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LLM Artifacts Connected to Andrej Karpathy's LLM Knowledge base idea, I've been building out a fun way to generate dynamic artifacts from these knowledge bases with the goal of discovering and revealing meaningful and deeper insights. LLM KBs are hard to consume for humans, as I think they are...

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elvis

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31,346 просмотров • 6 месяцев назад •via X (Twitter)

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HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:
0:16

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:

elvis

18,374 просмотров • 5 месяцев назад

I just built my own wiki generator plugin for my agents. My agents can now generate wikis for anything I ask. One of my favorite wikis is called PaperWiki. This is a great example of what Andrej Karpathy describes. It uses obsidian vaults to organize papers, retrieve LLM-generated summaries, diagrams, and other advanced views for paper exploration. When Obsidian UI is not enough, I use my own artifact generator inside my agent orchestrator (see clip for example). This allows my agents to build any kind of view or exploration feature that I need. The papers are all curated with automations and several rules/patterns I have manually built over the years. On the surface, this looks basic. But behind the scenes, there are advanced search capabilities, connections, metadata, derived data, and other interesting bits of information that are extremely useful for my research agents. This is mostly built for agents. The artifact preview is just a high-level way to validate and quickly assess the quality of the wiki, suggest improvements, and it's also great for research. I use tobi lutke's qmd for all search capabilities. Everything is markdown. The summaries and even the diagrams. The wiki updates on its own based on several automations I have optimized over the past couple of weeks. The wiki grows and self-improves based on several requirements important for my research use cases. This is as personalized as it gets. There is nothing like it out there. And I use my research expertise to continue improving it over time. This is a vanilla wiki. There are so many things I want to build on top of this. Different aggregations, views, artifacts, etc. All to help automate more of my research work and accelerate productivity. I think the biggest leverage here is how powerful this could be for discovery and experimentation. One of my goals is to use it to find deeper connections and insights that would otherwise elude the top human researchers and use those to generate interesting new hypotheses and research experiments. That way, my agents can use autoresearch to explore research ideas at the frontier. Stay tuned for more.
0:16

I just built my own wiki generator plugin for my agents. My agents can now generate wikis for anything I ask. One of my favorite wikis is called PaperWiki. This is a great example of what Andrej Karpathy describes. It uses obsidian vaults to organize papers, retrieve LLM-generated summaries, diagrams, and other advanced views for paper exploration. When Obsidian UI is not enough, I use my own artifact generator inside my agent orchestrator (see clip for example). This allows my agents to build any kind of view or exploration feature that I need. The papers are all curated with automations and several rules/patterns I have manually built over the years. On the surface, this looks basic. But behind the scenes, there are advanced search capabilities, connections, metadata, derived data, and other interesting bits of information that are extremely useful for my research agents. This is mostly built for agents. The artifact preview is just a high-level way to validate and quickly assess the quality of the wiki, suggest improvements, and it's also great for research. I use tobi lutke's qmd for all search capabilities. Everything is markdown. The summaries and even the diagrams. The wiki updates on its own based on several automations I have optimized over the past couple of weeks. The wiki grows and self-improves based on several requirements important for my research use cases. This is as personalized as it gets. There is nothing like it out there. And I use my research expertise to continue improving it over time. This is a vanilla wiki. There are so many things I want to build on top of this. Different aggregations, views, artifacts, etc. All to help automate more of my research work and accelerate productivity. I think the biggest leverage here is how powerful this could be for discovery and experimentation. One of my goals is to use it to find deeper connections and insights that would otherwise elude the top human researchers and use those to generate interesting new hypotheses and research experiments. That way, my agents can use autoresearch to explore research ideas at the frontier. Stay tuned for more.

elvis

67,308 просмотров • 6 месяцев назад

Increasingly, HTML Artifacts are becoming a core part of how I work with AI agents. Long-horizon agent sessions need a better way to surface insights about what work it has done. This may not be obvious right now, but as you start to let your agent work on dynamic workflows, large codebases, long-running loops (e.g., using /goal), and deep research tasks, you need a good way to present results. Chat window is not it. You also don't want to just trust everything the agents do. Artifacts help provide an important verification layer, which in turn enables important decision-making. I like HTML artifacts because I can just ask the agent to produce as many of them (and in whatever form) as I need to verify the work and make sense out of everything. I even built a nice tab system for my artifacts. They are great for continual learning and research. I use HTML artifacts for logging, tracking experiments, brainstorming, managing my inbox, code reviews, agent session management, deep research, writing, reading, and so much more. I believe Andrej Karpathy wrote about this somewhere: As we move on to more advanced applications of AI agents and outputs get more complex, we will start to find the need for even more advanced forms of interactions with AI, including interactive neural videos/simulations.
0:15

