- Updated: April 2, 2026
- 5 min read
Modolap & Codex Uncover Hacker News Trends – Rust, PostgreSQL, and Comment Lengths
Modolap, powered by OpenAI Codex, shows that Rust has overtaken Go in Hacker News mentions, PostgreSQL now leads MySQL, and the average comment length on Hacker News is gradually shrinking.
How Modolap & AI Codex Uncovered the Rise of Rust, the Decline of Go, and Shifting Comment Lengths on Hacker News
The full Hacker News archive—over 10 GB of parquet‑formatted data—has long been a goldmine for developers, investors, and data‑scientists eager to trace technology trends. In a recent experiment, the open‑source tool Modolap teamed up with OpenAI Codex to run rapid, code‑first queries on the entire dataset. The results reveal surprising shifts: Rust now enjoys more community chatter than Go, PostgreSQL edges out MySQL, and the average comment length is trending downward.
Read the original technical write‑up here: Modolap – Hacker News Analysis 1
What Is Modolap?
Modolap is a lightweight, command‑line‑driven analytics layer that lets you query massive parquet collections with simple SQL‑like syntax. By installing a single skill via npx, analysts can ask natural‑language questions and let Codex translate them into performant queries—no heavy ETL pipelines required.
Key Features
| Feature | Benefit |
|---|---|
| Zero‑setup parquet access | Works directly on the 10 GB Hacker News dump |
| AI‑driven query generation | Codex writes, tests, and refines SQL on the fly |
| Instant feedback loop | Small back‑and‑forth prompts produce production‑ready scripts |
For a deeper dive into Modolap’s capabilities, see our guide: UBOS platform overview.
Core Queries That Revealed the Trends
1. Rust vs. Go – Who’s Winning the Conversation?
Prompt to Codex: “Write a query that counts daily mentions of the keywords Rust and Go across all Hacker News story titles and discussion threads.”
- Rust surpassed Go in total mentions in early 2024 and has maintained a lead ever since.
- Peak Rust buzz aligns with the release of Rust 1.70 and growing adoption in systems‑level projects.
2. PostgreSQL vs. MySQL – Database Preference Shifts
Prompt to Codex: “Generate a query that tracks the frequency of the words PostgreSQL and MySQL in story titles over time.”
- PostgreSQL overtook MySQL in 2023, reflecting the community’s move toward advanced features like logical replication and JSONB support.
- The crossover point coincides with the launch of PostgreSQL 15, which introduced performance‑boosting parallel queries.
3. Comment Length – Are Discussions Getting Shorter?
Prompt to Codex: “Calculate the median (P50) and average character length of comments per month.”
- Both median and average comment lengths show a steady decline of roughly 2 characters per month since 2020.
- The trend suggests a shift toward concise, “quick‑answer” style replies, possibly driven by AI‑generated content and the popularity of short‑form platforms.
Why These Findings Matter
The three trends have concrete implications for developers, product teams, and business leaders:
- Technology Road‑Mapping: Companies can gauge community sentiment for language and database adoption, informing hiring and product‑roadmap decisions.
- Community Health Indicator: Shortening comment lengths may signal a move toward faster, less‑in‑depth discourse, prompting platform moderators to encourage richer conversations.
- AI‑First Analytics: The experiment showcases how AI‑assisted query generation can democratize big‑data insights without a dedicated data‑engineering team.
If you’re curious about other AI‑powered data tools, explore our article on AI marketing agents, which highlights how generative AI can automate content creation and analysis.
How to Replicate the Study Yourself
Follow these steps to run your own Hacker News trend analysis with Modolap:
Step‑by‑Step Tutorial
- Grab the dataset: The full Hacker News parquet files are hosted on Hugging Face. Download here.
- Install Modolap: Run
npx modolap@latest add codex-skillin your terminal. - Prompt Codex: Use natural‑language prompts (see the examples above) to generate queries.
- Execute & Export: Modolap can write results to CSV or JSON for downstream visualization.
- Visualize: Feed the output into your favorite charting library or into the Web app editor on UBOS to build interactive dashboards.
Need a ready‑made template to jump‑start your analytics UI? Check out the UBOS templates for quick start, which include pre‑built charts for time‑series data.
Next Steps for Data‑Driven Teams
The Modolap + Codex workflow proves that powerful insights are just a few prompts away. Whether you’re a startup looking to validate tech choices or an enterprise seeking to monitor ecosystem shifts, the approach scales.
Ready to accelerate your AI analytics?
- Explore the Enterprise AI platform by UBOS for end‑to‑end data pipelines.
- Join the UBOS partner program to get dedicated support and co‑marketing.
- Check out our UBOS pricing plans to find a tier that fits your budget.
For startups, the UBOS for startups page outlines how you can leverage AI without massive upfront investment. SMBs can benefit from UBOS solutions for SMBs, which include pre‑configured analytics workspaces.
If you love building AI‑powered tools, you might also enjoy our marketplace templates such as the AI SEO Analyzer, the AI Article Copywriter, and the AI Chatbot template. These can be combined with Modolap‑generated insights to create end‑to‑end knowledge products.
Conclusion
By marrying Modolap’s parquet‑native engine with OpenAI Codex’s code‑generation prowess, a single developer extracted actionable, longitudinal insights from a 10 GB news archive in minutes. The analysis confirms that Rust now enjoys broader community chatter than Go, PostgreSQL is edging out MySQL, and Hacker News comments are getting shorter—trends that echo broader shifts in the software ecosystem.
Stay tuned to UBOS Tech for more AI‑driven data stories, and feel free to reach out if you’d like a custom analysis of your own data lake.
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.