Salesforce built Koa, a reasoning model on Nvidia's Nemotron that's designed to handle complex CRM workflows like case routing and lead qualification. The company claims it outperforms general-purpose models on CRM tasks with three times fewer errors, though it hasn't published the benchmark or named competitors.

• Koa was trained entirely on synthetic data simulating 30 years of Salesforce's internal CRM deployments across 14 industries, not customer data.
• The model uses supervised fine-tuning combined with reinforcement learning and group relative policy optimization to handle multistep tool execution.
• General availability is set for winter 2024, with 1-800Accountant as the launch pilot customer.

Andrej Karpathy walks through practical daily AI workflows in a 2-hour video, covering model selection, reasoning models, code execution, and multi-chat memory — techniques most people never use. Someone extracted these methods into a Claude-specific guide with ready-to-use examples.

• Most people use only 10% of what AI models can do; this covers the remaining 90%
• Specific techniques shown include choosing the right model, deciding when reasoning models justify the cost, generating full research reports from single prompts, and automating code execution
• The guide translates Karpathy's video into Claude-specific features with immediately applicable examples

Marc Andreessen discusses the historical context and current state of AI, framing it as the result of decades of research rather than a fleeting trend. He argues that recent breakthroughs in AI, especially in reasoning and coding, signal a significant shift away from past boom-bust cycles. The conversation also touches on the implications for startups, infrastructure, and the role of open-source AI.

• Andreessen frames AI as an "80-year overnight success," arguing today's breakthroughs (especially reasoning and coding) are the payoff of decades of research, not hype
• Unlike the dot-com bubble, current AI infrastructure buildout is backed by cash-rich companies with real demand, not speculative investment
• Software capability is outpacing available hardware, driving up value of older NVIDIA chips and creating openings for startups to exploit underused existing models
• Open-source projects like DeepSeek and local/edge models are democratizing AI access and could gain ground as competition among major players intensifies

The article reviews significant trends and developments in the LLM space throughout 2025, highlighting breakthroughs in reasoning, the rise of coding agents, and the increasing use of LLMs in command-line interfaces. It notes the evolution of tools and models, including the impact of asynchronous coding agents and the normalization of YOLO mode for improved efficiency.

• Reasoning models became mainstream in 2025, significantly boosting LLM performance on complex tasks
• Coding agents surged in popularity, with asynchronous agents enabling developers to offload larger chunks of work
• "YOLO mode" (letting agents run commands without manual approval) went from risky novelty to normalized practice for efficiency
• LLMs increasingly moved into command-line interfaces, embedding AI more directly into developer workflows