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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 8 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.

Jurij Skornik

11,180 Aufrufe • vor 3 Monaten

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.
0:42

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 1 Jahr

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.
0:37

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 🦅

14,871 Aufrufe • vor 3 Monaten

your agent reviewing its own work is not a check. it is a second opinion from the same source. this is the most common gap in agent systems and it hides in plain sight, because the step exists. there is a review. it just cannot do the thing you think it does. here is the mechanism. the model produced an output from a context. you then ask the same model, holding the same context, whether that output is correct. it answers fluently, because that is what it does. and the answer is drawn from the same distribution that produced the thing being judged. same weights, same window, same blind spots. if the reason the output is wrong is something the model does not know, the review does not know it either. if the reason is something the context does not contain, the review has the same context. the failure mode and the detector share a cause. > why it feels like it works because most of the time the output is fine, and the review says fine. agreement is not evidence of detection. a reviewer that says pass on everything agrees with reality most of the time too. what you actually want to measure is what happens on the cases that are wrong. that is the only place a check earns its name, and it is exactly the place where a self-review is weakest. there is research on this. Huang and colleagues at DeepMind showed at ICLR 2024 that intrinsic self-correction, revising without external grounding, does not reliably help and often makes things worse. > what to actually do move the check outside the model. a test that runs, a schema that validates, a file that exists or does not, an exit code from something you did not write. these are not smarter than the model. they are just not correlated with it, and that is the entire value. when the judgement genuinely needs a model, at minimum use a different family. same family means shared blind spots, and frontier judges measurably inflate scores for outputs that look like their own. and split the work by kind. anything objectively checkable goes to code. only the genuinely semantic calls go to a judge, and those get a rubric written as one line. a review inside the loop tells you the model is confident. a check outside it tells you whether the work is done. save this - then read the eval setup below
0:50

your agent reviewing its own work is not a check. it is a second opinion from the same source. this is the most common gap in agent systems and it hides in plain sight, because the step exists. there is a review. it just cannot do the thing you think it does. here is the mechanism. the model produced an output from a context. you then ask the same model, holding the same context, whether that output is correct. it answers fluently, because that is what it does. and the answer is drawn from the same distribution that produced the thing being judged. same weights, same window, same blind spots. if the reason the output is wrong is something the model does not know, the review does not know it either. if the reason is something the context does not contain, the review has the same context. the failure mode and the detector share a cause. > why it feels like it works because most of the time the output is fine, and the review says fine. agreement is not evidence of detection. a reviewer that says pass on everything agrees with reality most of the time too. what you actually want to measure is what happens on the cases that are wrong. that is the only place a check earns its name, and it is exactly the place where a self-review is weakest. there is research on this. Huang and colleagues at DeepMind showed at ICLR 2024 that intrinsic self-correction, revising without external grounding, does not reliably help and often makes things worse. > what to actually do move the check outside the model. a test that runs, a schema that validates, a file that exists or does not, an exit code from something you did not write. these are not smarter than the model. they are just not correlated with it, and that is the entire value. when the judgement genuinely needs a model, at minimum use a different family. same family means shared blind spots, and frontier judges measurably inflate scores for outputs that look like their own. and split the work by kind. anything objectively checkable goes to code. only the genuinely semantic calls go to a judge, and those get a rubric written as one line. a review inside the loop tells you the model is confident. a check outside it tells you whether the work is done. save this - then read the eval setup below

Hanako

14,325 Aufrufe • vor 28 Tagen

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.
2:51

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

6,762,102 Aufrufe • vor 4 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.
5:50

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

20,298 Aufrufe • vor 8 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.
0:24

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

31,891 Aufrufe • vor 1 Monat

Perplexity CEO Aravind Srinivas on the brutal truth about who actually makes money in AI (and why it's not who you think): Aravind argues that the real value in AI comes from orchestration. He points to products like Codex, Claude Code, and Perplexity Computer: "What is that? It's an orchestration system. It takes a model, pairs it with an agent harness." And what is an agent harness? "The simplest way of describing it is like rules for how the agent loop should run. What are all the skills and sub-agents and connectors and tools it accesses? Without the harness, you don't necessarily capture and convert the intrinsic intelligence in the model into valuable output tokens." This leads to a blunt conclusion about who has a real business in AI, and who doesn't: "If you're literally just a reseller of model tokens, you have no business, because the model will get commoditized. So even if you're a model builder, you don't have a business. As an infra layer, you have some business on serving those output tokens. But as an application layer or model builder, you don't really have a business if you're just a reseller of tokens that come directly out of the model." So where does the value accrue? "You have a business if you know how to take the model, ground it in valuable context, orchestrate it with a really good agent harness, connected to the right set of tools and connectors (whether it's personal connectors or business connectors) and provide the experience to people in one single unified system." Aravind Srinivas then explains Perplexity's specific edge: Beyond orchestrating across tools, files, and connectors, they also orchestrate across models. "That is the differentiation that Anthropic and OpenAI cannot claim, because you wouldn't find GPT-5 inside the Claude Code harness. You wouldn't find Claude Opus inside the Codex harness. These are competing with each other. Whereas you would find both these models inside Perplexity Computer." Why does this matter? Because it all comes down to power. In Aravind's framing, the fundamental cost driver in AI is watts (the one input nobody can subsidize except the government). "Whoever provides the most valuable output tokens with the least amount of power expended to produce them generates the greatest value to the end user, has the most pricing power, has the most value. That is the orchestration problem to solve." His conclusion: "The one single most important metric in AI is token value per watt per user."
2:50