Increasingly, HTML Artifacts are becoming a core part of how I work with AI agents. Long-horizon agent sessions need a better way to surface insights about what work it has done. This may not be obvious right now, but as you start to let your agent work on dynamic workflows, large codebases, long-running loops (e.g., using /goal), and deep research tasks, you need a good way to present results. Chat window is not it. You also don't want to just trust everything the agents do. Artifacts help provide an important verification layer, which in turn enables important decision-making. I like HTML artifacts because I can just ask the agent to produce as many of them (and in whatever form) as I need to verify the work and make sense out of everything. I even built a nice tab system for my artifacts. They are great for continual learning and research. I use HTML artifacts for logging, tracking experiments, brainstorming, managing my inbox, code reviews, agent session management, deep research, writing, reading, and so much more. I believe Andrej Karpathy wrote about this somewhere: As we move on to more advanced applications of AI agents and outputs get more complex, we will start to find the need for even more advanced forms of interactions with AI, including interactive neural videos/simulations.

elvis

37,194 просмотров • 4 месяцев назад

We are entering an extremely exciting era for open-weight models. Kimi K2.6 now feels like a top agentic model. I took it for a spin via Fireworks AI fast inference APIs. Kimi K2.6 has impressive agentic capabilities, design skills, and the ability to synthesize large amounts of information. I built a little Skill that produces survey papers on any AI research topic you want. (see example in the clip) You can use the skill to tell your agent to generate a survey on whatever topic and watch it go to work. The artifact was fully generated by Kimi.ai's Kimi K2.6. It's cheap and fast. Next step for me is to explore ways to continue integrating the capabilities of these models on use cases like automating my LLM knowledge bases and augmenting my agent memory capabilities. Stay tuned for more.
0:19

We are entering an extremely exciting era for open-weight models. Kimi K2.6 now feels like a top agentic model. I took it for a spin via Fireworks AI fast inference APIs. Kimi K2.6 has impressive agentic capabilities, design skills, and the ability to synthesize large amounts of information. I built a little Skill that produces survey papers on any AI research topic you want. (see example in the clip) You can use the skill to tell your agent to generate a survey on whatever topic and watch it go to work. The artifact was fully generated by Kimi.ai's Kimi K2.6. It's cheap and fast. Next step for me is to explore ways to continue integrating the capabilities of these models on use cases like automating my LLM knowledge bases and augmenting my agent memory capabilities. Stay tuned for more.

elvis

47,678 просмотров • 6 месяцев назад

I just open-sourced my /learn skill. Learn anything with agents and HTML artifacts. I have been learning about all kinds of topics with it. Install the skill and interact with any agent to help you through any topic. Ask it to generate visual and interactive artifacts and help you go deeper or generate knowledge checks (e.g., quizzes). Upskilling myself on any topic is one of the most impactful ways I have been able to use AI agents. If you are a DAIR Academy pro member, you can use it with our AI Builder. Skill: Try now:
0:19

I just open-sourced my /learn skill. Learn anything with agents and HTML artifacts. I have been learning about all kinds of topics with it. Install the skill and interact with any agent to help you through any topic. Ask it to generate visual and interactive artifacts and help you go deeper or generate knowledge checks (e.g., quizzes). Upskilling myself on any topic is one of the most impactful ways I have been able to use AI agents. If you are a DAIR Academy pro member, you can use it with our AI Builder. Skill: Try now:

elvis

35,433 просмотров • 4 месяцев назад

Been exploring a new way to explore AI research papers to discover deeper insights. Agents are at the center of it. So far, I've built this little interactive artifact generator in my orchestrator to visualize things. This allows me to change views and insights (on-demand) from 100s of papers. Just scratching the surface here. More to share soon.
0:10

Been exploring a new way to explore AI research papers to discover deeper insights. Agents are at the center of it. So far, I've built this little interactive artifact generator in my orchestrator to visualize things. This allows me to change views and insights (on-demand) from 100s of papers. Just scratching the surface here. More to share soon.

