OpenAI bought Ona to power persistent, secure agents in its Codex platform, while Anthropic lifted its hidden safeguards after researchers flagged degraded outputs. The issue also covers Xiaomi’s MiMo Code AI assistant beating Claude on long tasks and dives into tokenizers, vintage LLM builds, compute markets, data debugging, and PyTorch optimizations.

• OpenAI acquired Ona to bring secure cloud execution and persistent, cross-session agent orchestration to its Codex platform.
• Anthropic secretly rerouted certain Claude requests (training rival models, debugging AI code, tweaking neural nets) to a weaker model, then reversed the policy after researchers and users complained.
• Xiaomi open-sourced MiMo Code V0.1.0, a terminal-native coding assistant that beats Claude Code on tasks over 200 steps using a memory subagent to track context.
• A developer built a full transformer from scratch for about $80 on a home PC.

OpenAI trained a new LLM, GPT-Rosalind, on 50 common biological workflows and major public databases to help researchers navigate massive genomic and protein datasets. The model links genotype to phenotype, suggests biological pathways, and prioritizes potential drug targets by leveraging mechanistic understanding.

• OpenAI's GPT-Rosalind is fine-tuned on the 50 most common biological workflows plus major public gene/protein/pathway databases
• It's designed to bridge jargon silos between biology subfields (e.g., helping a plant geneticist interpret neurobiology findings)
• Beyond summarizing data, it suggests biological pathways, links genotype to phenotype, and ranks drug targets to speed up hypothesis generation in drug discovery and synthetic biology

The article analyzes the unit economics of large language models (LLMs), focusing on the compute costs associated with training and inference. It discusses how companies like OpenAI and Anthropic manage their financial projections and cash flow, emphasizing the need for revenue growth or reduced training costs to achieve profitability.

• Inference costs are falling faster than training costs, so gross margins on deployed models improve over time even as frontier training runs get more expensive.
• OpenAI and Anthropic's path to profitability depends on either scaling revenue much faster than compute spend or finding ways to cut training costs, since current cash burn is dominated by training rather than serving models.
• Reported "profitability" claims from these labs often exclude massive R&D/training expenditures, making headline numbers misleading about true unit economics.