sorry to hear the stress that your cofounder caused. how did you know them before the venture?
cofounder arguments are common, esp in crypto. for your next venture, it's better to have a cofounder agreement with vesting schedule in place. once someone becomes irrational and unreasonable, it's a sign to get lawyers involved.
dont be too hard on yourself, happens to the best of us.
there's a lot of hype, but you already see it helpful at the fringes. alphazero, alphafold, and recently alphageometry is almost gold medalist level already.
it's turning learning and search into a compute bound problem accessibly by SWEs and DSs.
this has been a great resource. approachable and great for practitioners. it's frequently updated with new papers and techniques https://www.promptingguide.ai/
i heard the most important issues are cliff, vesting and the right of first refusal for selling shares. where would these issues typically be addressed?
Do you really need SOC2 compliance? If a customer problem is big enough and underserved, could you start serving customers without it? Or is it a way of potential customers telling you "no" while being nice about it?
Thanks. Focusing on high-impact, low-effort makes a ton of sense. I'm definitely staying close to paying customers. Wondering how you delineate core vs noncore tasks that you outsource?
Competitive landscape, just need to be aware of what they're doing, but not really focusing on them. There are probably at least 10-30 competitors doing something similar and different stages of venture backed.
Same idea. Probably 5 applicable accelerators, ~100-500 angel investors, ~50-100 seed stage investors. Going through accelerator interviews. Ignoring all investors until after I get more paying users. I haven't gone through raising before, but https://www.lore.vc/ has been a nice resource. Setting up the auction environment for the best fundraising market dynamics.
It's progressing, but maybe not in the way you think. 1 model will not rule them all, they'll get overtaken by vertical LLM's specialized with niche, high quality data sets.
Also prompt templates are rapidly progressing. "Tree of Thought" is the latest state of the art, but we're not applying predictive probabilistic tree traversal yet.
There's still a ton of low hanging fruit everywhere. Why are we inputting with language instead of code? Seems really backwards to use imprecise freeform language instead of intent templates.