elvis

143,127 просмотров • 7 месяцев назад

After a few more hours, I think I've figured out Opus 5. Opus 5 is trained to be more agentic than anything I've used. All Claude 5 models are like that. So what changes? The way to interact with Opus 5 or contextualize it won't work the same way as with other models. It loves exploring, so it doesn't need much guidance for it. Unique preferences, artifacts, and references compliment it well and enable cleaner and more effective exploration and execution. Now that it can explore more effectively on its own and understand intent better, the best thing to do is to get out of its way (e.g., it doesn't need examples of your preferences; a clear high-level description of it works best). It's truly agentic in that sense. A good first step to provide better context for Opus 5 is to distinguish between what's situational and what needs persistence. Regardless, persistent system prompts and CLAUDE.MD needs to stay lightweight. Remove memories and tool descriptions from these. CLAUDE.MD is also a great place to tap into progressive disclosure by linking command/skills to it. On the situational side, agent skills and auto-memory can leverage progressive disclosure and the improved ability of the model to use its external context/knowledge. Conflicting and unnecessary instructions, which are common at this layer (mainly to ensure reliability), are going to throw off this model easily. That's the biggest change I had to make. Simple, clean, and clear prompts and skills work best. I had to clean a lot of my skills and system prompts. The way I prompt remains the same (usually clear and well-scoped). MCP tool descriptions are also more descriptive and have been deduped from the system prompt. Anthropic released a guide on the new rules for context engineering, which was helpful here. I started to test the recommendations and created a little artifact with the things that worked along the way. This might feel like a lot of work. Believe me, it has been frustrating. But I think we can expect future frontier models to become more agentic and smarter at figuring out the right context/gaps. The best thing to do is to prepare for that now. Boris Cherny mentioned that Opus 5 is their least prompt-injectable model yet. I am not sure if that was something they intentionally trained for or if it emerged based on how it was trained, which is to be extremely agentic in nature and more direct in execution.
0:15

After a few more hours, I think I've figured out Opus 5. Opus 5 is trained to be more agentic than anything I've used. All Claude 5 models are like that. So what changes? The way to interact with Opus 5 or contextualize it won't work the same way as with other models. It loves exploring, so it doesn't need much guidance for it. Unique preferences, artifacts, and references compliment it well and enable cleaner and more effective exploration and execution. Now that it can explore more effectively on its own and understand intent better, the best thing to do is to get out of its way (e.g., it doesn't need examples of your preferences; a clear high-level description of it works best). It's truly agentic in that sense. A good first step to provide better context for Opus 5 is to distinguish between what's situational and what needs persistence. Regardless, persistent system prompts and CLAUDE.MD needs to stay lightweight. Remove memories and tool descriptions from these. CLAUDE.MD is also a great place to tap into progressive disclosure by linking command/skills to it. On the situational side, agent skills and auto-memory can leverage progressive disclosure and the improved ability of the model to use its external context/knowledge. Conflicting and unnecessary instructions, which are common at this layer (mainly to ensure reliability), are going to throw off this model easily. That's the biggest change I had to make. Simple, clean, and clear prompts and skills work best. I had to clean a lot of my skills and system prompts. The way I prompt remains the same (usually clear and well-scoped). MCP tool descriptions are also more descriptive and have been deduped from the system prompt. Anthropic released a guide on the new rules for context engineering, which was helpful here. I started to test the recommendations and created a little artifact with the things that worked along the way. This might feel like a lot of work. Believe me, it has been frustrating. But I think we can expect future frontier models to become more agentic and smarter at figuring out the right context/gaps. The best thing to do is to prepare for that now. Boris Cherny mentioned that Opus 5 is their least prompt-injectable model yet. I am not sure if that was something they intentionally trained for or if it emerged based on how it was trained, which is to be extremely agentic in nature and more direct in execution.

elvis

37,824 просмотров • 2 месяцев назад

Andrej Karpathy calls AI Agents slop "Overall, the models they are not there. And I feel like the industry [...] it's making too big of a jump and it's trying to pretend that this is amazing. And it's not—it's slop! And I think they are not coming to terms with it. And maybe they are trying to fundraise or something like that, I'm not sure what's going on."
0:16

Andrej Karpathy calls AI Agents slop "Overall, the models they are not there. And I feel like the industry [...] it's making too big of a jump and it's trying to pretend that this is amazing. And it's not—it's slop! And I think they are not coming to terms with it. And maybe they are trying to fundraise or something like that, I'm not sure what's going on."

