Anyone else encounter this thing where they post in a bunch of different groups and then 80% of the DMs end up just being long-winded, poorly made pitches? Whenever I see someone posting an issue, I see no point in waving a carrot in front of them. Just send them the resource/link and move on, build goodwill, what's with this pretense that you have to be compensated for every single idea you produce? I've gone back and forth maybe 4 or 6 times with the same talk track, the other end just mirroring the problem. Telling you they can fix it, then they push for you to give a budget and to then pay them in advance of them telling you HOW they'd fix it. Why is this so common? I've had this happen over a dozen times from all sorts of different Skool groups. I could truthfully deep research and google long enough to find answers, but I'm just trying to converse and learn through people too. I get it, if it's a repeat thing, sure, charge for support, but one-off without any rapport? "Compensate me for my time" while they're just doing the same search engine hopping you could do anyways. Boggles my mind Who's out here paying for ideas? Am I too Canadian? What is this? P.S. This happens primarily in AI groups, stinks the community experience if you're trying to charge everyone with a pulse

Looking to bring AI into the classroom in a way that'll help this group of grade 12 students actually make progress in their English learning journey. 8 Viet kids and 3 Korean, they've only experienced half-assed teachers and I'd like to be different This is a private school in Canada, Ontario for context

Happy Topless Tuesday 😎 Whether that means peeling off the doubt, dropping the stress, or literally just losing the hoodie because it’s hot show up confident and unapologetic today. Let’s keep it light, keep it bold, and keep building 💪🔥

https://research.google/blog/titans-miras-helping-ai-have-long-term-memory/ The TL;DR is they found a way to weigh memory into the inference points that a LLM goes through, rather than having it be separate, it assigns a weight to the memory as it forms. Worthwhile memory gets kept, not-so-important stuff gets purged ("weight decay"). As of December 4th, this is another huge leap on AI capability. Inference points are basically what LLMs use to make small jumps in understanding context, which makes or breaks the experience for a user. After their memory is bloated (thread gets too long), those jumps get harder and harder to make. Weights are an extra metric that evaluates whether or not it should forget in real time. Won't be long until this gets plugged into open source models, opening a new wave of vision-capable AI (think Kimi but in more hardware). Stringing a bunch of smaller AI agents as well, the implications can get scary The question is how much of this is marketing vs actual research-backed architecture? Links to the white papers: https://arxiv.org/abs/2501.00663 https://arxiv.org/pdf/2504.13173 "Infinite Memory" Claim Source "Instead of compressing information into a static state, this architecture actively learns and updates its own parameters as data streams in."

Seems like they're really going for a full ecosystem or suite of tools. No free version (outside of a trial) though, curious how it stands against its already market-dominant competitors Just came out today (December 3rd) https://workspace.google.com/studio/ Anyone playing with it yet? I imagine this would be an easier sell to enterprise clients that are already G-Suite native.