pressing challenges facing businesses today: how to implement AI in your business in a meaningful, aligned, and scalable way. We unpack the fragmented state of AI adoption across small to mid-sized businesses and explain why most organizations, despite interest, are either stuck in experimentation or using disconnected tools that don’t move the business forward. FOSTR AI is the answer to that problem—an execution intelligence layer that creates a company's “digital twin,” aligning AI usage with team structure, goals, and strategy from day one. We discuss how FOSTR helps companies: - Onboard and operationalize AI in a matter of minutes - Centralize AI usage across teams while maintaining control, security, and context - Reduce risk from siloed tools and misaligned AI use Whether you're running a 50-person business or leading an enterprise team, this episode explores how to unlock the full potential of AI, without losing visibility or control. If you’re an owner, operator, investor, or builder curious about the future of AI in business, this episode offers a first look at what that future will look like. 0:52 - Introducing FOSTR 2:40 - The challenges businesses face with AI today 5:14 - How companies and employees are misusing AI. 10:29 - Additional Challenges 12:33 - How FOSTR is creating alignment between companies and AI Solutions. 25:57 - Where FOSTR is in its life cycle 30:39 - How to get in touch with, work at, or invest in FOSTR

Chris Powers

41,751 Aufrufe • vor 1 Jahr

This is the correct response to what’s happening to Erika Kirk and what’s happening on the pro-Hamas Eurasianist slop “right.” And if more people have the courage to say this, I’m talking about people who are doing podcasts and who have lots and lots of viewers, we would be in a different place, but because you have a lot of people who are morally bankrupt they remain silent and pretend like that is the high ground. It’s not because your soul is not worth it a check. And if you are silent in the face of evil, that is the same thing as evil itself and God will not hold you blameless. I may not know a lot, but I definitely know that.
2:48

This is the correct response to what’s happening to Erika Kirk and what’s happening on the pro-Hamas Eurasianist slop “right.” And if more people have the courage to say this, I’m talking about people who are doing podcasts and who have lots and lots of viewers, we would be in a different place, but because you have a lot of people who are morally bankrupt they remain silent and pretend like that is the high ground. It’s not because your soul is not worth it a check. And if you are silent in the face of evil, that is the same thing as evil itself and God will not hold you blameless. I may not know a lot, but I definitely know that.

Insurrection Barbie

75,426 Aufrufe • vor 4 Monaten

THIS IS THE MOST DISTURBING THING I HAVE SEEN OR HEARD IN A LONG TIME. Same Marxist playbook every time. Stop antisemitism is a terrorist organization and a threat to the American people and everything we stand for. If you are Jewish specifically and watching this, you better stand up and speak out against this. If not, it’s only going to get worse. More hate, more divide, more chaos and more control. Which is ultimately what they want. But what do I know, I’m just a conspiracy theorist.
10:28

THIS IS THE MOST DISTURBING THING I HAVE SEEN OR HEARD IN A LONG TIME. Same Marxist playbook every time. Stop antisemitism is a terrorist organization and a threat to the American people and everything we stand for. If you are Jewish specifically and watching this, you better stand up and speak out against this. If not, it’s only going to get worse. More hate, more divide, more chaos and more control. Which is ultimately what they want. But what do I know, I’m just a conspiracy theorist.

Ian

125,188 Aufrufe • vor 6 Monaten

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesn’t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons I’ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, it’s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You don’t fix it . You start from the first principles concept that I’ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but it’s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that I’ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.
2:57

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesn’t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons I’ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, it’s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You don’t fix it . You start from the first principles concept that I’ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but it’s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that I’ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.

Brian Roemmele

72,312 Aufrufe • vor 7 Monaten

But now today, we have to agree, we have to accept, that the last shower of military reign is coming from Afghanistan, and from Khorasan. Whether the Afghan people are able to maintain unity in their ranks or not, because Shaytan is always at work, to try to bring rivalry, different warlords and disunity and fighting and civil war. We don’t know what will happen in the future. But so far the evidence is, that they had given a spectacular demonstration, that they are, the last shower of military reign in this Ummah.
0:50

But now today, we have to agree, we have to accept, that the last shower of military reign is coming from Afghanistan, and from Khorasan. Whether the Afghan people are able to maintain unity in their ranks or not, because Shaytan is always at work, to try to bring rivalry, different warlords and disunity and fighting and civil war. We don’t know what will happen in the future. But so far the evidence is, that they had given a spectacular demonstration, that they are, the last shower of military reign in this Ummah.

