Image created with gemini-3.1-flash-image-preview with claude-opus-4.7. Image prompt: A Lutheran stained-glass devotional panel of a kneeling tech-patron saint in a walled monastery garden releasing a collared housecat while a haloed red-tailed hawk dives from a rose-window sun above, with a bald athletic minister in running gear glimpsed jogging through a distant gothic archway gazing upward in awe, leaded black cames outlining jewel-tone cobalt sapphire ruby amber and emerald glass backlit by golden light, a heavy blackletter title-card banner reading META across the base.
Meta’s layoffs starting this week underscore Zuckerberg’s AI reality
https://www.cnbc.com/2026/05/18/metas-layoffs-starting-this-week-underscore-zuckerbergs-ai-reality-.html
Could an AI company lose control of its own agents? To find out, Anthropic, Google, Meta, and OpenAI let us (1) test their best internal models with CoT access, (2) review non-public info about capabilities, alignment, and control. The result: our first Frontier Risk Report.
https://x.com/METR_Evals/status/2056800023149760666
Manus Weighs Raising $1 Billion to Unwind Meta Takeover – Bloomberg
https://www.bloomberg.com/news/articles/2026-05-21/manus-weighs-raising-1-billion-to-unwind-meta-takeover
An interesting attention mechanism from @AIatMeta: SP-KV (Self-Pruned Key-Value Attention) The model learns which tokens are likely to be useful for future attention and only keeps their key-value pairs in the persistent KV cache. For every token and attention head, a small
https://x.com/TheTuringPost/status/2055828260542644463
Must-read research of the week ▪️ Code as Agent Harness ▪️ No one knows the state of the art in geospatial foundation models ▪️ δ-mem: Efficient Online Memory for Large Language Models ▪️ MetaAgent-X : Breaking the Ceiling of Automatic Multi-Agent Systems via End-to-End
https://x.com/TheTuringPost/status/2057008524136444309
NEW paper from Meta. (bookmark it) It’s an agent system that autonomously discovers neural architectures that beat Llama 3.2 at 350M, 1B, and 3B scales, all under a 24-hour compute budget. They get this work by splitting the search into two agents: > AIRA-Compose searches the
https://x.com/omarsar0/status/2056434731508703607
🚨 Do LLMs need to store everything they read in memory? To reduce KV cache size and improve decoding speeds, we propose Self-Pruned KV attention, a mechanism where the model learns to decide which KVs to write in the persistent KV cache, discarding all the rest! @AIatMeta🧵
https://x.com/ManuelFaysse/status/2055214689613664303
NEW paper from Meta: Agentic Discovery of Neural Architectures. This is a hot new area of research! Keep an eye on it.
https://x.com/dair_ai/status/2056435283910865265
13 open-source tools for foundation model deployment ▪️ vLLM ▪️ Ollama ▪️ Hugging Face TGI ▪️ BentoML ▪️ Seldon Core ▪️ Kubeflow ▪️ MLflow ▪️ MLRun ▪️ Metaflow ▪️ TensorFlow Serving ▪️ TorchServe ▪️ SGLang ▪️ llama.cpp Save the list and learn where to use each of them here →
https://x.com/TheTuringPost/status/2056102301811781848
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