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.

NVIDIA announced Nemotron 3.5 Lightning, a 30-billion-parameter mixture-of-experts open model designed for high-volume specialized tasks within multi-agent systems, claiming up to 4x faster output speed and 30% faster agentic task completion versus peers in its class. They also released NeMo Switchyard, an open source routing library that directs each request to the most suitable model across a mix of open, proprietary, and NVIDIA models without requiring application rewrites.

Why: If you are building multi-agent systems, the NeMo Switchyard routing library is the practically actionable piece here — it lets you mix models (open, proprietary, NVIDIA) behind a single router without rewriting your app, which is a real architecture decision worth evaluating. The 30B MoE model itself is a vendor claim with no independent benchmarks yet, so treat the speed numbers as unverified until third-party testing appears.

The provided article text contains only cookie consent and privacy policy boilerplate from Ars Technica. The actual article content about Meta's new open models and its AI strategy reboot is not present in the supplied text.

Why: No actionable takeaway can be derived because the article body is missing. Do not attempt to discuss Meta's model releases or strategy based on this text alone.