SShortSingh.
Back to feed

NetZero Tool Auto-Generates Kubernetes Network Policies Using Go and eBPF

0
·1 views

A developer has open-sourced NetZero, a zero-configuration tool written in Go and C that automates the creation of Kubernetes NetworkPolicy files for zero-trust environments. The tool addresses a common pain point where manually writing network policies often leads to missed dependencies and production outages. NetZero uses eBPF to hook into the Linux kernel at the socket-connection level, capturing outbound traffic metadata such as PID, destination IP, and port with minimal overhead. A Go-based user-space layer then enriches this raw data by querying the host's /proc filesystem and performing reverse DNS lookups to map IPs to stable domain names. After a test run completes, the tool aggregates the observed traffic and exports a ready-to-use Kubernetes YAML manifest reflecting actual runtime behavior.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

Beyond API Wrappers: Key Architecture Patterns for Defensible AI Apps

A large share of AI startups launched recently have struggled because they were built as thin interfaces over third-party LLM APIs, leaving them vulnerable when providers rolled out the same features natively. Experts argue that production-ready AI applications require deeper architectural investment, including Retrieval-Augmented Generation to give models access to long-term, company-specific context. Robust apps also implement LLM routing so that no single API failure can bring down the entire system. Data privacy is emerging as a competitive differentiator, with frameworks like Ollama enabling developers to run powerful models locally rather than sending sensitive data to external servers. Building competitive AI products today demands expertise in data pipelines, vector similarity search, and intelligent routing rather than simple API integration.

0
ProgrammingDEV Community ·

AI Agents Are Reshaping Developer Roles, Not Eliminating Them

A software engineering opinion piece published on DEV Community argues that AI will not replace developers but will replace those who refuse to adopt AI tools. The author notes that while AI agents can now autonomously research, plan, execute, and test complex multi-file code changes, they still require human oversight for business logic, security, and architectural decisions. The role of senior developers is described as shifting from writing code to orchestrating AI agents, with prompt engineering emerging as a core professional competency. The author contends that developers who master agentic workflows could match the output of an entire engineering team. The piece urges engineers to embrace AI-driven workflows within the next three years to remain professionally relevant.

0
ProgrammingDEV Community ·

MicroLoop: Open-Source Rust Tool Adds Runtime Safety Layer to AI Agents

A developer has released MicroLoop, an open-source runtime safety layer built in Rust, designed to prevent autonomous AI agents from getting trapped in costly execution loops. The tool intercepts and verifies every tool call made by an AI agent before it executes, addressing a gap that prompt engineering alone cannot reliably fill. MicroLoop uses a history tracker to detect repeated or looping actions and a rule engine that validates arguments via JSON Schema and regex before permitting execution. Written with a lightweight no_std Rust core, it achieves roughly 17 microseconds average verification time and can handle around 58,000 verifications per second. The project is designed to integrate with Python-based AI frameworks without requiring developers to rewrite their existing agent logic.

0
ProgrammingDEV Community ·

Chinese and open-weight AI models dominate developer usage on OpenRouter, data shows

A month-long daily tracking of OpenRouter's public usage rankings reveals that the top five most-used AI models by token volume are either Chinese-developed or open-weight, with DeepSeek V4 Flash leading at 4.72 trillion tokens per week. OpenRouter is a neutral API marketplace where developers pay per token and freely choose any model, making its rankings a real-world signal of developer demand. Notably, the first OpenAI model appears at rank 12 and the first Google Gemini model at rank 13, though the data excludes first-party consumer traffic from platforms like ChatGPT or claude.ai. A stark pricing gap — DeepSeek V4 Flash costs roughly $0.09 per million input tokens versus $5 for Claude Opus — appears to be a key driver, especially for token-intensive workloads like agent pipelines and batch processing. The analyst concludes that Chinese and open-weight models are now sufficiently capable for production API workloads while costing significantly less than leading US flagship models.

NetZero Tool Auto-Generates Kubernetes Network Policies Using Go and eBPF · ShortSingh