Pgvector

🔧 Summary

The General Reasoner paper shows how we can train LLMs to reason across domains using diverse data and a generative verifier. In this post, I walk through our open-source implementation showing how we built a modular reasoning agent capable of generating multiple hypotheses, evaluating them with an LLM-based judge, and selecting the best answer.

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🧠 What We Built

We built a GeneralReasonerAgent that:

• Dynamically generates multiple hypotheses using different reasoning strategies (e.g., cot, debate, verify_then_answer, etc.)
• Evaluates each pair of hypotheses using either a local LLM judge or our custom MR.Q evaluator
• Classifies the winning hypothesis using rubric dimensions
• Logs structured results to a PostgreSQL-backed system

All of this was integrated with our existing co_ai framework, which includes:

This is the second post in a 100-part series, where we take breakthrough AI papers and turn them into working code building the next generation of AI, one idea at a time.

🔧 Summary

In my previous post, I introduced co_ai a modular implementation of the AI co-scientist concept, inspired by DeepMind’s recent paper Towards an AI Co-Scientist.

But now, we’re going deeper.

This isn’t just about running prompts through an agent system it’s about building something radically different:

This is the fiOr you guys want to scare all of them All right problem this is where I say norst post in a 100-part series, where we take breakthrough AI papers and turn them into working code building the next generation of AI, one idea at a time.

🧾 Summary

In this post, I’ll walk through how I implemented the ideas from
AI Co-Scientist: Towards an AI Co-Scientist into a working system called co_ai.

Summary

Could the greatest playwright of all time have secretly shaped one of the most influential religious texts in history? Some believe William Shakespeare left his mark on the King James Bible hidden in plain sight. With the power of AI, we’ll investigate whether there’s any truth to this conspiracy.

You can read about the conspiracy here:

• Did Shakespeare write the King James Bible?

• Shakespeare and the King James Bible – Some Tentative Conclusions

Summary

Vector databases are essential for modern AI applications like semantic search, recommendation systems, and natural language processing. They allow us to store and query high-dimensional vectors efficiently. With the pgvector extension PostgreSQL becomes a powerful vector database, enabling you to combine traditional relational data with vector-based operations.

In this post, we will walk through the full process:

Installing PostgreSQL and pgvector Setting up a vector-enabled database Generating embeddings using Ollama Running similarity queries with Python By the end, you’ll be able to store, query, and compare high-dimensional vectors in PostgreSQL, opening up new possibilities for AI-powered applications.