Your Discovery Loop Is Broken
How do you find new ideas worth experimenting with?
Most developers today discover new ideas through social media feeds—Twitter threads, Reddit posts, LinkedIn shares.
Influencers share their favorite new arXiv papers, but rarely with any context about what you are building.
As a result:
Instead of advancing your ideas, you're sifting through noise for signal.
You're missing highly relevant work just because it doesn't trend.
Recently, I've been exploring HuggingFace's daily papers API, which is much more focused than the general social media feed. Their tools help you:
Chat with the authors
Ask bots to recommend similar papers
Using the APIs, you'll find:
Metadata: Titles, Authors, Abstracts, Links
Social signals: Upvotes, Discussions
Artifacts: Models, Datasets, Spaces
It's a goldmine - if the paper is relevant to you.
So, what's missing here is the context about what you're building to make relevant recommendations.
Imagine filtering these results through:
What are you building?
What have you explored before?
What questions are you asking?
By injecting the context of your experiment history, AI can recommend not just what's new but what's next for you.
HuggingFace's Daily Papers adds a metadata layer to arXiv—connecting AI research to the community and related artifacts, great for supporting search. We're extending that by adding an intelligence layer to match cutting-edge research to what you're building, so you can get contextually relevant alerts.
Last week, I discovered SpatialScore within hours of publication.
The work hadn't had time to trend on social media but was highly relevant to my work with VLMs. I didn't find it by chance, but because I was filtering the feed through my experimentation history using AI.
All I did was pass in a blob of text-based notes and prompt an LLM to match against recent research releases. It's really that simple.
So, where do personalized research recommendations go from here? In the future of engineering with AI, competitive advantage will go to those who can operationalize innovation fastest.
Managing the content overload and the challenges in discovering relevant results from the firehose of AI research will be critical to maintaining an edge in your initiatives.
AI is poised to learn what you're building and help you improve by observing the whole arc of your experimentation history and outcomes.
Developer discovery loops won't focus on the same trending topics in the future. Instead, recommendations will be grounded in what matters to you, helping you find the next great idea.