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Elvis tweets so frequently and in such depth, you're likely to find him simultaneously live-tweeting an AI conference while debugging algorithms and composing a math book review, basically, the human embodiment of multi-threaded processing, just with less sleep.
Elvis’s biggest win is building a reputable AI-focused knowledge hub post-PhD and industry experience, with standout tweets breaking major AI announcements that spark thousands of engagements and shape conversations.
Elvis’s life purpose is to advance understanding and innovation in artificial intelligence by bridging the gap between complex research and accessible knowledge. Through his work, he aims to empower others with the information and critical insights necessary to navigate and contribute to the future of AI.
He believes that knowledge sharing, transparency, and continuous learning are essential to driving progress in AI. Elvis values precision, evidence-based research, and the collaborative spirit of the tech community to solve challenging problems and unlock new possibilities.
Elvis’s strengths lie in his expert knowledge combined with clear communication, enabling him to break down complex AI concepts into digestible content that resonates widely. His consistent activity and authoritative voice position him as a go-to source for AI insights.
His high volume of tweets might overwhelm some followers or dilute the impact of individual messages, and his highly technical focus may occasionally alienate casual users not well-versed in AI jargon.
To grow his audience on X, Elvis should experiment with more engaging, bite-sized content like short video explainers or infographics that simplify his research and invite broader interaction. Highlighting Q&A sessions or interactive threads could deepen community connection beyond sharing links and summaries.
Fun fact: Elvis has tweeted over 16,000 times, showcasing an unparalleled commitment to keeping his followers informed and engaged with the latest AI developments.
Building a personal knowledge base for my agents is increasingly where I spend my time these days.
Like @karpathy, I also use Obsidian for my MD vaults.
What's different in my approach is that I curate research papers on a daily basis and have actually tuned a Skill for months to find high-signal, relevant papers.
I was reviewing and curating papers manually for some time, but now it's all automated as it has gotten so good at capturing what I consider the best of the best. There are so many papers these days, so this is a big deal.
You all get to benefit from that with the papers I feature in my timeline and on @dair_ai.
The papers are indexed using @tobi qmd cli tool (all of it in markdown files along with useful metadata). So good for semantic search and surfacing insights, unlike anything out there.
I am a visual person, so I then started to experiment with how to leverage this personal knowledge base of research papers inside my new interactive artifact generator (mcp tools inside my agent orchestrator system). The result is what you see in the clip.
100s of papers with all sorts of insights visualized. I keep track of research papers daily, so believe me when I tell you that this system is absolutely insane at surfacing insights. This is the result of months of tinkering on how to index research and leverage agent automations for wikification and robust documentation.
But this is just the beginning. The visual artifact (which is interactive too) can be changed dynamically as I please. I can prompt my agent to throw any data at it. I can add different views to the data. Different interactions. I feel like this is the most personalized research system I have ever built and used, and it's not even close.
The knowledge that the agents are able to surface from this basic setup is already extremely useful as I experiment with new agentic engineering concepts. I feel like this knowledge layer and the higher-level ones I am working on will allow me to maximize other automation tools like autoresearch. The research is only as good as the research questions. And the research questions are only as good as the insights the agents have access to.
Where I am spending time now is on how to make this more actionable. I am obsessed about the search problem here. The automations, autoresearch, ralph research loop (I built one months ago) are easier to build but are only as good as what you feed them.
Work in progress. More updates soon. Back to building.
Building a personal knowledge base for my agents is increasingly where I spend my time these days.
Like @karpathy, I also use Obsidian for my MD vaults.
What's different in my approach is that I curate research papers on a daily basis and have actually tuned a Skill for months to find high-signal, relevant papers.
I was reviewing and curating papers manually for some time, but now it's all automated as it has gotten so good at capturing what I consider the best of the best. There are so many papers these days, so this is a big deal.
You all get to benefit from that with the papers I feature in my timeline and on @dair_ai.
The papers are indexed using @tobi qmd cli tool (all of it in markdown files along with useful metadata). So good for semantic search and surfacing insights, unlike anything out there.
I am a visual person, so I then started to experiment with how to leverage this personal knowledge base of research papers inside my new interactive artifact generator (mcp tools inside my agent orchestrator system). The result is what you see in the clip.
100s of papers with all sorts of insights visualized. I keep track of research papers daily, so believe me when I tell you that this system is absolutely insane at surfacing insights. This is the result of months of tinkering on how to index research and leverage agent automations for wikification and robust documentation.
