Ad Astra Tech / Field Notes

What breaks after launch.

Notes from production recovery projects: security misses, scale failures, and codebase repair patterns in AI-assisted products.

These notes are written from live recovery work, not theory: where teams lose confidence, what gets fixed first, and what actually improves release quality.

auth drift queue pileups role mismatch latency spikes stale cache keys observability gaps hotfix regressions auth drift queue pileups role mismatch latency spikes stale cache keys observability gaps hotfix regressions
April 1, 20266 minStability

Your AI App Launched in 10 Days. Why Week 3 Feels Like Firefighting.

If each release adds bugs in new places, this is the usual failure pattern and the fastest stabilization path.

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April 1, 20267 minSecurity

The Founder-Friendly Security Checklist for AI-Built Products

A checklist for auth, secrets, dependencies, and logging that catches the most expensive launch risks.

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April 1, 20268 minScale

Why Scaling Breaks First in AI-Generated Backends

How to remove bottlenecks without rewriting everything or slowing down feature delivery.

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Published: April 1, 2026 / 6 min read / By Ad Astra Tech Team

Your AI App Launched in 10 Days. Why Week 3 Feels Like Firefighting.

Week one feels smooth. Week two gets noisy. By week three, hidden technical debt starts showing up in support requests, flaky integrations, and brittle release confidence.

Start with production triage: isolate high-error routes, lock down auth boundaries, and stabilize high-change modules. Most teams regain control in one focused sprint.

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Published: April 1, 2026 / 7 min read / By Ad Astra Tech Team

The Founder-Friendly Security Checklist for AI-Built Products

Security does not require mystery. A short checklist catches the majority of launch-time risk.

  • Secrets are never exposed in frontend bundles
  • RBAC is enforced server-side
  • All external input is validated and sanitized
  • Dependency vulnerabilities are patched quickly
  • Auth failures and suspicious behavior are logged
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Published: April 1, 2026 / 8 min read / By Ad Astra Tech Team

Why Scaling Breaks First in AI-Generated Backends

AI scaffolds endpoints quickly. It rarely gives you performance architecture by default.

  • N+1 queries hidden in service layers
  • No cache strategy for expensive reads
  • Latency spikes from synchronous chaining
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Upcoming topics

Topics requested by founders and product teams currently shipping AI-assisted applications:

  1. How to audit AI-generated code before your first paid user
  2. Seven API security mistakes in startup MVPs
  3. Avoiding mixed-style codebases across Cursor, Copilot, and Claude
  4. Prompt injection threat modeling for product teams
  5. When to choose queues over cron patches

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