A global workspace in language models (26 minute read)
Anthropic's new research paper introduces the concept of "J-space" in language models, revealed as internal neural patterns playing a unique role in Claude's processing. Emerging during training without explicit design, the J-space allows Claude to internally reason, modulate thoughts, and solve multi-step problems, distinguishing them from automatic processes. These patterns enable the monitoring of internal thoughts for AI misbehavior and provide insights into AI consciousness, suggesting a tangible distinction between deliberate and automatic decisions.
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Broadcom, Apple Extend Tie-Up to 2031 With New Custom Chips (2 minute read)
Broadcom and Apple have expanded their partnership through to 2031. The companies are working together to develop application-specific integrated circuit (ASIC) silicon for multiple generations of Apple products. ASIC chips are increasingly vital to the development of components for processing artificial intelligence-related tasks. Apple plans to deploy its advanced AI servers as early as 2027.
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Continual Learning for Agents (3 minute read)
Most production agents run on closed frontier models, so developers cannot update model weights and must focus instead on harness-level and context-level continual learning. To achieve this at Replit, the team built ViBench to evaluate functional app-building success from natural-language specs alongside Telescope, an automated system that clusters production failure traces into actionable issue groups.
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A Stargate for Data (6 minute read)
Data Labs are projected to surpass $100B/year in data spend by 2030 as demand shifts from compute-limited to data-limited regimes. The bottleneck now lies in collecting private, high-quality datasets, as public internet data becomes insufficient for AI training needs. This scarcity positions data as a strategic asset pivotal to economic and scientific progress, potentially spurring efforts to cultivate new data sources akin to major compute investments.
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Everyone Is Wrong About Open Source AI in the Enterprise (3 minute read)
Decagon runs roughly 90% of its workloads on open-source models because small, heavily fine-tuned models deliver the latency and task-specific performance required for customer service agents. Most enterprise AI use cases remain early-stage, so companies favor frontier models that maximize flexibility and intelligence. As deployments mature and workflows stabilize, many production workloads will likely migrate from closed models to specialized open-source alternatives.
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Hy3 (1 minute read)
Hy3 is a 295-billion-parameter Mixture-of-Experts model with 21 billion active parameters and 3.8 billion MTP layer parameters. Developed by Tencent, Hy3 outperforms similar-sized models and rivals flagship open-source models with two to five times more parameters. The full-sized model is available on Hugging Face. It is available for free on OpenRouter until July 21.
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Getting started with loops (11 minute read)
Loops are agents that repeat cycles of work until a stop condition is met. They can be categorized based on how they are triggered, how they are stopped, what Claude Code primitive is used, and what type of task is appropriate for each. This post covers the main loop types, when to use each, and how to maintain code quality while managing token usage.
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Bringing PyTorch Monarch to AMD GPUs: Single-Controller Distributed Training on ROCm (13 minute read)
Training state-of-the-art large language models with billions of parameters requires distributed training across hundreds or thousands of GPUs. At this scale, hardware failures are expected. PyTorch Monarch enables elastic, fault-tolerant distributed training on AMD GPUs. This post shows how Monarch dynamically recovers from node failures without halting the entire job and why this represents a significant step toward stable large-scale AI infrastructure.
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PACE: A Proxy for Agentic Capability Evaluation (2 minute read)
PACE framework predicts costly agentic LLM benchmark performance using a small subset of atomic evaluation instances, achieving high accuracy at a fraction of the cost. It uses a regression model with selected instances from non-agentic benchmarks to predict scores on agentic benchmarks. PACE reduces evaluation costs by over 99% while maintaining a mean absolute error under 4%, aiding model development and selection.
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xAI Is Dead. Long Live SpaceXAI (2 minute read)
xAI has rebranded to SpaceXAI. The rebranding brings new clarity to the business. The AI division is critical to Elon Musk's narrative about SpaceX's future. In this story, space infrastructure and exploration are inextricably linked to AI.
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State of CLI Coding Agents, Mid-2026 (37 minute read)
Claude Code, Codex CLI, and Omp are close enough in result quality that it wouldn't make sense to rank them. They can all read a serious repository, form a plan, edit across files, run checks, recover from failures, and land production-shaped patches. However, they differ in task clarity, repository hygiene, permissions, and whether the harness exposes the right tool at the right moment. OpenCode produces lower-quality results, but it tries the hardest to be good with every model.
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