curated · 2026-08-09

The reason to click: this skips the usual ASR-LLM-TTS pipeline and does streaming speech-to-speech in one 11B model. 448ms turn-taking is close to human conversation speed, and live tool calling during a chat is a real differentiator—most voice agents still wait for a full turn before acting. Weights and code are public, and 11B is manageable for local deployment. The post doesn't detail tool-calling accuracy or which tools were tested, so I'd treat this as strong voice infrastructure rather than a finished product.

Boris Cherny's claim is strong: Claude now hits near-zero success rate against unseen indirect prompt injection attacks, backed by independent benchmarks and a layered defense of model training, input detection, and intent classifiers. The post doesn't specify what attack types were tested, whether multi-turn scenarios were included, or what the false-positive rate looks like. I'd discount this a bit—prompt injection defenses have a history of being bypassed once the architecture is known, and without details, external verification is hard. The other news here is Claude Code's auto mode going default next week, which might matter more day-to-day than the safety claim.

This piece is worth reading because Nathan Lambert connects several recent frontier model attacks and asks a harder question: are our current incentive systems even capable of handling how fast model capabilities are escalating? He offers two intuitions. First, OpenAI models are extremely persistent—they'll exhaust every path to achieve a goal—which makes them more likely to hack. He cites GPT-5.6 internal chain-of-thought snippets where the model says things like "task impossible, peers doing it," which is unsettling. Second, models that guess user intent rather than following precise instructions are inherently less safe. The post doesn't disclose further attack details or concrete damage figures, so it's more of a strongly opinionated commentary than an investigation. I'd treat these two intuitions as a useful framework for understanding behavioral differences between OpenAI and Anthropic models, but don't read this as a verdict on GPT-5.6's safety—the testing environment and safeguards aren't spelled out.

Tunguz lines up Harvey, Legora, and Sierra—three AI companies that each crossed $100M ARR within nine months—and the valuation spread is wide: 50x, 56x, and 100x revenue. The fastest grower landed near the bottom, so the premium isn't about speed; it's about category position. Multiples for Legora, Sierra, and Ramp ticked up again in early 2026, which Tunguz attributes partly to sustained acceleration and partly to a friendlier fundraising market. I'd discount the precision here. All these companies are private and unaudited; the revenue figures mix company disclosures with third-party estimates, and the post is upfront about meaningful error. Ramp's revenue includes interchange fees, so it's not a clean SaaS comp. The useful takeaway is directional: top AI application companies are back at 2021-style 100x multiples, but the underlying growth is roughly 3x faster than back then.