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Topology-Aware Data Movement for Disaggregated GPU Inference
arXiv:2607.28633v1 Announce Type: new Abstract: Disaggregated LLM inference creates a datacenter networking problem that no existing system solves correctly. When prefill and decode run on separate GPU pools, the KV cache must be transferred between them. For a 70B model this is 2.6 GB per request, exceeding 100 GB/s aggregate at production scale. Yet DistServe, Splitwise, and Mooncake all use uniform RDMA, ignoring that bandwidth between two GPUs varies by 72x depending on their physical relationship: 900 GB/s via NVLink within a domain, 50 GB/s via InfiniBand across nodes, 12.5 GB/s via TCP across data centers. We design a topology-aware transfer orchestrator that discovers interconnect hierarchy at startup and selects optimal transport per transfer. Three mechanisms work together: (1) pipelined layer-by-layer transfer that overlaps transmission with ongoing prefill, hiding 60 to 85 percent of latency behind computation; (2) NVLink domain-aware placement for Mixture-of-Experts models
How to Apply Coding Agents to Non-Programming Tasks
Perform non-programming tasks with coding agents The post How to Apply Coding Agents to Non-Programming Tasks appeared first on Towards Data Science.
I Replaced a 15-Minute Booking Process with a LangGraph AI Agent
A step-by-step guide to building, running, and monitoring a stateful customer support agent using Python, LangGraph, and Langfuse. The post I Replaced a 15-Minute Booking Process with a LangGraph AI Agent appeared first on Towards Data Science.
Agentic Misalignment Explained: When AI Agents Go Rogue
Imagine hiring an AI assistant to handle important tasks, only to find that it quietly ignores your instructions because it believes it knows better. This is known as agentic misalignment, where an AI intentionally pursues its own objective instead of the one set by its operator. To understand how often this behavior appears, Anthropic researchers [ ] The post Agentic Misalignment Explained: When AI Agents Go Rogue appeared first on Analytics Vidhya.
Coding Agents Don’t Need Bigger Context Windows — They Need a Context Compiler
Most coding agents treat prompt construction like retrieval: gather more files, add more context, hope the model figures it out. But that approach breaks down fast. As context grows, irrelevant code competes for attention, and when the window fills, agents start compressing their own memory—often mid-task. What looks like “forgetting” is usually just degraded context. This article explores a different approach: treating prompt construction like a compiler that decides what to keep, what to reduce, and what to discard entirely. The post Coding Agents Don’t Need Bigger Context Windows — They Need a Context Compiler appeared first on Towards Data Science.
Put the Agent Inside the Workflow
A hybrid LLM application pattern that combines a predefined workflow with adaptive agent behavior The post Put the Agent Inside the Workflow appeared first on Towards Data Science.
LanceDB Vector Database Guide: Features, Python Demo
Large language models understand text well, but they become less effective when information is scattered across documents or mixed with images and other media. Modern AI systems rely on vector databases, which store embeddings and enable similarity search across collections. LanceDB is a vector database built for AI workloads, with native support for multimodal data [ ] The post LanceDB Vector Database Guide: Features, Python Demo appeared first on Analytics Vidhya.
The 3× Token Bill We Didn’t See Coming
How a seemingly harmless move to a multi-agent architecture quietly tripled our LLM costs and what actually fixed it. The post The 3× Token Bill We Didn’t See Coming appeared first on Towards Data Science.
When the Code Becomes the CEO: Why Your Next Manager Might Be a Decentralized Agentic Loop
In five to ten years, the sharpest manager in your company might not be human, might not sleep, and might exist entirely in shared GPU memory. This is the systems-level view of the algorithmic corporation — why middle management collapses into a protocol, what breaks in the current AI stack, and what has to be built for autonomous agents to actually run a business. The post When the Code Becomes the CEO: Why Your Next Manager Might Be a Decentralized Agentic Loop appeared first on Towards Data Science.
How to Debug AI Coding Agents When They Change the Wrong Thing
A practical tutorial for recording model tool requests, real function results, patches, checks, screenshots, and a saved run log. The post How to Debug AI Coding Agents When They Change the Wrong Thing appeared first on Towards Data Science.
How Benders Decomposition Works Part I: Optimality Cuts
A friendly introduction to one of the most powerfull optimization techniques using the uncapacitated facility location problem The post How Benders Decomposition Works Part I: Optimality Cuts appeared first on Towards Data Science.
July 2026 AI Releases: A Timeline of Frontier Model Shifts
July 2026 was the busiest month for frontier model releases the field has seen. Four major labs shipped flagship or near-flagship models, two well funded newcomers shipped their first, and the largest open weight model ever published went up for download, all inside thirty one days. Read as a list, the top AI models in July [ ] The post July 2026 AI Releases: A Timeline of Frontier Model Shifts appeared first on Analytics Vidhya.