# Introduction You have successfully set up Ollama, pulled a capable model, run a few queries in the terminal, and it worked. The responses were sharp. The latency was real. The whole thing ran on your own hardware with no API key and no cloud. Then you closed the terminal and walked away. The AI…
LLMs have gotten surprisingly good at writing GPU kernels[1][2][3], but almost all current benchmarks measuring that progress are single-GPU. In production, communication is often the bottleneck: communication overhead can account for over 20% of inference latency[4], and that gap keeps widening as compute scales faster than interconnect bandwidth. ParallelKernelBench (PKB) offers a benchmark and evaluation…
You shipped an agent. It worked in the demo. In production, a user phrased a question differently than you expected and the agent fell apart. AI evaluation is supposed to catch that issue before your users do, but the standard workflow asks you to build a reference dataset, hand-pick metrics, write LLM-as-a-judge prompts for each…
What You Should Know Overall median provider compensation climbed 4.3% in 2025, driven by escalating patient demand and highly competitive labor markets for clinical talent. A structural bottleneck has emerged: roughly half of all provider compensation growth is being funded by increased physician output (wRVUs up 2.4%), rather than commercial or federal reimbursement gains. A…
Earlier this year, a wave of new AI coding capabilities crystallized a shift that had been building for months: LLMs began to look capable of replacing – not just augmenting – enterprise software. The idea that AI could “eat” SaaS moved from speculation to something closer to inevitability, sparking what many dubbed the “SaaSpocalypse.” However,…
In this article, you will learn five practical strategies for managing context windows in long-running AI agent applications, along with the key tradeoffs each approach introduces. Topics we will cover include: Why context windows become a critical bottleneck in agent-based AI systems designed for sustained, autonomous operation. Five distinct context management strategies: sliding windows, recursive…
29th June 2026 – Link Blog Ornith-1.0: Self-Scaffolding LLMs for Agentic Coding. This is an interesting new open weights (MIT licensed) model, the first model release from DeepReinforce. […] with variants including 9B Dense, 31B Dense, 35B MoE, and 397B MoE. Built on top of pretrained Gemma 4 and Qwen 3.5, it achieves state-of-the-art performance…
Agentic systems often face a trade-off between accuracy and cost. The highest-performing proprietary frontier models and harnesses provide top accuracy but are expensive. Fine-tuning offers one way to address this problem. Smaller or more efficient open models starting with lower accuracy are taught to perform better with specific agents. However, fine-tuning requires expertise and hardware…
SpaceXAI’s first major post-Cursor test may arrive this week. The company and AI coding startup Cursor are preparing to launch their first jointly developed AI model as early as Wednesday, according to an internal staff memo first reported by The Information. The model is being positioned internally as competitive in some areas with Anthropic’s Opus…
The GitHub Copilot CLI brings AI-assisted coding into your terminal. Unlike the older gh copilot extension, which only suggests and explains shell commands, Copilot CLI is a standalone agentic app that answers questions, writes and debugs code, and interacts with GitHub.com services through natural-language prompts. For Python developers who feel at home on the command…