Buzz lets AI agents collaborate in shared workspaces using git worktrees, not sandboxes

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Buzz is an open-source workspace developed by Block, the company associated with Jack Dorsey, designed to let humans and AI agents work together in a unified environment. Unlike traditional AI tools bolted onto existing apps, agents in Buzz are treated as first-class members with their own identities, sharing the same channels, context, and audit trail as human users. The platform is model-agnostic, supporting Claude Code, Codex, and Goose via the Agent Client Protocol, and can be self-hosted or used through Block's relay. Notably, Buzz skips conventional sign-up entirely, instead generating a cryptographic identity key on first launch that serves as the user's account. Currently in developer preview, the project openly acknowledges it is unfinished, but is available for download at buzz.xyz as a desktop app with mobile clients.

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Developer tool lets cheap AI coding agents consult stronger models for hard problems

A developer has released ask-fable, an open-source MCP server designed to give budget AI coding agents on-demand access to more powerful reasoning models only when needed. The tool integrates with Claude Code and other MCP clients, allowing a cheaper, faster model to handle routine code writing while delegating complex problems — such as concurrency bugs or architectural decisions — to stronger models. Ask-fable offers four query modes: a standard single-model query, a council mode that polls multiple models and synthesizes their answers, a chained pipeline mode, and an adversarial debate mode for major design forks. The system supports a wide range of AI backends including Anthropic, Gemini, GPT, DeepSeek, and Grok, with gateway support for hundreds of additional models via OpenRouter. A built-in guard layer restricts the tool to software reasoning tasks, rejecting off-topic or offensive-security requests.

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ProgrammingDEV Community ·

Why Linux's 'cat' Command Is Misused and What to Use Instead

The 'cat' command, one of the first tools Linux users learn, was originally designed in 1971 by Ken Thompson and Dennis Ritchie to concatenate multiple files into a single stream — not to view individual files. Despite this, decades of tutorials have entrenched 'cat' as a default file viewer, a habit that causes real problems in production environments. When used on large log files or piped unnecessarily into other commands, it can freeze terminals, waste CPU cycles, and add kernel overhead. Modern CLI alternatives offer advantages like syntax highlighting, git-aware context, and better performance for interactive file inspection. Understanding the original purpose of 'cat' and adopting purpose-built tools can significantly improve how developers and administrators work on the Linux command line.

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ProgrammingDEV Community ·

How Business Growth Quietly Breaks Software That Once Worked Fine

Freelance software engineer Ayman Atif describes a recurring challenge in business software: applications that function well at launch begin to degrade as the business scales. A system handling a few hundred records may slow significantly when that number reaches tens of thousands, even without any code changes. The problem is compounded when users expect desktop-like behavior — such as viewing thousands of records in grids without pagination — placing simultaneous demands on the database, server, and browser. Atif notes that the instinct to fix the most visible symptom is often misleading, and that tracing the full request path from database to user interaction yields better results. The core issue, he argues, is that original design assumptions stop holding true as real-world usage grows.

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ProgrammingDEV Community ·

Arduino VENTUNO Q Combines Edge AI and Real-Time Microcontroller in One Board

Arduino has introduced the VENTUNO Q, a development board that pairs a Qualcomm Dragonwing IQ8 processor with an STM32H5 microcontroller to handle both AI inference and real-time physical control. The Dragonwing side manages neural-network workloads using its NPU, CPU, and GPU, while the STM32H5 handles time-sensitive operations such as motor control and GPIO. The board ships with 16 GB of LPDDR5 RAM, 64 GB of eMMC storage, and supports Ubuntu, Zephyr, and ROS 2, along with compatibility with Arduino UNO shields and Raspberry Pi accessories. Arduino positions the device for local large language models, computer vision, and speech applications, reducing dependence on cloud connectivity and keeping data processing on-device. The platform targets developers working across embedded programming, Linux tooling, and edge AI who want a single integrated system rather than multiple separate devices.

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