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Smelt — an open source test runner for chip developers

Hacker News

Smelt — an open source test runner for chip developers

Hey everyone, James from Silogy here. We’re excited to open-source our test runner, Smelt. Smelt is a simple and extensible test runner optimized for chip development workflows. Smelt enables developers to: * Programmatically define numerous test variants * Execute these tests in parallel * Easily analyze test results As chip designs get more complex, the state space that needs to be explored in design verification is exploding. In chip development, it's common to run thousands of tests, each with multiple hyperparameters that result in even more variation. Smelt offers a straightforward approach to generating test variants and extracting valuable insights from your test runs. Smelt integrates seamlessly with most popular simulators and other chip design tools. Key features: * Procedural test generation: Programmatically generate tests with python * Automatic rerun on failure: Describe the computation required re-run failing tests * Analysis APIs: All of the data needed to track and reproduce tests * Extensible: Define your tests with a simple python interface Yves ( https://github.com/silogy-io/yves ) is a suite of directed performance tests that we brought up with smelt – check it out if you’d like to see smelt in action. Repo: https://github.com/silogy-io/smelt We built Smelt to streamline the testing process for chip developers. We're eager to hear your feedback and see how it performs in your projects!

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
89%89% 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: apis, open · Missing: mac, agents, macos
80%80% 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, lua, open source · Missing: https docs, just released, exist
56%56% 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 · Strong signals: para · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
30%30% 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.

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