Ivo Tzanev

48 seconds is a very specific number — genuinely curious what's actually live at that point. Is the agent handling real traffic, or is it more of a configured scaffold waiting for final wiring? And what does iteration look like after that first deploy? The hard part with agent products is usually the feedback loop, not the initial setup.

Boilerplates saved me weeks on my last project. The tricky part is usually keeping them current as model APIs shift every few months. How often are you updating the starter, and what's the upgrade path for existing users?

When subagents run in parallel on the same codebase, how do you handle the merge problem? If agent A refactors a module while agent B adds new functionality that depends on it, does the orchestration layer catch the conflict before integration or does it surface as a failed merge? Wondering if there's a built-in dependency resolution step or whether that's still left to the user to coordinate.

The desktop automation category has been attempted a few times with mixed results mostly around reliability when the UI changes unexpectedly. How does Manus handle app state changes mid-workflow? Like if a dialog pops up it wasn't expecting, does it recover autonomously or does it pause and wait for human input? Curious how you've approached the failure mode design here.

What's the fallback behavior when the browser edits create conflicting suggestions for the coding agent? If I'm editing a component that has dependencies the agent doesn't have in its current context window, does it alert you or just apply? Trying to understand if this is designed for quick visual tweaks or if it handles more complex component refactoring workflows.

Real-time presence is genuinely useful for support and sales activation. The live chat trigger use case is real. What I keep running into with this category: knowing someone is on your pricing page is interesting. Knowing why they left without converting is the hard part. Real-time data is great for the "when" but rarely illuminates the "why." Does Sleek have any session replay or exit intent...

Most multi-model tools optimize the wrong step. Routing is easy. Synthesis is the hard part. The fact that the judge model is configurable is actually the interesting design decision here, not the parallel execution. A judge that just averages or picks longest is no better than a single good model. Curious whether the default judge behavior is documented somewhere, or if the quality of the...

The execution layer here makes sense. The part I want to understand is the strategy layer. When the system is running your GTM and it hits a decision point, say two customer segments are converting but at different LTVs, does Denovo make the call, or does it surface a choice and wait? Because a system that always waits isn't really autonomous. And one that always decides needs to have a strong...

Running agents at scale is the easy part to imagine. The harder problem is state coherence across the swarm — what happens when agent 47 and agent 12 reach conflicting conclusions about the same codebase and neither is obviously wrong. Does Mngr expose any shared state or consensus layer, or is resolution left entirely to the orchestrating workflow?

The distinction that's missing from this conversation: there are two types of learning, and "ship fast" only reliably generates one of them. Analytics tell you what users did. Conversations tell you what users meant. The first is cheap and fast. The second is slow and uncomfortable. Almost every team defaults to the first because it feels like learning without requiring you to sit with someone...

The consistency framing gets this exactly backwards. Posting every day doesn't build trust — posting something worth reading does. The advice I'd push back on harder: "lead with your best feature." Most AI tools do this, and it's why they all sound the same. The product that sticks is the one that starts with the problem you recognize, not the capability you've built. Features are claims. The...

The workspace governance piece is the part I keep coming back to. You can already wire Claude or Cursor into a Notion workspace via third-party connectors, but the access scoping is usually all-or-nothing. The fact that this runs admin controls at the workspace level — meaning you can limit which pages or databases a given agent can touch — is what makes this actually usable for team setups,...

The "over-buy vs under-buy" framing nails exactly why most SMB CRM adoption fails. I've watched small teams spend 3 months configuring Salesforce just to track 50 leads, the tool becomes the job. The real test for ZykoCRM will be whether the 5-minute setup actually holds when a team starts customising. That's where most "simple" CRMs quietly become complex. Congrats on the launch, Deyan.

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Claude Code inside VSCode, 3x MCPs (Sequential thinking, Context 7 and Augment's Context Engine MCP), Codex for front end (seems to be better currently), Vercel, MongoDB or Superbase, GitHub Desktop.

The "mission control" framing is exactly right for this problem. Once you move past single-agent experiments into multi-agent workflows, the operational overhead becomes the real bottleneck — not the agent logic itself. Most teams are cobbling together logs and dashboards from separate tools, which defeats the purpose of automation. > > How does AgentCenter handle runs where an agent spawns...

This is incredibly useful. When I was preparing our pitch deck, I spent days searching for real examples from companies at a similar stage. Having them in one place saves so much time. The ones I always learned the most from were the messy early-stage decks — not the polished Series B ones. Are you planning to tag them by stage so founders can filter?

The context engineering angle is what sets this apart from every other AI recorder. Most tools just transcribe everything — but knowing what the person found important during the conversation changes the output completely. As someone who takes a lot of founder calls and investor meetings, the signal-to-noise problem in notes is real. Smart approach.

The 30-page business plan in a couple of hours claim got my attention. I've written too many of those manually and it never gets easier. What I'm really curious about — does the AI challenge your assumptions or mostly structure what you feed it? Because the hard part of planning isn't the writing, it's figuring out where your thinking is wrong.