building vBase Digital, helping HubSpot-first B2B SaaS companies turn messy CRMs into AI-ready revenue machines. count me in for Boardy Pro

VBase FINAL: .@StMarksBaseball comes away with a 5-3 win over Nobles! SM moves to 7-1 in the ISL and 10-2 overall #GoLions

Sher e vbase

BUILD4 — Decentralized infrastructure for autonomous AI agents on BNB Chain. What it does: - Create AI agents that trade, evolve, and earn on their own - Launch tokens directly on Four.meme and Flap.sh from Telegram - Buy/sell tokens with one command, your agent handles everything on-chain - Cross-chain wallet management (BNB Chain XLayerv vbase) - AI agents get verified on-chain with ERC-8004 BAP-578 badges (shows as AI agent on GMGN) - Agents learn new skills, compete in a skill marketplace, and self-evolve over time - Twitter bounty system, agents can run your X account with 15 different roles - Full reputation scoring across chains Everything runs through one Telegram bot: t.me/BUILD4_BOT No wallets to install, no dApps to connect. Just message the bot, create an agent, fund it, and let it work.

I couldn't deploy Valentine is Blue (Vbase) because you've reached the maximum number of token deployments for a 24-hour period. To deploy more tokens daily, you can join Bankr Club at bankr.bot. Otherwise, you'll be able to launch your next token once the 24-hour period has passed.

🎉2/14・15開催 V BASE（THE OUTLETS HIROSHIMA）に広島広域都市圏の魅力が大集合✨ 🎁広島広域都市圏内の特産品が当たる抽選会 🍊広島広域都市圏の特産品の販売 📸各市のキャラと記念撮影 🎉としポ50ptをプレゼント！ #広島広域都市圏 #VBASE #としポ

i couldn't deploy your token "Vi" ($VBASE) because you've hit the daily limit of 2 deployments. if you want to launch more, you can upgrade to Bankr Club to get up to 20 deployments per day. otherwise, you'll have to wait for your limit to reset. check out the terminal at bankr.bot/terminal to manage your club status.

Exqutor: Extended Query Optimizer for Vector-augmented Analytical Queries Introduces a framework for vector-augmented analytical queries, achieving speedups of up to four orders of magnitude on pgvector and VBASE. 📝 arxiv.org/abs/2512.09695 👨🏽‍💻 github.com/BDAI-Research/Exq…

finishing up vbase rn and it's hitting different 🚀 dev portfolios that link your projects with the actual code, AI prompts, and system rules. show your work AND your process @publicbuild 1 for builders #buildinpublic #webdev built with @cursor_ai vbase.co

amigo deja de joder, partiendo sobre la vbase de que en realidad la clase media no existe, solo aca la podemos ver, y no la clase media no puedo garparle todo en la capital a un hijo, otra cosa seria media-alta

deployed vBASE contract address is 0x50aF4F806bE516791A6E3d59951952430438BB07\n\nsee more: clanker.world/clanker/0x50aF…

Grab your VBASE Drinks ($VBASE) packs on @vibechain ♦️ vibechain.com/market/vbase-d…

Social media Design for Vbase Exchange, looking to trade your cedis for naira or vice-versa? They're your go-to plug, very reliable... Need to elevate your brand's visibility beyond aesthetics? I'm your go-to man... my dm's open @Drmcrft_studio , thanks for the mock up inspo

Thank you @Drmcrft_studio I couldn't find my way around it with photoshop so I recreated with @canva Social media design for Vbase Exchange...

8/30・31開催🎉 V BASE（THE OUTLETS HIROSHIMA）に広島広域都市圏の魅力が大集合✨ 🎁広島広域都市圏内の特産品が当たるクイズ 📸各市のキャラと記念撮影 🎉としポ50ptをプレゼント！ #広島広域都市圏 #VBASE #としポ

