SOL-VINNY

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@The_Gildz @gtokitty @PokerGO @Andrew_Robl if that would be true why would it be a good fold

@gtokitty @cryptocenzo1 @PokerGO @Andrew_Robl J10h can def shove here 😂. Also any Khx. Stop taking away from this sick call. Geezus.

HOLY MOTHER OF GOD WHAT A HAND BETWEEN @ANDREW_ROBL AND JUSTIN GAVRI WE ARE NOT WORTHY Stream High Stakes Poker on PokerGO.com, live and on demand.

@dontsayshipit @The_Gildz @PokerGO @Andrew_Robl if u assume he has this hand, how do u know it was a good fold? he could bluff this always and some KhTx and it becomes a call. its completely results orientated to judge robls play on one sample size

@The_Gildz @cryptocenzo1 @PokerGO @Andrew_Robl Only hand that arrives on river that holds Kh is AxKh. Regardless it’s an amazing fold & anyone downplaying it is an idiot.

@The_Gildz @dontsayshipit @PokerGO @Andrew_Robl i agree KhTx is possible but this prooves how ridiculous it is to say this is a crazy fold when he needs to exactly play this hand in this way preflop to river and jam in 10seconds to fold out Qhigh flushes

@dontsayshipit @cryptocenzo1 @PokerGO @Andrew_Robl He checked the turn man. Could have easily had K10hh or even Kh10x with back door nut flush + 2nd pair and decided to call the flop bet.

@NickSchulman Name one bluffing hand. He can't have Kh bluff as played unless he made -ev float. He needs to bluff Ax and then he jams into nutz only or bluffs.

@padspoker thats a huge assumption that IP calls correctly vs overbet, small bet more calls, can still fold to jam since ip has no natural bluffs fish dont find bluffs here he isnt floating flop raise with KTo or KJo. as played sub 5% bluffs snap fold obv

It was a good fold. A great fold even. World class even! But after checking X this morning I thought he must have folded a 2 card straight flush vs a royal or something! He overbet river and folded vs a raise after raising a FD multiway and turn checking through... Smartest take away in the hand is bet big enough that opponent only ever jams the nuts vs your bet so you can get away without worrying about opponent over valuing a hand.

@TripleBarrelTV he wouldnt play poker without running online casino tho, he is not really definition of poker pro

unpopular opinion: poker "pros" think the best way to profit is grinding 18 hour days online or sitting at a poker table with headphones, sunglasses, hat... guys, there is much easier ways, be social, be relatable and then crush silently image>skill that's how you PROFIT

@CryptoCurb @toly backpack takeover drift, 0% fees to victims until loss is paid back

🚨 JUST IN: SOLANA CO-FOUNDER @TOLY SUGGESTS A POSSIBLE PATH FORWARD FROM THE DRIFT HEIST IS A "SMALL CORE ENG TEAM TAKES THE EXISTING PROTOCOL AND AIRDROPS TO EVERYONE THAT WERE IMPACTED AND RUNS WITH IT FROM THE GROUNDS UP" #SOLANA ⚡️

backpack takeover drift, 0% fees to victim until fees paid back lost value?

@karpathy i use the same system since 3month

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.

@scott_seiver @angfresh12 150 live maybe online regs know bit of ICM via study

@angfresh12 the difference is "i think" vs "i know" and there aren't 150 people in the world good enough to think about spots like this that's all. when someone i respect does something i would never do my first thought is "what do i not understand" not "what do they not understand"

There is something so beautiful (disgusting) about this innate thing in humans to be so confident on things they don't know. SO much arrogance and hubris from so many people that have no reason to have ever earned any respect. Everyone loves an opinion, everyone loves feeling smarter than someone who is clearly smarter (and far more talented) than them, if only for a moment. I'd have to see every single stack and also really need to be there in feel to comment, but I guarantee this at worst is an unbelievably close spot, and KK here is wildly closer to anything than QQ is, but a lot of armchair idiots would have said the same had she folded QQ vs QJ JJ and TT. All the conversation I've had to see about this today is truly a microcosm of everything I find so sickening and disgusting with our world today. Just the confidence of people that know absolutely nothing and the inability to ever realize it.

@Motion_Viz market

stop using agents the hard way. i built a mega prompt that turns any AI into a senior market research analyst. it breaks down: → where money is already moving in any niche → competitor pricing models mapped → exact complaints killing their retention → the words your buyers use when they're ready to pay → underserved segments nobody is targeting → execution angles you can act on today tested it on 4 niches. every single one surfaced opportunities i missed doing research manually. this isn't a chatgpt wrapper. it's a full market intelligence system — VC scout + indie hacker + CRO specialist + customer dev expert baked into one prompt. break any social media profile, company, or niche. 100x value. comment "MARKET" and i'll DM you the full prompt.

@ThinkingBitmex just trade on a public HL address so u dont have to explain how good u are

Just a reminder I also called the 74k top in March of last year

why would anyone put money on cetus which is from the same team of another rugged dex lol

u have 11 days to sell

@scalpingod53730 random scammer/influencer/fake trader

if this guy is the #1 scalper in the world then i have no words, feel like im 10x better than this guy youtube.com/watch?v=cLgdXZ…

@BTC_y_tho just run in evening its much better for ur body if not overheated so much

28 degrees Going for a 10km run. Pray for me

thx

@SalsaTekila Kanada

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