© 2026 AI-Buzz. Early access — data updated daily.
© 2026 AI-Buzz. Early access — data updated daily.
How we track AI companies and measure developer traction
AI-Buzz tracks developer community signals that traditional company databases miss. This page explains our data collection methods, update frequency, and curation process so you can verify the data cited in our articles.
All automated metrics sync daily. Manual curation ensures quality over quantity—we focus on companies that matter to developers, not comprehensive coverage.
We track npm and PyPI package downloads as our primary adoption signal — these show which AI tools developers are actually installing and using in production.
Downloads represent real developer adoption — someone ran npm install or pip install and integrated a package into their project. Unlike stars or mentions, downloads reflect actual usage in production code.
We use the Algolia HN Search API (free, no authentication required) to track how often companies are mentioned on Hacker News.
Hacker News is a leading indicator of developer interest. What's discussed there often becomes mainstream 6-12 months later. High HN mention counts signal that developers are actively engaging with a company's products or announcements.
For open-source AI companies, we track GitHub activity using the GitHub REST API (authenticated via GitHub token when available for higher rate limits).
github_repo field set (format: owner/repo)X-RateLimit-Remaining header and stops if <10 requests remainingGitHub stars and forks show developer adoption. Last commit date indicates whether a project is actively maintained. For open-source AI companies, these metrics complement funding data by showing real developer engagement.
We scan RSS feeds from trusted sources and use AI to extract structured funding data:
All detected funding rounds go through manual review before being added to the database:
funding_queue.jsonAutomated funding detection has false positives and misattributions. Manual review ensures accuracy—we'd rather miss a round than publish incorrect data. This quality bar is what differentiates AI-Buzz from automated aggregators.
Runs daily at 6 AM UTC via GitHub Actions workflow:
These require human intervention and update as needed:
Each company record includes a metrics_updated_at timestamp showing when automated metrics were last synced. This helps you understand data freshness when reading articles that cite AI-Buzz data.
We analyze Hacker News comment sentiment for each company using Claude Haiku (claude-haiku-4-5) to classify community reactions. Comments are fetched from the Algolia HN Search API and classified into three categories:
Classification is limited to 200 comments maximum per company, processed in batches of 50. Each batch is truncated to 300 characters per comment to stay within API token limits.
Comments are classified only when a company has HN mentions. If fewer than 10 analyzable comments exist after filtering, sentiment data may be incomplete. We prioritize broader search queries for companies with partial coverage (<10 samples) to increase sample size before marking data as final.
Confidence Tiers:
Each company receives an hn_sentiment_positive_pct score (0-100) representing the percentage of classified comments that were positive. This metric is paired with hn_sentiment_sample_size to indicate confidence—a 75% positive score from 50 comments is more reliable than 75% from 3 comments.
For repositories with commit history, we fetch weekly commit counts from the GitHub API and compute a velocity ratio comparing recent activity to historical trends.
GET /repos/{owner}/{repo}/stats/commit_activityThe velocity trend is a ratio: latest week commits / prior 4-week average.
Commit velocity reflects engineering activity only—not code quality, feature importance, or test coverage. A spike in commits may represent bug fixes, refactoring, or documentation changes, not necessarily new features. Use this metric in combination with other signals (GitHub stars, HN sentiment) for comprehensive project health assessment.
Google Trends Score measures relative search interest for each company over the last 30 days using the Google Trends API. The score reflects public awareness and mainstream attention beyond developer communities.
Google Trends data captures mainstream public interest that extends beyond developer communities tracked via Hacker News and GitHub. A rising Trends score indicates growing consumer awareness, media coverage, or market adoption. This complements developer-focused signals to show broader market reach.
Scores are relative, not absolute—a score of 80 for one company cannot be directly compared to 80 for another, as each is scaled to that term's own peak. Search volume varies significantly by search term specificity (e.g., "OpenAI" vs. "Anthropic"). Additionally, brand name searches may reflect negative attention or controversy rather than positive interest. Use this metric alongside HN sentiment and other signals for comprehensive company assessment.
AI-Buzz focuses on curated quality, not comprehensive coverage:
By design, we exclude:
We verify all funding rounds manually, but automated metrics (HN mentions, GitHub stats) are pulled from public APIs and may have occasional discrepancies. If you find an error, please report it via email.
See which AI companies are gaining developer traction right now.