Shrujal Mandawkar

This is a really interesting shift from “book a demo” to “experience the product instantly” Curious though — since the agent is interacting with real product flows and potentially prospect data, how are you handling things like environment isolation and permission controls? Especially to make sure demo agents can’t accidentally access sensitive data or trigger unintended actions inside the...

This is super interesting — “AI visibility” as a metric makes a lot of sense in the zero-click world Curious though — how do you validate attribution here? Since AI answers often synthesize multiple sources, how do you ensure a citation or mention is actually driving value back to the brand?

This is super useful — the gap between data and actual decisions is where most teams struggle Curious — how do you handle noisy or incomplete data? For example, if attribution is off or tracking isn’t perfect (which is pretty common), does ChatWithAds account for that while giving recommendations?

This is a really interesting direction — moving from text diffs to intent + AST-level tracking makes a lot of sense in an AI-first workflow Curious — how do you handle cases where the AI’s “intent” is correct at a high level, but the implementation subtly diverges across multiple files? Does Aura catch cross-file semantic inconsistencies as well or mainly within scoped changes?

This is a really powerful shift from “editing videos” to “designing video systems” Curious — how do you handle consistency across outputs? Like when generating multiple variants (A/B tests, reels, etc.), how do you ensure brand voice, pacing, and visual identity don’t drift across different agent workflows?

This is a really important space, especially with how easy it’s becoming to replicate someone’s identity with AI Curious — how do you handle false positives or misidentification in detection? Because in something as sensitive as identity tracking, even small inaccuracies could have serious consequences

Love how simple and practical this is 😄 Curious — since it’s hiding all active windows and muting everything instantly, are there any edge cases where certain apps (like screen recordings or notifications) might still leak through? Feels like those small things could break the illusion in real scenarios

This looks super powerful especially the always-on agents with long-term memory Curious — with persistent memory and 24/7 execution, how are you handling security around stored data and permissions? Especially making sure agents don’t take unintended actions or expose sensitive info over time

Love the local-first approach especially keeping everything fast and private Curious — since it can crawl and extract entire sites, how are you handling safeguards around accidentally pulling sensitive or restricted data? Feels like that could be important as usage scales

This is super interesting focusing on intent over volume makes a lot of sense Curious — with tracking signals from platforms like LinkedIn and automating outreach, how are you handling things like data privacy and account safety? Especially avoiding issues like platform restrictions or unintended access risks

Really impressive journey especially scaling to billions of queries Curious — with this kind of workload, how are you handling security around data access and isolation? Especially if teams are using it in multi-tenant or production environments

This is a really cool idea turning daily data into a podcast is super unique Curious — since you're pulling data from multiple sources (calendar, GitHub, etc.), how are you handling security & access between these integrations? Especially making sure everything stays isolated and safe

This is really interesting the “no compaction” part hits hard, that’s a real pain Curious — with long-running sessions and persistent memory, how are you thinking about security around stored context? Like preventing sensitive data leaks or unintended access over time?

This looks really solid love the idea of running multiple agents in parallel Quick question — with all these isolated environments, how are you handling security between them? Like making sure one agent can’t access another or any API-related risks?