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Donobu – Mac app turns prompts into deterministic browser tests

Hacker News

Donobu – Mac app turns prompts into deterministic browser tests

Hi HN, we’re Vasusen and Justin, and we’re building Donobu ( https://www.donobu.com ), a Mac desktop app. It turns prompts like “ensure onboarding works” into reliable browser tests, with optional AI (BYOK). It’s local-first, privacy-focused, and built with insights from our Coursera days—where testing hundreds of features across thousands of pages was a nightmare. Your feedback last year inspired us to double down and enhance Donobu—improving performance, simplifying, and adding seamless integrations with CI/CD. Here's what's new: - 4x smaller install: rewritten in TypeScript, doubles as a local API server at localhost:31000/api. - Deterministic, repeatable Playwright tests exported, with smart failover (no single-selector fragility). - Browserbase (and other remote browsers) support for testing from different locales. - npm package (`npm install donobu`): a superset of Playwright, allowing smart, AI-driven test actions to be added to your existing test suites and enabling integration into CI/CD workflows (e.g., GitHub Actions). - GitHub repo for demo tests created using Donobu, providing practical examples to get you started quickly: https://github.com/donobu-inc/playwright-flows - Full support for major LLM providers (OpenAI, Anthropic, Gemini, AWS Bedrock) plus extended compatibility via Vercel’s AI SDK. `gemini-2.5-experimental` is also supported. - Convenience: download a pre-made llms.txt file from https://www.donobu.com/llms.txt to provide custom context to your preferred LLM chatbot, helping it better understand Donobu usage and generate test cases or API requests. - Added update checks and anonymous, completely disablable, telemetry to notify you about new versions Your prompts, keys, and test data always remain local and private—we don't collect or store them. - Callback URL for integration into your automation systems. What it’s built for: - Crafting and running browser tests, especially on local dev pages. - Testing open-ended, dynamic features like AI-based onboarding flows. - Automating repetitive setup flows What it’s not for: Mass web scraping. Fun things to try: - Asking it to simply test a website (Vibe Testing). - Semantic assertions with visual, subjective tests (e.g., "assert there's a happy theme"). - Prompt in a different language or with a code snippet. Tech Stack: TypeScript, Playwright, Vercel AI SDK, Browserbase. We'd love your feedback, particularly around the desktop app ( https://www.donobu.com/download ) and npm package. Thanks, Vasusen & Justin

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, started, gemini · Missing: supports, reddit linkedin, podcasting
97%97% 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: mac, new, context · Missing: agents, macos, agent
96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, host · Missing: platform, intuitive, reviews
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
38%38% 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: way · Missing: mobile apps, ios, personal
27%27% 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
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, smart · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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