→Alexandr Wang shares Muse prompt: AI agent auto-adds travel time to calendar
Alexandr Wang shared a Muse prompt that scans the next 14 days of calendar and auto-adds travel time blocks for off-site meetings. The prompt also works with Instinct and Grok @bot. The post does not disclose the prompt format or usage limits.
#Alexandr Wang#Muse#Instinct
editor take
Alexandr Wang shared a Muse prompt that auto-adds travel buffers for off-site meetings—but the post doesn't include the prompt format.
FEATUREDAI HOT (Curated Pool)· aihot-apiZH00:07 · 09·19
→Anthropic delays IPO to November, targeting ~$2T valuation
Anthropic pushed its IPO from October to November, aiming to show Q3 financials first. The target valuation is around $2 trillion, with a raise of up to $100 billion—both would top SpaceX's record. The company expects annualized revenue above $110 billion by end of 2026. The delay was decided before a former researcher's public warning about AI speed, but investors will still ask how a slower model rollout could hit financials. Existing backers think the impact is limited since current models already generate strong revenue. Meanwhile, OpenAI won't go public before 2027 and is in early talks for a new round that could value it above $1.2 trillion; some Anthropic investors worry that could weaken demand for Anthropic's offering.
#Anthropic#OpenAI#SpaceX
why featured
Featured · importance 88 · hook + knowledge + resonance
editor take
Anthropic delayed its IPO to November to show Q3 revenue and justify a ~$2T valuation, but OpenAI's next funding round could steal demand.
sharp
The headline numbers are what make this worth opening: a ~$2 trillion valuation and up to $100 billion raised, both topping SpaceX's June 2026 record. The delay from October to November has a simple logic—Anthropic wants Q3 financials in hand to back up the story with hard revenue. The company expects annualized revenue above $110 billion by end of 2026, and existing backers argue that even a slower model rollout won't hurt much since current models already generate strong income.
Where I'd discount the hype: OpenAI is in early talks for a new round that could value it above $1.2 trillion, and it's explicitly not going public before 2027. Some Anthropic investors themselves worry that if OpenAI raises a comparable sum first, demand for Anthropic's offering could weaken. The former researcher's public safety warning adds another layer—investors will press on whether a safety-first stance helps or hurts under post-IPO shareholder scrutiny. The article doesn't spell out how much of that $110B revenue projection is already contracted versus aspirational, so I'd treat the valuation as a negotiation opener, not a settled number.
STILL DEVELOPING · 1dFEATUREDAI HOT (Curated Pool)· aihot-apiZH00:00 · 09·19
→OpenRouter benchmarks Jev against LLMs for customer support: 100x cheaper but cannot generate text
OpenRouter benchmarked TypeSafe's Jev 1.13 against GPT Luna and Claude Opus on 60 support tickets. Jev classified and flagged escalation at $0.025 per 1,000 tickets with 194 ms median latency, versus $0.09 for Luna and $2.88 for Opus. It returns typed probabilities directly—no JSON parsing needed. Jev is text-only, weak at arithmetic, and can't generate prose. The post recommends routing with Jev first, then handing off to an LLM for replies.
#OpenRouter#TypeSafe#Jev
why featured
Featured · importance 82 · hook + knowledge
editor take
OpenRouter benchmarked Jev 1.13 against Claude Opus 5 on support ticket triage: same accuracy, 100x cheaper, 10x faster latency — but it can't write the reply.
sharp
OpenRouter published a blog post benchmarking Jev 1.13, GPT Luna, and Claude Opus 5 on 60 support ticket triage cases. Jev matched Opus 5 on accuracy but cost $0.025 per 1,000 tickets vs Opus's $2.88, with latency dropping from ~2 seconds to 194ms. Both sources covering this are pulling from the same OpenRouter blog, so the agreement is just a single data point echoing — no independent replication yet.
Jev's pitch is narrow: it does classification and yes/no decisions, returns typed labels with probabilities, and doesn't generate text. The pattern OpenRouter suggests is route with Jev first, then hand off to an LLM for the actual reply. That split isn't new, but having public numbers from a neutral platform makes it easier to evaluate.
I'd discount two things. The test set is tiny — 60 examples, one day, one prompt design. And Jev's own docs flag that it degrades with irrelevant detail and can't handle arithmetic or date comparison reliably. If your tickets include screenshots, long chat histories, or date math, these numbers won't hold. What's missing: larger-scale results, multi-language performance, and behavior on noisy real-world data.