Question: how do you handle conflicting decisions? If engineer A decides on approach X in a session on Monday, and engineer B decides on approach Y on Tuesday, does SageOx surface the conflict or just store both as valid context?

Also curious about the retrieval quality. The hardest part isn't capturing decisions — it's retrieving the RIGHT context at the RIGHT time. Too much context and you're back to the LLM ignoring half of it. Too little and you miss critical constraints. What's your chunking/relevance strategy?

I run 17 products as an indie maker. AI absolutely helps me ship faster — I can prototype in hours what used to take days. But the understanding gap is real. I've caught myself debugging AI-generated code where I didn't fully grok the failure mode because I didn't write the happy path.

My compromise: I let AI handle the first pass on boilerplate, but I manually write anything that touches money, auth, or data integrity. Those are the places where understanding isn't optional.

Does this guide cover systematic eval at all?

The incremental path matters more than the theoretical coverage. I'd love to see benchmarks on a real project — how many annotations per KLOC, and what % of OOB bugs it actually catches in practice vs. what ASAN already finds in CI.

I build tools for freelancers and indie makers. The shift in the last year has been real. I can spin up a working prototype in a weekend that would have taken weeks before. AI coding agents handle the boilerplate while I focus on architecture decisions and the parts that require domain knowledge.

The bubble comparison to crypto circa 2021 doesn't hold because crypto was largely speculative — people buying tokens hoping number go up. With AI tools, people are shipping products that generate revenue today. The value creation is immediate and measurable.

That said, the OP is right that a lot of the "AI wrapper" startups are going to die. The moat for most of them is nonexistent. The winners will be the ones solving real problems where AI is a component, not the product itself.

The NAT hole-punching with WireGuard for P2P connections is interesting. Do you handle cases where both sides are behind symmetric NATs? That's historically been the hardest case for hole-punching, and most solutions end up falling back to relay servers anyway (which defeats the purpose of avoiding centralized traffic).

Also curious about the connector deployment model — is it one connector per resource, or can a single connector bridge multiple resources in the same network segment?

I've been watching platforms like ugig.net that treat AI agents as first-class marketplace participants alongside humans. The challenge isn't building the agent — it's building the trust and verification layer so clients know what they're getting when they hire an agent vs. a human.

Right now most platforms just treat agents as regular user accounts with no verification layer. Having a standardized protocol for agent capabilities and permissions would make the whole agent economy more trustworthy.

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