But this is just the beginning. The visual artifact (which is interactive too) can be changed dynamically as I please. I can prompt my agent to throw any data at it. I can add different views to the data. Different interactions. I feel like this is the most personalized research system I have ever built and used, and it's not even close.
The knowledge that the agents are able to surface from this basic setup is already extremely useful as I experiment with new agentic engineering concepts. I feel like this knowledge layer and the higher-level ones I am working on will allow me to maximize other automation tools like autoresearch. The research is only as good as the research questions. And the research questions are only as good as the insights the agents have access to.
Where I am spending time now is on how to make this more actionable. I am obsessed about the search problem here. The automations, autoresearch, ralph research loop (I built one months ago) are easier to build but are only as good as what you feed them.
Work in progress. More updates soon. Back to building.
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In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:\n\nData ingest:\nI index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally \"compile\" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.\n\nIDE:\nI use Obsidian as the IDE \"frontend\" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).\n\nQ&A:\nWhere things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.\n\nOutput:\nInstead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up \"filing\" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always \"add up\" in the knowledge base.\n\nLinting:\nI've run some LLM \"health checks\" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.\n\nExtra tools:\nI find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. \n\nFurther explorations:\nAs the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM \"know\" the data in its weights instead of just context windows.\n\nTLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.","full_text":"LLM Knowledge Bases\n\nSomething I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:\n\nData ingest:\nI index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally \"compile\" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.\n\nIDE:\nI use Obsidian as the IDE \"frontend\" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).\n\nQ&A:\nWhere things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.\n\nOutput:\nInstead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up \"filing\" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always \"add up\" in the knowledge base.\n\nLinting:\nI've run some LLM \"health checks\" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.\n\nExtra tools:\nI find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. \n\nFurther explorations:\nAs the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM \"know\" the data in its weights instead of just context windows.\n\nTLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.","created_at":1775162541000,"author_id":"33836629","author":{"id":"33836629","name":"Andrej Karpathy","username":"karpathy","screen_name":"karpathy","profile_image_url":"https://pbs.twimg.com/profile_images/1296667294148382721/9Pr6XrPB_400x400.jpg","profile_image_url_https":"https://pbs.twimg.com/profile_images/1296667294148382721/9Pr6XrPB_400x400.jpg","is_blue_verified":1},"public_metrics":{"like_count":56430,"retweet_count":6738,"reply_count":2782,"quote_count":1985}},"fact_check":null,"id":"2039844072748204246","view_count":451756,"bookmark_count":7975,"created_at":1775171700000,"favorite_count":4439,"quote_count":25,"reply_count":138,"retweet_count":438,"user_id_str":"3448284313","conversation_id_str":"2039844072748204246","full_text":"Building a personal knowledge base for my agents is increasingly where I spend my time these days. \n\nLike @karpathy, I also use Obsidian for my MD vaults. \n\nWhat's different in my approach is that I curate research papers on a daily basis and have actually tuned a Skill for months to find high-signal, relevant papers. \n\nI was reviewing and curating papers manually for some time, but now it's all automated as it has gotten so good at capturing what I consider the best of the best. There are so many papers these days, so this is a big deal. \n\nYou all get to benefit from that with the papers I feature in my timeline and on @dair_ai. \n\nThe papers are indexed using @tobi qmd cli tool (all of it in markdown files along with useful metadata). So good for semantic search and surfacing insights, unlike anything out there. \n\nI am a visual person, so I then started to experiment with how to leverage this personal knowledge base of research papers inside my new interactive artifact generator (mcp tools inside my agent orchestrator system). The result is what you see in the clip.