The AI space moves fast.
We built the radar.
SignalAI exists for one reason: to give developers, PMs, and founders a continuous, structured view of what is actually happening in AI - without the noise, the doomscrolling, or the FOMO.
The Problem
The AI frontier is a firehose.
Every day, dozens of research papers drop on arXiv. GitHub sees hundreds of new AI repositories. Company blogs, X threads, Hacker News posts, and Reddit discussions pile up simultaneously. No one person can read all of it.
The result? You either dedicate hours to staying current and still miss things, or you rely on second-hand takes that are already 48 hours stale. Both options cost you clarity when it matters most.
Information overload
22+ relevant sources publishing daily. No human reads all of it without burning out.
Stale intelligence
By the time a trend surfaces on social media, the builders already acted on it 72 hours ago.
No structure
Raw feeds give you links, not answers. What happened, why it matters, what to do - those are on you.
Context switching
Jumping between arXiv, HuggingFace, Reddit, and GitHub every morning is a half-day gone.
Our Approach
One continuous loop. Ingest, analyze, cluster, brief.
A fully automated pipeline runs every six hours. It pulls from 22 live sources, passes every item through an LLM for structured extraction, groups related signals using semantic embeddings, and surfaces the highest-velocity clusters first. You open SignalAI and the important things are already ranked and explained.
Under the Hood
Built on three technical layers.
No dashboards built from hand-curated RSS feeds. No hourly manual curation. SignalAI's pipeline is code from ingestion to briefing, using modern LLM tooling and vector search at every step.
LLM Structured Extraction
Every ingested item is passed through a structured LLM prompt that extracts six fields: a plain-language summary, why it matters technically, the target persona, the category, impact level (High / Medium / Low), and a relevance score 1-5. Reliable, typed output at scale.
Semantic Embeddings
After extraction, each article is passed through an embedding model to produce a dense vector. These vectors live in a vector-indexed Postgres table. Cosine similarity lookup determines whether a new signal belongs to an existing trend cluster or starts a new one.
Velocity-Ranked Clusters
Related signals are grouped into clusters using a threshold-based similarity algorithm. Each cluster carries a velocity score comparing article count in the current 7-day window vs the prior 7 days. Fastest-accelerating clusters surface first in the Trends view.
Principles
What we stand for, specifically.
01
Signal over volume
More sources does not mean more insight. We rank by impact and velocity, not by recency alone. The most recent article is not always the most important one.
02
Structure beats summaries
A one-line summary is not enough. Every signal includes what happened, why it matters technically, who it affects, and what to do next. We extract that structure because prose alone does not transfer into action.
03
Automation, not curation
Human curation does not scale across 22 sources updated daily. The pipeline is fully automated so coverage is consistent, fast, and free from editorial drift. We build tooling, not editorial calendars.
04
Build in public
SignalAI is an early-stage product shipping new features regularly. We iterate based on what users actually use. No roadmap theater.
The Story
Built by someone who needed it.
Hi, I am Mayank. SignalAI started as a personal tool because I was spending two to three hours every morning trying to keep up with AI research, open-source releases, and company announcements - and most of what surfaced was noise anyway. The signal-to-noise ratio was broken.
The first version was a scraper and a spreadsheet. The second added an LLM extraction step. The third added embeddings and clustering. At some point it became a product worth sharing, so I shipped it.
This is still a one-person operation. I build it, use it daily, and iterate based on what actually works. That means your feedback goes directly to the person writing the code.
Have feedback or a feature request?
Reach out directly. Every message gets read by Mayank.
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