alexhr
It’s a local, daemonless kernel your agents invoke on demand: shared state, an audit trail of why changes happened, todos that persist across sessions, and proof-gated “truth” to prevent drift from quietly rotting a codebase.
Git-native. No SaaS. Model- and agent-agnostic.
https://github.com/DecapodLabs/decapod
cargo install decapod
Decapod: A local, daemonless control plane for AI agents
Location: Bend, Oregon
Remote: Yes (since 2012 on global teams (such as Zendesk and Coinbase))
Willling to relocate: No, unless worthwhile
Technologies: Kubernets, Go, Terraform, DataDog, Python, eBPF, Cillium, Nginx, AWS, Kafka, EventBridge, many DBs, GitHub Actions, TravisCI, Bazel, CloudFormation, GCP, GraphQL, AppSync, Neptune, Apollo, Postman, Docker, Hbase, Hadoop, Vault, Figma, GitHub Enterprise, Linux, macOS
Resume: https://www.linkedin.com/in/alex-raber-412a7348/
Email: [email protected]
Remote: Yes (since 2012 on global teams (such as Zendesk and Coinbase))
Willling to relocate: Yes
Technologies: Kubernets, Go, Terraform, DataDog, Python, eBPF, Cillium, Nginx, AWS, Kafka, EventBridge, many DBs, GitHub Actions, TravisCI, Bazel, CloudFormation, GCP, GraphQL, AppSync, Neptune, Apollo, Postman, Docker, Hbase, Hadoop, Vault, Figma, GitHub Enterprise, Linux, macOS
Resume: https://www.linkedin.com/in/alex-raber-412a7348/
Email: [email protected]
Show HN: FlowHawk – ultra fast eBPF network security monitor with ML
https://github.com/alexhraber/flowhawk
I built FlowHawk, a high-performance network security monitor that uses eBPF/XDP to analyze packets in real-time and detect threats like DDoS attacks, port scans, and botnet activity. It’s written in Go and C is used for the eBPF program. Includes ML anomaly detection and a real-time dashboard. Currently over 80% test coverage and I would love your feedback and contributions!