DEEBO AUTONOMOUS DEBUGGING MCP SERVER FOR AI CODING AGENTS

Debugging AI Server LPO

Debugging AI Server LPO

This guide covers all of it: unit testing tool implementations, integration and end-to-end testing with mock LLM responses, regression testing with golden datasets, performance profiling, and the debugging techniques that make agent failures diagnosable rather than mysterious. Complete guide to debugging AI agents in production: 5 failure modes, debugging primitives, and when to use agent-first observability tools like Latitude. By Latitude · March 23, 2026 Key Takeaways Agent debugging requires thinking about failure at the session level — the failures that matter. DebugMCP is an MCP server that gives AI coding agents full control over the VS Code debugger. Instead of reading logs or guessing, your AI assistant can autonomously set breakpoints, launch debug sessions, step through code line by line, inspect variable values, and evaluate expressions — just like. Debugging production MCP servers requires moving beyond local STDIO to inspect raw JSON-RPC traffic, handle HTTP 429 rate limits, and normalize third-party API errors before they reach your AI agent.

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AI Server Power Connection

AI Server Power Connection

This blog post explores innovations in power devices, gate drivers and advanced controllers with Digital Signal Processing (DSP) capabilities to meet Artifical Intelligence (AI) servers' power and efficiency needs. This AI selector guide simplifies the selection process, helping designers quickly find solutions that achieve high efficiency while meeting crit density, reliability, and performance. Recently, we finished turnkey OEM (original equipment manufacturing) for our client, providing AI server contract manufacturing from prototypes to bulk production, from PCB fabrication, component sourcing, PCB assembly, custom accessories including high-power cables and prong connectors, box-build. The document is particularly relevant for design engineers and component purchasers specifying. An AI server is a specially designed and optimized server that may have one or more high-performance GPUs (Graphics Processing Units) or dedicated AI accelerators, such as Google's Tensor Processing Units (TPU) or NVIDIA's AI accelerator cards, among others. Where traditional server racks once operated at around 5–10 kW, modern AI environments are pushing far beyond that, often reaching 30 kW, 60 kW or even over 100 kW per rack.

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Iran inquires about 200G AI server

Iran inquires about 200G AI server

Iran has issued a new threat against a US interest: the $30 billion Stargate AI data center in Abu Dhabi. Stringer/Getty Images Welcome back to In the Loop, TIME's new twice-weekly newsletter about AI. Tech companies have been funnelling billions of dollars into AI infrastructure projects in the Middle East over the past few years, drawn in by cheap and readily available energy and land, alongside local government support. Iran's strikes on data centres in the Gulf signal a new phase in warfare, where digital infrastructure becomes a target. Home / Blog / Iran's AI Ambitions: From Strategic Goals to Offensive Capabilities I recently read the in-depth analysis of Iran's national strategy regarding AI by Recorded Future Insikt Group®. Despite facing economic and technological constraints, Iran is developing a state-led, militarized AI.

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FTTH uses a 400G AI server

FTTH uses a 400G AI server

Based on the 3D-mesh architecture of AI DCs, ISP optical transport and premium private line solution adds 400G ultra-high-speed planes in hotspot areas to guarantee high bandwidth and SLAs for AI computing power. These components are not mere upgrades but foundational necessities to support the data-heavy operations of AI. AI infrastructure and applications will bring new opportunities to ISPs and operators, including new traffic brought by AI DCI and AI application device-cloud synergy, as well as value-added sales of network latency brought by real-time interactive applications. The definitive guide to selecting, deploying, and maximizing 400G optical transceivers for network architects, procurement managers, and operations teams building the infrastructure that powers today's AI, cloud, and carrier networks. This article explains how 400G/800G Ethernet fabrics enable scalable, low-latency, high-bandwidth AI data center networks, addressing GPU traffic, congestion control and modern architecture needs. AI can enable more efficient network design and management, reducing costs, while improving service and flexibility – providing certain preconditions are met. How is AI changing FTTH network design? The global FTTH network design market is expected to grow from $1.

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Do we need a server to deploy AI

Do we need a server to deploy AI

Yet, to implement AI models effectively, one needs powerful computing capacity, which is where an AI GPU server is needed. Using GPU-accelerated infrastructure provides accelerated model training and inference, and thus it is an essential part of AI-powered businesses. Choosing the right AI server setup for your workload is crucial to ensuring optimal performance and scalability. Imagine running complex machine learning models, generating stunning AI-driven visuals, or training large language models, all from a server you've designed and.

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