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Chad Collins

1,096 articles published

How to Scale AI-Assisted Development Without Overloading Git or Weakening Code Review

What Happened GitHub is redesigning its Git infrastructure for repositories where developers and agents work concurrently. Monthly Git events more than doubled between September 2025 and August 2026, reaching 473.3 billion. Its proposed architecture coordinates reference updates while parallelizing other push work, moves maintenance off the serving path, and separates compute from durable storage. GitHub…

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How to Choose AI Hardware and Test Agents for Real-World Workloads

What Happened Two recent research publications address different gaps in AI planning. The Lincoln AI Computing Survey now tracks more than 120 commercial accelerators, up from 57 in its first survey. It compares publicly reported peak performance and power across CPUs, GPUs, ASICs, FPGAs and dataflow systems, while examining how architecture affects performance. Earlier work…

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Polars 2.0 Makes Larger-Than-Memory Analytics Easier—but Migration Testing Still Matters

What Happened Python Polars 2.0 enables out-of-core execution by default, targeting 80% of available RAM and using a default 64 GB disk budget. It adds out-of-core sorting and improves streaming group-by, window, join, and approximate-quantile execution. The release also expands SQL support with grouping sets, ROLLUP, CUBE, GROUPING(), and additional window options [1]. Query-planning changes…

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How to Choose an Agent Framework Without Compromising Security or Reliability

What Happened Recent releases show agent tooling improving at two different layers. LangGraph 1.2.14 was announced without substantive change details in the available release note; its Python SDK 0.4.6 percent-encodes thread and assistant IDs in stream requests, a targeted interoperability fix [3][4]. Claude Code 2.1.290–2.1.292 added agent-effort controls, plugin-install options and workflow-agent hook details while…

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How to Choose GPU Infrastructure Without Locking AI Workloads to One Stack

What Happened Recent NVIDIA materials point to three infrastructure problems that become more visible as AI moves into production. GPU applications may need to initiate data movement without putting the CPU on every network transaction; multiple components within one process need predictable access to GPU resources; and Kubernetes GPU clusters require compatible versions of drivers,…

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What Recent llama.cpp Updates Mean for Safer, More Portable Local AI Inference

What Happened The developments in these notes are concentrated in llama.cpp and ggml, not new open-weight model releases. llama.cpp added support for the pplx-decider model, while text, vision and audio support for embeddinggemma2 is tracked as a request rather than a confirmed release. [5] [8] Inference changes include RPC support for tensor split mode, with…

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AI Agents Are Moving Into Business Workflows — Here’s What Enterprises Need to Control

What Happened Several announcements point to agents becoming operational software, not just chat interfaces. Meta, Walmart, Stripe and others published the Personal Agent Protocol to standardize and secure interactions between consumer agents and businesses [2]. SAP said its Autonomous Enterprise architecture will become generally available this month, while Cohere introduced North 2 for multi-step agent…

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What New AWS AI, Data and Security Releases Mean for Production Systems

What Happened On October 5, 2026, AWS announced updates across AI models, data platforms, infrastructure and access controls: AI: Z.ai’s GLM 5.3 became generally available to eligible Amazon Bedrock enterprise customers. It offers a 1-million-token context window and selectable reasoning effort. Amazon Nova 2.5 Sonic also became generally available for real-time speech-to-speech agents, alongside Strands…

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How to Build Production AI Platforms Without Losing Control of Cost, Security and Reliability

What Happened Recent infrastructure announcements point to a broader shift: inference, agent execution, retrieval and evaluation are moving into managed cloud services. That reduces infrastructure work, but leaves businesses responsible for workflow reliability, access control and spending. Agent execution is moving off the laptop. Anthropic’s redesigned Cowork runs both inference and a separate per-session sandbox…

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