Overview

We track real AI activity across companies - not just press releases and marketing hype. Our scoring system evaluates three core factors (Quality, Sentiment, Urgency) plus bonus components to separate companies that are actually shipping from those just talking about it.

Rankings update every 5 minutes as new events are detected. Events decay exponentially over time (10% per week), giving more weight to recent activity. We use AI-powered quality filtering and pattern analysis to ensure only meaningful activity affects scores.

Scoring Dimensions

Our scoring system combines three key factors, each normalized to 0-100 scale:

1. Quality Score

Weight: 40%

Evaluates content quality and technical substance through comprehensive AI analysis:

Technical depth - working demos, code releases, API documentation
Benchmark performance on standardized tests (MMLU, HumanEval, etc.)
Research quality - arXiv papers, peer review, reproducibility
Production readiness - API reliability, uptime metrics
Developer experience - documentation quality, example code

Quality assessment focuses on substance over hype. Events with working products, benchmarks, and code score higher than vague announcements.

2. Sentiment Score

Weight: 30%

Analyzes community perception from technical communities (0-1 scale, converted to 0-100% for display):

Reddit discussions in r/MachineLearning, r/artificial, r/LocalLLaMA, r/singularity, r/OpenAI
Company blogs and tech news RSS feeds
arXiv research paper citations and discussion
Event-level sentiment analyzed per-event, then time-weighted and aggregated

Sentiment ranges for display: 80-100% is strong positive (≥0.6 in database), 60-80% is generally positive (0.2 to 0.6), 40-60% is mixed (-0.2 to 0.2), below 40% is negative (<-0.2).

3. Urgency Score

Weight: 30%

Evaluates event importance and priority (0-10 scale, normalized):

High urgency - product launches, major releases, funding rounds
Medium urgency - research papers, partnerships, feature updates
Lower urgency - blog posts, hiring announcements, general updates
Time sensitivity - breaking news weighted higher than scheduled releases

Urgency reflects how important an event is in the AI landscape. Major breakthroughs and product launches score higher than routine updates.

Bonus Components

Additional points added to the base score (not percentages):

Frequency Bonus: min(10, event_count × 0.1) - rewards consistent activity, capped at +10 points
Recency Bonus: up to +5 points for very recent events (within last week)
Time Decay: exp(-0.1 × age_in_weeks) - events lose 10% weight per week, emphasizing recent activity

These bonuses ensure that actively shipping companies with recent momentum score higher than those with sporadic or old activity.

How Scoring Works

Each event is scored using three core factors (Quality, Sentiment, Urgency), multiplied by time decay, then aggregated across all events. Bonuses are added to the final score:

Score = Σ[(Quality × 40% + Sentiment × 30% + Urgency × 30%) × Time Decay] + Frequency Bonus + Recency Bonus

We apply AI-powered quality filters, exponential time decay (10% per week), and pattern analysis to ensure scores reflect genuine AI progress rather than marketing noise.

Update Frequency: Rankings update every 5 minutes. Event detection varies by source (RSS ~15min, Reddit ~30min). Sentiment is calculated per-event (instant). Hype Gap updates every 5 minutes (requires minimum 3 events for accuracy).

What We Track

✅ Real Activity

→ Product launches with actual demos
→ Research papers on arXiv/journals
→ GitHub activity and code releases
→ Substantive partnerships
→ Validated funding rounds
→ Hiring/layoffs (market signals)

❌ Marketing Noise

→ Vague press releases
→ "Exploring AI" announcements
→ Repackaged old news
→ Executive quotes about strategy
→ Generic AI blog posts
→ Marketing without product

Our Principles

✓All companies evaluated with identical criteria - no special treatment
✓Rankings cannot be bought or gamed - we track what's actually happening
✓Updates every 5 minutes based on fresh data from multiple sources
✓High-level methodology shared publicly, implementation details proprietary

Scoring Methodology