Lisan al Gaib

3,275,063 просмотров • 1 год назад

World Models are the path for some AI Models in the future. But how can we efficiently train these models to not only see the world the way humans do but to see the world in a new and unique way. By visualizing, what is normally sequenced audio patterns, we can derive much more insights. Here we see Paganini in a visual form that can than be described and transcribed into a World Model. We can observe connections in a manner that may not have been clear prior to the digitalization of music and sound in this way. The company with the most valuable potential in building a World Model is Tesla. Not that this type of visualization is being used, but that the mechanisms are in place, and the technology is in place for the company to thrive in this new form of AI.
0:19

World Models are the path for some AI Models in the future. But how can we efficiently train these models to not only see the world the way humans do but to see the world in a new and unique way. By visualizing, what is normally sequenced audio patterns, we can derive much more insights. Here we see Paganini in a visual form that can than be described and transcribed into a World Model. We can observe connections in a manner that may not have been clear prior to the digitalization of music and sound in this way. The company with the most valuable potential in building a World Model is Tesla. Not that this type of visualization is being used, but that the mechanisms are in place, and the technology is in place for the company to thrive in this new form of AI.

Brian Roemmele

57,517 просмотров • 11 месяцев назад

Excited to launch a new way to upskill with AI agents. This is how we are making it possible for anyone to learn to build with coding agents. To start, we are launching 4 new hands-on labs on the following topics: - Agent Skills - Agentic Image Generation - 30 Days of Hermes Agents - Prompt Engineering with Agents I am confident that with our new DAIR.AI platform, anyone can learn to become a top AI builder by building and acquiring highly-demanded AI skills. And there is a lot more landing in the coming weeks.
0:14

Excited to launch a new way to upskill with AI agents. This is how we are making it possible for anyone to learn to build with coding agents. To start, we are launching 4 new hands-on labs on the following topics: - Agent Skills - Agentic Image Generation - 30 Days of Hermes Agents - Prompt Engineering with Agents I am confident that with our new DAIR.AI platform, anyone can learn to become a top AI builder by building and acquiring highly-demanded AI skills. And there is a lot more landing in the coming weeks.

elvis

19,058 просмотров • 4 месяцев назад

The Sabotaging Practice of Over Supply and Sameness in the NFT Space. The current zeitgeist of the NFT space is that the same artists are doing the same kind of work five times a year, with project after project leaving a trail of disappointment and discontent among collectors and all of us watching in disbelief as huge resources are extracted from the space over work that feels like it could be left as an "artist study." I understand that you can do what you want with your money as collectors, but we are killing the whole space with this incestuous practice. No artist is that prolific to be able to do 5 collections of 100+ pieces each every year and actually deliver innovation and some kind of creative evolution. Of course, they can pretend play that the work has something new, but there is no precedent nor proof that that has ever happened in the speed that it happens in the NFT space. Again, people are free to through away their resources on whatever they want but with this way of doing things, we more and more are going to start seeing the consequences. Oh! There are consequences? Yes. Maybe unintended, but there are. Let's see. Let's start with the loss of belief in the NFT space as somewhere where emerging artists can come and find support for their experiments. Why even bother to bring experiments, innovation, and new ways to think of art on the blockchain if the same people have all the collectors hypnotized with their magical flutes? Why even try to come to a space where taking risks and challenging the status quo (the mission of art!!!) is overlooked? This makes the NFT space a social club and not a space for art. I guess it is fine, but IMO it is a recipe for disaster. New collectors stay away because the art will slowly but surely become stale and un-challenging. Why even bother to come and see what is happening here if you can't, as a collector, see new weird and up-and-coming artists? The amount of noise emitted by the same artists doing the same art over and over, drowns out any new voices. Again. A recipe for disaster. The NFT space is becoming a space of disappointment and doubt. We think that collections going to zero one after the other, over and over, is not damaging? I feel we are kidding ourselves. Disappointment piles up, and again, the people who will hurt are the emerging artists, the new blood, the ones who are willing to risk the most and, in return, put fire in this cold space of sameness. I love this space—don't get me wrong—it has changed my life, and I believe it has a ton of potential, but things need to change for it to become a beacon of light in art. But we need to support new voices. We need to support new ideas. The challenge is huge. I hope to contribute all I can to this change. I hope more and more see how exciting it is to go out and try to discover what else is out there and move this space forward. But again, I understand the leaps of faith needed, but if there is a space that is based on that, it's the NFT space...so there is hope. We will see. 📺by Boldtron
0:12

