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![What a day! I did onboarding demos for:
🍑 #1 clinic partner network for colonoscopy in the UK
🔞 a top 15 OnlyF*ns creator
⌚️ an international Swiss watch brand
(3 very different use cases, but at least it proves VIDEO AI ME works across any industry 😅)
Also got: 125 signups → 113 started onboarding → 88 completed → 13 requested a demo video → 3 sales.
Onboarding proves real user interest, demos cost me time+money, so I need to filter before giving a free video. Most people complain about no free trial; I’m not at that stage yet.
Next step: automate the demo video post-onboarding to boost sales. to reach the [ demo → sales ] at 20%+](/_next/image?url=https%3A%2F%2Fpbs.twimg.com%2Famplify_video_thumb%2F1994440999674777600%2Fimg%2FVit9sAh69HBcr_g3.jpg&w=3840&q=75 "What a day! I did onboarding demos for:
🍑 #1 clinic partner network for colonoscopy in the UK
🔞 a top 15 OnlyF*ns creator
⌚️ an international Swiss watch brand
(3 very different use cases, but at least it proves VIDEO AI ME works across any industry 😅)
Also got: 125 signups → 113 started onboarding → 88 completed → 13 requested a demo video → 3 sales.
Onboarding proves real user interest, demos cost me time+money, so I need to filter before giving a free video. Most people complain about no free trial; I’m not at that stage yet.
Next step: automate the demo video post-onboarding to boost sales. to reach the [ demo → sales ] at 20%+")
What a day! I did onboarding demos for: 🍑 #1 clinic partner network for colonoscopy in the UK 🔞 a top 15 OnlyF*ns creator ⌚️ an international Swiss watch brand (3 very different use cases, but at least it proves VIDEO AI ME works across any industry 😅) Also got: 125 signups → 113 started onboarding → 88 completed → 13 requested a demo video → 3 sales. Onboarding proves real user interest, demos cost me time+money, so I need to filter before giving a free video. Most people complain about no free trial; I’m not at that stage yet. Next step: automate the demo video post-onboarding to boost sales. to reach the [ demo → sales ] at 20%+
I just launched a new startup today → VIDEO AI ME → Create your own AI clone and make studio-quality videos that go viral → https://t.co/eU2hD1uyoV
i mean, it's a language model, how big should it be? 1 million parameters?
Artisanal baker of reasoning models @pleiasfr
Reminder to get the premium license of typingmind at 60% off! 23 hours left
https://t.co/dNyvDu6yiB
I'm sorry this is painful to look at. Exposed metal mechanics forced through gravel and sand. This won't do, this isn't an excavator. These robots must be shown in a realistic deployment gear.
We're in a race. It's not USA vs China but humans and AGIs vs ape power centralization. @deepseek_ai stan #1, 2023–Deep Time «C’est la guerre.» ®1
![10 years ago: the reinforcement learning (RL) prompt engineer [1] (Sec. 5.3). Adaptive chain of thought: an RL neural net learns to query its "world model" net for abstract reasoning & decision making. Going beyond the 1990 neural world model [2] for millisecond-by-millisecond planning and the 1991 adaptive neural subgoal generator [3,4] for hierarchical planning.
[1] J. Schmidhuber (JS, 2015). On Learning to Think: Algorithmic Information Theory for Novel Combinations of RL Controllers and Recurrent Neural World Models. ArXiv 1210.0118
[2] JS (1990). Making the world differentiable: On using fully recurrent self-supervised neural networks for dynamic reinforcement learning and planning in non-stationary environments. TR FKI-126-90, TUM. (This report also introduced artificial curiosity and intrinsic motivation through generative adversarial networks.)
[3] JS (1991). Learning to generate sub-goals for action sequences. Proc. ICANN'91, p. 967-972.
[4] JS & R. Wahnsiedler (1992). Planning simple trajectories using neural subgoal generators. Proc. SAB'92, p 196-202, MIT Press.](/_next/image?url=https%3A%2F%2Fpbs.twimg.com%2Fmedia%2FG62qakxWcAATu16.png&w=3840&q=75 "10 years ago: the reinforcement learning (RL) prompt engineer [1] (Sec. 5.3). Adaptive chain of thought: an RL neural net learns to query its \"world model\" net for abstract reasoning & decision making. Going beyond the 1990 neural world model [2] for millisecond-by-millisecond planning and the 1991 adaptive neural subgoal generator [3,4] for hierarchical planning.
[1] J. Schmidhuber (JS, 2015). On Learning to Think: Algorithmic Information Theory for Novel Combinations of RL Controllers and Recurrent Neural World Models. ArXiv 1210.0118
[2] JS (1990). Making the world differentiable: On using fully recurrent self-supervised neural networks for dynamic reinforcement learning and planning in non-stationary environments. TR FKI-126-90, TUM. (This report also introduced artificial curiosity and intrinsic motivation through generative adversarial networks.)
[3] JS (1991). Learning to generate sub-goals for action sequences. Proc. ICANN'91, p. 967-972.
[4] JS & R. Wahnsiedler (1992). Planning simple trajectories using neural subgoal generators. Proc. SAB'92, p 196-202, MIT Press.")
10 years ago: the reinforcement learning (RL) prompt engineer [1] (Sec. 5.3). Adaptive chain of thought: an RL neural net learns to query its "world model" net for abstract reasoning & decision making. Going beyond the 1990 neural world model [2] for millisecond-by-millisecond planning and the 1991 adaptive neural subgoal generator [3,4] for hierarchical planning. [1] J. Schmidhuber (JS, 2015). On Learning to Think: Algorithmic Information Theory for Novel Combinations of RL Controllers and Recurrent Neural World Models. ArXiv 1210.0118 [2] JS (1990). Making the world differentiable: On using fully recurrent self-supervised neural networks for dynamic reinforcement learning and planning in non-stationary environments. TR FKI-126-90, TUM. (This report also introduced artificial curiosity and intrinsic motivation through generative adversarial networks.) [3] JS (1991). Learning to generate sub-goals for action sequences. Proc. ICANN'91, p. 967-972. [4] JS & R. Wahnsiedler (1992). Planning simple trajectories using neural subgoal generators. Proc. SAB'92, p 196-202, MIT Press.
Invented principles of meta-learning (1987), GANs (1990), Transformers (1991), very deep learning (1991), etc. Our AI is used many billions of times every day.
RT @markminervini: The only way to master love is to practice love. You don't need to justify love. You don't need to explain love. You jus…
Quaker Libertarian Vegetarian Domain Investor #chess player @impervious, https://t.co/hbatJ5R7Mo, https://t.co/1kKnSOfXQL. Previous https://t.co/I6PIEzagKA