Tracking what developers ship, investors fund, and enterprises adopt
From GitHub commits to SEC filings, scored by AI to separate real trends from noise.
Machine Learning
Machine learning is a method of data analysis that automates analytical model building.
5/6
Sources with signal
Strong
Signal strength
1.3k
Evidence points
Hot
Signal strength
B2B
Market tracked
Signal breakdown by source
We track real-time data from developer platforms, startup ecosystems, academic research, and enterprise filings to identify trends backed by hard evidence.
Hacker News
Community awareness · Last 14 days
Upvotes from the tech community. More points = more attention.
Stories and discussion on Hacker News indicate tech community awareness. High HN activity often precedes mainstream adoption by 1-3 months.
-Quantum Machine Learning: A Pragmatic Guide for Classical ML Engineers
-Show HN: AutoML Agents
-Linear Algebra for Computer Vision, Robotics, and Machine Learning [pdf]
GitHub
Developer creation velocity · Last 30 days
Stars are bookmarks from developers. More stars = higher quality projects.
New repositories signal active developer investment. GitHub creation velocity is a 3-12 month leading indicator of market adoption.
-anknpolley123/BTCDump: BTC Dump is a professional Bitcoin price prediction tool …
-turkiyeyapayzekaakademisi/Machine-Learning:
-soumili-here/Crime-Analysis-Against-Children-in-India: This project deals with d…
Y Combinator
Startup funding signal · Last 4 batches
YC runs batches twice a year. More batches = sustained investor interest over time.
YC-backed companies in this space represent the strongest conviction signal: venture capital betting on market potential.
-OnDeck AI: Analyze any footage, without training a model
-Chamber: The AIOps Agent for ML Teams
-Anto Biosciences: A Foundation Model for Microbial Communities.
Stack Overflow
Developer adoption · Last 90 days
Stack Overflow questions indicate developers actively using the technology. A 3-6 month leading indicator of production adoption.
-best pure .net machine learning libraries for IoT
-What is the best online training for Machine Learning course?
-I have been trying to learn how to do machine learning for a long time , but don…
Academic Research
Research publication signal · Last 24 months
Citations count how often other researchers reference this work. More citations = higher influence.
Academic papers represent the earliest indicator. Researchers publishing findings that drive the next wave of applied innovation.
-Using Machine Learning to Identify Diseases and Perform Sorting in Apple Fruit
-Evaluation metrics and statistical tests for machine learning
-PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformatio…
SEC Corporate Filings
Enterprise adoption signal · Latest filings
Mentions in SEC 10-K/10-Q filings mean public companies are disclosing this technology as material to their business. This is the strongest enterprise validation signal.
All data is sourced from public APIs: Hacker News (Algolia), GitHub (Search API), Stack Overflow (Stack Exchange API), Y Combinator (public batch data), OpenAlex (academic database), and SEC EDGAR (regulatory filings). Numbers reflect verified matches. Each item is checked by AI to confirm it's genuinely about the topic, not just a keyword overlap.
What the data tells us
5 independent sources show real activity around Machine Learning. Developers are building tools (GitHub), the tech community is talking about it (Hacker News), investors are backing startups in the space (Y Combinator), researchers are publishing papers, and enterprises are disclosing it in public filings. When this many unrelated sources agree, it's not speculation. It's market movement.
Why this data matters
We don't guess. We count what's actually happening.
Most trend reports are opinions. Inqvey Trends is different . We track what developers are building, what investors are funding, what researchers are publishing, and what enterprises are reporting to regulators. When multiple independent sources point to the same trend, it's not hype. It's a real market shift.
Hacker News
Community awareness (1-3 month lead)
GitHub
Developer creation velocity (3-12 month lead)
Y Combinator
Startup funding conviction signal
Stack Overflow
Technology adoption (3-6 month lead)
OpenAlex
Academic research (earliest indicator)
SEC EDGAR
Enterprise disclosure (strongest validation)
Each source is scored on a 0-100 scale based on activity volume. When a topic shows up across multiple unrelated sources (developers on GitHub, startups at Y Combinator, filings at the SEC) that agreement between independent signals is what separates real trends from noise. We use AI to verify that each result is genuinely about the topic, not just a keyword match.
Data freshness
Sources are scanned daily. Hacker News and GitHub reflect the last 14–30 days. Y Combinator covers the last 4 batches. Academic papers span the last 24 months. SEC filings cover the most recent quarterly and annual reports.
Go from trend to market intelligence
Knowing what's trending is step one. Knowing what the market actually wants is what drives product decisions.
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