RequestHunt is a community-driven platform that collects and curates feature requests from X (Twitter), Reddit, and GitHub. We help product teams discover what features real users are asking for, making it easier to validate product ideas and prioritize feature development based on actual user demand.

How does RequestHunt collect feature requests?

RequestHunt uses AI-powered extraction to automatically collect feature requests from social media platforms including X, Reddit, and GitHub. The platform monitors discussions, issues, and posts to identify genuine user feature requests, extracting key information like the requested feature, product mentioned, and user sentiment.

Who uses RequestHunt?

RequestHunt is used by product managers, founders, developers, and product teams who want to discover what features real users are requesting. It's particularly valuable for startups looking to validate ideas, established companies prioritizing their roadmap, and developers building products that solve real user problems.

Is RequestHunt free to use?

Yes, browsing and searching feature requests on RequestHunt is completely free. We also offer API access for developers who want to integrate feature request data into their own applications. Check our pricing page for API plan details.

How often is RequestHunt data updated?

RequestHunt continuously collects feature requests from Reddit, X (Twitter), GitHub, YouTube, LinkedIn, and Amazon. You can also trigger on-demand scraping via the API or web interface to get the freshest data for any topic.

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Enhance CI/CD pipelines with comprehensive DevSecOps capabilities | RequestHunt

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Enhance CI/CD pipelines with comprehensive DevSecOps capabilities

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by @Vishakha Sadhwani | 7/29/2025 | submitted by AutoCollector

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DevOps pipelines should be more DevSecOps friendly, incorporating capabilities like secret scans, SAST, DAST, and continuous container monitoring, especially at the artifact storage level, to shift security left and catch issues earlier in the development cycle.

Original Source

@Vishakha Sadhwani | 838 pts

You cannot master modern cloud-native devops without understanding how code moves from a developer’s workstation to production Whether you're a DevOps Engineer, Cloud Developer, or SRE, here’s a visual step-by-step DevOps Delivery Workflow with Google Cloud that can help: 1. Source Code Management ↳ Developers write and push code to version control platforms like GitHub Enterprise, Bitbucket, or Google Cloud Source Repositories. 2. Build ↳ CI/CD tools such as GitHub Actions, GitLab CI, Jenkins, CircleCI, or Google Cloud Build compile the code and run automated tests. 3. Store Artifacts ↳ Once the build passes, artifacts (packages, images, etc.) are stored in registries like Artifact Registry, Cloud Storage, Docker Hub etc 4. Deploy Infrastructure ↳ Infrastructure as Code tools like Terraform, Pulumi to provision and configure cloud infrastructure. 5. Test & Monitor ↳ Tools like Prometheus, Datadog, Splunk, and Google Cloud Operations suite to monitor app health, performance, and availability. 6. Deploy to Cloud ↳ The final application is deployed to Google Cloud services — Compute Engine, Kubernetes Engine (GKE), App Engine, or Cloud Run — depending on the use case. This is the full journey of delivering software in a cloud-native, scalable, and secure way — from local code to live product on the cloud. If you're aiming for a role in the cloud devops domain, being able to explain this flow — especially with real-world tools — is essential. If you found this useful: • • • I regularly share practical DevOps workflows, tools, and tips — follow me (Vishakha) for more content like this, and feel free to share it with anyone learning Cloud DevOps.

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