Scraper
A paid bulk-image workflow rebuilt around constant-time lookups, retries and checkpoints after the original job failed at real data volume.
Problem
A client image-download job used linear lookups and had no durable record of progress, so a failure forced work to restart instead of resume.
How I solved it
- 01
Replaced repeated linear lookup work with set-based constant-time checks, removing the scaling bottleneck in the selection path.
- 02
Recorded checkpoints per row and resumed from saved progress after interruption, then added retry rounds so a transient failure did not restart the full run.
- 03
Exposed multi-worker processing and operator progress in the public Python and Next.js source; the lack of a committed regression suite and private client outcome remain explicit limits.
My role / what I owned
Freelance developer
- Replaced linear lookup work with constant-time set-based checks.
- Added checkpointing so interrupted work can resume from recorded progress.
- Added retry and multi-worker surfaces visible in the public source.
How it was built
- Team
- A private client engagement. The client identity and downstream result are not public.
- Tools
- Python
- Next.js
- Checkpointing
- Retry handling
- Use of AI
- No project-specific AI contribution is documented for this work.
Source and current state
- 18 public commits at the August 2026 review.
- Public Python and Next.js source showing the checkpoint, retry and multi-worker implementation.
- No committed test files were found in the audit.
- Status
- Paid delivery with public source; the client result remains self-reported.
- What it shows
- The public code shows the constant-time lookup and resume strategy that replaced the failed workflow.