How to Build an AI Tool That Downloads Knowledge from Twitter in Under Two Hours

A former startup CTO has created an impressive open-source tool called XGPT that allows users to download and query knowledge from Twitter (X) users. This innovative solution overcomes Twitter’s API restrictions to create personalized knowledge bases from the platform’s brightest minds.

Why Twitter is a Goldmine for Learning

Twitter (X) hosts countless brilliant minds sharing valuable insights daily. However, its restrictive API makes it difficult to systematically extract and learn from this knowledge. Even the platform’s search function, including “deep search,” falls short when trying to mine specific information from particular users.

How XGPT Works

XGPT is a TypeScript tool built with BUN that offers several key features:

Smart tweet scraping from any public Twitter/X user
Advanced filtering options for content selection
Conversion of tweets into 1536-dimensional vector embeddings for semantic search
Natural language question-answering about the collected content

Using the Tool

The CLI tool offers an interactive mode with several configuration options:

Enter a Twitter handle to scrape
Select content type (tweets, replies, or both)
Choose scope (all posts or keyword-filtered posts)
Set time range (e.g., last month)
Specify number of tweets to scrape
Select rate-limiting profile (aggressive, moderate, or cautious)
Generate embeddings after scraping

The tool stores collected data in a SQLite database using Drizzle, allowing for efficient querying and timestamp tracking. This enables setting up cron jobs for automatic updates to maintain an up-to-date knowledge base.

Building with Augment

The developer built XGPT using Augment’s remote agents for specific tasks:

Research spikes (time-blocked research sprints)
Design iterations
Documentation
Code optimization

The approach involved explaining desired features as ordered lists of operations to match customer outcomes. Remote agents helped identify improvements like vectorization and timestamp tracking that weren’t in the initial design.

Effective Prompting Techniques

The developer noted that smaller, more focused prompts work better with today’s advanced AI models:

Maximum of four sentences in system prompts
Just-in-time context for precise problem-solving
Using Anthropic’s prompt templates as starting points

This “context engineering” approach allows for more targeted, efficient AI assistance.

Performance Optimization

After building the initial tool, the developer used Augment to analyze the codebase and suggest optimizations:

More efficient database queries using CTEs
Implementing optimized similarity search with caching
Using TypedArrays for 60% faster performance
Addressing memory management issues

Comparison with Other Tools

When compared to alternatives like Cloud Code, Augment was highlighted for its strengths:

More deliberate approach with context
Better understanding of the codebase
More affordable pricing ($50 for 600 threads vs. $200/month)

The XGPT tool is available as open-source software through the VAI organization’s GitHub repository, alongside other tools like VAI platform and AIS DLC.