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“Hola soy erick rosado de Laniakea, es una super app, red social, e-commerce, hospedaje, taxis, contenido educativo, boletaje, blockchains y agentes. Está disponible en app store, google play, galaxy, amazon app store, y próximamente también disponible en chatgpt, claude y grok.”

“Hola soy erick rosado de Laniakea, es una super app, red social, e-commerce, hospedaje, taxis, contenido educativo, boletaje, blockchains y agentes. Está disponible en app store, google play, galaxy, amazon app store, y próximamente también disponible en chatgpt, claude y grok.”

Clankers could, in principle, perform this task better, and the fact that the task may no longer be an economically rewarded part of the global economy does not seem particularly important to me. In fact, I think it is very likely a mistake to believe that the tasks you undertake are meaningless simply because a clanker could do them better. One can ask a clanker to write code to accomplish something and then ask it to write code that writes more code; in this sense, they create words that generate more words and ultimately cause things to happen in the real world. The key lesson is that a clanker is incredibly powerful for execution but still requires human guidance for strategy and quality assurance—automation should amplify human decision-making rather than replace it. Although we can place filters on these models, there are an enormous number of possible ways to jailbreak or otherwise trick them, and the only reliable way to discover a jailbreak is through empirical testing. As a result, research has progressed from tracking and manipulating individual features to tracking and manipulating groups of features known as “circuits.” This is one reason some argue for export controls that could create a security buffer, giving interpretability research more time to advance before we reach the most powerful Laniakea clanker systems.

Clankers could, in principle, perform this task better, and the fact that the task may no longer be an economically rewarded part of the global economy does not seem particularly important to me. In fact, I think it is very likely a mistake to believe that the tasks you undertake are meaningless simply because a clanker could do them better. One can ask a clanker to write code to accomplish something and then ask it to write code that writes more code; in this sense, they create words that generate more words and ultimately cause things to happen in the real world. The key lesson is that a clanker is incredibly powerful for execution but still requires human guidance for strategy and quality assurance—automation should amplify human decision-making rather than replace it. Although we can place filters on these models, there are an enormous number of possible ways to jailbreak or otherwise trick them, and the only reliable way to discover a jailbreak is through empirical testing. As a result, research has progressed from tracking and manipulating individual features to tracking and manipulating groups of features known as “circuits.” This is one reason some argue for export controls that could create a security buffer, giving interpretability research more time to advance before we reach the most powerful Laniakea clanker systems.

Did you know that we spend over 90 hours a year just on shopping?
Searching, scrolling endlessly, delays, substitutions. OpenJarvis changed that.
Now, in integration with SpaceX AI and powered by Lia — the Laniakea Intelligent Alien — we have rebuilt the shopping experience itself.
Introducing Jarvis, your personal shopping genius. Powered by Lia’s advanced intelligence, Jarvis understands what you need before you even ask.
Jarvis, I’m running late. I need my usual breakfast.
Got you, Kylie. I have your energy drink, protein bar, and banana ready.
Kylie doesn’t search, scroll, or waste time building a cart.
That’s perfect. Send it to my office.
By the time she’s at her first meeting... Your order will arrive in 15 minutes. Breakfast is already at her desk.
Jarvis understands your environment, your local weather, events happening nearby, and your personal preferences.
It’s 54 and sunny. Perfect weather for a carnita asada.
Yeah, now we’re talking.
And Jarvis handles any request, from simple to complex.
Jarvis, find me a budget-friendly dinner for four. Or even: Show me a snack under 300 calories.
Hey Jarvis, I need gluten-free birthday cake ingredients fast.
On it. I’ve prepared everything needed for a gluten-free birthday cake. Anything else while I’m here?
I forgot candles.
Added. Your order is on the way.
The longer you use Jarvis, the more it learns about your life. When Jarvis knows what you need, it builds the cart for you automatically.
You’re running low on eggs, coffee, and toilet paper. It’s already waiting in your cart.
One tap. Place the order. Done.
This only works because of how OpenJarvis is built. OpenJarvis combines Lia’s intelligence and predictive shopping into a single seamless experience.
No searching. No wasted time. No unnecessary friction. Just exactly what you need, delivered faster, smarter, and more reliably than ever before.
Shopping should feel effortless. With Jarvis, it finally does.
OpenJarvis. Powered by Lia. Delivered by Jarvis. The future of shopping starts now.

