Calling an LLM API is not an architecture. Here’s what actually is.There’s a conversation I keep seeing play out on engineering teams right now, and it goes something like this.A tech lead gets pulled into a demo. The AI feature looks incredible – it a…
The Scissor Effect: Quantifying the Transition From Unstructured DevOps to Product-led Platform EngineeringHow Platform Engineering focuses on improving the productivity and efficiency of an industry.For several decades, DevOps has been one of the most…
How to set up a generate-test-fix cycle where AI writes, tests, and repairs its own code — without you in the middle.Continue reading on Towards AI »
The biggest bottleneck in deploying agents isn’t reasoning quality — it’s error accumulation. Here’s the architecture that fixes it.Photo by julien Tromeur on UnsplashYour multi-agent pipeline passed every demo you ran. In production, it silently accum…
In the current AI landscape, the ‘context window’ has become a blunt instrument. We’ve been told that if we simply expand the memory of a frontier model, the retrieval problem disappears. But as any AI professionals building RAG (Retrieval-Augmented Generation) systems knows, stuffing a million tokens into a prompt often leads to higher latency, astronomical […]
The post Chroma Releases Context-1: A 20B Agentic Search Model for Multi-Hop Retrieval, Context Management, and Scalable Synthetic Task Generation appeared first on MarkTechPost.
NVIDIA researchers introduced ProRL AGENT, a scalable infrastructure designed for reinforcement learning (RL) training of multi-turn LLM agents. By adopting a ‘Rollout-as-a-Service’ philosophy, the system decouples agentic rollout orchestration from the training loop. This architectural shift addresses the inherent resource conflicts between I/O-intensive environment interactions and GPU-intensive policy updates that currently bottleneck agent development. The […]
The post NVIDIA AI Unveils ProRL Agent: A Decoupled Rollout-as-a-Service Infrastructure for Reinforcement Learning of Multi-Turn LLM Agents at Scale appeared first on MarkTechPost.
Two paradigms, one revolution — why the future belongs to developers who know when to prompt, and when to delegate.Continue reading on Towards AI »
Welcome to the era of sprawling, idiosyncratic tooling.
A real integration story from the database schema to the first query running in productionContinue reading on Towards AI »
How one engineer and an AI agent built a CI system that tested, corrected, and extended itself — feature by featureContinue reading on Towards AI »