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🎁6월 30일 오후 12시 (KST) #블랙핑크# 와 함께하는 트위터 블루룸 라이브🎁 #Ask_BLACKPINK# 해시태그와 함께 트윗으로 질문을 보내주시면 블랙핑크가 라이브에서 직접 답변해드려요🖤💘 #Ask_BLACKPINK# #TwitterBlueroom# LIVE Q&A with #BLACKPINK#

Get ready for #NewMusicDaily# Presents BLACKPINK with @AppleMusic. Come celebrate the release of #THEALBUM# with us! Sign up for a chance to join & ask questions live. Reserve your spot (US Only): #BLACKPINK# #JISOO# #JENNIE# #ROSÉ# #LISA# #YG#

for the first time ever, I'm sharing everything I do across all my SaaS to rank on Google and get mentioned by AI every SEO and AEO lesson I've learned is here: 1. we show up in Reddit threads. AI pulls a lot of its answers from Reddit, so Bazzly finds the threads that are fresh, and even the ones already ranking on Google, and gets our products into the top reply 2. we get our products listed #1# in listicles that already rank on Google, with Outrank. when someone asks ChatGPT for the best tool, those are the pages it reads 3. we turn Reddit questions into blog articles. Bazzly shows us what people actually ask, and we feed those questions to Outrank 4. I regularly sit down and find new niches where our products could be useful. I research each one and feed it to Outrank, and Outrank finds new ones on its own too, so our keyword plans keep expanding into new markets 5. we publish an SEO article on our own blogs every day, also with Outrank 6. Outrank helps us refresh old articles regularly. AI and Google both prefer recent pages, so every page has to stay current 7. we build new backlinks every week with Outrank, and every placement gets verified. we also pitch guest posts using Outrank's free guest post database of 278 checked sites, all DR 20+ 8. we regularly ship extremely useful free tools for potential users, and each one ranks on Google and brings people into our products 9. every month we check which AI answers name our products and which don't Outrank's AI Visibility Checker asks ChatGPT, Claude, Gemini and Perplexity real buyer questions, then shows whether we got named, which competitors did, and which sources got cited we feed those sources back into Bazzly, Outrank and our internal social content all of this brings in new users, and then we run loops that turn them into champions who spread the word 10. we incentivize our users to share honest reviews of our products on Instagram, TikTok, X and LinkedIn, and on public review sites 11. we let our users become affiliates, and then we help them make better content about our products. in the last 30 days, ~$8K has been generated by Revid affiliates alone basically every SEO and AEO lesson I learn building Outrank and Bazzly goes straight into every product I run and the idea behind all of it is simple AI recommends the most mentioned product so we make sure every one of our products gets mentioned everywhere Google and AI look: all sorts of blogs, listicles, Reddit, review sites and social and this setup does the job extremely well, for every product we run

This is Farzapedia. I had an LLM take 2,500 entries from my diary, Apple Notes, and some iMessage convos to create a personal Wikipedia for me. It made 400 detailed articles for my friends, my startups, research areas, and even my favorite animes and their impact on me complete with backlinks. But, this Wiki was not built for me! I built it for my agent! The structure of the wiki files and how it's all backlinked is very easily crawlable by any agent + makes it a truly useful knowledge base. I can spin up Claude Code on the wiki and starting at index.md (a catalog of all my articles) the agent does a really good job at drilling into the specific pages on my wiki it needs context on when I have a query. For example, when trying to cook up a new landing page I may ask: "I'm trying to design this landing page for a new idea I have. Please look into the images and films that inspired me recently and give me ideas for new copy and aesthetics". In my diary I kept track of everything from: learnings, people, inspo, interesting links, images. So the agent reads my wiki and pulls up my "Philosophy" articles from notes on a Studio Ghibli documentary, "Competitor" articles with YC companies whose landing pages I screenshotted, and pics of 1970s Beatles merch I saved years ago. And it delivers a great answer. I built a similar system to this a year ago with RAG but it was ass. A knowledge base that lets an agent find what it needs via a file system it actually understands just works better. The most magical thing now is as I add new things to my wiki (articles, images of inspo, meeting notes) the system will likely update 2-3 different articles where it feels that context belongs, or, just creates a new article. It's like this super genius librarian for your brain that's always filing stuff for your perfectly and also let's you easily query the knowledge for tasks useful to you (ex. design, product, writing, etc) and it never gets tired. I might spend next week productizing this, if that's of interest to you DM me + tell me your usecase!

LLM Knowledge Bases Something 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: Data ingest: I 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. IDE: I 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). Q&A: Where 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. Output: Instead 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. Linting: I'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. Extra tools: I 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. Further explorations: As 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. TLDR: 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.

My Watchlist got smaller this week, and that felt like progress. @GeckoTerminal shows me the list I actually use, not the list I wish I had, and keeping it short is the whole point for me. Every pool on there has a reason I can say out loud: what I am trying to learn from it, or what question it helps me answer. If I cannot say that reason clearly, it comes off. If I can, it stays, even when nothing is happening with it, because I am tracking to learn, not to chase movement. I check it the same way I tidy a desk. I look at what is still earning its place, I ask what has changed since I added it, and I let go of those that no longer teach me anything. That keeps my research honest, and it keeps my attention on the pools I already understand best. That is only how I keep mine, not advice for anyone else. My setup is here if you want a look: [your Watchlist link] What did you last take off your Watchlist, and why did it lose its place?

Who decides the answer to a prediction market? Usually the creator. Or an AI on a server nobody can check. So we built Noa Court - an AI resolver for Noa, live on NEAR mainnet. In the video we ask Gemini a vague question: "will the NEAR hackathon get more than 2000 projects?" - It finds the real source (Nearhackathon API) and samples it live: 1,803 right now - Turns the vague question into precise, hashed rules - We review the market and sign with a NEAR wallet - Market #27# goes live on Noa, published with readable rules How it resolves: - One Court contract, one pinned resolver running in an @out_layer TEE - Anyone can trigger it. Nobody can pick the answer - Numbers? Decided by code - Words? 3 NEAR AI judges must all agree and quote the evidence Asked "did a crypto hack make the news?", all 3 judges quoted a headline that never says "hack". It refuses to guess: - Prompt injection - flagged - Dead or changed source - void + refund Works with prices, NEAR tokens (powered by Intear & JustHoot), news feeds, web pages, any JSON API. Plug it into any market, contract or agent. Add the MCP to Claude, ChatGPT, Gemini - any AI that speaks MCP: Code: What would you never let an AI resolve? Built at NEAR HACKATHON @NEARProtocol @NearBuilders #NEARHackathon#

holy f*ck... Elon Musk's team just made airport rebooking lines useless my flight got cancelled and 187 people were ahead of me at the desk. my Grok Bot rebooked me before the line moved 17 people Grok Bot makes phone calls now. it sits on hold, talks to the agent and says up front it's an AI what it did while I stood in line: → sat through 11 minutes of hold music → got me on the 7:40 AM flight tomorrow → the airline cancelled, so it asked for a hotel and meals. got both → "aisle seat is $49 extra" it didn't pay. it asked me. I took the free middle seat → texted my wife the new landing time (signed Grok Bot) and set my alarm 14 minutes on the phone. I walked out of the line, 168 people still in it all it took was 3 rules I sent it once: 1. on every call, say you're an AI calling for me 2. ask for everything I'm owed, nothing made up 3. anything that costs money: ask me first the Grok Bot Research Desk setup is in my article. read it below ↓