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Author page: Chad Collins

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Chad Collins

1,105 articles published
Illustration for the Kimbodo News & Research briefing “Why llama.cpp’s Recent Releases Make Local, Cross‑Platform Inference Practical — and What Leaders Should Do Next” (Open-Source Models & Communities).

Why llama.cpp’s Recent Releases Make Local, Cross‑Platform Inference Practical — and What Leaders Should Do Next

Open-Source Models & CommunitiesAugust 27, 2026234Views0Likes0Comments
What Happened The open-source llama.cpp project published a large set of engineering and backend changes that materially reduce friction for running large models locally across Windows, macOS, Linux, Android and specialized architectures. The changes fall into four practical categories: backend/kernel expansion, memory and I/O optimizations, platform/runtime hygiene, and operational/benchmark tooling. Backend and kernel…

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Prepare Your AI Stack for Persistent Agents, Faster Models and Escalating Cyber Risk

AI Industry NewsAugust 27, 2026174Views0Likes0Comments
What Happened Today’s AI headlines coalesced around three themes: agentic capabilities becoming persistent and integrated into user devices and workflows, major shifts in model and hardware economics, and coordinated warnings about AI-enabled cyberattacks. Anthropic is planning a blockbuster IPO with secondary share sales and atypical lockup discussions, and simultaneously published a Model Hardware…

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How to Adopt AI Safely as Compute Scarcity, Agent Risk and Open-Source Supply Chains Converge

Industry NewsAugust 27, 2026137Views0Likes0Comments
What Happened The last day’s technology news points to a clear shift: AI adoption is no longer mainly a model-selection problem. It is becoming an infrastructure capacity, governance, security and product-design problem. AI compute demand is tightening the whole hardware stack. Nvidia reported record data-center revenue and is nearing a scale normally associated…

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Illustration for the Kimbodo News & Research briefing “How to Build Production AI Platforms That Control Deployment Cost, Evaluation Risk and Agent Complexity” (Research).

How to Build Production AI Platforms That Control Deployment Cost, Evaluation Risk and Agent Complexity

ResearchAugust 26, 2026164Views0Likes0Comments
What Happened Several recent AI infrastructure updates point to the same operating reality: enterprise AI is moving from isolated model experiments to platforms that must manage model choice, telemetry, evaluation, data access, cost controls and workflow integration. Open-weight multimodal models are becoming more deployment-relevant. Qwen3.8-Flash-Next was presented as an open-weights multimodal Mixture-of-Experts model…

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GitHub Release Monitoring — August 26, 2026

GitHub Release MonitoringAugust 26, 2026146Views0Likes0Comments
What Happened Summary of releases v0.33.1: Small point release adding Qwen3.8 "Flash Next" support, mlxrunner structured output and Metal GPU load-time timeout avoidance; CMake external-compatibility patches made idempotent [1][2]. v5.16.0: Large platform/model release that added many model ports (Qwen4‑Exp, Granite Speech 5.0 Turbo CTC, Step‑3.7‑Flash sparse MoE, CohereCompass base, ESMC/ESMFold2 ports),…

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Curated AI Newsletters & Summaries — August 26, 2026

Curated AI Newsletters & SummariesAugust 26, 2026137Views0Likes0Comments
What Happened Two tightly related developments surfaced in this week’s reporting: advances in physics‑centric foundation models and progress closing the model training→serving loop at model, environment and infrastructure layers. Anima Anandkumar’s team demonstrated that physics problems (weather, plasma) can be modeled at production quality using neural operators (Fourier Neural Operator and spherical‑harmonics variants)…

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Protect AI Gateways and Agent Runtimes: Prevent Credential Theft, Cryptomining and VM Escapes

AI Security & CybersecurityAugust 26, 2026167Views0Likes0Comments
What Happened Two converging trends in 2026 make AI infrastructure a uniquely valuable attacker target: (1) adversaries are compromising AI gateways, retrieval/orchestration platforms and runtimes to steal provider credentials, establish persistence and monetize compute; and (2) advanced autonomous agents can discover and weaponize zero‑days to escape virtual machines and operate as APTs. Gateways and orchestration…

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AI Coding & Developer Tools — August 26, 2026

AI Coding & Developer ToolsAugust 26, 2026169Views0Likes0Comments
What Happened GitHub introduced a Copilot app automation template to automate Dependabot pull request triage: it groups open Dependabot PRs by risk (safe patch, minor, major), verifies CI status, produces short summaries with next-step recommendations, and can start an interactive Copilot session from the automation context. Automations support manual, hourly, daily, weekly, or…

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How Recent AI Research Lowers Cost and Risk for Production Systems — Practical Signals for CIOs and ML Engineers

AI Research & PapersAugust 26, 2026162Views0Likes0Comments
What Happened A cluster of recent papers across materials, model architecture, agent systems, evaluation methodology and auditing propose practical advances that reduce compute cost, improve reliability, or expose operational failure modes. Highlights: Faster, more-valid materials design: CrysVCD enforces valence constraints up‑front and combines an LM for formulas with a diffusion structure generator, cutting…

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How PyTorch’s 10 New Projects Change Production ML — and What Data Science Teams Using Python and R Should Do Next

Data Science, Python & RAugust 26, 2026136Views0Likes0Comments
What Happened The PyTorch Ecosystem Landscape added ten projects that expand training, inference, routing, dataset, visualization and domain-specific tooling: Perforated, AReaL, TorchJD, RLinf, Miles, SMG, FiftyOne, TokenSpeed, VisualTorch, and TorchSurv. These projects aim to increase visibility and community collaboration around PyTorch-native tooling [1]. Perforated: a data-efficiency library that injects neuron-specific RL signals via…

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Build Production-Grade RAG: Practical Choices for Vector Databases, Hybrid Search and Safe ES|QL Behavior

Retrieval, RAG & SearchAugust 26, 2026143Views0Likes0Comments
What Happened Elasticsearch 9.5 introduced an unmapped_fields option for ES|QL that prevents queries from failing when they reference fields missing from index mappings. The option accepts NULLIFY (return NULLs) or LOAD (read values from _source) and resolves partially unmapped non-keyword fields (PUNKs) by injecting an “unmapped” field into the query plan so the planner and…

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