The Sabotaging Practice of Over Supply and Sameness in the NFT Space. The current zeitgeist of the NFT space is that the same artists are doing the same kind of work five times a year, with project after project leaving a trail of disappointment and discontent among collectors and all of us watching in disbelief as huge resources are extracted from the space over work that feels like it could be left as an "artist study." I understand that you can do what you want with your money as collectors, but we are killing the whole space with this incestuous practice. No artist is that prolific to be able to do 5 collections of 100+ pieces each every year and actually deliver innovation and some kind of creative evolution. Of course, they can pretend play that the work has something new, but there is no precedent nor proof that that has ever happened in the speed that it happens in the NFT space. Again, people are free to through away their resources on whatever they want but with this way of doing things, we more and more are going to start seeing the consequences. Oh! There are consequences? Yes. Maybe unintended, but there are. Let's see. Let's start with the loss of belief in the NFT space as somewhere where emerging artists can come and find support for their experiments. Why even bother to bring experiments, innovation, and new ways to think of art on the blockchain if the same people have all the collectors hypnotized with their magical flutes? Why even try to come to a space where taking risks and challenging the status quo (the mission of art!!!) is overlooked? This makes the NFT space a social club and not a space for art. I guess it is fine, but IMO it is a recipe for disaster. New collectors stay away because the art will slowly but surely become stale and un-challenging. Why even bother to come and see what is happening here if you can't, as a collector, see new weird and up-and-coming artists? The amount of noise emitted by the same artists doing the same art over and over, drowns out any new voices. Again. A recipe for disaster. The NFT space is becoming a space of disappointment and doubt. We think that collections going to zero one after the other, over and over, is not damaging? I feel we are kidding ourselves. Disappointment piles up, and again, the people who will hurt are the emerging artists, the new blood, the ones who are willing to risk the most and, in return, put fire in this cold space of sameness. I love this space—don't get me wrong—it has changed my life, and I believe it has a ton of potential, but things need to change for it to become a beacon of light in art. But we need to support new voices. We need to support new ideas. The challenge is huge. I hope to contribute all I can to this change. I hope more and more see how exciting it is to go out and try to discover what else is out there and move this space forward. But again, I understand the leaps of faith needed, but if there is a space that is based on that, it's the NFT space...so there is hope. We will see. 📺by Boldtron

alejandro cartagena

98,261 просмотров • 2 лет назад

Usually I don’t speak on political issues often however Charlie Kirk’s death (RIP) hits a little too close to home. Perhaps it is because anyone who has some resemblance of a following who is outspoken against any narrative online has probably received some sort of threat in their inbox. (I know I have) It is baffling to me that one could harbour such a large amount of hate inside of them to want to have another silenced for having differing opinions than their own. I think about all my outspoken friends who are in positions faced to the public and of course myself and what this potentially means for all of us. This of course, would never stop me from speaking out to what’s right, so perhaps that is also why I wanted to make a comment. To me it is disgusting behaviour to mock/be happy about the death of people you disagree with that have done nothing wrong. I think about all my friends, family who could be wrongfully persecuted based off the delusions of perceived “sides”. The amount of hate manufactured and projected onto you for just living your life and not complying to a certain ideal is insane. I don’t like fear mongering but I also think it’s important to stay vigilant to what’s going on around you. It is a very real reality that there are people out there that hate you just for simply being you and what you represent to them. We’ve seen evidence of this twice this week and I suspect more to come unfortunately. I am always hopeful so I will leave this message on a positive note, that just as much hate has also been met with just as much love. There are many amazing kind hearted people out there standing up for what’s right and building community, coming together. Even if they are moving in silence behind the scenes or in front stage, I see you and I honour your work.
0:12

Usually I don’t speak on political issues often however Charlie Kirk’s death (RIP) hits a little too close to home. Perhaps it is because anyone who has some resemblance of a following who is outspoken against any narrative online has probably received some sort of threat in their inbox. (I know I have) It is baffling to me that one could harbour such a large amount of hate inside of them to want to have another silenced for having differing opinions than their own. I think about all my outspoken friends who are in positions faced to the public and of course myself and what this potentially means for all of us. This of course, would never stop me from speaking out to what’s right, so perhaps that is also why I wanted to make a comment. To me it is disgusting behaviour to mock/be happy about the death of people you disagree with that have done nothing wrong. I think about all my friends, family who could be wrongfully persecuted based off the delusions of perceived “sides”. The amount of hate manufactured and projected onto you for just living your life and not complying to a certain ideal is insane. I don’t like fear mongering but I also think it’s important to stay vigilant to what’s going on around you. It is a very real reality that there are people out there that hate you just for simply being you and what you represent to them. We’ve seen evidence of this twice this week and I suspect more to come unfortunately. I am always hopeful so I will leave this message on a positive note, that just as much hate has also been met with just as much love. There are many amazing kind hearted people out there standing up for what’s right and building community, coming together. Even if they are moving in silence behind the scenes or in front stage, I see you and I honour your work.

