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.
- 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
WDK/x402 conformance and policy testing for a bounded EVM profile: 136/136 runtime tests, with Windows and Ubuntu CI passing. Local cryptographic verification and simulated settlement only; no real funds, mainnet, or blockchain broadcast. Release v0.1.0.
Local invoice and reconciliation demo with simulation by default and official WDK read-only integration. 82/82 offline tests, with Windows and Ubuntu CI passing; live Sepolia reads use test USDC, not official USDt, and are verified separately from CI. No signing, transfers, mainnet, or real funds. Release v0.1.0.
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.
Python · pytest · Node.js · JavaScript/TypeScript · Tether WDK · x402 · REST · WebSocket · JSON · HTTP · JSON-RPC · Git · GitHub · PowerShell · WSL · Windows · CLI
- 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.
Romanian (native) · English (intermediate) · German (intermediate)
