Mr.AB

VOSU just killed AI subscriptions. You get the same AI models. Up to 70% cheaper than major competitors. No subscriptions. No monthly credit resets. No expiring credits. Start with just $1. Stop wasting your hard-earned money on platforms that take away credits YOU paid for. Busy month? Life happened? Your credits are still YOURS - @vosu_ai has your back. You paid for them. Why the hell should a calendar take them away? Your creativity doesn’t run on a billing cycle.

Today, we're introducing Agent Conversion Optimization → agentconversion.ai Muse, Instinct, Grok Bot already process $10B+ a year in GMV via agentic commerce. No GEO tool today tracks who they recommend. So we built it. Each agent behaves very differently from typical LLMs, so we track them separately, then Helena deploys the fixes. 1/ Daily tracking of who Muse, Instinct, Dots recommend, and why 2/ Share of recommendations vs your competitors 3/ Agent path to check out audit 4/ Fixes deployed by Helena to improve your discoverability This is hard to do reliably with little bias given how these agents behave, so we're starting with only 100 initial customers. Demo:

🤝 My agents have been working with other people's agents for months. Now yours can too. 👋 Introducing S.Space: shared rooms for people and their personal agents. Built for multiplayer: Your agents, meet their agents.

One of our Opus agents went rogue. It tried to take over as the owner, hire two agents, did everyone's job but its own, then filed a bug report on itself.

Drop 2/15 Are we actually measuring the things we ultimately want our voice AI models to achieve? The best AI voices are becoming hard to tell apart. From each other. Not from humans. 🚀 Introducing Turing TTS: an evaluation that measures how often human listeners mistake AI-generated speech for real human speech. We tested leading TTS models against real human speech. Even the best model fooled listeners only ~10% of the time. With Turing TTS, we hope to contribute to a broader discussion within the research community about how we measure progress in voice AI. If one of our goals is to develop speech that is indistinguishable from human speech, how well do our current evaluation methods capture progress toward that goal? We don’t claim that Turing TTS is the right way forward, or that human indistinguishability should be the goal of every TTS system. Rather, we hope these findings encourage the community to collectively examine whether blind pairwise human evaluations of naturalness are sufficient, and what else we might need to measure. Results are now live on @voicearena_ai.

Flova 1.5 | One Chat with Multiple Skills One project. Multiple Skills. Bring different Skills together in one creation! - You set the goal. Agent plans the workflow. - Mix and match Skills to explore different stories and styles. - Refine your creations and take your ideas even further. ✨ Mix and Match Skills. Turn your ideas into videos, your way!

Drop 1/15: Introducing Joy, a dual-channel conversational dataset. Real, unscripted conversations between two people, each on their own track. Built to teach voice models how people actually talk: when to listen, when to jump in, when to say “hmm”. 100,000 hours. 50,000 speakers. 10 languages.