ApiArk – Open-source, local-first API platform (Postman alternative)

Hacker News

I built ApiArk because I got tired of Postman eating 800 MB of RAM, requiring a login, and storing my API keys on someone else's cloud. ApiArk is a desktop API client built with Tauri v2 (Rust + React). Everything is stored locally as plain YAML files - no account, no cloud, no lock-in. Your collections are just files in a folder, so they work naturally with Git. Key differences from Postman: - No login required - just download and start working - Git-friendly - plain YAML files, not proprietary JSON blobs - Broadest protocol support I know of - REST, GraphQL, gRPC (all 4 streaming types), WebSocket, SSE, MQTT, and Socket.IO in one tool - Local mock servers with Faker.js data and latency simulation - TypeScript scripting for pre/post-request logic - Collection runner with data-driven testing (CSV/JSON) - CLI for CI/CD run collections in GitHub Actions, GitLab CI, etc. - Import from Postman, Insomnia, Bruno, Hoppscotch, OpenAPI, cURL The app also includes a proxy capture mode, response diffing, scheduled monitoring with webhook alerts, and a plugin system (JS + WASM). Built with Tauri v2, so the backend is Rust (reqwest, tokio, tonic for gRPC, axum for mock servers) and the frontend is React + TypeScript. Available on macOS, Windows (including Microsoft Store), and Linux. - GitHub: https://github.com/berbicanes/apiark - Webpage: https://apiark.dev I'd love feedback on what protocols or workflows you'd want to see next.

Visit product View on Hacker NewsMar 25, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, open · Missing: agents, agent, cursor

94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io, including · Missing: https docs, excited, just released

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

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

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

13%13% 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.

Incorrect prediction on native model

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