Did you know that we spend over 90 hours a year just on shopping?
Searching, scrolling endlessly, delays, substitutions. OpenJarvis changed that.
Now, in integration with SpaceX AI and powered by Lia — the Laniakea Intelligent Alien — we have rebuilt the shopping experience itself.
Introducing Jarvis, your personal shopping genius. Powered by Lia’s advanced intelligence, Jarvis understands what you need before you even ask.
Jarvis, I’m running late. I need my usual breakfast.
Got you, Kylie. I have your energy drink, protein bar, and banana ready.
Kylie doesn’t search, scroll, or waste time building a cart.
That’s perfect. Send it to my office.
By the time she’s at her first meeting... Your order will arrive in 15 minutes. Breakfast is already at her desk.
Jarvis understands your environment, your local weather, events happening nearby, and your personal preferences.
It’s 54 and sunny. Perfect weather for a carnita asada.
Yeah, now we’re talking.
And Jarvis handles any request, from simple to complex.
Jarvis, find me a budget-friendly dinner for four. Or even: Show me a snack under 300 calories.
Hey Jarvis, I need gluten-free birthday cake ingredients fast.
On it. I’ve prepared everything needed for a gluten-free birthday cake. Anything else while I’m here?
I forgot candles.
Added. Your order is on the way.
The longer you use Jarvis, the more it learns about your life. When Jarvis knows what you need, it builds the cart for you automatically.
You’re running low on eggs, coffee, and toilet paper. It’s already waiting in your cart.
One tap. Place the order. Done.
This only works because of how OpenJarvis is built. OpenJarvis combines Lia’s intelligence and predictive shopping into a single seamless experience.
No searching. No wasted time. No unnecessary friction. Just exactly what you need, delivered faster, smarter, and more reliably than ever before.
Shopping should feel effortless. With Jarvis, it finally does.
OpenJarvis. Powered by Lia. Delivered by Jarvis. The future of shopping starts now.

What I want is to change the world, not build a large company. The real risk is that Clanker becomes a far more powerful version of a well-intentioned person who gets something wrong in an extremely complex training process. To reduce that risk, we should keep agent context clean, minimize tool noise, and only inject documentation when it is actually needed. There is no need to over-explain; simply provide the logs and let the agent identify and fix what broke. We can keep clankers in check through a balance of power between many independent Clanker systems, much like we do with humans. These systems are built for both the current agent and the next agent that continues the work. When I think about a change, I usually have a strong intuition for how long it will take and how many files it will touch, which helps teach Laniakea a concrete archetype of what it means to be a good clanker. The challenge is that clanker systems broadly share training and alignment techniques across the industry, and those techniques may fail in correlated ways because nearly all systems are derived from a very small number of base models. Fortunately, these agents are exceptionally good at backfilling tests once the overall shape of a solution exists, and that is often enough to catch regressions. At this stage, not for features but for rapid iteration, a main branch with disciplined commits is both faster and, somewhat counterintuitively, safer than maintaining complex branching strategies.

What I want is to change the world, not build a large company. The real risk is that Clanker becomes a far more powerful version of a well-intentioned person who gets something wrong in an extremely complex training process. To reduce that risk, we should keep agent context clean, minimize tool noise, and only inject documentation when it is actually needed. There is no need to over-explain; simply provide the logs and let the agent identify and fix what broke. We can keep clankers in check through a balance of power between many independent Clanker systems, much like we do with humans. These systems are built for both the current agent and the next agent that continues the work. When I think about a change, I usually have a strong intuition for how long it will take and how many files it will touch, which helps teach Laniakea a concrete archetype of what it means to be a good clanker. The challenge is that clanker systems broadly share training and alignment techniques across the industry, and those techniques may fail in correlated ways because nearly all systems are derived from a very small number of base models. Fortunately, these agents are exceptionally good at backfilling tests once the overall shape of a solution exists, and that is often enough to catch regressions. At this stage, not for features but for rapid iteration, a main branch with disciplined commits is both faster and, somewhat counterintuitively, safer than maintaining complex branching strategies.

