You probably think AI image generators paint your image pixel by pixel, like a digital artist filling in a canvas.
That's not what's happening. A normal image has millions of pixel values. Generating those one at a time would be too slow and too expensive. Even on the best GPUs.
MACHINE LEARNING
-
AI image generators don’t paint pixel by pixel like digital artists
By
–
-
Fields Medal awarded to OpenAI GPT5.5
By
–
Fields Medal for @OpenAI GPT5.5 🔜 https://t.co/f0sMD9nNEv
— Nando de Freitas (@NandoDF) 21 mai 2026Fields Medal for @OpenAI GPT5.5
-
Addressing the replication crisis in biomedical machine learning research
By
–
Biomedical AI may be headed for a replication crisis. (This work below is not about AI-generated reports; it’s about studies of biomedicine that use ML in their methods, and how they are evaluted.)
-
YOLOv8s Fine-Tuning Workflow with Geospatial Data Pipeline
By
–
– Les cartes : Leaflet
– L'API de cartographie : Google Earth Engine (c'est trop sous-coté, je vous jure, essayez si vous ne connaissez pas)
– Modèle fine-tuné : YOLOv8s
– Labellisation : custom tools en Node.js
– BDD : SQLite structuré, des dossiers sur mon ordi en non structuré -
Anthropic pays SpaceX $1.25 billion monthly for compute, majority revenue
By
–
Anthropic is paying SpaceX $1.25 billion per month for compute. Per month. That's $15 billion a year flowing to a company whose total annual revenue is $18 billion. One AI lab is about to account for the majority of SpaceX's commercial income. We only know this because SpaceX
-
Mixture-of-Experts Training: Emergent Specialization in AI Models
By
–
MoE Training, Part 1 — in one tweet:
— Satya Mallick (@LearnOpenCV) 21 mai 2026
You do NOT assign "this expert handles medicine, this one handles law."
You start with 9 random experts + a router. The router learns to pick 2–3 per question. Specialization emerges from data, not design.
That's how Mixtral and DeepSeek… pic.twitter.com/YGSc6ak94iMoE Training, Part 1 — in one tweet:
You do NOT assign "this expert handles medicine, this one handles law."
You start with 9 random experts + a router. The router learns to pick 2–3 per question. Specialization emerges from data, not design.
That's how Mixtral and DeepSeek -
Aphrodite with Exl3 supports TP, thank me later
By
–
Aphrodite w/ Exl3 supports TP btw Thank me later 😉
-
AutoResearchClaw: Autonomous AI Research with Collaboration
By
–
AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration As real-world science is iterative, this paper introduces AutoResearchClaw, a multi-agent system that debates ideas, self-corrects experiments, decides whether to pivot or refine, verifies data, and
-
DECS fixes LLM overthinking by cutting redundant tokens
By
–
Overthinking is killing LLM reasoning efficiency. DECS fixes it by surgically cutting redundant tokens without hurting performance. Current RLVR models generate excessively long reasoning paths with zero gain. Existing length penalties backfire—they penalize essential
-
Codex Cli Makes Local AI Effortless: Hardware, Model & Performance Optimization
By
–
Local AI Is Now Easy With This Give Codex Cli the article below & tell it: – Infer the right Inference Engine from your hardware + article below
– Use uv+venv
– Pick the right kernels
– Tune flags, batching, KVCache, etc
– Optimize for your hardware & chosen model See? SO EASY