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@kaptain shared a link, 1 month, 1 week ago
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From Deterministic to Agentic: Creating Durable AI Workflows with Dapr

Dapr droppedDurable Agents- a mashup of classic workflows and LLM-driven agents that can actually get things done and survive rough edges. They track reasoning steps, tool calls, and chat states like a champ. If things crash, no problem: Dapr Workflows and Diagrid Catalyst bring it all back... read more  

From Deterministic to Agentic: Creating Durable AI Workflows with Dapr
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@kaptain shared a link, 1 month, 1 week ago
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Implementing assurance pipeline for Amazon EKS Platform

AWS released a full-stack CI/CD validation pipeline forAmazon EKS. It pulls in six layers of testing,Terraform,Helm,Locustload testing, and evenAWS Fault Injectionfor pushing resilience to the edge. The goal: bake policy checks, functional tests, and brutal load tests right into pre-deployment. Fewe.. read more  

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@kaptain shared a link, 1 month, 1 week ago
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v1.35: New level of efficiency with in-place Pod restart

Kubernetes 1.35, as you may know, introducedin-place Pod restarts(alpha). It's a real reset: all containers, init and sidecars included - without killing the Pod or kicking off a reschedule. Think restart without the cloud drama. Big win for workloads with heavy inter-container dependencies or massi.. read more  

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@kaptain shared a link, 1 month, 1 week ago
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1.35: Enhanced Debugging with Versioned z-pages APIs

Kubernetes 1.35 makes a quiet-but-crucial upgrade: z-pages debugging endpoints now returnstructured, machine-readable JSON. That means tools- not just tired humans - can parse control plane state directly. The responses areversioned, backward-compatible, and tucked behind feature flags for now... read more  

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v1.35: Watch Based Route Reconciliation in the Cloud Controller Manager

Kubernetes v1.35 sneaks in an alphafeature gatethat flips the CCM route controller from "check every X minutes" to "watch and react." It now usesinformersto trigger syncs when nodes change - plus a light periodic check every 12–24 hours... read more  

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@kala shared a link, 1 month, 1 week ago
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The 2026 Data Engineering Roadmap: Building Data Systems for the Agentic AI Era

Data engineering’s getting flipped.AI agentsandLLMsaren’t just tagging along anymore - they’re the main users now. That means engineers need to buildcontext-aware, machine-readable data systemsthat don’t just store info but actually make sense of it. Think:vector databases,knowledge graphs,semantic .. read more  

The 2026 Data Engineering Roadmap: Building Data Systems for the Agentic AI Era
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@kala shared a link, 1 month, 1 week ago
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Streamlining Security Investigations with Agents

Slack broke down how it's threading AI into its product without torching user trust.Slack AIleans hard ontenant-specific data isolationandzero data retention- no leftover crumbs from LLM interactions. Instead of piping user data through someone else’s APIs, Slack runs LLMs onits own infrawhere it ca.. read more  

Streamlining Security Investigations with Agents
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@kala shared a link, 1 month, 1 week ago
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2025: The year in LLMs

2025 was the year LLMs stopped just answering questions and started building things.Reasoning modelslike OpenAI’s o-series and Claude Code took over tool-driven workflows. Asynchronous coding agentsbroke out. These models didn’t just write code - they ran it, debugged it, then did it again. That loo.. read more  

2025: The year in LLMs
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@kala shared a link, 1 month, 1 week ago
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Meet the ‘Mad Max’-Loving CEO Challenging Nvidia With a Renegade Chip

June Paik spurned a takeover offer from Meta Platforms last year. Now his South Korean company, FuriosaAI, has an AI chip entering mass production... read more  

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@kala shared a link, 1 month, 1 week ago
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My LLM coding workflow going into 2026

Anthropic saysClaude Code writes about 90% of its own code now. Why? Because devs are getting smart with AI. They're slicing problems into tight, testable chunks and running structured workflows that keep LLMs on a short leash. It's not just prompts anymore. Think context packaging, multi-agent setu.. read more  

My LLM coding workflow going into 2026
AIStor is an enterprise-grade, high-performance object storage platform built for modern data workloads such as AI, machine learning, analytics, and large-scale data lakes. It is designed to handle massive datasets with predictable performance, operational simplicity, and hyperscale efficiency, while remaining fully compatible with the Amazon S3 API. AIStor is offered under a commercial license as a subscription-based product.

At its core, AIStor is a software-defined, distributed object store that runs on commodity hardware or in containerized environments like Kubernetes. Rather than being limited to traditional file or block interfaces, it exposes object storage semantics that scale from petabytes to exabytes within a single namespace, enabling consistent, flat addressing of vast datasets. It is engineered to sustain very high throughput and concurrency, with examples of multi-TiB/s read performance on optimized clusters.

AIStor is optimized specifically for AI and data-intensive workloads, where throughput, low latency, and horizontal scalability are critical. It integrates broadly with modern AI and analytics tools, including frameworks such as TensorFlow, PyTorch, Spark, and Iceberg-style table engines, making it suitable as the foundational storage layer for pipelines that demand both performance and consistency.

Security and enterprise readiness are central to AIStor’s design. It includes capabilities like encryption, replication, erasure coding, identity and access controls, immutability, lifecycle management, and operational observability, which are important for mission-critical deployments that must meet compliance and data protection requirements.

AIStor is positioned as a platform that unifies diverse data workloads — from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets — within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.