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Iovanny Olguín Ávila
Posts
Theoretical Limits of Recursive Self-Improvement: Implications for Next-Gen GPU Design
Recursive self-improvement GPU roadmaps often assume autonomous training loops require ever-more accelerators. Hector Zenil’s analysis (arXiv:2601.05280, January 2026 preprint, King’s College London) models recursive self-training...
Self-Play RL: How SWE-RL Cuts Human Data Dependencies and Multiplies Training Efficiency
SWE-RL self-play GPU workloads differ from supervised fine-tuning pipelines. Meta’s SSR (Self-play SWE-RL) (Wei et al., arXiv:2512.18552, December 2025 preprint) trains one LLM policy to...
The Agent Autonomy Curve: What It Means for Your GPU Infrastructure in 2026–2027
Agent autonomy GPU planning should anchor on measurable autonomy curves, not hype. METR’s Frontier Risk Report (Feb–Mar 2026 assessment window, published May 2026) documents how...
AlphaEvolve in Production: Algorithm Optimization Already Saving Millions on GPU Clusters
AlphaEvolve GPU optimization is no longer confined to academic benchmarks. Google DeepMind’s May 2026 impact report documents production deployments that cut training time, storage amplification,...
The Self-Improvement Paradox: Why HyperAgents Won’t Spike GPU Demand the Way You Expect
Most infrastructure leaders assume that HyperAgents GPU infrastructure planning should mirror large-scale model training: more self-improvement cycles mean more GPUs, linearly or exponentially. That mental...
NVIDIA AMD AI chips in 2026: Blackwell, MI400, Gaudi & export rules
Updated: May 12, 2026. Technical disclaimer: Specifications, cloud SKU names, and regulatory thresholds cited here reflect public sources as of this publication date and can...
Unified Memory AI Comparison (2026): DGX Spark vs Mac Studio M4 Ultra vs AMD Ryzen AI Max+ vs GMKtec EVO-X2
Last updated: May 2026. This unified memory AI comparison pits NVIDIA DGX Spark, Apple Mac Studio M4 Ultra, OEM AMD Ryzen AI Max+ 395 desktops,...
The Complete Hardware Guide for Running Powerful AI Models Locally (2026)
Building the right hardware for running powerful AI models locally is the single most consequential technical decision you’ll make as an AI practitioner in 2026....