🔮 The future of data analysis is powerful, intuitive, and beautiful. Stay tuned as Aiden grows into a game-changer for your data challenges! #AI #DataAnalytics #Visualization. Which tools are you excited about for large datasets? Let’s chat! 🗨️.

9/ While Aiden’s foundation is in development, we’re deeply exploring tools like Vega-Lite, Perspective, Mosaic, and Observable Plot. Our goal? To craft an AI data analyst that’s interactive, scalable, and user-first. 🧠✨.

8/ Python Code Sandbox: Aiden’s future in Code interpreters?. With Mosaic-like tools, Aiden could enable real-time exploration of data within Python environments:.📊 Write visualization specs. 🔍 Query data directly. 🎯 Render interactive visuals.

7/ Exploring hybrid execution models:. Aiden could use a local + cloud compute strategy:.🌐 Cloud = Heavy lifting. 💻 Browser = Snappy interactivity. Paired with cloud backends, might give Aiden the ability to process queries efficiently while staying user-friendly.

6/ Perspective: Designed for big data and real-time interactivity. ✨ Highlights:.• C++ engine + WebAssembly = Cross-platform adaptability. • Handles streaming datasets, nested data, & real-time updates. • Built-in and custom UI plugins.

5/ Mosaic: An architecture built for scalable, interactive visualizations. 💡 Key ideas we love:.• Uses DuckDB + WebAssembly for in-browser data processing. • Declarative specs (JSON/YAML) make it adaptable. • Links user inputs directly to visual outputs.

4/ Observable Plot: Perfect for fast, efficient visualizations. It skips unnecessary layers, going straight from data to graphics. For Aiden, it means rapid prototyping and effortless data-to-visual connections. ⚡.

3/ Libraries we’re exploring:. Vega/Vega-Lite: JSON-based declarative visualization languages:.• Vega = Rich interactivity. • Vega-Lite = Lightweight & quick prototyping. These offer the flexibility Aiden needs to scale across frameworks and languages. 🌐.

2/ Key challenges.✅ Infinite scrolling for big datasets (no clunky pagination). ✅ Smarter grids for complex data. ✅ Real-time streaming with reliable interactivity. We’re seeking tools to make these effortless.

1/ The challenge: Building a next-gen data analyst like Aiden means balancing raw power and seamless user experience. ➡️ BI tools = Simple but rigid. ➡️ Code libraries = Flexible but complex. We’re exploring ways to bridge this gap for better, smarter visualizations! 🌉.

At Aiden, we’re exploring cutting-edge visualization libraries to power Aiden’s ability to handle massive datasets interactively, scalably, and beautifully. Here’s a sneak peek into our process 🧵👇.

At AIDAX, we’re rethinking AI-powered data analysis for the enterprise. If you’re dealing with messy schemas, SQL pain, or complex queries, we’d love to talk. Drop us a DM or follow for more insights on the future of AI & enterprise data. 👇. #AI #SQL #DataAnalysis.

Data is your competitive edge, but most of it is locked behind massive complexity and siloed tools. An AI that understands your data like a human analyst — but faster, smarter, and at scale — changes everything. No more bottlenecks. No more guesswork. Just insights. 🚀.

We're developing Aiden, an AI system for enterprise data that can map and interpret massive schemas, handle multiple SQL dialects, pull meaning from various sources, and generate complex SQL. We're not solving toy problems—we're solving for your data.

7/ Enterprise teams spend hours writing SQL or wrangling fragmented data. What if an AI could:.✅ Understand massive schemas automatically.✅ Generate SQL for complex queries, across dialects.✅ Ground itself in existing code, docs, and even tribal knowledge.

6/ Unstructured data (logs, docs, code). Text-to-SQL in the real world demands reasoning, grounding, and deeper understanding.

5/ Real-World Data ≠ Toy Examples.Why can’t most models keep up? 🤔. Most solutions train on clean, curated datasets that don’t reflect real enterprise mess:.• Terabyte-scale data lakes.• Multi-turn workflows.

4/ Complex Queries:.Think multi-joins, nested subqueries, and 100+ lines of SQL with advanced transformations.

3/ Incomplete Documentation:.The data lives in tables. The meaning? Often scattered across outdated docs and tribal knowledge.

2/ Multiple SQL Dialects:.BigQuery ≠ Snowflake ≠ Oracle. Real systems mix dialects, adding complexity.