I’m working on DeepCost.ai, an AI-driven cloud cost optimization tool for AWS, GCP, and Azure.
The goal is to help small teams and fast-growing startups understand where cloud spend is leaking and automatically reduce waste (idle resources, over-provisioned workloads, inefficient Kubernetes setups, and AI API usage). Setup is lightweight, and we focus on actionable recommendations rather than massive dashboards.
We’re still early and testing with a few teams who want better cost visibility without running a full FinOps practice.
I’m building an open-source tool called kafy, which is a kubectl-style cli for Kafka. It’s meant to handle all Kafka use cases and some basic AWK-like operations for inspecting topics, brokers, and consumer groups.
The tool is free and open-source, so anyone can use it for their own Kafka clusters. It’s very much a “crappy but functional” project at this stage — nothing fancy, just a practical CLI for certain tasks.
Our use case is to execute test scripts in a sandbox mode. This is multi host and multi region setup. We might run millions of test scripts per day.
One of our engineers found https://testcontainers.com. We find it interesting and it seems like it won’t maintain container live. Instead, it start and remove the container for each test. We might need to implement lock mechanism to run only maximum number of containers at a time. I don’t know whether it fits for highly scalable test cases.
Recently I tried rork.app to generate mobile games for my kids. It is really amazing. They also give published URL which we can directly use in the mobile.
Is there any significant difference in code generation?
I had same feeling when I build a free tools to unlock the password protected pdf. It can be easily done with OSX Preview. Then I see that people who don’t have technical knowledge and tools, they can easily unlock pdf from browser itself.
The goal is to help small teams and fast-growing startups understand where cloud spend is leaking and automatically reduce waste (idle resources, over-provisioned workloads, inefficient Kubernetes setups, and AI API usage). Setup is lightweight, and we focus on actionable recommendations rather than massive dashboards.
We’re still early and testing with a few teams who want better cost visibility without running a full FinOps practice.
Website: https://deepcost.ai