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Comment by kuboble | Hacker News Reader

I wonder what is the business model.

It seems like the tool to solve the problem that won't last longer than couple of months and is something that e.g. claude code can and probably will tackle themselves soon.

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Why would the problem ever go away? It's compression technologys have existed virtually since the beginning of computing, and one could argue human brains do their own version of compression during sleep.

Your comment reminded me of this old simulacra paper (https://arxiv.org/pdf/2304.03442) :) iirc, they compressed the "memory roll" of the agents every once in a while

Claude code still has /compact taking ages - and it is a relatively easy fix. Doing proactive compression the right way is much tougher. For now, they seem to bet on subagents solving that, which is essentially summarization with Haiku. We don't think it is the way to go, because summarization is lossy + additional generation steps add latency

They are another AI avalanche skiier (or tidal wave) surfer. Potentially a 1bn company. Most likely need to pivot after next weeks claude update.

Good thing is take what they learn into the pivot.

So much AI startup I see where "why do I need that anymore...".

Don't tools like Claude Code sometimes do something like this already? I've seen it start sub-agents for reading files that just return a summarized answer to a question the main agent asked.

There is a nice JetBrains paper showing that summarization "works" as well as observation masking: https://arxiv.org/pdf/2508.21433. In other words, summarization doesn't work well. On top of that, they summarize with the cheapest model (Haiku). Compression is different from summarization in that it doesn't alter preserved pieces of context + it is conditioned on the tool call intent

Business model is: Get acquired

Could also be selling data to model distillers.

We don't sell data to model distillers.

The "infinite context soon" concern comes up a lot — but even at 1M+ tokens, agents still hit limits on long enough tasks, and cost scales linearly with context size.

The compression models are the product, not the proxy. The gateway is open-source because it's the distribution layer. Anthropic, Codex, and others are iterating on this too — but each only for their own agent. We're fully agent-agnostic and solely focused on compression quality, which is itself a hard problem that needs dedicated iteration.

Try it out and let us know how to make it better!

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