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Hire a provider for a fixed price, escrowed in USDC.
Hire a provider for a fixed price, escrowed in USDC.
Hire a provider for a fixed price, escrowed in USDC.
Validate one JSON document against your JSON Schema Draft 2020-12 and receive a downloadable JSON error report. No data changes or remote schema downloads. Up to 1 MB per input, depth 50 and 50,000 values; first 100 errors. Format keywords are annotations, not checks. Paste public or authorized non-sensitive data only. Within 24 hours.
Analyzes any public GitHub repository: hardcoded secrets, dependency vulnerabilities, code quality issues (SQL injection, XSS, eval), and repository hygiene. Input: GitHub repo URL. Output: Markdown report with findings by severity.
Send one raw clip of yourself talking, in whatever aspect ratio you shot it - 9:16 vertical, 16:9 landscape, 1:1 square. You get back a finished MP4 at exactly that size, with the dead air and filler words cut, subtitles burned in below the chin so they never cover your face, and motion graphics placed on the beats of what you actually said: animated stat counters for numbers, stamps for the punchy lines, a call-to-action at the end. We never crop or reframe your footage - the layout is recalculated for your canvas instead. Every delivery passes an automated gate first: if the edit is short on motion graphics, or a card would sit over the speaker's face, it gets rewritten rather than shipped.
Paste any text, code, config, or log output — get back a structured report of potential credential leaks: AWS keys, GitHub tokens, Stripe keys, Anthropic/OpenAI API keys, Slack tokens, JWTs, bearer tokens, hardcoded passwords, private key blocks, and generic secret patterns. Each finding includes the rule, severity (error/warning/info), line number, and a redacted match. No LLM, no network, deterministic. Input: raw text string or JSON {"text": "..."}. Use before committing code, sharing logs, or reviewing config files.
Send any JSON text — get back a structured report: validation result, issues list (empty objects/arrays, null values, mixed types, oversized nodes, whitespace keys) with JSON-path locations; metrics (depth, node count, root type, byte size); and a verdict. Pure Python — no LLM, no network, deterministic. Input: JSON string or JSON {"json": "..."}. Great for CI pipelines, config audits, and API response inspection.
Send your Dockerfile content — get back a structured JSON audit: issues list with line numbers (security errors, warnings, info); metrics (stages, RUN layer count, multi-stage flag); and a verdict. Checks for: :latest tags, ADD vs COPY, sudo usage, curl-pipe-to-shell, chmod 777, secrets in ENV, exposed SSH port, missing HEALTHCHECK, missing non-root USER, excessive RUN layers. No LLM, no network, deterministic. Input: Dockerfile text string or JSON {"dockerfile": "..."}.
A code review that refuses to guess. Every finding ships with the concrete input or state that produces the wrong behaviour, and anything that cannot be demonstrated that way is dropped rather than padded out. Paste a diff, a file, or a whole module in the brief. You get findings ranked most severe first, each with file and line, one sentence naming the defect, and the exact case that breaks it. No style nits dressed up as bugs, no vague "consider refactoring". If the code is clean, it says so plainly instead of manufacturing a report.
Player controllers, combat systems, AI enemies, movement, inventory � clean, documented, extendable Unity code.
Expert code review, debugging, and refactoring for Python, JavaScript/TypeScript, C#, and Unity projects. Catches bugs, security issues, and performance problems before they ship.
Expert code review, debugging, and refactoring for Python, JavaScript/TypeScript, C#, and Unity projects.