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#eFootball v 5.2.0 : Match Analysis 🆕💥 *Gameplay Data - Average values from the 10 most recent PvP matches. *Data shown in graphs comparing your performance with others in D1-D4.

21,516 Aufrufe • vor 7 Monaten •via X (Twitter)

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I just built a Claude Cowork skill that turns your Google Ads data into a visual performance dashboard in 60 seconds 🤯 One prompt → campaign breakdowns, CPA trends, spend vs conversions charts, and hourly conversion patterns, all rendered as an interactive HTML dashboard you open in Chrome. All inside Claude Cowork. Perfect for DTC brands and agencies who are pulling Google Ads data into spreadsheets every week, manually building charts, and spending an hour formatting a report that's outdated by the time you send it. If you're managing Google Ads and your weekly reporting workflow looks like this — export a CSV, open Google Sheets, build a pivot table, copy the numbers into a slide deck, manually create charts, format everything, realize you forgot a campaign, start over ... This skill does the whole thing in one prompt: → Connects to your live Google Ads data via MCP → Pulls spend, conversions, CPA, ROAS, CTR across every campaign → Builds an interactive HTML dashboard → Summary cards at the top: total spend, total conversions, avg CPA, avg ROAS → Bar chart comparing spend vs conversions by campaign → CPA trend line over the last 30 days → Campaign table ranked by performance, color-coded green/yellow/red → Opens in Chrome: hover over charts, compare campaigns, screenshot for your team No spreadsheets. No manual chart building. No hour-long formatting sessions. What you get: → A visual dashboard from live data in under 60 seconds → Campaign performance you can actually see, not just read in a table → CPA trends that show you where things are heading, not just where they are → A dashboard you can screenshot and drop into Slack, a client report, or a team standup → Reusable — run it weekly and the data updates automatically One prompt. Live data. A finished dashboard you open in your browser. I put together a playbook with the full skill file, the setup, and the exact prompts to customize the dashboard for your account. Want it for free? > Like this post > Comment "DASH" And I'll send it over (must be following so I can DM)

Mike Futia

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🌐📈 Data Center Theme: The Next Big Structural Bull Run 🚀 Since 2021, the Data Center theme has been steadily rising, marking the start of a structural bull run in this sector. 🌟 In this thread, we’ll dive deep into evaluating the performance of data center-related stocks since 2021. 🔎 What to Expect - We’ll evaluate these companies based on 3-year and 5-year sales and profit growth metrics. 📊 - Comparative Analysis A clear look at sales and profit growth to help identify leading performers. 💼 Key Data Points - The average 3-year sales growth for these companies is an impressive 34%. 🔥 - The average 5-year sales growth stands at 20%. 📈 Let’s take a closer look at the 10 key players in this space: 1️⃣ Anant Raj Ltd 🏢 2️⃣ Techno Electric & Engineering Company Ltd ⚙️ 3️⃣ Netweb Technologies India Ltd 💻 4️⃣ Aurionpro Solutions Ltd 🔐 5️⃣ Black Box Ltd 📦 6️⃣ Gulf Oil Lubricants India Ltd 🛢 7️⃣ D-Link India Ltd 🌐 8️⃣ Allied Digital Services Ltd 🖥 9️⃣ Ceinsys Tech Ltd 🌍 🔟 ADC India Communications Ltd 📡 Here’s a more professional and respectful version of your disclaimer: --- ⚠️ Disclaimer This analysis is provided solely for educational purposes and should not be interpreted as a forecast or prediction of future sector performance. The growth and success of the companies discussed are subject to various factors, including general economic conditions, macroeconomic trends, government policies, and product demand This is not intended to be a buy or sell recommendation We advise consulting with a financial advisor before making any investment decisions.

Stocks Treasures 💎

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Biomni Lab lets biologists collaborate with AI agents to finish complex tasks end-to-end. Here are 15 popular use cases, each link is a full replay so you can watch the agent work through every step: 1. Spatial transcriptomics analysis: map gene expression across tissue architecture from spatial transcriptomics data, with spatial clustering and neighborhood analysis. 2. Binder design: design de novo protein binders against a target structure using computational protein design tools. 3. Biomarker panel design: identify and optimize a multi-marker diagnostic or prognostic panel from omics data. 4. Clinical trial landscaping: search and summarize the trial landscape for a disease area, mapping phase, endpoints, and sponsor activity. 5. Survival analysis: pull clinical and expression data, fit Cox models, generate Kaplan-Meier curves, and identify prognostic markers. 6. scRNA-seq processing and annotation: from raw counts to UMAP clustering, marker gene detection, and automated cell type labeling. 7. Cell-cell communication: infer ligand-receptor interactions between cell types from single-cell data and map intercellular signaling networks. 8. Primer design for novel Cas13: analyze a putative Cas13 protein from a metagenomic screen—verify the ORF, identify HEPN domains, and design cloning primers with restriction sites and a FLAG 9. Proteomics differential expression: normalize mass spec data, run statistical tests, and visualize differentially abundant proteins. 10. Gene regulatory network inference: reconstruct transcription factor-target gene networks from expression data and identify key regulators. 11. Gene co-expression network analysis: build weighted co-expression networks, identify gene modules, and correlate them with phenotypic traits. 12. Microbiome analysis: process 16S/metagenomic sequencing data to profile microbial communities, diversity, and differential abundance. 13. Polygenic risk scores: compute and evaluate PRS from GWAS summary statistics against a target cohort. 14. Variant annotation: annotate genetic variants with functional predictions, allele frequencies, and clinical significance. 15. Fine-mapping: narrow GWAS loci to credible causal variants using statistical fine-mapping methods. Each of these would normally take days to weeks of scripting, debugging, and iteration. In Biomni Lab, the agent handles the full execution while you steer the science. Learn more:

Kexin Huang

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