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Analyzing effectiveness of ping-ponging orbs with low orb count agents on Corrode. Also Corrode map structure facilitating AOE util agents also, rather than the strict neon/waylay meta. Finally discussing Astra+Sage viability 🎦Full video on Youtube ↓

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Santiago

213,012 Aufrufe • vor 1 Monat

LLM Wikis are being slept on. I argue that creating knowledge bases with LLMs or coding agents is one of the most valuable applications of AI today. It's about being intentional in building and scaling your intelligence stack. To showcase this, I wanted to share an LLM Wiki I have built over the last couple of months. It's called PaperWiki, and I use it across all my research workflows, along with my research agents. In fact, I also use it to curate papers I share with my communities, newsletter, and on X. The PaperWiki is updated regularly with automations, so I basically have agents on a loop maintaining it. All the entries are ingested from different sources and stored in a vault (Obsidian) and further indexed using qmd. And then further presented via an HTML artifact. So all of it is easily accessible to all my agents and easily searchable through full-text search and rich semantic search. The structure of the wiki has proven significantly useful to start interesting and exciting cutting-edge research projects with my research agents (from building tiny and more efficient gpt/difussion llms to building out SoTA harnesses and memory systems). It turns out that agents love markdown files and can more easily navigate the papers given the rich metadata structure of the wiki. I am just getting started on this, but it's clear to me that we should all be experimenting with LLM Wikis. Here's why: Building LLM knowledge bases gets you into the habit of leveraging AI outputs in all kinds of creative ways. It's the good kind of tokenmaxxing we should all be pushing for. LLM Wikis can be maintained automatically in a loop. I use an automation that updates the wiki every day based on papers I curate. The curation is another automation I run in a loop (with a bit of human in the loop), so I get to build on all my previous knowledge and expertise, and all of it compounds the deeper the integration/layers. One interesting result of this process is that I feel like I can better spot high-quality papers and remove noise more easily. Social media could never solve that. And most paper aggregators use metrics I simply don't trust. I like that agents can help with the noise vs. signal problem. This is important for research. Lots of people consider agents to produce mostly slop. But it doesn't have to be that way. Careful curations, prompts, automations, verifiers, and human-in-the-loop can produce some astonishing results. And you really don't need frontier models for this. I use a combination of frontier models (opus-4.8) and open-weight models (deepseek-v4-flash) to maintain this. An exciting future work (we are working on this DAIR.AI) is to tune specialized models on top of this to allow LLMs to quickly understand cutting-edge research ideas and can better conceptualize research strategies that further accelerate scientific research agents. I plan to open-source a bunch of this work, including the artifact, but this is currently work in progress, and I was excited to share some thoughts as I continue working on it. Sharing more as I go. Stay tuned!
0:27

LLM Wikis are being slept on. I argue that creating knowledge bases with LLMs or coding agents is one of the most valuable applications of AI today. It's about being intentional in building and scaling your intelligence stack. To showcase this, I wanted to share an LLM Wiki I have built over the last couple of months. It's called PaperWiki, and I use it across all my research workflows, along with my research agents. In fact, I also use it to curate papers I share with my communities, newsletter, and on X. The PaperWiki is updated regularly with automations, so I basically have agents on a loop maintaining it. All the entries are ingested from different sources and stored in a vault (Obsidian) and further indexed using qmd. And then further presented via an HTML artifact. So all of it is easily accessible to all my agents and easily searchable through full-text search and rich semantic search. The structure of the wiki has proven significantly useful to start interesting and exciting cutting-edge research projects with my research agents (from building tiny and more efficient gpt/difussion llms to building out SoTA harnesses and memory systems). It turns out that agents love markdown files and can more easily navigate the papers given the rich metadata structure of the wiki. I am just getting started on this, but it's clear to me that we should all be experimenting with LLM Wikis. Here's why: Building LLM knowledge bases gets you into the habit of leveraging AI outputs in all kinds of creative ways. It's the good kind of tokenmaxxing we should all be pushing for. LLM Wikis can be maintained automatically in a loop. I use an automation that updates the wiki every day based on papers I curate. The curation is another automation I run in a loop (with a bit of human in the loop), so I get to build on all my previous knowledge and expertise, and all of it compounds the deeper the integration/layers. One interesting result of this process is that I feel like I can better spot high-quality papers and remove noise more easily. Social media could never solve that. And most paper aggregators use metrics I simply don't trust. I like that agents can help with the noise vs. signal problem. This is important for research. Lots of people consider agents to produce mostly slop. But it doesn't have to be that way. Careful curations, prompts, automations, verifiers, and human-in-the-loop can produce some astonishing results. And you really don't need frontier models for this. I use a combination of frontier models (opus-4.8) and open-weight models (deepseek-v4-flash) to maintain this. An exciting future work (we are working on this DAIR.AI) is to tune specialized models on top of this to allow LLMs to quickly understand cutting-edge research ideas and can better conceptualize research strategies that further accelerate scientific research agents. I plan to open-source a bunch of this work, including the artifact, but this is currently work in progress, and I was excited to share some thoughts as I continue working on it. Sharing more as I go. Stay tuned!

