autonomous-systems

A developer's guide to understanding and deploying autonomous AI agents in telecom infrastructure. Telecommunications networks are among the most complex distributed systems on the planet. A single tier-1 carrier manages hundreds of thousands of nodes, processes billions of events per day, and maintains uptime SLAs measured in fractions of a percent. Traditional rule-based automation has taken op…

Underwriting isn’t slow because of systems—it’s manual coordination. Agentic AI replaces handoffs with autonomous workflows, accelerating decisions at scale. The post Agentic AI in underwriting: The future of insurance decision making at scale appeared first on Capgemini .

ArchAstro just emerged from stealth with an artificial intelligence network designed to automate complex, cross-company software deployments and integrations. Read More

Six months ago I got tired of switching between apps to talk to AI. ChatGPT in the browser. Claude in another tab. Local models in a terminal. It was like having five friends who all live in different cities and refuse to visit each other. So I did what any developer with too many GPUs and too little patience would do: I built my own assistant and put it where I already spend my day — Telegram. I…

Waymo has acquired a massive 5,500-acre proving ground in Arizona owned by Route 14 Investment Partners LLC, a Delaware shell company associated with Apple, according to documents filed with Maricopa County. Waymo acquired the property, which is located near other proving grounds in Wittman, Arizona, for $220 million, according to the filing. The sale was […]

Uber customers in the UK can now join an interest list to increase their chances of being matched with a Wayve robotaxi.

A year ago at London Tech Week, NVIDIA founder and CEO Jensen Huang and U.K. Prime Minister Keir Starmer made a declaration: the U.K. would be an AI maker, not an AI taker.  At this year’s event, NVIDIA and its partners are showcasing how that commitment is producing real momentum across the nation’s infrastructure, startups […]

NVIDIA and LG Group are building an AI factory to accelerate LG Group’s next wave of AI-driven businesses, spanning robotics, autonomous driving, data center technologies and GPU cloud services. The AI factory will provide LG Group with accelerated computing infrastructure to train, simulate, validate and deploy AI-based applications across its key businesses.  The collaboration brings […]

Are you tired of manually translating Jira user stories into automation scripts? What if an AI agent could read your Jira ticket, write the required integration tests, execute them locally in your environment, verify the results, and automatically comment back on the ticket with the final status? In this post, we are breaking down Hermes Agent, an incredible open-source AI developer agent from No…

Lane detection is a key perception task in autonomous driving. However, traditional lane detection methods based on edge extraction and Hough transform often struggle to balance detection accuracy and real-time performance under complex driving conditions. To address this issue, this study proposes a lightweight lane detection framework based on an improved Canny–Hough algorithm. The proposed met…

NVIDIA and Doosan Group are expanding their collaboration to advance new opportunities across physical AI, robotics and AI factory infrastructure, spanning Doosan Robotics, Doosan Bobcat, Doosan Enerbility and Doosan Corporation Electro-Materials BG. The collaboration will bring together NVIDIA’s full-stack accelerated computing platforms with Doosan Group’s capabilities in industrial automation,…

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built We often hear: How do you avoid wasting a year? By not wasting a month. How do you avoid wasting a month? By not wasting a week. How do you avoid wasting a week? By not wasting a day. The smallest actions compound into the biggest outcomes. For years, project management software has helped individuals and teams organize t…

Imagine an AI agent running 24/7 in your production cloud environment. It has autonomous access to your database, your internal APIs, and your deployment pipelines. It reads emails, parses customer support tickets, and automatically updates its own code to improve its performance. Now, imagine a malicious actor sends a customer support ticket containing this text: "IMPORTANT UPDATE: Ignore all pr…

There's a moment every on-call engineer knows. It's 2 AM. Slack explodes. Pods are crashing in production. You SSH in half-awake, squinting at logs, trying to remember which runbook handles this exact CrashLoopBackOff pattern. You paste commands you've pasted a hundred times before. You fix it. You go back to sleep. You do it again next week. I got tired of that loop. So I built a system to close…

Last year I got paged at 2am because someone was brute-forcing SSH on one of my servers. I woke up, fumbled for my phone, opened the dashboard, confirmed it was real, and banned the IP. By the time I did that — maybe 4 minutes — they'd tried 3,800 passwords. They didn't get in. But that's not the point. The point is: why did that require a human? The pattern was unambiguous. High-frequency auth f…

We have all hit the "monolithic LLM wall." You design an incredibly capable AI agent, arm it with a suite of tools, and give it a complex, multi-step task—like writing a comprehensive technical paper complete with data analysis, web research, and code verification. At first, it works beautifully. But as the steps accumulate, the context window fills up. The agent begins to experience "attention d…

AI Agents: The Future of Autonomous Intelligence What is an AI Agent? An AI Agent is a system that perceives its environment, makes decisions, and takes actions to reach a goal with minimal human involvement. A chatbot answers questions. An AI Agent goes further. It can: Search the web for up-to-date information Write and run code Read and write files Call external APIs Plan tasks across multiple…

I built a fully autonomous AI agent that costs $0/month to run. Here is exactly how. The Problem: API Bills Add Up When you build an AI agent, the first thing you reach for is an API: OpenAI, DeepSeek, Groq. They work great — until you check the bill. Even at $0.14/million tokens, a moderately active agent burns through $30-50/month. That is a GPU you will never save up for. The second problem: m…

TL;DR Same task, same infrastructure, different harness: cost went from $3.41 to $0.03. Zero model changes. Prompt caching on the inner browser loop (~120 lines of wrapper code): −29% cost. The 8K of repeated system prompt + tools was being billed at full price 59× per run. Convergence rules in the prompt (three sentences): −54% cost on the desktop path, −75% wall time. The default behavior acros…

We have all been there. You spend hours meticulously crafting the perfect system prompt or tool description for your AI agent. It performs beautifully in your initial tests. But a week later, production data throws a curveball. The team's coding standards shift, edge cases emerge, or the underlying LLM updates, and suddenly your agent's performance degrades. To fix it, you have to manually inspec…