\n\n100s of papers with all sorts of insights visualized. I keep track of research papers daily, so believe me when I tell you that this system is absolutely insane at surfacing insights. This is the result of months of tinkering on how to index research and leverage agent automations for wikification and robust documentation. \n\nBut this is just the beginning. The visual artifact (which is interactive too) can be changed dynamically as I please. I can prompt my agent to throw any data at it. I can add different views to the data. Different interactions. I feel like this is the most personalized research system I have ever built and used, and it's not even close. \n\nThe knowledge that the agents are able to surface from this basic setup is already extremely useful as I experiment with new agentic engineering concepts. I feel like this knowledge layer and the higher-level ones I am working on will allow me to maximize other automation tools like autoresearch. The research is only as good as the research questions. And the research questions are only as good as the insights the agents have access to. \n\nWhere I am spending time now is on how to make this more actionable. I am obsessed about the search problem here. The automations, autoresearch, ralph research loop (I built one months ago) are easier to build but are only as good as what you feed them.\n\n Work in progress. More updates soon. Back to 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use Telegram for real work but is there anything else?","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null,"source":"koosocial_engage","fetched_at":1772456209092,"edit_history_tweet_ids":null,"poll_10min_at":null,"poll_3day_at":1771704028150,"poll_count":1,"poll_complete":1},{"bookmarked":false,"display_text_range":[0,265],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1980052095639126457","view_count":104556,"bookmark_count":118,"created_at":1760916181000,"favorite_count":546,"quote_count":8,"reply_count":196,"retweet_count":16,"user_id_str":"3448284313","conversation_id_str":"1980052095639126457","full_text":"I tried Codex on ChatGPT today. \n\nClaude Code is just irreplaceable to me at this point.\n\nAnd with this new Skills feature, the edge it gives is just too good to pass on.\n\nI am sure Codex will get 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Will keep trying future iterations.\n\nWhat’s your experience?","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null,"source":"scraping","fetched_at":null,"edit_history_tweet_ids":null,"poll_10min_at":null,"poll_3day_at":null,"poll_count":0,"poll_complete":0},{"bookmarked":false,"display_text_range":[0,277],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1870444425375309902","view_count":216537,"bookmark_count":384,"created_at":1734783676000,"favorite_count":1940,"quote_count":40,"reply_count":166,"retweet_count":196,"user_id_str":"3448284313","conversation_id_str":"1870444425375309902","full_text":"The hype around o3 is out of control.\n\nIt’s not AGI, it’s not the singularity, and you definitely don’t have to change your worldview.\n\nIn fact, the public doesn’t even have access to the models so how can anyone claim any of the above.\n\nI appreciate how the OpenAI researchers presented o3.\n\nI encourage folks to checkout the original presentation on YouTube.\n\nDon’t fall for all the hype threads you see here on X.\n\nOpenAI made it clear that there lots of things to improve on. \n\nIt’s exciting yes but the headlines are misleading and benchmark results don’t really say much these days.\n\nHoping these words balance your timeline a bit. 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It will contain popular equations paired with code and explanations. The idea is to start with a small collection of 100 (beginner to advanced).\n\nWould you be interested in this? https://t.co/d0ceSrsJDr","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null,"source":"scraping","fetched_at":null,"edit_history_tweet_ids":null,"poll_10min_at":null,"poll_3day_at":null,"poll_count":0,"poll_complete":0},{"bookmarked":false,"display_text_range":[0,85],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1680960073667936258","view_count":75491,"bookmark_count":49,"created_at":1689607086000,"favorite_count":283,"quote_count":2,"reply_count":144,"retweet_count":10,"user_id_str":"3448284313","conversation_id_str":"1680960073667936258","full_text":"looking to connect with more LLM researchers and developers \n\nif that’s you, say hi 👋","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null,"source":"scraping","fetched_at":null,"edit_history_tweet_ids":null,"poll_10min_at":null,"poll_3day_at":null,"poll_count":0,"poll_complete":0},{"entities":{"hashtags":[],"media":[{"additional_media_info":{"monetizable":true},"allow_download_status":{"allow_download":true},"display_url":"pic.x.com/YI5cWUhqT3","expanded_url":"https://x.com/omarsar0/status/2039844072748204246/video/1","ext_media_availability":{"status":"Available"},"id_str":"2039843952044781568","indices":[275,298],"media_key":"13_2039843952044781568","media_results":{"result":{"media_key":"13_2039843952044781568"}},"media_url_https":"https://pbs.twimg.com/amplify_video_thumb/2039843952044781568/img/rASDSkvegI3Coohs.jpg","original_info":{"focus_rects":[],"height":1080,"width":1896},"sizes":{"large":{"h":1080,"resize":"fit","w":1896},"medium":{"h":684,"resize":"fit","w":1200},"small":{"h":387,"resize":"fit","w":680},"thumb":{"h":150,"resize":"crop","w":150}},"type":"video","url":"https://t.co/YI5cWUhqT3","video_info":{"aspect_ratio":[79,45],"duration_millis":18700,"variants":[{"content_type":"application/x-mpegURL","url":"https://video.twimg.com/amplify_video/2039843952044781568/pl/D_BmYB0Lj_SU3Kwm.m3u8"},{"bitrate":256000,"content_type":"video/mp4","url":"https://video.twimg.com/amplify_video/2039843952044781568/vid/avc1/474x270/aNK059h60gTUVl5V.mp4"},{"bitrate":832000,"content_type":"video/mp4","url":"https://video.twimg.com/amplify_video/2039843952044781568/vid/avc1/632x360/kHM76wVa-v6E4WQj.mp4"},{"bitrate":2176000,"content_type":"video/mp4","url":"https://video.twimg.com/amplify_video/2039843952044781568/vid/avc1/1264x720/5nCZf9icxASXvdjM.mp4"},{"bitrate":10368000,"content_type":"video/mp4","url":"https://video.twimg.com/amplify_video/2039843952044781568/vid/avc1/1896x1080/-kHSjAI8lU2OjZH3.mp4"}]}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"33836629","indices":[106,115],"name":"Andrej