Apple Music to add Transparency Tags to distinguish AI music, says report - TechCrunch
Apple Music to add Transparency Tags to distinguish AI music, says report TechCrunch
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I am building a Notebook Environment for SQL Inside a Database Client
This post is also available on tabularis.dev . You know the drill. Write a query, get a table. Need to build on that result? Copy-paste into the next query. Need a chart? Export CSV, open a spreadsheet. Want to document the analysis? Paste SQL into a doc and pray nothing drifts. I got tired of this loop, so I'm building Notebooks into Tabularis — a cell-based SQL analysis environment that lives inside the database client. No Jupyter, no Python runtime, no context switching. Just SQL + markdown cells, inline charts, and a few features that make multi-query analysis way less painful. It's still in development, but the core works. Here's what it looks like and how it's shaping up. How It Works A notebook is a sequence of cells — SQL or markdown. SQL cells run against your database and show re
Fuzzing REST APIs in Industry: Necessary Features and Open Problems
arXiv:2604.01759v1 Announce Type: new Abstract: REST APIs are widely used in industry, in all different kinds of domains. An example is Volkswagen AG, a German automobile manufacturer. Established testing approaches for REST APIs are time consuming, and require expertise from professional test engineers. Due to its cost and importance, in the scientific literature several approaches have been proposed to automatically test REST APIs. The open-source, search-based fuzzer EvoMaster is one of such tools proposed in the academic literature. However, how academic prototypes can be integrated in industry and have real impact to software engineering practice requires more investigation. In this paper, we report on our experience in using EvoMaster at Volkswagen AG, as an EvoMaster user from 2023
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I am building a Notebook Environment for SQL Inside a Database Client
This post is also available on tabularis.dev . You know the drill. Write a query, get a table. Need to build on that result? Copy-paste into the next query. Need a chart? Export CSV, open a spreadsheet. Want to document the analysis? Paste SQL into a doc and pray nothing drifts. I got tired of this loop, so I'm building Notebooks into Tabularis — a cell-based SQL analysis environment that lives inside the database client. No Jupyter, no Python runtime, no context switching. Just SQL + markdown cells, inline charts, and a few features that make multi-query analysis way less painful. It's still in development, but the core works. Here's what it looks like and how it's shaping up. How It Works A notebook is a sequence of cells — SQL or markdown. SQL cells run against your database and show re
DIGIT Deal Roundup March 2026
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How to build an MCP server from scratch (Python, 2026 guide)
If you're using Claude Code, Cursor, or any MCP-compatible AI tool, you can extend its capabilities by building custom MCP servers. This guide walks through building one from zero. What's an MCP server? Model Context Protocol (MCP) servers are tools that AI coding assistants can call. Think of them as plugins — each server exposes a set of tools (functions) that the AI can invoke during conversation. When you say "get me the current Bitcoin price" in Claude Code, an MCP server handles the actual API call and returns structured data. Prerequisites Python 3.10+ uv package manager (or pip) Claude Code, Cursor, or any MCP client Step 1: Project setup mkdir my-mcp-server cd my-mcp-server uv init uv add mcp httpx Create the structure: my-mcp-server/ ├── pyproject.toml ├── src/ │ └── my_mcp_serve
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