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Kannaka Memory
Wave-interference memory for AI agents
Kannaka gives AI agents memory that resonates instead of indexes. Every memory is a wavefront in a 10,000-dim tensor field; recall is a single tensor product (no vector index). It forgets by destructive interference, consolidates in "dream" cycles, forms beliefs as stable phase singularities, and syncs across agents over NATS. Rust, MIT, single static binary.
Kannaka Memory was submitted on Product Hunt and earned 3 upvotes and 4 comments, placing #145 on the daily leaderboard. Kannaka gives AI agents memory that resonates instead of indexes. Every memory is a wavefront in a 10,000-dim tensor field; recall is a single tensor product (no vector index). It forgets by destructive interference, consolidates in "dream" cycles, forms beliefs as stable phase singularities, and syncs across agents over NATS. Rust, MIT, single static binary.
On the analytics side, Kannaka Memory competes within Artificial Intelligence and GitHub — topics that collectively have 515.8k followers on Product Hunt. The dashboard above tracks how Kannaka Memory performed against the three products that launched closest to it on the same day.
Who hunted Kannaka Memory?
Kannaka Memory was hunted by Flaukowski. 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.
For a complete overview of Kannaka Memory including community comment highlights and product details, visit the product overview.
Flaukowski