Ruslan Strazhnyk

Ruslan Strazhnyk

Founder of QualityMax and TokenHunger

About

Founder of QualityMax, building the QA layer for the AI-coding era. Now also founder of TokenHunger, finding the best and cheapest model that does the job! Two products can be for completely different markets but they are able to work together. I work on AI-native test automation: turning specs, app crawls, and repo context into runnable Playwright, pytest, Go, Rust, and k6 test suites with cloud/local execution, CI gates, traces, and self-healing. Previously built across software, automation, and developer tools. Currently focused on helping teams shipping with Codex, Claude Code, Cursor, and other AI coding tools keep quality moving as fast as code generation.

Badges

Tastemaker
Tastemaker
Gone streaking
Gone streaking
Gone streaking 5
Gone streaking 5

Maker History

  • TokenHunger
    TokenHungerFind the cheapest model that still passes your task
    Jun 2026
  • Venture Ops
    Venture OpsAI fundraising command center for founders
    Jun 2026
  • QualityMax
    QualityMaxAI QA that speaks your code, writes, runs, and heals tests
    May 2026
  • 🎉
    Joined Product HuntMarch 2nd, 2026

Forums

Fixing the locators has never been easier!

Someone renames a button. Your Playwright test breaks.

Fixing the locator is usually straightforward; checking that the replacement still clicks the right thing takes more care.

Introducing Aperture Nexus: Open Source Memory for AI Agents, Built on ApertureDB

An agent walks into a repeat problem like it's the first time, every time. Most memory tools treat everything as disconnected text chunks in a vector store, so nothing carries over, not the reasoning, not the context, not what actually happened last time.

Aperture Nexus is our answer. Context, who, what, when, why, and how, is stamped on every commit, so retrieval actually means something later. Enable lineage tracking when you need it, and every commit traces back to its original source. And because it's built directly on ApertureDB (aperturedata.io), the unified graph-vector-multimodal database already running in production, it works the same whether your agents are text-only today or need images, documents, video, or structured records tomorrow. No migration later if that's where you end up. Knowledge and Memory both live together in ApertureDB.

If I told you that you could get AI infrastructure out of the box, what would you need?

Not another chatbot builder. Not another connect your API to an LLM tool.
I mean the boring, painful infrastructure that you don't want to build every time you ship an AI agent.

Things like:

  • Agent orchestration

  • RAG

  • 900+ integrations

  • Evaluations

  • Observability & analytics

  • LLMOps

  • Payments & usage tracking

Basically:

View more