The Sure Path

10,514 Aufrufe • vor 5 Monaten

How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇
0:59

How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇

Zeb Evans

425,244 Aufrufe • vor 5 Monaten

Chanhyuk: Company doesn’t matter, but it’s about the people you work with. “There’s also something like this. Even though we say we created a “company,” we just used that word because it’s easier for people to understand. But in the end, it’s not really about the company, it’s about the people. Like, who you work with. And what kind of experiences you share with them, that’s what really matters the most. So with these people, whether we go, volunteering or travel somewhere, we always move together as one group. Because if not and later on, when I write and release a song, if we hadn’t shared those experiences, I’d have to explain everything. Like, “I went somewhere and had this kind of experience…” But since we’ve all lived it together, I can just say, “Hey, I wrote this kind of song this time,” and they’ll be like, “Oh, yeah, I get it. I know exactly what that is.” It just makes everything so much faster, right?”
0:47

Chanhyuk: Company doesn’t matter, but it’s about the people you work with. “There’s also something like this. Even though we say we created a “company,” we just used that word because it’s easier for people to understand. But in the end, it’s not really about the company, it’s about the people. Like, who you work with. And what kind of experiences you share with them, that’s what really matters the most. So with these people, whether we go, volunteering or travel somewhere, we always move together as one group. Because if not and later on, when I write and release a song, if we hadn’t shared those experiences, I’d have to explain everything. Like, “I went somewhere and had this kind of experience…” But since we’ve all lived it together, I can just say, “Hey, I wrote this kind of song this time,” and they’ll be like, “Oh, yeah, I get it. I know exactly what that is.” It just makes everything so much faster, right?”

akmerch ⚔️

194,013 Aufrufe • vor 3 Monaten

You were told to question everything. So you looked past the mainstream… and picked a side that felt like the alternative. But why does every path still lead back to the same people? Different countries. Different leaders. Different narratives, yet the outcome keeps moving in the same direction… no matter who’s “winning.” The “multipolar world” isn’t what people think. The WEF is just one piece of it. Look closer, and the same names and networks keep showing up. From the League of Nations—now the United Nations—to Bilderberg, the Council on Foreign Relations, and the Trilateral Commission. So why are so many people convinced this is a battle between the “good guys” and the “bad guys”? Independent journalist James Corbett has been tracking this for 19 years, and he’s come to a conclusion most people don’t want to hear: no one is coming to save us. No matter which side appears to be “winning,” the direction doesn’t change. It leads to the same place. Technocracy. And if you look back at COVID, you’ve already seen how this works. Maybe every event isn’t perfectly orchestrated… But the outcome? That’s a different story. 🧵
2:06

You were told to question everything. So you looked past the mainstream… and picked a side that felt like the alternative. But why does every path still lead back to the same people? Different countries. Different leaders. Different narratives, yet the outcome keeps moving in the same direction… no matter who’s “winning.” The “multipolar world” isn’t what people think. The WEF is just one piece of it. Look closer, and the same names and networks keep showing up. From the League of Nations—now the United Nations—to Bilderberg, the Council on Foreign Relations, and the Trilateral Commission. So why are so many people convinced this is a battle between the “good guys” and the “bad guys”? Independent journalist James Corbett has been tracking this for 19 years, and he’s come to a conclusion most people don’t want to hear: no one is coming to save us. No matter which side appears to be “winning,” the direction doesn’t change. It leads to the same place. Technocracy. And if you look back at COVID, you’ve already seen how this works. Maybe every event isn’t perfectly orchestrated… But the outcome? That’s a different story. 🧵

The Vigilant Fox 🦊

35,430 Aufrufe • vor 2 Monaten