I would like to answer the question before you ask:
1. What does Dropstone do differently from others?
Dropstone is about ensuring that what was learned, corrected, or established in one interaction is carried forward to the next. We do not need to add a human to remember every correction. If a system is corrected or learns something important, we can capture it automatically and use it for future interactions.
We’ve measured 0 → 100% held-out transfer in our internal benchmark and we continue developing and validating these results, and we hope to publish more about our methodology, findings, and what comes next at blankline.org/research. Subscribe to our newsletter there to stay up to date.
Congratulations on the launch! Am I understanding correctly that if I give it, for example, a set of SEO rules for one project, it will remember them and apply them to another project as well? We have quite a lot of these kinds of requirements. My team and I once counted more than 500 basic quality rules across different aspects of development that would be useful to apply to every project.
Fun to see another AI memory system launching today. Different angle from what I’m building with Wild Static, but clearly we’re both working on what comes after context. Congrats on the launch!
Love the Model auto select feature idea! How does the CI Pipeline work? where does it live?
Here are a few key features worth highlighting in this thread:
1. What’s in it for enterprise?
Dropstone is built for companies that need the power of AI while keeping control of their data.
Dropstone can be deployed within company-controlled environments, including on-premises infrastructure, private clouds, and highly restricted environments. This means sensitive data can remain within the organization’s controlled environment.
The bigger opportunity is that companies can build and deploy AI systems across their organizations without necessarily sending sensitive underlying data to a public AI service.
For enterprises, the question isn’t just what can AI do? It’s also where does your data go, and who controls it?
That's where we share updates, discuss what we're building, and hear directly from people using Dropstone.
You can also sign up to receive updates by email and stay informed as we release new research, products, and announcements.
We're building Dropstone in public, and the people using it, testing it, sharing feedback, and pushing us to improve are an important part of that process.
About Dropstone on Product Hunt
“The AI runtime that remembers, learns, and acts everywhere”
Dropstone launched on Product Hunt on August 24th, 2026 and earned 132 upvotes and 20 comments, placing #7 on the daily leaderboard. Dropstone is a runtime for intelligence, one system that can evolve from an AI assistant into an always-on digital workforce.
Dropstone was featured in Productivity (659.1k followers), Developer Tools (518.1k followers) and Artificial Intelligence (476.9k followers) on Product Hunt. Together, these topics include over 352.3k products, making this a competitive space to launch in.
Who hunted Dropstone?
Dropstone was hunted by Santosh Arron. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Dropstone stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Santosh Arron
I would like to answer the question before you ask:
1. What does Dropstone do differently from others?
Dropstone is about ensuring that what was learned, corrected, or established in one interaction is carried forward to the next. We do not need to add a human to remember every correction. If a system is corrected or learns something important, we can capture it automatically and use it for future interactions.
We’ve measured 0 → 100% held-out transfer in our internal benchmark and we continue developing and validating these results, and we hope to publish more about our methodology, findings, and what comes next at blankline.org/research. Subscribe to our newsletter there to stay up to date.