Perplexity CEO Aravind Srinivas on the brutal truth about who actually makes money in AI (and why it's not who you think): Aravind argues that the real value in AI comes from orchestration. He points to products like Codex, Claude Code, and Perplexity Computer: "What is that? It's an orchestration system. It takes a model, pairs it with an agent harness." And what is an agent harness? "The simplest way of describing it is like rules for how the agent loop should run. What are all the skills and sub-agents and connectors and tools it accesses? Without the harness, you don't necessarily capture and convert the intrinsic intelligence in the model into valuable output tokens." This leads to a blunt conclusion about who has a real business in AI, and who doesn't: "If you're literally just a reseller of model tokens, you have no business, because the model will get commoditized. So even if you're a model builder, you don't have a business. As an infra layer, you have some business on serving those output tokens. But as an application layer or model builder, you don't really have a business if you're just a reseller of tokens that come directly out of the model." So where does the value accrue? "You have a business if you know how to take the model, ground it in valuable context, orchestrate it with a really good agent harness, connected to the right set of tools and connectors (whether it's personal connectors or business connectors) and provide the experience to people in one single unified system." Aravind Srinivas then explains Perplexity's specific edge: Beyond orchestrating across tools, files, and connectors, they also orchestrate across models. "That is the differentiation that Anthropic and OpenAI cannot claim, because you wouldn't find GPT-5 inside the Claude Code harness. You wouldn't find Claude Opus inside the Codex harness. These are competing with each other. Whereas you would find both these models inside Perplexity Computer." Why does this matter? Because it all comes down to power. In Aravind's framing, the fundamental cost driver in AI is watts (the one input nobody can subsidize except the government). "Whoever provides the most valuable output tokens with the least amount of power expended to produce them generates the greatest value to the end user, has the most pricing power, has the most value. That is the orchestration problem to solve." His conclusion: "The one single most important metric in AI is token value per watt per user."

Big Brain AI

42,319 Aufrufe • vor 29 Tagen

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

33,442 Aufrufe • vor 1 Jahr

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

16,629 Aufrufe • vor 5 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

58,360 Aufrufe • vor 6 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

Hidden camera recording catches a data center developer in Piedmont, Oklahoma saying it doesn’t matter if residents oppose his data center project The reason their city was chosen was because of the power infrastructure they need, resident outrage doesn’t matter Resident “I mean, but the citizens here are telling you that they don't want you here. Do, do you like going to communities where you're not really wanted?” Developer “Um, we work in a lot of different communities and have a lot of different reactions” (obviously negative reactions) Resident “But these people are clearly showing you that they don't want you here. So do you not think that when a citizens in a, in a community shows you that they don't want you here in their town, that maybe you should look to go locate your business elsewhere?” Developer “We look for strong spots on the grid, and that's what we have here” Resident “So you're just basically here not because you like the city of Piedmont, but because you like the power infrastructure and things like that?” Developer “The power infrastructure is very good” They openly admit they don’t care about the residents, they don’t care about the town or the issues the data center will cause, they are here for the power
1:43

Hidden camera recording catches a data center developer in Piedmont, Oklahoma saying it doesn’t matter if residents oppose his data center project The reason their city was chosen was because of the power infrastructure they need, resident outrage doesn’t matter Resident “I mean, but the citizens here are telling you that they don't want you here. Do, do you like going to communities where you're not really wanted?” Developer “Um, we work in a lot of different communities and have a lot of different reactions” (obviously negative reactions) Resident “But these people are clearly showing you that they don't want you here. So do you not think that when a citizens in a, in a community shows you that they don't want you here in their town, that maybe you should look to go locate your business elsewhere?” Developer “We look for strong spots on the grid, and that's what we have here” Resident “So you're just basically here not because you like the city of Piedmont, but because you like the power infrastructure and things like that?” Developer “The power infrastructure is very good” They openly admit they don’t care about the residents, they don’t care about the town or the issues the data center will cause, they are here for the power

Wall Street Apes

296,337 Aufrufe • vor 4 Tagen

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 6 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

125,333 Aufrufe • vor 8 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 9 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 ⚔️

194,243 Aufrufe • vor 4 Monaten