Mockd – Mock Server for HTTP, gRPC, GraphQL, WebSocket, MQTT, SSE, Soap

Hacker News

I kept ending up running WireMock for REST, some random Node for WebSocket, a hacky gRPC mock and whatever else I needed ad-hoc. So I built one that does all of them. The core is a shared engine (matching, state management, templates, chaos injection) and each protocol is just another interface on top of it. Once that clicked it got fun to add more. The part I'm most interested in feedback on: digital twins. You import a real API's OpenAPI spec, bind endpoints to stateful tables, and get a mock that behaves like the real service. stripe-go runs 49/49 tests against mockd. twilio-go passes 13/13. As all things tend to start from annoyance, I built this because I got annoyed while at a client with a normal modern stack that integrates with legacy bank SOAP APIs. Your options were basically spin up WireMock or don't test it... Sooo ~120K lines of Go late .. I built mockd, no CGO, Apache 2.0. Single binary, 12ms startup.

Visit product View on Hacker NewsMar 25, 2026

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: stripe, single, apis · Missing: mac, agents, macos

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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