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h2so4nackl-code/README.md

Hi, I'm Ioan 👋

Independent technical tester building practical evidence in AI evaluation, software QA, API testing, and technical troubleshooting.

I am interested in entry-level and project-based opportunities where careful manual testing, reproducible bug reports, structured evaluation, and clear technical documentation matter. The work below consists of personal portfolio projects and independent testing—not employer or client work unless explicitly stated.

What I work on

  • Manual, exploratory, and regression testing
  • REST API and WebSocket contract testing
  • AI/LLM response and endpoint evaluation
  • Test-case design and reproducible bug reporting
  • Technical troubleshooting across Windows, WSL, Git, GitHub, and CLI workflows
  • Basic Python automation for repeatable checks and reports

Featured projects

Local-first Python checks for OpenAI-compatible endpoints: HTTP status, JSON and response shape, latency, timeout, retry, empty content, and safe JSON reports.

Deterministic REST and WebSocket QA examples covering contracts, malformed messages, reconnect attempts, event ordering, duplicate detection, latency, and JUnit reporting.

Fictional manual and API test cases, reproducible bug reports, regression coverage, and an exploratory testing charter written for recruiter review.

Forty-three fictional, model-agnostic AI response evaluations with anchored scoring, evidence, controlled comparisons, and reusable templates.

Read-only JSON-RPC validation, error classification, transaction-field inspection, state checks, and sanitized reports. No wallets, signing, secrets, or private endpoints.

A practical handbook for evidence-based AI response review, scoring consistency, reviewer calibration, and uncertainty handling.

Tools and technologies

Python · pytest · REST · WebSocket · JSON · HTTP · JSON-RPC · Git · GitHub · PowerShell · WSL · Windows · CLI

Working principles

  • Test observable behavior and record exact evidence.
  • Separate severity, priority, facts, and assumptions.
  • Include positive, negative, boundary, recovery, and privacy cases.
  • Use synthetic or sanitized data in public artifacts.
  • Describe independent testing accurately and avoid unsupported claims.

Languages

Romanian (native) · English (intermediate) · German (intermediate)

Contact

Connect with me on LinkedIn

Pinned Loading

  1. ai-evaluation-portfolio ai-evaluation-portfolio Public

    Practical AI response evaluation portfolio with evidence-based scoring, worked reviews, and controlled response comparisons

  2. ai-response-evaluation-lab ai-response-evaluation-lab Public

    Model-agnostic AI response evaluation handbook with evidence-based rubrics, calibration guides, case studies, and reusable templates

  3. ai-api-evaluation-toolkit ai-api-evaluation-toolkit Public

    Local-first Python toolkit for OpenAI-compatible endpoint health, schema, latency, timeout, retry, and reporting checks.

    Python

  4. api-websocket-test-suite api-websocket-test-suite Public

    Local REST and WebSocket QA suite with contract, reconnect, malformed-message, ordering, latency, and JUnit reporting examples.

    Python

  5. blockchain-dapp-testing-toolkit blockchain-dapp-testing-toolkit Public

    Read-only JSON-RPC and dApp QA toolkit for health, error classification, transaction inspection, state checks, and reproducible reports.

    Python

  6. qa-test-cases-bug-reports qa-test-cases-bug-reports Public

    Fictional QA portfolio with manual and API test cases, reproducible bug reports, regression coverage, and exploratory testing examples.