WedVault#23: Midjourney v7, Claude Artifacts, AI for Business Startups

Welcome to the WedVault

Today in WedVault 📂

• AI + Humans = The GTM Dream Team

• Midjourney v7 Image model is here

• Automation with Ryse: Smart Home

• AI CheatSheets

• AI Resource

• AI Week: Major Breakthroughs & New Tools

• AI Funding

• AI Jobs

• A Prompt to Investigate AI in Climate Risk Forecasting

AI + Humans = The GTM Dream Team 

Stop relying on AI for the wrong parts of your GTM process.

Harness the strengths of humans + AI with Bounti– your AI teammate doing all the research and prep work for you, our humans to make sure the content meets your needs, and you–the expert closer. In minutes, Bounti gives you a toolkit with everything you need to close target accounts, so you can:

✔️ know your prospects and what they care about
✔️ land your pitch by connecting to buyer business objectives
✔️ thoughtfully engage them with personalized outreach

Midjourney v7 Image model is here

Automation with Ryse: Smart Home

Could RYSE Be the Next Ring?

When Amazon bought Ring for $1.2B, Kevin O’Leary called it the biggest miss in Shark Tank history—a 66,756% return lost.

Now, a new smart home disruptor is gaining traction: meet RYSE. Their patented technology automates existing window shades—no replacements needed.

With $10M+ in revenue, 200% YoY growth, and distribution in 127 Best Buy stores (with Home Depot launching in 2025), RYSE is scaling fast in the $158B smart home market.

Unlike Ring, you can still invest early—shares are just $1.90 each.

Earn up to 25% in bonus shares.

_{Past performance is not indicative of future results. Email may contain forward-looking statements. See US Offering for details. Informational purposes only.}

AI Cheat Sheets

AI Resources

Generative AI for smart grid modeling

How AI is reshaping the legal profession

How to use Claude Artifacts to visualize data

How to humanize AI content: strategies + tools

How to ask Adobe's AI assistant to summarize files

How to use ChatGPT to analyze PDFs and more for free

Used AI to create a marketing plan 2 ways — here’s how

How the A.I. that drives ChatGPT will move into the physical world

How AI can help companies better disclose their financial information

AI Week: Major Breakthroughs & New Tools

A Prompt to Investigate AI in Climate Risk Forecasting

Assume the role of a climate intelligence architect tasked with designing a next-generation system for climate risk forecasting using artificial intelligence. Your objective is to deeply investigate how AI methodologies — including machine learning, deep learning, and hybrid modeling — can be integrated with environmental data sources to accurately predict, analyze, and mitigate climate-related risks across various regions and industries.

Start by identifying the critical variables involved in climate risk forecasting: temperature anomalies, sea-level rise, precipitation patterns, soil moisture, extreme weather event frequency, and greenhouse gas concentrations. Explore how satellite imagery, remote sensing data, IoT climate sensors, and historical weather records can be ingested, processed, and transformed into structured formats suitable for predictive modeling.

Design an architecture for training models capable of forecasting both short-term and long-term climate phenomena. Include deep learning networks (e.g., LSTMs, CNNs, Transformers) for pattern recognition across time series and geospatial datasets. Discuss ensemble methods and hybrid physical-AI models that merge scientific climate simulations (e.g., General Circulation Models) with machine learning to enhance accuracy and resolution.

Incorporate anomaly detection frameworks to identify early signals of climate disruption — such as drought onset, flood risk, wildfire likelihood, or heatwave intensity. Propose AI pipelines that can monitor regions in real time, quantify vulnerability, and rank areas by potential impact on human populations, agriculture, infrastructure, and biodiversity.

Explore the ethical and geopolitical dimensions of AI-driven climate forecasting, including bias in data availability, transparency in model outputs, and the risk of overfitting due to limited localized data. Recommend governance frameworks or interpretability tools (like SHAP, LIME, or counterfactuals) to ensure responsible deployment in policymaking and disaster preparedness.

The deliverable should be a strategic AI-driven forecasting system with:

A modular data pipeline integrating real-time and historical climate data.

Model architecture(s) for regional and global climate risk prediction.

Visualizations of risk zones with predictive heatmaps or dashboards.

A strategy for integrating the system into early warning systems or policy frameworks.

Recommendations for improving transparency, scalability, and model trustworthiness.

Design the system to be scalable across continents, adaptive to new data inputs, and accessible for governments, NGOs, and scientific institutions. Emphasize proactive intervention and sustainable decision-making guided by AI-driven insights.

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