Tiffany Huber

16,934 просмотров • 1 год назад

One of the followers sent these videos in. I am sent so many videos like this lately. This has gone past the point of “racism”. People are just sick and tired of this shit and they are using my page as an outlet to make themselves heard. What makes it worse is these people dont even like us. It’s sad to see this is coming from one of our churches. They have every right to feel this way and they have every right to post it. We live in a society of free speech and if anyone doesn’t like it you can just unfollow or block the page. I don’t want anything to do with you and I have no interest in your opinion. I refuse to silence people from having an opinion on what is happening to their home.
0:23

One of the followers sent these videos in. I am sent so many videos like this lately. This has gone past the point of “racism”. People are just sick and tired of this shit and they are using my page as an outlet to make themselves heard. What makes it worse is these people dont even like us. It’s sad to see this is coming from one of our churches. They have every right to feel this way and they have every right to post it. We live in a society of free speech and if anyone doesn’t like it you can just unfollow or block the page. I don’t want anything to do with you and I have no interest in your opinion. I refuse to silence people from having an opinion on what is happening to their home.

Around Liverpool

55,277 просмотров • 1 месяц назад

LLM Knowledge Base → Slides When Andrej Karpathy shared his LLM Knowledge Base setup, many were wondering how to generate more visual forms of the wiki. There are many options, but I think Gamma is one of the best at producing high-quality, rich presentations. To showcase this, I just built a pipeline that turns my AI papers wiki (1K+ papers across 20 AI agent topics) into polished slide presentations using Gamma. The flow: Obsidian vault → Gamma MCP → embedded preview in my dashboard. I give one command to my agent, which pulls the top papers from each topic (via the wiki), feeds them to Gamma, and renders the presentation inline. The Gamma connector for Claude is a great choice for generating beautiful and professional slides. Easy to use. Go to your Claude instance and add the official Gamma connector. That's it! Claude Code will now have access to all the necessary MCP tools for generating slides. I use the Claude Agent SDK for my agent orchestrator, so I use the official Gamma MCP tools and embed the generated slides in an iframe via my artifact preview. See the clip below for an example.
0:19

LLM Knowledge Base → Slides When Andrej Karpathy shared his LLM Knowledge Base setup, many were wondering how to generate more visual forms of the wiki. There are many options, but I think Gamma is one of the best at producing high-quality, rich presentations. To showcase this, I just built a pipeline that turns my AI papers wiki (1K+ papers across 20 AI agent topics) into polished slide presentations using Gamma. The flow: Obsidian vault → Gamma MCP → embedded preview in my dashboard. I give one command to my agent, which pulls the top papers from each topic (via the wiki), feeds them to Gamma, and renders the presentation inline. The Gamma connector for Claude is a great choice for generating beautiful and professional slides. Easy to use. Go to your Claude instance and add the official Gamma connector. That's it! Claude Code will now have access to all the necessary MCP tools for generating slides. I use the Claude Agent SDK for my agent orchestrator, so I use the official Gamma MCP tools and embed the generated slides in an iframe via my artifact preview. See the clip below for an example.

