📣 Headlines

• Meta introduced Ray-Ban Display smart glasses with an in-lens screen and AI assistant plus a Neural Band wrist controller , signaling a more capable HUD-class wearable platform.

• Zoom unveiled AI Companion 3.0 with agentic capabilities spanning meetings, CX, marketing, sales, and frontline workflows.

• In healthcare, Akido Labs' ScopeAI runs appointments and drafts diagnoses under physician review, while more clinicians turn to ChatGPT for second opinions , raising benefit and privacy questions.

• As attackers weaponize AI, CrowdStrike pushed scaling defensive AI and backed Terra Security’s agentic offensive platform via its accelerator with Nvidia and AWS support.

• To curb GPU dependency, the industry is pursuing alternatives and open networks as firms seek to escape the 'Nvidia tax' , highlighted by Upscale AI’s $100M seed for open-standards AI networking .

• Biosecurity spotlight: researchers used AI-designed DNA to create bacteriophages that infected and killed E. coli , demonstrating real-world bioactivity from AI-generated genomes.

• Materials discovery advance: MIT’s SCIGEN steers diffusion models to generate candidate quantum materials with target lattice geometries (e.g., Kagome, Archimedean).

• Microsoft expanded its data stack as Fabric adds a LinkedIn-derived native graph engine and real-time geospatial maps integrated with OneLake.

🔧 Company Engineering Blogs

Gemini achieves gold-level performance at the International Collegiate Programming Contest World Finals (deepmind​.google) . Gemini 2.5 Deep Think achieves gold-medal level at the 2025 ICPC World Finals, solving 10/12 problems with advanced reasoning and reinforcement learning techniques

Meet the GitHub MCP Registry: The fastest way to discover MCP Servers (github​.blog) . GitHub introduces the MCP Registry to centralize MCP server discovery for Copilot, agents, and MCP-enabled tools

Scaleway on Hugging Face Inference Providers 🔥 (huggingface​.co) . Scaleway joins Hugging Face Inference Providers, enabling serverless inference with Scaleway API keys and HF routing

Learn Your Way: Reimagining textbooks with generative AI (research​.google) . Google Research explores Learn Your Way, using GenAI to generate multimodal, personalized educational materials and measure learning efficacy

🤖 Agentic systems: data, operations, and real workflows

Supporting our AI overlords: Redesigning data systems to be Agent-first (muratbuffalo​.blogspot​.com) . Agent-first data systems: LLM agent workloads, agentic speculation, multi-query optimization, memory stores, and neurosymbolic collaboration in DBMS redesign

Clouded Judgement 9.19.25 - The AI Shift: Static Software vs. Living AI Systems (cloudedjudgement​.substack​.com) . AI products evolve like living systems, requiring continuous evaluation, observability, and hot-swappable models and prompts

Why Digital Work is the Perfect Training Ground for AI Agents (thedataexchange​.media) . Upwork CTO Andrew Rabinovich explains Uma, RLEF, RAG with knowledge graphs, and human-in-the-loop evaluation for AI agents in digital work

What happens when coding agents stop feeling like dialup? (martinalderson​.com) . Discusses AI coding agents, reliability, token speeds, OpenRouter data, Claude Code, Cerebras Code, Gemini CLI, and implications for developer workflow and pricing

⚙️ LLM performance engineering: inference, profiling, and embeddings

Lessons from the trenches: why llama.cpp works best (today) (visokio​.com) . llama.cpp beats vLLM for running GPT-OSS models locally, with reliability and interactive capabilities highlighted

Scaled dot-product attention profiling (aarnphm​.xyz) . Scaled dot-product attention profiling with naive, sdpa, and tensorboard tracing using UV, Modal, and PyTorch on CPU/CUDA

How to Reduce the costs of Running LLMs by 10-15x [Investigations] (artificialintelligencemadesimple​.substack​.com) . Techniques for cost-efficient LLM inference: batching, compiler graphs, FlashAttention, quantization, KV caches, sparse architectures, MoE, and spec decoding

Qwen-8B Embeddings: Near-SOTA Performance at 600x the Speed (alexdong​.com) . Qwen-8B embeddings enable near-SOTA text classification, 600x faster than LLM classifiers, achieving MAP ~0.944 on Kaggle with simple MLP