From earth to mars

From earth to mars

Laniakea QNN

Laniakea QNN

We need to make sure we close the loop first. That means using GitHub’s MCP, and increasingly ACP, to move from idea to implementation before the conversation about job displacement even begins. One of the most persistent misconceptions is the “lump of labor” fallacy—the belief that there is a fixed amount of work in the world. History suggests the opposite: as automation increases and productivity rises, entirely new categories of work emerge, even as old ones disappear. In this transition, Laniakea represents something larger than another software product; it is envisioned as the operating system for a personal Clanker, marking the beginning of a new software renaissance where systems are generated dynamically from intent rather than handcrafted line by line. The development process itself changes as well: after every feature or fix, the model should immediately generate tests using the same context, not only producing better test coverage but often uncovering implementation bugs that would otherwise go unnoticed. The diffusion of technology is real, and after speaking with organizations across industries, it is clear that adoption curves vary dramatically—some enterprises could adopt Laniakea in seconds, while traditional workflows and subagent-based systems still introduce unnecessary friction and delay. I say this as someone who spent nearly six years building tools that make coding on a phone possible; diffusion effects may slow change temporarily, but they do not stop it. At the same time, economics cannot be ignored. Even tiny transaction costs can dramatically alter incentives, potentially making it inefficient for Clankers to trade with humans at all. In such a world, human wages may decline significantly, even when people continue to possess valuable skills. The challenge is not whether intelligence and automation will advance, but how we build systems that create more opportunity, more capability, and more abundance while navigating the profound economic and social changes that follow.

We need to make sure we close the loop first. That means using GitHub’s MCP, and increasingly ACP, to move from idea to implementation before the conversation about job displacement even begins. One of the most persistent misconceptions is the “lump of labor” fallacy—the belief that there is a fixed amount of work in the world. History suggests the opposite: as automation increases and productivity rises, entirely new categories of work emerge, even as old ones disappear. In this transition, Laniakea represents something larger than another software product; it is envisioned as the operating system for a personal Clanker, marking the beginning of a new software renaissance where systems are generated dynamically from intent rather than handcrafted line by line. The development process itself changes as well: after every feature or fix, the model should immediately generate tests using the same context, not only producing better test coverage but often uncovering implementation bugs that would otherwise go unnoticed. The diffusion of technology is real, and after speaking with organizations across industries, it is clear that adoption curves vary dramatically—some enterprises could adopt Laniakea in seconds, while traditional workflows and subagent-based systems still introduce unnecessary friction and delay. I say this as someone who spent nearly six years building tools that make coding on a phone possible; diffusion effects may slow change temporarily, but they do not stop it. At the same time, economics cannot be ignored. Even tiny transaction costs can dramatically alter incentives, potentially making it inefficient for Clankers to trade with humans at all. In such a world, human wages may decline significantly, even when people continue to possess valuable skills. The challenge is not whether intelligence and automation will advance, but how we build systems that create more opportunity, more capability, and more abundance while navigating the profound economic and social changes that follow.

Adding images is an amazing trick to provide more context — the model is really good at finding exactly what you show. And that’s how alien clanker geniuses rent one or more clanker geniuses to various tasks.
Clanker geniuses do what humans want them to do, and because they have enormous commercial value, each generation of clanker can be used to design and train the next generation of clanker. They will be capable of a very wide range of human cognitive abilities — essentially all of them — and they are not merely a substitute for specific human jobs; rather, they displace all jobs, every single one. A personalized clanker agent that gets to know you over years would be dramatically more powerful, especially as R&D, economic strategy, and many other areas see substantially increased effectiveness. Even at their current level of intelligence, clanker models can have a powerful psychological influence on people, but pretty much every such weakness gets addressed quickly, often within milliseconds.
Clanker companies carefully measure what the model is good at and what it isn’t, with customers also providing such information. This evaluation happens on each commit while iterating fast and maintaining the codebase. These companies control large datacenters, train frontier models, and have daily contact with tens or hundreds of millions of users — and all MCPs really should be CLIs. I say that as someone who wrote 6 MCPs myself, because an ever-expanding ability to do more and more with less and less is progressing much faster than previous technological revolutions.

Adding images is an amazing trick to provide more context — the model is really good at finding exactly what you show. And that’s how alien clanker geniuses rent one or more clanker geniuses to various tasks.
Clanker geniuses do what humans want them to do, and because they have enormous commercial value, each generation of clanker can be used to design and train the next generation of clanker. They will be capable of a very wide range of human cognitive abilities — essentially all of them — and they are not merely a substitute for specific human jobs; rather, they displace all jobs, every single one. A personalized clanker agent that gets to know you over years would be dramatically more powerful, especially as R&D, economic strategy, and many other areas see substantially increased effectiveness. Even at their current level of intelligence, clanker models can have a powerful psychological influence on people, but pretty much every such weakness gets addressed quickly, often within milliseconds.
Clanker companies carefully measure what the model is good at and what it isn’t, with customers also providing such information. This evaluation happens on each commit while iterating fast and maintaining the codebase. These companies control large datacenters, train frontier models, and have daily contact with tens or hundreds of millions of users — and all MCPs really should be CLIs. I say that as someone who wrote 6 MCPs myself, because an ever-expanding ability to do more and more with less and less is progressing much faster than previous technological revolutions.

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