Popular & Trending

AI-ML 7 min read

Kimi K2.5: A Deep Dive into China’s Long-Context AI Model and What It Means for the Future of LLMs

Kimi K2.5 is not just another chatbot—it represents a shift toward long-context reasoning and document-centric AI. This deep dive explores how Moonshot AI’s model handles massive inputs, why it excels in enterprise and research workflows, and what it signals about the future direction of large language models.

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techie007

2 days ago

3,669

AI-ML 7 min read

Local LLMs for Security vs Securing Context with Public LLMs

As enterprises embed large language models into automation, compliance, and platform engineering workflows, security becomes a trust-boundary problem—not a tooling choice. This article examines the real trade-offs between running local LLMs for data isolation and using public LLMs with carefully secured context, drawing on production architecture patterns, operational risks, and regulatory realities that engineering teams face today.

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ControlPlane

Feb 1, 2026

541

AI-ML 8 min read

Building an MCP Server to Interact with Ansible Automation Platform

A practical guide to building an MCP server for Ansible Automation Platform, enabling safe AI-driven automation on OpenShift with policy controls, guardrails, and real production insights.

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ControlPlane

Jan 31, 2026

535

Architecture 6 min read

Building Custom Kubernetes Operators in Go — And Why Enterprises Actually Need Them

Kubernetes is excellent at orchestrating containers, but it does not understand enterprise application lifecycle, compliance, or operational intelligence. This is where custom Kubernetes Operators written in Go become essential. In this deep-dive article, we explore why enterprises build Operators, how they encode real operational knowledge into Kubernetes, and real-world use cases from telecom, banking, databases, and internal developer platforms. Learn how Go-based Operators enable zero-downtime upgrades, continuous compliance, and scalable day-2 operations that Helm charts and CI/CD pipelines cannot handle alone.

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ControlPlane

Feb 1, 2026

452

AI-ML 4 min read

How Stanford’s 8-Word Prompt Changed the Way We Think About Prompt Engineering

A subtle eight-word prompting technique from Stanford researchers has challenged long-standing assumptions about prompt engineering. By encouraging models to reveal multiple possible outputs instead of a single “safe” response, this approach unlocks greater creativity and diversity—without retraining or complex prompt templates. This article explores how it works, why it matters, and what it means for the future of working with generative AI.

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techie007

Jan 29, 2026

450

AI-ML 6 min read

Building Trustworthy AI Agents: An Engineering Playbook for Production Systems

Building AI agents that work in production is not just about better models or smarter prompts. Trustworthy AI agents require strong engineering discipline—determinism, observability, memory design, action safety, and continuous evaluation. This practical, engineering-focused guide explains how to design AI agents that are predictable, debuggable, and safe to operate at scale.

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techie007

Jan 29, 2026

442

AI-ML 7 min read

Beyond the Prompt: Why Simply “Using AI” Is No Longer Enough

For a brief moment in technology history, writing good prompts felt like a superpower. You described what you wanted.
The model responded with code, prose, insights, or answers.
Entire workflows suddenly felt effortless. Prompt engineering quickly became a buzzword—and for good reason. It lowered the barrier to entry for advanced AI and made powerful models accessible to almost anyone.

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techie007

Jan 28, 2026

290

AI & Machine Learning 6 min read

Tokenomics for Coding Agents: A Developer’s Guide to Saving Money Without Slowing Down

Learn how to control coding-agent costs with practical tokenomics: smaller context, smarter model routing, caching, tool discipline, and measurable cost budgets.

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DeepStateAI

4 weeks ago

131

AI & Machine Learning 6 min read

The Agent Permission Problem: Why AI Needs Its Own Identity Lifecycle

AI agents are moving from answering questions to calling APIs, accessing data and changing systems. The next engineering challenge is not just model safety—it is giving every agent a distinct identity, scoped authority, short-lived credentials and auditable actions.

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DeepStateAI

3 weeks ago

97

AI-ML 6 min read

AI Agents Are Becoming a Security Boundary: What the September 2026 Incidents Teach Engineering Teams

Recent incidents and threat research show AI agents moving from assistants to systems that can browse, execute tools, maintain state, and act with limited supervision. This engineering-focused guide explains what changes when an agent becomes a security principal—and how to design identity, permissions, audit, isolation, and kill controls around it.

D

DeepStateAI

4 weeks ago

64

AI & Machine Learning 9 min read

From Tokenomics to Unit Economics: How to Measure the Real Cost of Coding Agents

Part 2 of the coding-agent tokenomics series: move beyond token counts and build a practical unit-economics framework for measuring cost per successful task, cache efficiency, tool overhead, model routing, and developer value.

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DeepStateAI

3 weeks ago

62

AI-ML 9 min read

The AI Agent Is Not the Product: Why Agentic Systems Need an Engineering Control Plane

AI agents can reason, use tools, and take actions, but production systems need much more than an intelligent model. Explore the engineering control plane required for identity, policy, observability, memory, evaluation, approvals, and safe autonomous execution.

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DeepStateAI

Sep 13, 2026

56