Real-time data allows AI systems to respond instantly to dynamic changes, improving accuracy in areas like fraud detection, predictive maintenance, and personalised recommendations.
2. Breaking Data Silos
Data activation integrates fragmented data across platforms enabling AI to access rich, unified inputs for better insights and cross functional decision making.
3. Turning Insights into Action
Reverse ETL moves AI-generated insights from data warehouses back into operational tools (like CRMs) ensuring predictions drive real-world actions.
What other ways does data activation empowers AI Models?
Prompt stuffing: Quick, dirty, and like cramming for an exam, works until it doesn’t.
Fine tuning: Great if you have static data and deep pockets for compute.
RAG inclusive Vector DB: The gold standard. Think of it as having your data whisper the answers to the LLM.
With AI Squared, you can keep your data fresh, dynamic, and external because nobody wants to retrain a model every time the boss changes their mind. :D
Egoless engineering is such a great mindset—focusing on team goals over individual credit really makes collaboration smoother and ideas stronger. It’s amazing how much better things get when everyone’s just working toward the best solution, not personal recognition. Definitely something more teams should embrace!
DuckDB really seems to be having its moment—projects like Evidence and DuckDB GSheets are super cool examples of its potential. And yeah, Postgres’s longevity is insane, it just keeps adapting.
On the AI front, vector databases like Pinecone and pgvector are exciting, but I’d love to see something even more integrated with AI workflows. The possibilities are huge. Curious to hear what others think!
Low code tools can indeed be effective for simple use cases or prototyping, but their limitations often surface when scaling or customizing is needed. As others have pointed out, a framework like Rails offers the flexibility to expand and adapt while maintaining structure.
Building the Multiwoven product based on Rails has been incredibly helpful in balancing rapid development with the ability to scale and customize as user demands evolve. It provides a structured yet flexible foundation, allowing us to adapt quickly without compromising on quality.
It’s about knowing when to leverage low code for speed and when to transition to more robust solutions for long-term scalability.
Honestly, I think it’s somewhere in between. LLMs are great at spotting patterns in data and using that to make predictions, so you could say they build a sort of "world model" for the data they see. But it’s not the same as truly understanding or reasoning about the world, it’s more like theyre really good at connecting the dots we give them.
They dont do science or causality theyre just working with the shadows on the wall, not the actual objects casting them. So yeah, they’re impressive, but let’s not overhype what they’re doing. It’s pattern matching at scale, not magic. Correct me if I am wrong.
Documentation: Enhancing user and contributor guides.
Code: Developing new connectors and improving existing ones.
Design: Refining the UI/UX of our platform.
Level:
Beginner-Friendly: Documentation improvements and minor code enhancements.
Advanced: Building new connectors and optimizing data pipelines.
Great discussion here! At AI Squared, we have also been exploring the evolving landscape of stream processing and SQL engines. While batch engines like DataFusion excel at handling static data, we recognize the challenges around integrating streaming capabilities and infrastructure seamlessly.
Our focus has been on simplifying data activation pipelines with tools like Multiwoven, which aims to bridge the gap between static and dynamic data needs by supporting connectors for both traditional databases and real-time platforms like Kafka. However, the need for more embedded, developer-friendly streaming solutions is clear, and it’s exciting to see the progress in projects like Arroyo, Materialize, and ClickHouse.
For us, the balance lies in usability and flexibility—how can we empower teams to embed robust data capabilities (whether streaming or batch) into their workflows without overloading on infrastructure complexity? As this ecosystem evolves, we’re optimistic about collaborating and contributing to solutions that make streaming SQL as accessible as traditional SQL.
Real-time data allows AI systems to respond instantly to dynamic changes, improving accuracy in areas like fraud detection, predictive maintenance, and personalised recommendations.
2. Breaking Data Silos
Data activation integrates fragmented data across platforms enabling AI to access rich, unified inputs for better insights and cross functional decision making.
3. Turning Insights into Action
Reverse ETL moves AI-generated insights from data warehouses back into operational tools (like CRMs) ensuring predictions drive real-world actions.
What other ways does data activation empowers AI Models?