Let's prioritize safety, ethics, and global cooperation to ensure AI benefits us all. Please let me know what your thoughts are. In contrast, Emmanuel Macron's interview with Micode shows a much more viable long-term approach to it, even if that will be hard to compete.

It's worth noting that Vance's stance aligns with the broader Trump administration's focus on profit and deregulation. While economic growth is essential, we must ensure it doesn't come at the cost of safety and ethical standards. Let's strive for a future where AI benefits everyone.

Geopolitical Cooperation: Vance hinted at an AI race, but this shouldn't be at the expense of global cooperation. We need shared ethical standards to guide AI's development. Let's work together!

Economic Balance: Vance fears regulation could stifle the AI industry. But unchecked AI poses risks like job displacement. We need a balanced approach to foster growth while managing risks.

Global Trust: AI needs public confidence to thrive. Vance's focus on lighter regulation misses the mark on ensuring safety and preventing misuse. We need international standards to build trust.

Learning from Europe: Vance criticized European regulations as "risk-averse." But Europe's approach balances innovation and protection. The EU's AI Act aims to build trust through safety and transparency. That's a win-win!

Safety First: Vance warned that "excessive regulation" could hinder AI's growth. But let's not forget that safety is paramount. We need guidelines to protect us from potential harm. It's like driving a car - rules keep us safe!

🎯 Native Gemini API integration is neat, but the real test will be real-world performance at scale. Previous managed RAG solutions have struggled with latency and cost. Let's see if Google cracks this nut. #CloudComputing

⚡️ Deep customization for parsing (chunk sizes), retrieval, and generation. But here's the thing: more knobs to turn = more complexity. Will be interesting to see if this actually simplifies RAG or adds another layer of decisions. #LLMs

🤔 Key differentiator: Flexibility. Mix & match your favorite models (Gemini, Claude, Llama), vector DBs (Pinecone, Weaviate), and data sources. No vendor lock-in is surprisingly un-Google, but smart move. #VertexAI

And yes, my deployment solution of choice, Upsun, has it out of the box.

Bonus: And let's not forget its excellent JSON support with JSONB - giving you NoSQL flexibility when you need it, while keeping all the benefits of a relational database. 🎁

Finally: The extensibility through custom functions, operators, and data types is amazing. 🔌 Want geospatial capabilities? PostGIS. Need full-text search? Built right in. PostgreSQL isn't just a database, it's a platform you can build upon. 🏗️

Window functions and Common Table Expressions (CTEs) in PostgreSQL are incredibly powerful. 💪 Complex analytical queries that used to require multiple temporary tables or application-level processing now run in a single, elegant query. ✨

Game-changing reason: PostgreSQL's native vector support through pgvector! 🧠 Being able to store and query embeddings directly in the database is incredible for AI applications. Similarity searches, nearest neighbor queries, and cosine distance calculations right in SQL 🎯 #ai #vectors #embeddings

First up: PostgreSQL's ACID compliance and transaction handling is rock solid. 🔒 The way it handles concurrent operations and maintains data integrity, especially in high-traffic situations, gave me peace of mind I never knew I needed. No more late-night data consistency nightmares! 😴

Wait is that 1.46 or 1.54?