Interactive Git Rebase Explained: A Beginner-Friendly Guide

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A new tutorial published at cachebag.sh aims to demystify the git rebase -i command, which many developers find intimidating. The article walks readers through interactive rebasing in Git, breaking down the process into approachable steps. Interactive rebasing is a powerful tool that allows developers to edit, squash, reorder, or clean up commits in their version history. The post has gained attention on Hacker News, attracting early discussion from the developer community.

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Four-Pillar Framework for Safely Extracting SaaS Data Into a Data Lake

A software engineer has outlined a platform-agnostic security framework for pulling large volumes of enterprise SaaS data into a data lake without requiring write permissions. The approach rests on four pillars: a least-privilege read-only API role, a dedicated non-human service account, OAuth 2.0 client-credentials authentication, and an asynchronous bulk-export job pattern. The read-only role limits blast radius in case of credential leaks, while a named service account improves auditability and prevents pipeline failures tied to employee turnover. Using OAuth 2.0 client credentials replaces the risky practice of reusing admin passwords or running pipelines under personal employee accounts. The framework is designed to work with virtually any modern SaaS platform that exposes a REST API.

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DeepSeek halts fundraise amid chip shortage; Hugging Face seeks $100M after OpenAI breach

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Why AI Agents for Legal and Accounting Firms Must Prepare, Not Decide

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How to Stop AI Analytics Tools From Leaking Data Across Users

As developers embed natural language analytics into products and dashboards, a critical security gap has emerged: AI assistants often connect to databases through a single powerful service account, bypassing the tenant-level access controls enforced in the regular app interface. This means a user asking a simple business question could inadvertently trigger queries that return data belonging to other customers or roles. The root problem is a broken identity chain, where the database sees the AI service's credentials rather than the individual user's permissions. A safe architecture treats the AI as an untrusted query planner, with a dedicated analytics gateway responsible for verifying user identity, enforcing row-level security, and scoping every query before it reaches the warehouse. This layered approach ensures that tenant filters, role restrictions, and audit trails remain intact regardless of whether a question comes through the standard UI or an AI-generated SQL path.

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