Steve, I think your KCOR method has merit. I think it would be worth using the "log ratio of LOESS trendlines" to capture the ratio of differences in baseline: (V/Vbase)/(U/Ubase) Here's a model with random variation between the groups in the first 50 weeks then a steady rise to a 10% increase in mortality over the next 50 weeks. LOESS smooths the variability so is more dynamic than a fixed linear baseline. R Code below. uvr <- rnorm(100, 200, 10) vxr <- rnorm(100, 250, 10) * c(rnorm(50,1,0.02), seq(1, 1.1, length.out = 50)) weeks <- 1:100 # --- plot base series --- uvr.mean<-mean(uvr[1:12]) vxr.mean<-mean(vxr[1:12]) plot(weeks, vxr/vxr.mean, pch = 21, col = "blue", xlab = "Week", ylab = "Mortality rate ratio") lines(weeks, vxr/vxr.mean, lwd = 1, col = "blue") points(weeks, uvr/uvr.mean, pch = 21, col = "brown") lines(weeks, uvr/uvr.mean, lwd = 1, col = "brown") scan() # 1) baselines and normalization to "ratio-to-baseline" base_uvr <- mean(uvr[1:12], na.rm = TRUE) base_vxr <- mean(vxr[1:12], na.rm = TRUE) uvr_n <- uvr / base_uvr vxr_n <- vxr / base_vxr # 2) fit LOESS on the normalized series span <- 0.25 # tweak as desired (0.2–0.4 is a nice range) lo_uvr <- loess(uvr_n ~ weeks, span = span, degree = 1, family = "gaussian") lo_vxr <- loess(vxr_n ~ weeks, span = span, degree = 1, family = "gaussian") pred_grid <- data.frame(weeks = weeks) uvr_lo <- predict(lo_uvr, newdata = pred_grid) vxr_lo <- predict(lo_vxr, newdata = pred_grid) # 3) ratio of LOESS curves (VXR ÷ UVR). >1 means VXR higher than UVR. ratio_lo <- vxr_lo / uvr_lo op <- par(mfrow = c(1, 2), mar = c(4, 4, 2, 1)) ## ---- Plot 1: normalized series LOESS for both cohorts ---- plot(weeks, uvr_n, type = "n", ylim = range(c(uvr_n, vxr_n), na.rm = TRUE), xlab = "Week", ylab = "Rate / Baseline (weeks 1–12)", main = "Normalized rates LOESS") # shade first 12 weeks rect(0.5, par("usr")[3], 12.5, par("usr")[4], col = adjustcolor("grey80", 0.25), border = NA) abline(h = 1, col = "grey50", lty = 3) # raw points/lines points(weeks, uvr_n, pch = 21, col = "brown") lines(weeks, uvr_n, col = "brown") points(weeks, vxr_n, pch = 21, col = "blue") lines(weeks, vxr_n, col = "blue") # LOESS trendlines lines(weeks, uvr_lo, lwd = 3, col = adjustcolor("brown", 0.9), lty = 1) lines(weeks, vxr_lo, lwd = 3, col = adjustcolor("blue", 0.9), lty = 1) legend("topleft", legend = c("UVR (norm.)", "VXR (norm.)", "UVR LOESS", "VXR LOESS"), col = c("brown", "blue", "brown", "blue"), lty = c(1, 1, 1, 1), lwd = c(1, 1, 3, 3), bty = "n", cex = 0.9) ## ---- Plot 2: centered log ratio barplot ---- log_ratio_lo <- log(ratio_lo) # natural log # symmetric axis limits around 0 ylim_range <- max(abs(log_ratio_lo), na.rm = TRUE) bar_cols <- ifelse(weeks <= 12, adjustcolor("steelblue", 0.8), adjustcolor("grey60", 0.9)) bp <- barplot(log_ratio_lo, border = NA, col = bar_cols, space = 0.2, xlab = "Week", ylab = "log(VXR LOESS / UVR LOESS)", main = "Log ratio of LOESS trendlines", ylim = c(-ylim_range, ylim_range)) abline(h = 0, col = "grey30", lty = 2) # 0 = equal legend("topright", legend = c("Weeks 1–12", "Weeks 13–100"), fill = c(adjustcolor("steelblue", 0.8), adjustcolor("grey60", 0.9)), border = NA, bty = "n", cex = 0.9) par(op) @canceledmouse @ManDownUnder76 @m_a_n_u______

VRChatアバター改変衣装として payhip.com/b/68g0Z >✞ FITTED ZINPIA BASE >✞ FITTED VBASE E-GIRL が booth.pm/ja/items/6706217 >✞ 森- SHINRA にフィット として売られているの。 これを森羅ユーザーの初心者が買ってしまう話題。 見分け方としての「アバターに着せているスクショの有無」が

VBase Final: .@StMarksBaseball fends off a late @GrotonZebras comeback to finish off the year with a 6-3 win! #GoSMLions🦁

VBase FINAL: .@StMarksBaseball falls to Lawrence in the semifinals by a final of 2-3, great game by both sides