ROBOMARKET
Stay Informed
Daily coverage from 2970 trusted sources — robotics, AI agents, and industry analysis for the GCC.
Last Updated: 10:00 AM (1h ago)
Auto-refresh every 2 hours
Perforce charges $500 for training training videos.. and it's AI narrated
Comments
OpenAI reduces Codex Model Context Size from 372k to 272k
Comments
My Daily Content Runs on Seven Platforms. Here Is the System Behind It
I publish every day across YouTube, Instagram, TikTok, LinkedIn, Facebook, X and Threads. People assume that means I spend my whole day Continue reading on Medium »
Building a Custom C++/CUDA Deep Learning Framework to Beat PyTorch’s Speed
How I stripped away the abstractions of modern AI tools to build Aakaar from scratch.Continue reading on Medium »
The End of Vibe Coding: Why AI-Native Software Engineering Is the Next Evolution
Vibe coding proved that anyone can build software. AI-native software engineering will determine who can build software that actually Continue reading on Medium »
Beyond Coding Benchmarks: Why AI Observability Needs Its Own Evaluation Framework
How Grafana s o11y-bench is redefining the way we measure AI agents in production observability workflows.Continue reading on Medium »
The cleanup trap: Stop asking RAG to fix bad data
The enterprise technology ecosystem is caught in a costly cycle. Over the past two years, millions of dollars have been funneled into generative AI pilots, yet many of these initiatives stall out before ever reaching a live production environment.When a project fails, the immediate instinct of technical leadership is often to blame the model: The context window was too restrictive, the latency was too high, or the reasoning capabilities simply were not there.But as data engineers building the scaffolding for these systems, we often see a different reality: The model receives the blame, but the pipeline usually contains the root cause. Production gen AI rarely fails because of model limitations alone. More often, it fails because the enterprise data foundation underneath it is fundamentally unready.This is what I call the Cleanup Trap : The false belief that an organization can pipe fragmented, inconsistent, and ungoverned legacy data into a large language model (LLM) orchestrator and s
What Are Long-Running AI Agents? A Beginner’s Guide
If you ve used ChatGPT or Claude, you know how it works: you ask a question, you get an answer, and that s the end of it.Continue reading on Towards Dev »
Stop Asking AI “What Side Hustle Should I Start?” Make It Interview You Instead.
The generic list AI hands you ignores the one thing that decides success: whether the hustle fits your actual life. Here s the better Continue reading on Write Rise »
LLM-Integrated Multivariable Calculus Course
Comments
Day 2 | Giving an Old Laptop a Second Life
(Or Why My AI Companion Accidentally Became a Linux Debugging Assistant)Continue reading on Medium »
Most companies don’t have a technology problem. They have an adoption problem.
Every few months a company buys a powerful new tool, announces a transformation, and quietly goes back to the old way of working within a Continue reading on Medium »