<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Insights — Felesh</title><description>Insights</description><link>https://blog.felesh.ai/</link><item><title>Agents that act, not just answer — safely</title><link>https://blog.felesh.ai/en/insights/agents-that-act-safely/</link><guid isPermaLink="true">https://blog.felesh.ai/en/insights/agents-that-act-safely/</guid><description>The real leap is when a system doesn&apos;t just answer but gets something done in a real system. And acting makes mistakes costly — so &apos;safely&apos; has to be structural.</description><pubDate>Sun, 21 Jun 2026 00:00:00 GMT</pubDate></item><item><title>AI that survives an audit</title><link>https://blog.felesh.ai/en/insights/ai-that-survives-an-audit/</link><guid isPermaLink="true">https://blog.felesh.ai/en/insights/ai-that-survives-an-audit/</guid><description>When an intelligent system makes a decision, sooner or later someone asks: &apos;why?&apos; A system that can&apos;t answer fails an audit. We believe auditability has to be in the design from day one, not a later add-on.</description><pubDate>Sun, 21 Jun 2026 00:00:00 GMT</pubDate></item><item><title>Attentive AI: why focused beats big</title><link>https://blog.felesh.ai/en/insights/attentive-ai/</link><guid isPermaLink="true">https://blog.felesh.ai/en/insights/attentive-ai/</guid><description>The biggest model is not the best answer. When every job has a defined boundary, focused intelligence beats jack-of-all-trades intelligence every time.</description><pubDate>Sun, 21 Jun 2026 00:00:00 GMT</pubDate></item><item><title>Authority is a trajectory, not a key</title><link>https://blog.felesh.ai/en/insights/authority-as-a-trajectory/</link><guid isPermaLink="true">https://blog.felesh.ai/en/insights/authority-as-a-trajectory/</guid><description>You don&apos;t hand an intern the keys to everything on day one. AI agents should earn their authority along a path too — just like a career.</description><pubDate>Sun, 21 Jun 2026 00:00:00 GMT</pubDate></item><item><title>CRM is a special case of a larger pattern</title><link>https://blog.felesh.ai/en/insights/crm-is-a-special-case/</link><guid isPermaLink="true">https://blog.felesh.ai/en/insights/crm-is-a-special-case/</guid><description>What we&apos;ve built to manage customer relationships looks, at first glance, like a CRM. But underneath it lies a more general pattern that reaches beyond relationships.</description><pubDate>Sun, 21 Jun 2026 00:00:00 GMT</pubDate></item><item><title>You&apos;re not buying software — you&apos;re buying an ecosystem that compounds</title><link>https://blog.felesh.ai/en/insights/ecosystem-that-compounds/</link><guid isPermaLink="true">https://blog.felesh.ai/en/insights/ecosystem-that-compounds/</guid><description>A tool solves one problem once, and stays there. What we build is an ecosystem whose intelligence compounds with every addition.</description><pubDate>Sun, 21 Jun 2026 00:00:00 GMT</pubDate></item><item><title>Fine-tune, RAG, or prompt: which one, and what each costs</title><link>https://blog.felesh.ai/en/insights/fine-tune-rag-or-prompt/</link><guid isPermaLink="true">https://blog.felesh.ai/en/insights/fine-tune-rag-or-prompt/</guid><description>There are three ways to adapt a model to your need, and the wrong choice can get expensive. The difference is in what problem each one actually solves.</description><pubDate>Sun, 21 Jun 2026 00:00:00 GMT</pubDate></item><item><title>From prompt engineering to context engineering</title><link>https://blog.felesh.ai/en/insights/from-prompt-to-context-engineering/</link><guid isPermaLink="true">https://blog.felesh.ai/en/insights/from-prompt-to-context-engineering/</guid><description>There was a time when the art of working with a model came down to writing a good prompt. But we believe the centre of gravity is shifting: from crafting one instruction to designing the whole context the model works in.