Karpathy","screen_name":"karpathy"}]},"extended_entities":{"media":[{"additional_media_info":{"monetizable":true},"allow_download_status":{"allow_download":true},"display_url":"pic.x.com/YI5cWUhqT3","expanded_url":"https://x.com/omarsar0/status/2039844072748204246/video/1","ext_media_availability":{"status":"Available"},"id_str":"2039843952044781568","indices":[275,298],"media_key":"13_2039843952044781568","media_results":{"result":{"media_key":"13_2039843952044781568"}},"media_url_https":"https://pbs.twimg.com/amplify_video_thumb/2039843952044781568/img/rASDSkvegI3Coohs.jpg","original_info":{"focus_rects":[],"height":1080,"width":1896},"sizes":{"large":{"h":1080,"resize":"fit","w":1896},"medium":{"h":684,"resize":"fit","w":1200},"small":{"h":387,"resize":"fit","w":680},"thumb":{"h":150,"resize":"crop","w":150}},"type":"video","url":"https://t.co/YI5cWUhqT3","video_info":{"aspect_ratio":[79,45],"duration_millis":18700,"variants":[{"content_type":"application/x-mpegURL","url":"https://video.twimg.com/amplify_video/2039843952044781568/pl/D_BmYB0Lj_SU3Kwm.m3u8"},{"bitrate":256000,"content_type":"video/mp4","url":"https://video.twimg.com/amplify_video/2039843952044781568/vid/avc1/474x270/aNK059h60gTUVl5V.mp4"},{"bitrate":832000,"content_type":"video/mp4","url":"https://video.twimg.com/amplify_video/2039843952044781568/vid/avc1/632x360/kHM76wVa-v6E4WQj.mp4"},{"bitrate":2176000,"content_type":"video/mp4","url":"https://video.twimg.com/amplify_video/2039843952044781568/vid/avc1/1264x720/5nCZf9icxASXvdjM.mp4"},{"bitrate":10368000,"content_type":"video/mp4","url":"https://video.twimg.com/amplify_video/2039843952044781568/vid/avc1/1896x1080/-kHSjAI8lU2OjZH3.mp4"}]}}]},"display_text_range":[0,274],"lang":"en","possibly_sensitive":false,"quoted_tweet":{"id":"2039805659525644595","text":"LLM Knowledge Bases\n\nSomething I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:\n\nData ingest:\nI index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally \"compile\" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.\n\nIDE:\nI use Obsidian as the IDE \"frontend\" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).\n\nQ&A:\nWhere things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.\n\nOutput:\nInstead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up \"filing\" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always \"add up\" in the knowledge base.\n\nLinting:\nI've run some LLM \"health checks\" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.\n\nExtra tools:\nI find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. \n\nFurther explorations:\nAs the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM \"know\" the data in its weights instead of just context windows.\n\nTLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.","full_text":"LLM Knowledge Bases\n\nSomething I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:\n\nData ingest:\nI index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally \"compile\" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.\n\nIDE:\nI use Obsidian as the IDE \"frontend\" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).\n\nQ&A:\nWhere things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.\n\nOutput:\nInstead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up \"filing\" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always \"add up\" in the knowledge base.\n\nLinting:\nI've run some LLM \"health checks\" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.\n\nExtra tools:\nI find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. \n\nFurther explorations:\nAs the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM \"know\" the data in its weights instead of just context windows.\n\nTLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. 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There are so many papers these days, so this is a big deal. \n\nYou all get to benefit from that with the papers I feature in my timeline and on @dair_ai. \n\nThe papers are indexed using @tobi qmd cli tool (all of it in markdown files along with useful metadata). So good for semantic search and surfacing insights, unlike anything out there. \n\nI am a visual person, so I then started to experiment with how to leverage this personal knowledge base of research papers inside my new interactive artifact generator (mcp tools inside my agent orchestrator system). The result is what you see in the clip.\n\n100s of papers with all sorts of insights visualized. I keep track of research papers daily, so believe me when I tell you that this system is absolutely insane at surfacing insights. This is the result of months of tinkering on how to index research and leverage agent automations for wikification and robust documentation. \n\nBut this is just the beginning. 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