elvis

55,566 Aufrufe • vor 1 Monat

Nigerian Rapper Ycee has lamented what he described as Nigeria's growing obsession with wealth, warning that the pursuit of money is eroding societal values. He also revealed he once aspired to study Computer Science but missed the admission cut-off, reflecting on his academic struggles and love for reading. The music star dismissed claims that his comments were elitist, insisting they were aimed at encouraging a shift in societal values rather than criticising people's backgrounds. Visit to watch full video. The music star dismissed claims that his comments were elitist, insisting they were aimed at encouraging a shift in societal values rather than criticizing people's backgrounds.
1:09

Nigerian Rapper Ycee has lamented what he described as Nigeria's growing obsession with wealth, warning that the pursuit of money is eroding societal values. He also revealed he once aspired to study Computer Science but missed the admission cut-off, reflecting on his academic struggles and love for reading. The music star dismissed claims that his comments were elitist, insisting they were aimed at encouraging a shift in societal values rather than criticising people's backgrounds. Visit to watch full video. The music star dismissed claims that his comments were elitist, insisting they were aimed at encouraging a shift in societal values rather than criticizing people's backgrounds.

The Guardian Nigeria

68,744 Aufrufe • vor 1 Monat

Thesis: 1000x I've been accumulating $1000X as one of my highest-conviction moves this cycle. $1000x is a social token with buybacks founded by Avi and Jonah The buybacks are currently sourced from ad revenue from Avi and Jonah's 1000x podcast and their discretionary trading profits. In the future, the buybacks will also be fueled by revenue from the 1000x terminal. The terminal lets you interact with 1000xAgent, which aspires to be an AI incarnation and extension of Avi and Jonah’s collective trading faculties. It has the potential to become a full-blown AI market intelligence platform akin to aixbt. Avi and Jonah are successful traders who served respectively as Head of Digital Assets Trading at GoldenTree and Global Head of Trading at Cumberland. $1000x is essentially a bet on two highly competent and high-integrity actors. There is a direct link between their success and the success of the token via buybacks. 1000x is built on Virtuals on Base, so it is also a bet on Base and the ascent of AI agents. The fact that 1000x has revenue and buybacks and is founded by a pair rather than a single individual differentiates it from almost all social and creator tokens. Finally, it has a killer name: 1000x. Think about it. As always, NFA. I'm just a degenerate primate that occasionally makes a great call. (Music composed by Jonah)
0:32

Thesis: 1000x I've been accumulating $1000X as one of my highest-conviction moves this cycle. $1000x is a social token with buybacks founded by Avi and Jonah The buybacks are currently sourced from ad revenue from Avi and Jonah's 1000x podcast and their discretionary trading profits. In the future, the buybacks will also be fueled by revenue from the 1000x terminal. The terminal lets you interact with 1000xAgent, which aspires to be an AI incarnation and extension of Avi and Jonah’s collective trading faculties. It has the potential to become a full-blown AI market intelligence platform akin to aixbt. Avi and Jonah are successful traders who served respectively as Head of Digital Assets Trading at GoldenTree and Global Head of Trading at Cumberland. $1000x is essentially a bet on two highly competent and high-integrity actors. There is a direct link between their success and the success of the token via buybacks. 1000x is built on Virtuals on Base, so it is also a bet on Base and the ascent of AI agents. The fact that 1000x has revenue and buybacks and is founded by a pair rather than a single individual differentiates it from almost all social and creator tokens. Finally, it has a killer name: 1000x. Think about it. As always, NFA. I'm just a degenerate primate that occasionally makes a great call. (Music composed by Jonah)

BΞN

13,377 Aufrufe • vor 11 Monaten