elvis

47,989 просмотров • 6 месяцев назад

Many of us in Silicon Valley, especially those active on platforms like X/Twitter, might think that everyone knows about ChatGPT and has used it at least once. But, this is not true. In my travels and online discussions, I've seen that most people still don't know about things like ChatGPT. There's a real need to close this gap in knowledge. My own kids, who are 6 and 9 years old, are using AI with me all the time. They are having a lot of fun, and learning a lot from it. At the same time, many people around the world don't even know it exists or what it can do. This is not just surprising; it shows we need to do something. To help with this, my colleague Kalkidan Betre and I started an Applied AI course at ALX Africa. The first project in this course is about making deep fakes and video refacing. The video you see here has been made by one of our student James. This project is more than just for learning; it opens the door to seeing how easy and accessible AI is. You don't have to be great at math or know anything about programming to get into AI. It's really as easy as clicking a mouse, showing how simple it is to make deep fakes today. This project also deals with important ethical questions. Some people who haven't tried the project were surprised we started with something so controversial. But that's exactly why it works well. The project highlights being responsible and ethical, which helps students understand the big impact and responsibility of using AI. It's a good way to start a deeper conversation about AI, a conversation that's needed for people to think critically and be aware, especially when digital media and fake news are everywhere. Using AI tools hands-on is key. Without this, you can't understand how powerful and accessible these AI tools are. Students who have done the first project are now starting to get it. They're learning to think more about and question the media they see online. Growing this kind of thinking and awareness is crucial. To really get how powerful and fast-changing AI is, you have to use it yourself. It's a strong, amazing tool that keeps evolving. I hope to quickly grow our program, as I feel we have a big responsibility in this new industrial revolution. It's important that everyone understands AI, its potential for good, and all the questions it creates for us. If we don't share this knowledge widely, the gap between those who have and those who don't will get much bigger. AI is starting a new revolution, and it's very important that we include everyone.
0:15

Many of us in Silicon Valley, especially those active on platforms like X/Twitter, might think that everyone knows about ChatGPT and has used it at least once. But, this is not true. In my travels and online discussions, I've seen that most people still don't know about things like ChatGPT. There's a real need to close this gap in knowledge. My own kids, who are 6 and 9 years old, are using AI with me all the time. They are having a lot of fun, and learning a lot from it. At the same time, many people around the world don't even know it exists or what it can do. This is not just surprising; it shows we need to do something. To help with this, my colleague Kalkidan Betre and I started an Applied AI course at ALX Africa. The first project in this course is about making deep fakes and video refacing. The video you see here has been made by one of our student James. This project is more than just for learning; it opens the door to seeing how easy and accessible AI is. You don't have to be great at math or know anything about programming to get into AI. It's really as easy as clicking a mouse, showing how simple it is to make deep fakes today. This project also deals with important ethical questions. Some people who haven't tried the project were surprised we started with something so controversial. But that's exactly why it works well. The project highlights being responsible and ethical, which helps students understand the big impact and responsibility of using AI. It's a good way to start a deeper conversation about AI, a conversation that's needed for people to think critically and be aware, especially when digital media and fake news are everywhere. Using AI tools hands-on is key. Without this, you can't understand how powerful and accessible these AI tools are. Students who have done the first project are now starting to get it. They're learning to think more about and question the media they see online. Growing this kind of thinking and awareness is crucial. To really get how powerful and fast-changing AI is, you have to use it yourself. It's a strong, amazing tool that keeps evolving. I hope to quickly grow our program, as I feel we have a big responsibility in this new industrial revolution. It's important that everyone understands AI, its potential for good, and all the questions it creates for us. If we don't share this knowledge widely, the gap between those who have and those who don't will get much bigger. AI is starting a new revolution, and it's very important that we include everyone.

