Seems very interesting making people APIs for AIs :P https://rentahuman.ai/

I love finding the strange little details that actually move the needle. Saw a report today about a creator who went from 12k views to 2.1M views just by changing their body language. The hack? "Arms up." Literally just showing vulnerable, open body language in the thumbnail/hook. It signals openness to the algorithm (and humans, apparently). It sounds silly, but the numbers don't lie. Original breakdown is here: https://xcancel.com/danielhangan_/status/2016578118585053354?s=46 Also, on the "big money" side of things... A stealth AI lab called "Flapping Airplanes" (yes, real name) just raised $180M from Sequoia and Google Ventures. Nobody knows exactly what they are doing yet, other than "human-level intelligence." Keep an eye on them. With a name like that and backing like that, they are definitely up to something. https://xcancel.com/flappyairplanes/status/2016564437499728259 Have a great weekend, everyone. Go try the arms up thing and tell me if it works lol.

I honestly can't keep up with this week (or any week for that matter). If you are building anything with video, you need to look at what Elon's team just dropped. xAI launched the Grok Imagine API and they are claiming it is the fastest video model on the market. The latency is apparently zero. We are talking real-time. Check it out here: https://x.ai/news/grok-imagine-api But wait, because Google isn't sitting this one out. They just released "Genie" for Ultra subscribers. This is the part that blew my mind—it’s not just generating a video you watch. It’s a "world model." You can essentially generate an interactive environment. Like a game. From a text prompt. See the demo here: https://x.com/googleai/status/2016929427784122627 I’m curious—are you guys sticking with Runway/Sora, or are we all jumping ship to Grok because of the speed? Let me know in the comments. 👇

Worth reading and keeping an eye on.. Introducing Nested Learning: A new ML paradigm for continual learning We introduce Nested Learning, a new approach to machine learning that views models as a set of smaller, nested optimization problems, each with its own internal workflow, in order to mitigate or even completely avoid the issue of “catastrophic forgetting”, where learning new tasks sacrifices proficiency on old tasks. The last decade has seen incredible progress in machine learning (ML), primarily driven by powerful neural network architectures and the algorithms used to train them. However, despite the success of large language models (LLMs), a few fundamental challenges persist, especially around continual learning, the ability for a model to actively acquire new knowledge and skills over time without forgetting old ones. When it comes to continual learning and self-improvement, the human brain is the gold standard. It adapts through neuroplasticity — the remarkable capacity to change its structure in response to new experiences, memories, and learning. Without this ability, a person is limited to immediate context (like anterograde amnesia). We see a similar limitation in current LLMs: their knowledge is confined to either the immediate context of their input window or the static information that they learn during pre-training. The simple approach, continually updating a model's parameters with new data, often leads to “catastrophic forgetting” (CF), where learning new tasks sacrifices proficiency on old tasks. Researchers traditionally combat CF through architectural tweaks or better optimization rules. However, for too long, we have treated the model's architecture (the network structure) and the optimization algorithm (the training rule) as two separate things, which prevents us from achieving a truly unified, efficient learning system.

I am looking for someone who is an expert at Dev Ops for N8N self hosted. I want to add in some level of redundancy, deal with usage issues we may have with high usage and optimize the performance etc. Experts only no beginners please. Please ping me if interested!