AI Agents Trained on Employee Work Habits Are Quietly Taking Over Jobs

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A game company in China's Shandong province replaced a departing employee with an AI agent built from his work records, drawing attention to a broader trend of employers capturing how workers think and operate. Around the same time, a viral GitHub project and an internal Meta memo revealed that major tech firms are exploring ways to systematically record employee workflows, keystrokes, and screen activity. Platforms like Mercor and Handshake AI are also paying professionals to hand over past work products, effectively commodifying human expertise for AI training. Coding tools such as Claude Code and Codex already log full session histories — including every prompt, edit, and rejected attempt — as local files, often without the user's active awareness. The emerging question is not just whether AI will automate jobs, but who owns the detailed behavioral data being collected in the process.

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SnapMind App Shifts to Agent-Based Architecture in Major Rebuild

SnapMind, a desktop app that binds AI prompts to hotkeys, is undergoing a significant rebuild by its developer. The creator has decided to move away from a prompt-based model toward an agent-based architecture, citing rapid advancements in AI as the motivation. A key upcoming feature will allow users to create custom AI agents and assign them to hotkeys. The developer also plans to replace the current LLM provider service with a more robust solution, as frequent API endpoint changes have made the existing setup difficult to maintain. The latest release, v0.5.5, is available on the project's website and GitHub repository.

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ProgrammingDEV Community ·

DeepSeek Harness Builds Entire Agent Framework as Cordis Plugins, Guide Explains

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ProgrammingDEV Community ·

LSHIY Campaign Exploited OAuth ROPC and IPv6 to Spray Passwords at Scale

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ProgrammingDEV Community ·

One Complex Query Beats Ten Simple Ones for Testing PostgreSQL-ChatGPT Connections

When connecting PostgreSQL to ChatGPT, developers should not rely on multiple easy questions with plausible-sounding answers to validate the integration. A single well-designed business query — incorporating a known answer, tenant boundary, join, versioned metric, and a defined reporting cutoff — provides a far more rigorous acceptance test. The test should also verify identity, data freshness, query limits, truncation handling, and provenance of results. Negative test cases, such as unauthorized tenant access, stale replicas, and forced timeouts, must also be executed to expose failure modes. Critically, the system's responses should preserve incomplete, stale, or unknown states rather than silently collapsing them into empty or misleading output.

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