Auto-generate chaos tests for your external APIs this weekend

Every engineer has a story. Mine involves a Stripe timeout that returned a 200 with an empty body, which our code interpreted as success, which meant we charged cards without creating orders, which we discovered via angry customer emails at 2am on a Saturday. The unit tests passed. The staging environment was green. Nobody had written a test for "what if Stripe just... doesn't finish the response."

This is the problem that Automated Edge-case Test & Chaos Generator for External APIs is trying to solve. And honestly, it's a real one.

What the MVP actually does

The pitch is simple: a GitHub Action that watches your outbound HTTP calls during test runs, records what real API responses look like, then uses an LLM to generate plausible failure scenarios from those actual payloads. Not generic "503 service unavailable" errors. Specific ones, like "what if Stripe returns the charge object but with `amount` as null instead of 0" or "what if the Twilio response is valid JSON but missing the `sid` field your code assumes is always there."

The MVP has three pieces:

• A proxy interceptor (written in Go or Rust, distributed as a GitHub Action) that captures outbound HTTP traffic during test runs via environment variable injection. No code changes. No sidecar. No security team conversation.
• A synthesis engine that takes those recorded request/response pairs and uses GPT-4o with few-shot prompting to generate a library of failure scenarios specific to your actual payloads.
• A CI report that tells you which failure scenarios your app handled gracefully and which ones caused it to crash, with copy-paste reproduction code.

The dashboard is Next.js + Supabase on Vercel. Billing is Stripe (appropriately). The whole thing is designed so that the "aha moment" happens within 48 hours: user sees their first CI run catch an unhandled edge case they never would have written a test for.

Validate before you write a single line of code

I want to be honest about the risks here, because there are real ones.

The first is that the core market, SREs and backend engineers at well-resourced companies, is extremely capable of building their own version of this with Hoverfly + Toxiproxy + a few hundred lines of Python and an OpenAI key. Your ideal customer is also your most dangerous potential competitor. A good "build your own chaos testing setup" blog post could become the default community recommendation and undercut you entirely.

The second is that Postman acquired Akita Software in 2023 specifically for API traffic observation. They have 800 engineers. The synthesis step is probably on their roadmap. You have maybe 9-12 months before this gets commoditized from above.

So before you build anything, do this: put up a Notion or Typeform landing page with "Join the beta — we'll personally analyze your top 3 external API failure risks for free" as the CTA. Post it on r/ExperiencedDevs, r/sre, and HackerNews as a Show HN. DM 30 SREs at Series A-C fintechs on LinkedIn with a cold note that references a specific third-party outage their stack is exposed to. Manually deliver the "failure analysis" as a Google Doc to the first 10 respondents.

Your success metric: 5 companies agree to pay $99-$299/month before you've written any code, credit card captured. If you can't get 5 people to pay for the manual version of this, the automated version won't save you.

This validation approach also gives you something genuinely useful: real incident reports and real failure patterns from real teams. Feed those into your synthesis engine's few-shot prompts and your day-one scenario quality will be miles ahead of a competitor starting from a generic prompt.

The stack and timeline

This is a 5-7 week solo build if you're using Cursor or Lovable to accelerate the dashboard and boilerplate.

Weeks 1-2: the proxy interceptor binary and recording mechanism. This is the hard part and the part where AI coding tools won't save you as much. Go is the right call here because distributing a single static binary as a GitHub Action is straightforward and the performance characteristics matter for a proxy.

Weeks 3-4: the synthesis engine. Start by hand-curating failure scenario libraries for Stripe, Twilio, AWS S3, and SendGrid before you even touch GPT-4o. These four APIs alone cover probably 60% of your initial customer base. Use real production incident reports in your few-shot prompts. The quality of this output is make-or-break for retention.

Week 5: GitHub Action packaging and CI report generation. This is where Cursor earns its keep.

Weeks 6-7: billing dashboard, onboarding flow, and the weekly digest email that tells users about new failure scenarios discovered across the customer base for their specific API providers.

How it makes money

Free tier: 2 APIs, 10 scenarios per month. This exists for community building and top-of-funnel, not charity.

$99/month Solo: 5 APIs, unlimited scenarios, 1 CI integration.

$249/month Team: unlimited APIs, unlimited scenarios, 5 CI integrations, Slack alerts.

$799/month Growth: everything, plus SSO, audit logs, dedicated onboarding, SLA.

You break even at 22 customers on the Team plan, covering roughly $5,500/month in infrastructure and a modest founder salary. At 82-88% gross margins (your main COGS is OpenAI API calls at $0.10-$0.50 per customer per month and Supabase/Vercel), the unit economics are good.

The retention mechanism is built into the product: once the GitHub Action is embedded in `.github/workflows`, removing it is an active decision that requires a team conversation. That's a real switching cost, not a made-up one.

Where to find your first customers

Write a post-mortem-style blog post titled something like "The 7 ways Stripe will break your app and how to test for them." Publish it on your personal dev blog and cross-post to HackerNews and dev.to. In the comments, offer a free API resilience audit. From audit leads, filter to fintech and e-commerce teams with 10+ engineers and DM the SRE or lead backend engineer.

SLOconf Slack, Hangops Slack, and the CNCF #sig-testing channel are high-density SRE communities. Post war stories about third-party API outages, not product pitches. Engineers in these communities can tell the difference immediately.

For longer-term SEO, target "how to test Stripe timeouts in CI" and "mock Twilio failures GitHub Actions." High intent, low competition, exactly the engineers who would pay for this.

My honest take

The idea scores a 7/10 opportunity with a "vulnerable" survival verdict, and I think that's fair. The architectural tension between needing production traffic to generate good scenarios and only having access to test environment traffic is genuinely unsolved. The AI quality ceiling is real: GPT-4o doesn't know about Stripe's 2023 silent partial charge failure unless you tell it, and those underdocumented edge cases are exactly the ones your customers need most.

But the pain is real. G2 reviews, SRE subreddits, and honestly just any engineer who's been paged at 2am over a third-party API edge case will tell you the same thing. The manual version of this problem is already being solved by teams spending weeks writing mocks that still miss things in production.

If you can validate 5 paying customers before you build, and if your synthesis quality is genuinely good on day one rather than "good enough," this is worth building. The window before Postman or Gremlin close the gap is probably 12 months. That's enough time to get to 200+ customers and start building the cross-customer failure pattern database that makes the product genuinely defensible.

Start with the validation. Write the Stripe blog post this week. See who responds.