Chapter 2 -- The 50-Day Plan For Building A Personal Assistant Agentic System (PAAS)

PHASE 2: API INTEGRATIONS (Days 11-25)

In this phase, you'll build the data collection foundation of your PAAS by implementing integrations with all your target information sources. Each integration will follow a similar pattern: first understanding the API and data structure, then implementing core functionality, and finally optimizing and extending the integration. You'll apply the foundational patterns established in Phase 1 while adapting to the unique characteristics of each source. By the end of this phase, your system will be able to collect data from all major research, code, patent, and financial news sources.

Day 20-22: Startup And Financial News Integration

These three days will focus on researching the ecoystem of startup news APIs and also integrating with financial news. You will want o focus upon startup funding, startup acquisitions, startup hiring data sources to track business developments in the AI sector. You'll learn how to monitor investment activity, company formations, and acquisitions that indicate where capital is flowing in the technology ecosystem. You'll develop systems to track funding rounds, acquisitions, and strategic partnerships that reveal the commercial potential of different AI approaches. You'll create analytics to identify emerging startups before they become well-known and to understand how established companies are positioning themselves in the AI landscape. Throughout, you'll connect these business signals with the technical developments tracked through your other integrations.

  • Morning (3h): Study financial news APIs

    • News aggregation services: Explore financial news APIs like Alpha Vantage, Bloomberg, or specialized tech news aggregators, understanding their content coverage, data structures, and query capabilities. Develop strategies for filtering the vast amount of financial news to focus on AI-relevant developments while avoiding generic business news.
    • Company data providers: Research company information providers like Crunchbase, PitchBook, or CB Insights that offer structured data about startups, investments, and corporate activities. Create approaches for tracking companies across different lifecycles from early-stage startups to public corporations, focusing on those developing or applying AI technologies.
    • Startup funding databases: Study specialized databases that track venture capital investments, angel funding, and grant programs supporting AI research and commercialization. Develop methods for early identification of promising startups based on founder backgrounds, investor quality, and technology descriptions before they achieve significant media coverage.

  • Afternoon (3h): Implement financial news tracking

    • Monitor startup funding announcements: Build systems that track fundraising announcements across different funding stages, from seed to late-stage rounds, identifying companies working in AI and adjacent technologies. Implement filtering mechanisms that focus on relevant investments while categorizing startups by technology domain, application area, and potential impact on the field.
    • Track company news and acquisitions: Develop components that monitor merger and acquisition activity, strategic partnerships, and major product announcements in the AI sector. Create entity resolution systems that can track companies across name changes, subsidiaries, and alternative spellings to maintain consistent profiles over time.
    • Analyze investment trends with Rust processing: Create analytics tools that identify patterns in funding data, such as growing or declining interest in specific AI approaches, geographical shifts in investment, and changing investor preferences. Implement Rust-based data processing for efficient analysis of large financial datasets, using Rust's strong typing to prevent errors in financial calculations.