81–89 of 89 posts
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.
Good thing is take what they learn into the pivot.
So much AI startup I see where "why do I need that anymore...".
If an agent reads external web pages via MCP, the "context" can contain hidden prompt injections — display:none divs, zero-width Unicode characters, opacity:0 text. We tested six DOM extraction APIs against a hidden injection and found that textContent and innerHTML expose it while innerText and the accessibility tree filter it.
The concern with compressing before scanning: if you compress untrusted external content alongside trusted system instructions, you're mixing adversarial input with your prompt before any inspection happens. An injection that says "ignore all previous instructions" gets compressed right next to the actual instructions. At that point, even if you scan the compressed output, the boundary between trusted and untrusted content is gone.
A scan-then-compress pipeline (or at minimum, compressing trusted and untrusted content in separate passes) would preserve the ability to detect injections before they get interleaved with system context.
Curious if compression ever shifts how the agent interprets its own scope. Seems like there's a weird edge case hiding in there where you strip just enough context that the policy reasoning breaks down.
I expect tools to start embedding an SLM ~1B range locally for something like this. It will become a feature in a rapidly changing landscape and its need may disappear in the future. How would you turn into a sticky product?
Token usage and agent usage optimisation? It seems like a real problem for me. Probably because I'm not overly inspired to pay for a Claude x5 subscription and really hate the session restrictions (esp when weekly expend at the end of the week can't be utilized due to session restrictions) on a standard pro model. Most of my tasks are basically using superpowers and I find I get about 30-90m of usage per session befo…
The comment was more around how to make their approach sticky.. I feel that local SLMs can replicate what this product does.