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🐸’s 본업 모먼트ㅣBEHIND THE [EROS] 🎥 #LEECHANHYUK# #이찬혁# #AKMU# #악뮤# #2ndFULLALBUM# #EROS# #비비드라라러브# #Vivid_LaLa_Love# #BEHIND_THE_EROS# #HIGHLIGHTCLIP# #YG#

God mercy on this grouuuuuund 🤙ㅣBEHIND THE [EROS] 🎥 #LEECHANHYUK# #이찬혁# #AKMU# #악뮤# #2ndFULLALBUM# #EROS# #멸종위기사랑# #Endangered_Love# #BEHIND_THE_EROS# #HIGHLIGHTCLIP# #YG#

이런 동생 본 사람 🐰ㅣBEHIND THE [EROS] 🎥 #LEECHANHYUK# #이찬혁# #AKMU# #악뮤# #2ndFULLALBUM# #EROS# #돌아버렸어# #Out_of_My_Mind# #BEHIND_THE_EROS# #HIGHLIGHTCLIP# #YG#

Behind-the-Scenes of ‘비비드라라러브’ 🎥 NOW ON YOUTUBE : #LEECHANHYUK# #이찬혁 ##AKMU# #악뮤# #2ndFULLALBUM# #EROS# #비비드라라러브# #Vivid_LaLa_Love# #YG#

A few projects made some serious moves behind the scenes: @XOOBNetwork continues rewarding real users, distributing Nomisen IDs to top campaign contributors, Nomisma participants, and Genesis NFT holders. always good to see ecosystems rewarding consistency instead of pure hype. @useTria keeps proving that consumer crypto is no longer just theory: • $100M+ card spend • $200M+ routed via BestPath • nearly $1B futures volume • $40M+ AUM • $5M+ distributed back to users they’re building a system where crypto actually feels usable in daily life spend, swap, trade, travel, all connected. travel feature drops in 2 weeks 👀 @sleepagotchi is evolving far beyond “sleep-to-earn.” Now positioning itself as an AI-powered wellness intelligence layer with wearable integrations (WHOOP, Oura, Apple Watch), personalized coaching, and user-owned health data tied into $SLEEP utility. definitely one of the more ambitious pivots in the health x AI space. @quipnetwork still cooking quietly in the quantum sector: post-quantum security, decentralized hybrid compute, and real quantum randomness infrastructure. while most people wait for the future, they’re already building for it. @TheARCTERMINAL dropped an important reminder: privacy isn’t “trust us, we won’t look.” real privacy means the architecture itself makes it impossible for servers to read your data. AI sovereignty will matter more and more from here. and shoutout to @River4fun too 🌊 still one of the more underrated communities grinding consistently, building engagement organically, and keeping the timeline alive while others disappear after the hype cycle. bear market or not… builders still building. real users still active. that’s what matters.

Behind the scenes 📷 Two languages, one soundtrack. The Great Wall stands witness to everlasting bonds. Let the song carry us — @layzhang sings for China-Pakistan friendship atop this historic landmark! 🔗 #LAY# #LAYZHANG# #ZHANGYIXING# #张艺兴#

Behind the MiMo API Price Reduction: The deepest price cut, up to 99%, is for Input (Cache Hit). The core reason is our inference framework now supports hierarchical KV cache optimization for SWA. Production inference engine tests show this optimization increases cached token capacity by 5x, equivalent to an 80% reduction in caching costs. Combined with Cache Read Overlap among multiple Full Attention modules in the Hybrid model, actual costs are further reduced. Prices for Input (Cache Miss) and Output are also reduced by 60%-80%. This mainly benefits from the extreme 1:7 Full:SWA sparsity ratio brought by the model architecture (the prefill compute of the 70-layer MiMo-V2.5-Pro roughly equals a 10-layer GQA model). This kept our original inference costs well below the industry average, naturally leaving a 2x-3x profit margin in pricing. This price adjustment simply reflects our decision to pass these structural cost efficiencies directly to developers. Operating at these newly reduced API prices, our production inference engine is running at near full capacity, and we can still essentially break even. We previously advised LLM companies not to "blindly cut prices" precisely because very few model architectures and inference optimizations can keep API costs from running at a loss. If more architectures that save compute and KV cache emerge, along with better inference Infra to drive down API costs, this will form an excellent virtuous cycle in the industry. More crucially, affordable, high-performance model APIs will drive real, sustained, and at-scale inference demand. This upstream demand pulls forward the development of the entire AI infrastructure chain—including chips, servers, optical transceivers, PCBs, liquid cooling, power, energy storage, and data centers—serving as a strategic fulcrum for a systemic revaluation of AI hardware. In the long run, this injects more affordable and accessible compute into both training and inference pipelines, accelerating the parallel evolution of global AGI across multiple regions and technical routes. For more technical details, we will release a detailed Blog post later.

🚨 Claude Code made me 6 trading bots in 15 mins In the US alone, emotional retail traders lost more than $1.8 billion on liquidations While billions of amateur traders were staring at charts, overtrading, and getting wrecked on fees, a quiet group of algorithmic traders treated prediction markets like a hyper-liquid data engine They didn't guess outcomes -> they knew the structural price gaps in advance Here is how they did it, and why manual trading is completely dead: It's all about removing emotion and deploying cross-market statistical arbitrage Linear Spread Cointegration Formula: S_t = P_P,t - β * P_K,t - μ Ornstein-Uhlenbeck Continuous Dynamics Formula: dS_t = θ(μ - S_t)dt + σ dW_t Euler-Maruyama Discretization (MLE Calibration) Formula: S_t_i = S_t_i-1 * e^(-θΔt) + μ(1 - e^(-θΔt)) + ε_t Level 1 Order Book Imbalance (OBI) Formula: I_t = (V_b(t) - V_a(t)) / (V_b(t) + V_a(t)) Volume-Weighted Micro-Price Prediction Formula: P_micro(t) = P_mid(t) + I_t * (Δspread / 2) Cross-Venue Predictive Signal Optimization Formula: ΔP_Kalshi(t + δ) = f(I_Polymarket(t), P_micro,Polymarket(t) - P_micro,Kalshi(t)) In the era of advanced AI, the winner is not the one who guesses the score, but the one who lets automated systems execute with absolute patience and discipline. AI does the hard parts now -> you don't even need a CS degree to build this The full behind-the-scenes live system build is now available to the public 📝