About ClarityStack

AI news for builders, not spectators.

ClarityStack is a daily AI news system built for people who actually ship — not just follow the hype cycle. It surfaces meaningful developments across AI research, tooling, and infrastructure, filtered through a simple question: does this matter if you're building something real?

This is not a news aggregator. Every post includes opinionated analysis — what I would use, what I would ignore, and where I think the real leverage is. The goal is to replace noise with signal, and give you the kind of context you'd normally get from a sharp engineer on your team.

How It Works

ClarityStack runs on a fully automated pipeline that I designed and built from scratch — optimized for reliability, controllability, and zero-trust execution:

• Source ingestion — ArXiv, GitHub Trending, and Hacker News are continuously scanned for high-signal AI developments.
• Structured extraction — Gemini Flash converts raw content into structured JSON, extracting key facts, relevance signals, and source traceability.
• Opinionated synthesis — Claude generates first-person analysis using a strict style guide that enforces clarity, specificity, and non-generic opinions.
• Privacy enforcement — A dual-pass privacy scanner runs before and after generation to guarantee no sensitive data leaks into output.
• Human gating — Every post requires explicit approval. A custom Discord bot pushes drafts to my phone for review before anything is published.
• Multi-platform delivery — Approved content is automatically deployed to this blog (Cloudflare Pages) and LinkedIn. Automated publishing to X is planned.

The system runs inside an NVIDIA NemoClaw sandbox with kernel-level isolation (Landlock + seccomp), strict network whitelisting, and effectively zero marginal cost.

This is not just a content pipeline — it's a controlled AI system: deterministic where it matters, automated where it scales.

About Me

I'm Rui Zhang (Ray), a software engineer focused on AI systems, security architecture, and developer tooling.

I built ClarityStack as both a daily-use product and a proof of how AI automation should be done: multi-model orchestration, strong safety boundaries, and human-in-the-loop control that doesn't become a bottleneck.

My current interests sit at the intersection of AI agents, system safety, and real-world deployment — building systems that are not just powerful, but predictable and trustworthy.

You can find me on LinkedIn or explore the ClarityStack source code on GitHub.

Open Source, Opinionated

The pipeline behind ClarityStack is open source. The opinions are mine.

If something here is wrong or missing context, I'd rather hear about it than let it sit — feel free to reach out.