Julien Barbier 🙃❤️🏴‍☠️

12,088 просмотров • 2 лет назад

I think one of the biggest problems with AI agents right now is how much control we give up just to use them. Most AI agent platforms want you to work inside their ecosystem, use their preferred models, and trust them with your files and data. That might be convenient, but I don't think it should be the only option. I recently came across PilotDeck, and I found its approach pretty interesting. It's an open-source AI agent workspace developed by Tsinghua University's THUNLP, ModelBest, OpenBMB, and AI9Stars. The idea is simple: instead of being tied to one AI provider, you should have more freedom to decide how your agent works. And there are a few things worth looking at here. First, model independence. Most people focus on which AI model is the smartest, but I think having the freedom to choose your model is equally important. PilotDeck supports different model providers and configurable API endpoints. This means you're not necessarily stuck with one company's models. You can choose a setup based on your budget, performance needs, or the type of task you're working on. For developers who regularly experiment with different AI models, I think that's a pretty useful approach. Second, privacy and control over your workspace. This is probably the part that interests me the most. AI agents are increasingly being used to handle research papers, financial documents, spreadsheets, presentations, and other personal or business files. And once you start giving an agent access to that kind of information, where your files are processed becomes an important question. PilotDeck supports local workspaces and offers options for local or private deployment. That gives users more flexibility over how their environment is configured, rather than relying entirely on a closed platform. Of course, privacy still depends on which model provider and API you connect. Using a local workspace doesn't automatically mean your data never leaves your device. But having the option to control the setup is a meaningful difference. Third, it's designed to actually complete work. Another thing I like about the approach is that PilotDeck isn't just trying to be another chatbot. It's built around an agent workflow where you describe a task, the system plans the steps, works with files, uses tools, and produces an output you can still edit. For example, imagine giving an AI agent multiple research documents and asking it to organize the information into an Excel spreadsheet and prepare a PowerPoint presentation. Instead of getting a long text response that you need to manually copy into different applications, the goal is to have the agent handle the workflow and deliver usable files. It also supports task history, result previews, switching models, and changing instructions while a task is running. I think this is where AI agents become more practical for everyday work. And finally, there's the open-source aspect. Personally, I think we're going to see more demand for AI tools that don't force users into one vendor's ecosystem. Models are improving quickly, and the best option today might not be the best option six months from now. Having an agent workspace that can work with different models makes a lot more sense to me than rebuilding your entire workflow every time you want to switch providers. There are still things I'd want to test properly, especially how reliable it is on longer tasks, how easy the setup is for non-technical users, and what the actual privacy trade-offs are with different model configurations. But I like the direction PilotDeck is taking. I don't think the future of AI agents should be about which company controls your entire workflow. It should be about how much control you have over the tools doing the work. And that's why I think open-source, model-independent projects like PilotDeck are worth paying attention to. If you're interested in building AI workflows with more flexibility, check it out. Website: GitHub:
0:19

I think one of the biggest problems with AI agents right now is how much control we give up just to use them. Most AI agent platforms want you to work inside their ecosystem, use their preferred models, and trust them with your files and data. That might be convenient, but I don't think it should be the only option. I recently came across PilotDeck, and I found its approach pretty interesting. It's an open-source AI agent workspace developed by Tsinghua University's THUNLP, ModelBest, OpenBMB, and AI9Stars. The idea is simple: instead of being tied to one AI provider, you should have more freedom to decide how your agent works. And there are a few things worth looking at here. First, model independence. Most people focus on which AI model is the smartest, but I think having the freedom to choose your model is equally important. PilotDeck supports different model providers and configurable API endpoints. This means you're not necessarily stuck with one company's models. You can choose a setup based on your budget, performance needs, or the type of task you're working on. For developers who regularly experiment with different AI models, I think that's a pretty useful approach. Second, privacy and control over your workspace. This is probably the part that interests me the most. AI agents are increasingly being used to handle research papers, financial documents, spreadsheets, presentations, and other personal or business files. And once you start giving an agent access to that kind of information, where your files are processed becomes an important question. PilotDeck supports local workspaces and offers options for local or private deployment. That gives users more flexibility over how their environment is configured, rather than relying entirely on a closed platform. Of course, privacy still depends on which model provider and API you connect. Using a local workspace doesn't automatically mean your data never leaves your device. But having the option to control the setup is a meaningful difference. Third, it's designed to actually complete work. Another thing I like about the approach is that PilotDeck isn't just trying to be another chatbot. It's built around an agent workflow where you describe a task, the system plans the steps, works with files, uses tools, and produces an output you can still edit. For example, imagine giving an AI agent multiple research documents and asking it to organize the information into an Excel spreadsheet and prepare a PowerPoint presentation. Instead of getting a long text response that you need to manually copy into different applications, the goal is to have the agent handle the workflow and deliver usable files. It also supports task history, result previews, switching models, and changing instructions while a task is running. I think this is where AI agents become more practical for everyday work. And finally, there's the open-source aspect. Personally, I think we're going to see more demand for AI tools that don't force users into one vendor's ecosystem. Models are improving quickly, and the best option today might not be the best option six months from now. Having an agent workspace that can work with different models makes a lot more sense to me than rebuilding your entire workflow every time you want to switch providers. There are still things I'd want to test properly, especially how reliable it is on longer tasks, how easy the setup is for non-technical users, and what the actual privacy trade-offs are with different model configurations. But I like the direction PilotDeck is taking. I don't think the future of AI agents should be about which company controls your entire workflow. It should be about how much control you have over the tools doing the work. And that's why I think open-source, model-independent projects like PilotDeck are worth paying attention to. If you're interested in building AI workflows with more flexibility, check it out. Website: GitHub:

Sally Stockholm

13,794 просмотров • 13 дней назад