The Daily Index — Friday, September 18, 2026 · TLDR Index

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plasma.org

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HOW CLAUDE IS UPLIFTING BIOMOLECULAR MODELING

Claude enhanced biomolecular modeling by optimizing over 30 models, achieving a 4x speed increase and creating a low-memory mode for predicting larger systems on a single NVIDIA GPU. These improvements, now open-sourced, allow for efficient protein design and structure prediction, potentially accelerating drug discovery. A protein design competition co-sponsored with Adaptyv Bio offers up to $1 million in Claude credits and highlights these advancements.

anthropic.com · 12 min

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PROJECTS REDESIGNED: FROM FOLDER TO CONVERSATION

Claude Code Projects let users manage builds by automating task delegation, coordination, and result assembly across cloud sessions. Projects utilize threads for parallel operations, adapting based on progress, and draw on shared memory for efficient task execution. Available now in beta for select subscribers, with wider access coming soon, projects promise enhanced workflow management and context retention.

claude.com · 4 min

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NOAM BROWN – AGENT SWARMS, ALIGNMENT, & RECURSIVE SELF-IMPROVEMENT

This post features a transcript of an interview with Noam Brown, a research scientist at OpenAI who works on reasoning, reinforcement learning, self-play, and multi-agent AI. Brown was a foundational contributor to the development of reasoning models. In this interview, Brown talks about multi-agent, Navier-Stokes, the current explosion of maths progress, automating AI research, and how we will know whether models are actually aligned before recursive self-improvement kicks off. A video of the interview is available.

dwarkesh.com · 72 min

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LLM CLASSIFICATION IS FEATURE ENGINEERING

Getting LLMs into shape to reliably serve as classifiers is hard work but potentially highly impactful. More and more research is relying on LLMs for classification, so these tools need to output high-quality results. This post shows how classification is just a feature engineering problem.

minimallysufficient.com · 20 min

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ANTHROPIC SAYS ITS AI NOW DOES A QUARTER OF ITS RESEARCH WORK

Anthropic says Claude now leads 26% of its AI research work and oversees tens of thousands of active internal agents. Its new measurements track how much AI is helping build the next models, whether people can still oversee those agents, and the computing driving the work.

anthropic.com · 25 min

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TOWARD RECURSIVE SELF-IMPROVEMENT: HOW GLM BUILT ITS OWN INFERENCE INFRASTRUCTURE

Z.ai used a GLM-5.3-powered Infra Agent to help build GLM-5.3-Flash's production serving stack on 100,000+ Chinese accelerators in under two weeks. Dense feedback, kernel fixes, and system-level optimization tripled throughput while keeping humans responsible for objectives and risk.

z.ai · 15 min

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unscriptedconf.io

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INTRODUCING BONSAI 2 27B: NEAR-LOSSLESS COMPRESSION IN A 9X SMALLER FOOTPRINT

Ternary Bonsai 2 27B brings stronger reasoning, coding, vision, and agentic capability to the Bonsai series. It uses ternary {−1, 0, +1} weights with FP16 group-wise scaling, for 1.76 effective bits per weight and a total model footprint of 5.9GB. The model supports a 262K-token context window and multimodal text-and-image input. It can be run on Nvidia GPUs via CUDA and on Apple devices via MLX through custom low-bit kernels.

prismml.com · 7 min