🛠️ Hands-on builds and experiments: vLLM, Android RAG, diffusion, and personal projects

Summer 2025 in Review (bengubler​.com) . Summer 2025 recap of AI projects, tokenizers, and WebGPU shading library shade, plus dataset tooling and a LessWrong piece

How I Built the Database of my Dreams (blog​.apiad​.net) . BeaverDB: a Pythonic, SQLite-backed multi-modal data store for vectors, text, lists, queues, pub-sub and more

Setting Up LLaVA/BakLLaVA with vLLM: Backend and API Integration (pyimagesearch​.com) . Guide to setting up vLLM with CUDA for LLaVA/BakLLaVA, offline Python inference, and OpenAI-compatible API serving

Running a RAG powered language model on Android using MediaPipe (darrylbayliss​.net) . Step-by-step guide using MediaPipe to run a RAG-powered language model on Android with Gemma, embeddings, and a local vector store

arkaine - an experiment in AI tooling (hlfshell​.ai) . Arkaine: an AI tooling framework for agents with tool calling, contexts, PythonEnv backend, Spellbook, and lessons learned

Diffusion models: image generation (konradb​.substack​.com) . DIY diffusion-image generation with Flux, Hugging Face diffusers, and prompts automation in Colab

🔎 RAG in production: evaluation, selective retrieval, and vector stores

RAG talk recap from DevConf.US 2025 (major​.io) . RAG with LLMs explained through a Fellowship metaphor, failures, strategies, and practical lessons for production systems

Evaluating Your RAG Solution (towardsdatascience​.com) . RAG pipeline construction with OpenAlex abstracts, FAISS vector store, LangChain, and DeepEval for retriever and generator evaluation

Deciding When Not to Retrieve: Adaptive RAG, Part 2 (blog​.reachsumit​.com) . Selective Retrieval in Adaptive RAG: pre-generation decisions using external features and popularity-based triggers

How do vector databases work? (hclimente​.github​.io) . Vector embeddings, cosine similarity, UMAP visualizations, and HNSW-based vector databases (Qdrant) for RAG with LLMs

🧪 Rethinking learning: test-time diffusion, layer-wise decoding, and RL efficiency

Deep researcher with test-time diffusion (research​.google) . TTD-DR uses test-time diffusion with self-evolution and retrieval-denoising to draft and revise long-form research reports

Making LLMs more accurate by using all of their layers (research​.google) . SLED decoding uses all LLM layers to align outputs with factual knowledge without external data or fine-tuning

Prediction is hard, especially about the future (strangeloopcanon​.com) . Forecasting with tiny LLMs: Varro RL environment, GSPO training, semantic similarity, and daily headline predictions

The Extreme Inefficiency of RL for Frontier Models (tobyord​.com) . New scaling paradigm: RL’s information efficiency vs pre-training; long-horizon tasks, token-entropy, METR/HCAST, o1/o3/o3 models, latency and inference costs

The Shift to Reinforcement Learning Greatly Reduces Learning-Efficiency (tobyord​.com) . RL training learns far less per hour than pre-training, impacting scalability, generality, and frontier task efficiency in AI systems

📚 Academic Research

LLM-I: LLMs are Naturally Interleaved Multimodal Creators (arxiv:cs) . LLMs orchestrate tools like online image search, diffusion generation, code execution, and image editing for interleaved multimodal creation

Understand Before You Generate: Self-Guided Training for Autoregressive Image Generation (arxiv:cs) . Self-guided training for autoregressive image generation improves visual understanding and FID for LlamaGen models

Hierarchical Self-Attention: Generalizing Neural Attention Mechanics to Multi-Scale Problems (arxiv:stat) . Hierarchical self-attention for multi-scale, multi-modal data using entropy-minimizing mechanics and dynamic-programming-accelerated transformers

MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer (arxiv:cs) . Manzano proposes a unified multimodal framework with a hybrid image tokenizer, shared vision encoder, dual adapters, and a unified LLM for text and image token generation

AToken: A Unified Tokenizer for Vision (arxiv:cs) . AToken: a unified transformer-based visual tokenizer for images, videos, and 3D with 4D rotary embeddings and adversarial-free training

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