AI Weekly Malaysia

River AI, founded by xAI co-founder Igor Babuschkin, raised $1.1B in a seed/Series A led by General Catalyst and AMP PBC, with Nvidia, AMD Ventures, Y Combinator, and Temasek participating. The company, which exited stealth in June 2026, already offers an API billed per million tokens that supports RL and LoRA fine-tuning on open models, positioning itself as an alternative to prompt engineering by letting developers train models they own rather than steer ones they don't.

Why: If you're currently relying on prompt engineering against closed models, River's API offers a concrete alternative: fine-tune open models with RL and LoRA and serve them as endpoints you control. Temasek's participation signals sovereign-fund interest in AI infrastructure that could ripple into Southeast Asian deployment and partnerships. Evaluate whether per-million-token fine-tuning economics beat your current prompt-heavy workflow.

Nathan Lambert's post-training textbook 'Reinforcement Learning from Human Feedback: Aligning and Post-training LLMs' is now published by Manning and freely available online, accompanied by a 12-hour video course, slides, a codebase with exercises, and model comparison examples. It covers topics like rejection sampling, outcome reward models, and character training at a foundational level, targeting readers with a CS background rather than beginners. The print edition is 50% off until August 19 with code PBLambert.

Why: If you're an AI/ML learner or developer moving from model usage to model fine-tuning, the free online book plus 12-hour course gives you a structured path into RLHF and post-training techniques that are otherwise thinly documented. The 50% discount code expires Aug 19, so decide before then if you want the print version.