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Continuum
A runtime that reuses computation across AI workflows
AI workflows today are built from disconnected calls that do not share work. The same prompts and tokens are recomputed across steps, which wastes time and cost. Continuum treats workflows as executable graphs where tokens, tensors, and tools live in one system. It can reuse shared computation, such as long prompt prefixes, and optimize execution across runs. The result is faster agents, lower costs, and a system that understands what it is doing.
The idea came from a simple frustration. In most AI systems, we kept recomputing the same prompts and tokens across steps, even when most of the work was identical. Caching helped a bit, but only at the output level.
Continuum takes a different approach. It treats AI workflows as programs, so it can reuse computation across steps and across runs. For example, if multiple calls share most of a long prompt, it skips recomputing that shared part instead of starting from scratch each time.
Early tests show consistent latency and cost reductions on multi step agents.
Would love feedback, especially from people building complex workflows or agents.
About Continuum on Product Hunt
“A runtime that reuses computation across AI workflows”
Continuum was submitted on Product Hunt and earned 6 upvotes and 2 comments, placing #52 on the daily leaderboard. AI workflows today are built from disconnected calls that do not share work. The same prompts and tokens are recomputed across steps, which wastes time and cost. Continuum treats workflows as executable graphs where tokens, tensors, and tools live in one system. It can reuse shared computation, such as long prompt prefixes, and optimize execution across runs. The result is faster agents, lower costs, and a system that understands what it is doing.
On the analytics side, Continuum competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Continuum performed against the three products that launched closest to it on the same day.
Who hunted Continuum?
Continuum was hunted by Rithul Kamesh. 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 Continuum including community comment highlights and product details, visit the product overview.
Rithul Kamesh
Thanks for checking out Continuum.
The idea came from a simple frustration. In most AI systems, we kept recomputing the same prompts and tokens across steps, even when most of the work was identical. Caching helped a bit, but only at the output level.
Continuum takes a different approach. It treats AI workflows as programs, so it can reuse computation across steps and across runs. For example, if multiple calls share most of a long prompt, it skips recomputing that shared part instead of starting from scratch each time.
Early tests show consistent latency and cost reductions on multi step agents.
Would love feedback, especially from people building complex workflows or agents.