About Astrava
Astrava is a developer intelligence platform. It exists to reduce information overload: hundreds of technical changes ship every week, and only a handful will matter to the work you do. Astrava tracks developments that are gaining momentum and ranks them by the evidence behind them.
It covers artificial intelligence, AI agents, foundation models, developer tools, AI infrastructure, open-source software, robotics, quantum computing, cybersecurity and computing infrastructure.
What it is not
Astrava does not predict the future. A Breakout Score is an analytical ranking derived from observable signals; it is not a forecast, a recommendation to adopt something, or a judgement of quality. Early signals are not guaranteed outcomes.
Principles
- Evidence before narrative
- A signal starts with something observable — a release, a paper with code, an integration, a benchmark. Commentary is context, not proof.
- Show the working
- Every score has a visible breakdown. Every statement is tagged as fact, analysis or estimate, and facts link to sources.
- Say what is missing
- When evidence is thin, the page says “insufficient evidence” rather than generating a plausible answer.
- Penalise hype
- Superlatives, self-reported-only benchmarks and discussion without substance reduce a score.
- Early ≠ inevitable
- Most early signals fade. The aim is to notice the few that matter while there is time to evaluate them calmly.
Kunal Shenoy
Kunal Shenoy designed and built Astrava. He works at the intersection of AI, data and product strategy, turning ambiguous problems into data-driven products.
Kunal leads technology initiatives at ICICI Prudential Life Insurance as Assistant Manager – Technology, building Business Intelligence models that support real-time decisions and automating workflows across retail and group businesses.
Before that he was Director of Product & Technology at Devlancers, owning end-to-end delivery of more than five client-facing AI and web platforms, and an AI/ML intern working with Google Vertex AI at Unstop, where he earned a letter of recommendation.
He has published IEEE research (OTCON 2024) comparing machine-learning approaches to predicting Indian stock-market movements, built RAG applications such as PagePal, competed in hackathons including Smart India Hackathon, and contributed to open source through Social Summer of Code. He is a Lean Six Sigma Green Belt and studied at NMIMS School of Technology, Management & Engineering.
The developments that end up mattering to developers are usually visible early — in repositories, papers and package registries — but buried under hype. Astrava ranks them by evidence and shows the working.
- Product strategy
- Applied AI & LLMs
- Business intelligence
- Workflow automation
About the data on this deployment
Signals are collected from official public APIs — GitHub, Hacker News, arXiv, Hugging Face and npm — and recomputed every hour. An optional AI layer drafts context, but a statement is kept as fact only when its quoted excerpt is found verbatim in the cited source. If every live source is unavailable, the site falls back to a clearly labelled fictional demo dataset. The bar at the top of every page says which you are looking at. See the methodology for exactly how it is processed and scored.