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Author page: Chad Collins

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Chad Collins

1,102 articles published
Illustration for the Kimbodo News & Research briefing “Act Now: Key AI and Cloud Platform Updates That Change Deployment, Cost and Operations” (Industry News).

Act Now: Key AI and Cloud Platform Updates That Change Deployment, Cost and Operations

Industry NewsSeptember 14, 2026127Views0Likes0Comments
What Happened Kubernetes Changed Block Tracking (CBT) for CSI drivers moved from v1alpha1 to cbt.storage.k8s.io/v1beta1 (schema unchanged) and shipped with external-snapshot-metadata v1.0.0; requires Kubernetes >= 1.33 and CSI spec >= 1.10. Upgrade requires re-applying the v1.0.0 CRD and updating manifests and clients (no automatic conversion) — CBT targets block volumes only [1]. …

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How to Build Production AI Agents Without Losing Control of Cost, Security, and Operations

ResearchSeptember 14, 2026144Views0Likes0Comments
What Happened Recent enterprise AI platform patterns show a clear shift: production teams are moving beyond standalone chatbots toward orchestrated agent systems with managed runtime isolation, governed identity, task-specific models, deterministic business rules, and cloud-native observability. Amazon Bedrock AgentCore is being used as managed infrastructure for agent execution, including serverless code-interpreter sandboxes that run in…

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The Few AI Sources That Predict Practical Breakthroughs — A Kimbodo Executive Briefing

UncategorizedSeptember 14, 202699Views0Likes0Comments
What Happened Two signal items this week underline where you need to focus: DevFest 2026 is returning as a global, in-person/online network aimed at helping developers and organizations build, secure and scale for the agentic AI era [2]. Separately, high-profile conversations (for example, Google research leadership engaging explorers and domain experts) continue to signal how…

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Stay deployment-safe: how to track and respond to breaking changes and new features in AI/ML open‑source libraries

GitHub Release MonitoringSeptember 14, 2026100Views0Likes0Comments
What Happened Three related upstream updates surfaced that matter to teams running production AI/ML stacks: A v0.34.1 release (covering changes from v0.34.0 → v0.34.1-rc1) that includes UI fixes, MLX/runner memory and lifecycle changes, and LLM engine adjustments such as raising the token repeat limit to 100 and returning explicit errors for over‑limit inputs;…

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How Recursive’s “Eureka Machine” Could Cut AI Training Costs and Reshape Enterprise Model Ops

Curated AI Newsletters & SummariesSeptember 14, 2026116Views0Likes0Comments
What Happened Richard Socher’s Recursive announced a large strategic seed focused on a “Eureka Machine” — a recursive, auto‑research stack that optimizes AI infrastructure and models end to end. The company reported early wins where its auto‑research system outperformed humans on NanoChat/NanoGPT and discovered CUDA kernel improvements, and it plans to prioritize “AI for AI”…

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Use Behavioral Clustering on Cloud Audit Logs to Find Risky Identities — and Turn Detections into SQL-First Alerts

AI Security & CybersecuritySeptember 14, 2026100Views0Likes0Comments
What Happened Unit 42 published a practical detection pattern that maps cloud identity behavior by clustering audit-log activity and converting those behavioral patterns into standard SQL queries for continuous threat detection. The method uncovers anomalous or risky accounts by grouping identities by role-like behavior and operationalizing those groups as repeatable, queryable detections rather than one-off…

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AI Coding & Developer Tools — September 14, 2026

AI Coding & Developer ToolsSeptember 14, 2026157Views0Likes0Comments
What Happened The supplied notes describe two GitHub product updates that change how organizations control cost/quality trade-offs and perform large-scale code changes: GitHub Copilot introduced an auto model-selection feature with three policy tiers — Efficiency, Balance, and Intelligence — that evaluate each prompt and choose the best model instance for that prompt (so…

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Which Recent AI Research Should Business Leaders Adopt Now — and How to Turn Papers into Production-Grade Capabilities

AI Research & PapersSeptember 14, 2026117Views0Likes0Comments
What Happened A broad wave of applied AI research published across labs and preprints this cycle converges on four actionable trends for product and operations teams: (1) deploying constraint-aware generative models for safety‑critical outputs, (2) building domain-specific, low-label supervision pipelines, (3) operational hardening for agentic systems and edge deployments, and (4) efficiency and interpretability advances…

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How ellmer 0.5.0 Makes R-first AI Apps Safer, Cheaper and Easier to Integrate with Posit, Quarto and Shiny

AI Application DevelopmentSeptember 14, 2026119Views0Likes0Comments
What Happened ellmer 0.5.0 was released on CRAN (install.packages("ellmer")). The release introduces lifecycle and return-type changes, file/citation/cost tooling, provider default model updates, structured streaming and new request hooks. Key items: tool return types tightened (data frames/lists deprecated); file upload and document APIs added; citation capture from major LLM providers; token counting across multiple providers; default…

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Choose the Right GPU, Cloud and Deployment Stack to Ship Secure, Cost-Effective AI Products

AI Infrastructure, GPUs & DeploymentSeptember 14, 2026137Views0Likes0Comments
What Happened The recent release of Perplexity's Portable Computer for Windows — a local, multistep agent accelerated by NVIDIA RTX — highlights a clear trend: AI agents and capable models are moving off centralized clouds and onto endpoint GPUs to reduce latency and keep sensitive data local [1]. At the same time, enterprises must balance…

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Illustration for the Kimbodo News & Research briefing “Why the latest llama.cpp / ggml updates make multiplatform, low-cost inference realistic for production” (Open-Source Models & Communities).

Why the latest llama.cpp / ggml updates make multiplatform, low-cost inference realistic for production

Open-Source Models & CommunitiesSeptember 14, 2026180Views0Likes0Comments
What Happened The llama.cpp project published a major v0.4.1 milestone and associated commits that together expand model support, backend coverage, server reliability and observability while hardening correctness across CPU/GPU/accelerator backends. New models & formats: adds Maple 20B‑A1B (ternary MoE, CPU) and a Tencent Hy 4 preview; conversion and format flags (e.g., --fuse-qkv) to…

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