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Chapar – A Native API Testing Tool for Developers

Hacker News

Chapar – A Native API Testing Tool for Developers

Hi HN, I'm excited to share Chapar, a native API testing tool I've been building with GoLang. I created it out of frustration with other API testing tools and aimed to make something fast, minimalistic, and more flexible, especially when it comes to gRPC. Features: Supports both HTTP and gRPC protocols. Workspaces to organize your API endpoints. Environments for storing variables and configurations for different API setups. Send requests using GET, POST, PUT, DELETE, PATCH, HEAD, OPTIONS, CONNECT methods. Flexible request body types like Form, Raw, Binary, and content types such as JSON, XML, Text, HTML. Different authentication methods like Basic, Bearer, API Key, or No Auth. Support for gRPC reflection, proto files, and loading sample request structures. Chaining requests with Pre/Post request options for more complex testing workflows. Dark mode. All data is stored locally, ensuring privacy—nothing gets sent to any server. Import Postman collections. Cross-platform and lightweight, without any JS, ensuring it's fast and easy to run. Upcoming Features: Support for WebSocket and GraphQL. Python as a scripting language for pre/post requests. Tunneling to servers and Kubernetes clusters as pre-request actions. You can try it out by downloading the binary from the [GitHub release page]( https://github.com/chapar-rest/chapar/releases ) or building it yourself. Since it's written in GoLang and has no JavaScript dependencies, it's lightweight and fast to start and use. I'm still in the early stages and there's a lot of room to grow, so I'd love feedback and suggestions. Feel free to give it a try and let me know your thoughts!

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, created · Missing: reddit linkedin, podcasting, latex
85%85% 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: using · Missing: mac, agents, macos
83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist
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.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
34%34% 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
33%33% 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
14%14% 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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