vbrown.skr

@plugs0l welcome back plug

im back niggas

@TracyWesterman Australia shouldn't be paying for geopolitical ego trips. When functioning agreements get scrapped for political branding, everyday people end up covering the cost through higher fuel prices, inflation, and instability. $AUSPOL #auspol

Let this sink in. In 2015, the Iran nuclear deal capped uranium enrichment at 3.5% with full international inspections. The IAEA repeatedly confirmed Iran was complying. Trump’s own Secretary of Defence testified it was in America’s national security interest to keep it. Trump tore it up in 2018 because it had Obama’s name on it. Iran immediately accelerated its nuclear program — exactly what the deal was designed to prevent. Eight years, a war, thousands of lives, and billions of dollars later — Trump’s envoys are now trying to negotiate the same enrichment limits and verification measures that already existed in the deal he destroyed. Read that again. He broke a working agreement. Created the crisis. Started a war. And is now trying to negotiate his way back to where we started — except now with less leverage, more enemies, and a body count. This isn’t dealmaking. This is ego dressed up as foreign policy. A man who burned down the house and is now asking for credit because he called the fire brigade. Meanwhile, the rest of the world — including Australia — is paying the price. Fuel shortages. Economic instability. A region in flames. All because one man couldn’t tolerate his predecessor’s name on a functioning agreement. The Art of the Deal in reverse.

Imagine waking up tomorrow and seeing news that a Nordic alien had a private meeting with Donald Trump 😅 What's the first question you'd ask? Mine would be: "How long have y'all been visiting Earth?" 👀🛸

$RIPS WILL RIPPP

@Rr210ra nice card

@RonPull 58vJgT9gVbU1YWuxB8pUnuktLbUiuyAxVCgAKJiMWeMd

Just got more funds to fund my giveaways + ape more dips on $Jotchua, $GIGATROLL and $kins Btw who want to be my next winner ? Drop $SOL addys below and check your DMs in like 3-4 hours 😈💎

Holy shit 800k followers and bull posting about $Jotchua Thanks to this cult I m finally on something very easy to hold and can make finally rich af

@RonPull 58vJgT9gVbU1YWuxB8pUnuktLbUiuyAxVCgAKJiMWeMd

One breakfast in $SOL 🔔❤️♻️ + $SOL addys below Ends in only 180 minutes Yeah ofc, if you don’t engage with me, you have less chance than someone who actually does 😈

@raycrypto_1 @39billion 8072054509 Sporty ID

@39billion Funding 20 sportybet ID as usual

Universe I dropped it here!

EDIT ✅ XF813BBB @39billion

Elon Musk just became the world's first trillionaire. Love him or hate him, you're witnessing history.

@dukexbt_ never got in on any of them

worst info fi edition goes to ?

@MicMetaX__ @solanafunded spot gotten

Most creator programs are a scam that pay you peanuts for way too much work. @solanafunded is actually different, they pay real cash just for posting good content, no matter how many followers you have. If your posts get views, you get paid. Link》》creators.solanafunded.com

@kaitegrace201 @solanafunded spot gotten

Tired of the same old influencer campaigns that require 50k followers just to say hello? @solanafunded actually gets it. They're stripping away the gatekeeping and paying out based on what actually matters: your ability to engage. ​Get in now: creators.solanafunded.com

@NALU_HQ @solanafunded will try it out

@solanafunded is a new creator program that pays you in USDC based on your content's performance. Unlike traditional programs, there are no follower counts or "influencer" requirements. Post content that gets views and get paid. ​Check it: creators.solanafunded.com

Gmonad legends 💜 Here are some ongoing campaigns in the Monad ecosystem and how you can participate: → @idgafonmonad Hosting weekly NFT giveaways for holders with at least 1M $IDGAF. → Register your wallet: idgafonmonad.xyz/raffle → @Levr_Bet Levr is offering a $10 bet-back reward if your first World Cup bet loses. → Join the action: levr.bet/home?league=WC → @OpenBuildxyz Running a 1-day intensive hackathon focused on AI agents and programmable payments on Monad. -> Prize Pool: $1,500 -> Ends: June 27 → @perpltrade Hosting a trading tournament with $100,000 in prizes. → @monu_hq Launching the $EMO airdrop campaign. → Spin daily → Complete a 3-day streak → Qualify for the claim Miss the streak and your allocation rolls into the leftover pool shared by successful participants. That’s a wrap for now. Follow me and the projects above for more Monad ecosystem updates, and don’t miss out on these opportunities.

@Jannat63598 @solanafunded I'm gonna apply

@solanafunded is actually a cheat code right now. No follower gatekeeping, no 20 page legal contracts, no begging for brand deals. They’re dropping real USDC and funded accounts for people who can actually post content. ​ Put in the work and get paid creators.solanafunded.com

@RonPull 58vJgT9gVbU1YWuxB8pUnuktLbUiuyAxVCgAKJiMWeMd

10$ in $SOL - Engage quoted post - Repost and like - Drop $SOL addys below ! Ends in only 6 hours get in now or stay broke 😈 Good luck

@qlib_quant love the analysis

Financial markets pose fundamental challenges for asset return prediction due to their high dimensionality, non-stationarity, and persistent volatility. Despite advances in large language models and multi-agent systems, current quantitative research pipelines suffer from limited automation, weak interpretability, and fragmented coordination across key components such as factor mining and model innovation. In this paper, we propose R&D-Agent for Quantitative Finance, in short RD-Agent(Q), the first data-centric multi-agent framework designed to automate the full-stack research and development of quantitative strategies via coordinated factor-model co-optimization. RD-Agent(Q) decomposes the quant process into two iterative stages: a Research stage that dynamically sets goal-aligned prompts, formulates hypotheses based on domain priors, and maps them to concrete tasks, and a Development stage that employs a code-generation agent, Co-STEER, to implement task-specific code, which is then executed in real-market backtests. The two stages are connected through a feedback stage that thoroughly evaluates experimental outcomes and informs subsequent iterations, with a multi-armed bandit scheduler for adaptive direction selection. Empirically, RD-Agent(Q) achieves up to 2X higher annualized returns than classical factor libraries using 70% fewer factors, and outperforms state-of-the-art deep time-series models on real markets. Its joint factor-model optimization delivers a strong balance between predictive accuracy and strategy robustness.