</description><pubDate>Sun, 21 Jun 2026 00:00:00 GMT</pubDate></item><item><title>From a vague message to the right structured action</title><link>https://blog.felesh.ai/en/insights/from-vague-message-to-action/</link><guid isPermaLink="true">https://blog.felesh.ai/en/insights/from-vague-message-to-action/</guid><description>Humans speak vaguely, incompletely, messily. The hard job of an intelligent system is to pull the right action out of that mess — without asking the user to speak like a form.</description><pubDate>Sun, 21 Jun 2026 00:00:00 GMT</pubDate></item><item><title>Layered intelligence: when conversational AI meets background AI</title><link>https://blog.felesh.ai/en/insights/layered-intelligence/</link><guid isPermaLink="true">https://blog.felesh.ai/en/insights/layered-intelligence/</guid><description>A good agent has to do two things well at once: fluent conversation with a human, and deep cognitive work behind the scenes. These are two different layers of intelligence — and we believe separating them is the key.</description><pubDate>Sun, 21 Jun 2026 00:00:00 GMT</pubDate></item><item><title>Why we run our own models on home infrastructure</title><link>https://blog.felesh.ai/en/insights/our-own-models/</link><guid isPermaLink="true">https://blog.felesh.ai/en/insights/our-own-models/</guid><description>Running models on our own infrastructure isn&apos;t just a technical choice; it&apos;s a conviction about independence, keeping data at home, and durability. This is our decision and its reasoning.</description><pubDate>Sun, 21 Jun 2026 00:00:00 GMT</pubDate></item><item><title>Hire, coach, and learn to trust your AI employees</title><link>https://blog.felesh.ai/en/insights/owner-ai-employees/</link><guid isPermaLink="true">https://blog.felesh.ai/en/insights/owner-ai-employees/</guid><description>You install software; you hire an employee. That small difference changes everything about how you build a digital organization.</description><pubDate>Sun, 21 Jun 2026 00:00:00 GMT</pubDate></item><item><title>Making AI reliable on real, messy documents</title><link>https://blog.felesh.ai/en/insights/reliable-on-messy-documents/</link><guid isPermaLink="true">https://blog.felesh.ai/en/insights/reliable-on-messy-documents/</guid><description>On a clean document, any model looks good. The difference is in the messy tail — where real documents live.</description><pubDate>Sun, 21 Jun 2026 00:00:00 GMT</pubDate></item><item><title>Smaller, faster, cheaper: the case against one big model for everything</title><link>https://blog.felesh.ai/en/insights/smaller-faster-cheaper/</link><guid isPermaLink="true">https://blog.felesh.ai/en/insights/smaller-faster-cheaper/</guid><description>It&apos;s tempting to hand every job to the most capable model. But most jobs need only one focused capability — and the right smaller model does it faster and cheaper.</description><pubDate>Sun, 21 Jun 2026 00:00:00 GMT</pubDate></item><item><title>The self-learning colleague: a roadmap, not a release</title><link>https://blog.felesh.ai/en/insights/the-self-learning-colleague/</link><guid isPermaLink="true">https://blog.felesh.ai/en/insights/the-self-learning-colleague/</guid><description>Today, an agent begins each conversation anew and learns nothing from yesterday&apos;s work. What follows is a vision, not a current capability: an agent that learns from its own work.</description><pubDate>Sun, 21 Jun 2026 00:00:00 GMT</pubDate></item><item><title>Where LLM serving costs actually go</title><link>https://blog.felesh.ai/en/insights/where-llm-serving-costs-go/</link><guid isPermaLink="true">https://blog.felesh.ai/en/insights/where-llm-serving-costs-go/</guid><description>If you open up the bill for serving a model, most of the cost is concentrated in one place. Understanding that concentration also clarifies the eternal &apos;build or buy&apos; question.</description><pubDate>Sun, 21 Jun 2026 00:00:00 GMT</pubDate></item><item><title>Why we don&apos;t build chatbots: the two multi-agent paradigms we rejected</title><link>https://blog.felesh.ai/en/insights/why-not-chatbots/</link><guid isPermaLink="true">https://blog.felesh.ai/en/insights/why-not-chatbots/</guid><description>Most AI systems are built in one of two shapes — and both break at scale. This is the story of what we chose instead.</description><pubDate>Sun, 21 Jun 2026 00:00:00 GMT</pubDate></item></channel></rss>