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Trending on GitHub

Sunday, September 27, 2026 · 3 stories, curated & summarized — click any story for the source.

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GitHub Trending (daily) githubrepos ↺ since 09-26 ⚠ unverified date/source

Paperclip: Open-source orchestration for teams of AI agents

Paperclip is an open-source Node.js server and React UI designed to orchestrate teams of AI agents. It functions as a management layer where users bring their own agents, assign specific business goals, and track work and costs from a single dashboard. The platform emphasizes governance, budgeting, and goal alignment over traditional code management.

  • Built with Node.js and React for open-source agent orchestration.
  • Centralized dashboard for defining goals and tracking agent costs.
  • Supports bringing your own agents with flexible team structures.
  • Focuses on business outcome governance rather than PR management.
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PostgreSQL News database ↺ since 09-23

pgsql-test: Bringing Fast Testing Loops to PostgreSQL Logic

pgsql-test addresses the gap in testing database logic by enabling rapid, automated feedback loops similar to those in application code. It allows developers to verify triggers, foreign keys, and row-level security policies without relying on mocks or manual checks. This tool helps keep enforcement logic within Postgres rather than shifting it to the application layer.

  • Mocks fail to validate actual DB behavior like triggers or RLS policies.
  • pgsql-test enables fast iteration for database-specific logic.
  • Reduces architectural pressure to move constraints to app code.
  • Supports verifying queries run under correct database roles.
TRADE-OFFTesting Database LogicMock-Based TestingFails to validate triggersMisses RLS policiesIgnores actual DB behaviorpgsql-Test ApproachVerifies actual DB behaviorKeeps logic in PostgresEnables fast iterationvs
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GitHub Trending (daily) githubrepos ↺ since 09-26 ⚠ unverified date/source

Rohit's AI Engineering Curriculum: 523 Lessons Across Python, Rust, and TS

This GitHub repository hosts a comprehensive AI engineering curriculum comprising 523 lessons across 20 phases, totaling approximately 342 hours of study. It covers multiple languages including Python, TypeScript, Rust, and Julia, with a focus on building reusable artifacts for each lesson. The project includes internationalization support and leverages the creator's prior work on persistent agent memory.

  • Covers Python, TypeScript, Rust, and Julia for broad engineering relevance.
  • Each lesson produces a reusable artifact rather than just theoretical code.
  • Includes i18n support with machine-translated branches for global access.
  • Integrates concepts from the creator's persistent agent memory project.
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