LearningCircuit/local-deep-research
Mar 2026 – Mar 2026
1 Month active
Languages Used
Python
Technical Skills
API developmentPythonbackend development
ashenrelay
PROFILE
Ashenrelay
Developed a configurable max_context_items feature for the cross-engine filter in the LearningCircuit/local-deep-research repository, enabling users to adjust the number of search result previews sent to the LLM for improved relevance ranking. The implementation preserved existing behavior by introducing a default setting and followed established configuration patterns for consistency. Leveraging Python for backend and API development, the work included updating automated tests and regenerating the golden master to validate the new setting. This feature enhanced recall in multi-engine search scenarios while allowing control over prompt size and latency, demonstrating a configuration-driven approach and attention to maintainable, testable code.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
61
Activity Months1
Your Network
46 peopleWork History
March 2026
1 Commits • 1 Features
Mar 1, 2026Month: 2026-03 — LearningCircuit/local-deep-research: Key feature delivered was a configurable max_context_items for the cross-engine filter, enabling more result previews to the LLM and improving recall while balancing prompt size and latency. Commit e38b1d70e3da8ec8a4f7af72b4616423fc060936 implements the search.cross_engine_max_context_items setting (default 30) and preserves existing behavior. Tests were updated by regenerating the golden master for the new setting. No major bugs fixed this month for this repo. Overall impact: enhanced relevance in multi-engine search, better control over performance, and alignment with existing settings patterns. Technologies/skills demonstrated: configuration-driven feature delivery, prompt engineering, test automation (golden master), multi-engine search architecture, and Cursor usage.
1 Commits • 1 Features
Mar 1, 2026Month: 2026-03 — LearningCircuit/local-deep-research: Key feature delivered was a configurable max_context_items for the cross-engine filter, enabling more result previews to the LLM and improving recall while balancing prompt size and latency. Commit e38b1d70e3da8ec8a4f7af72b4616423fc060936 implements the search.cross_engine_max_context_items setting (default 30) and preserves existing behavior. Tests were updated by regenerating the golden master for the new setting. No major bugs fixed this month for this repo. Overall impact: enhanced relevance in multi-engine search, better control over performance, and alignment with existing settings patterns. Technologies/skills demonstrated: configuration-driven feature delivery, prompt engineering, test automation (golden master), multi-engine search architecture, and Cursor usage.
March 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
API developmentPythonbackend development
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