It depends, there are a lot of options including the age old blogger, wordpress, substack, ghost, medium etc. May be you can checkout substack (https://substack.com/for-bloggers)
What about the overall value of the code itself? Are the developers making similar amount of money using their code as they were before the advent of the LLMs?
At that stage usually her PhD advisor or her PostDoc advisor or her own network should be chipping in... I am not sure how many years of experience she has as a postdoc, the few I knew ended up with 6-9 years of temporary postdoc positions before they got a tenure track job. Industry is bit more welcoming but might demand some amount of exclusive interview prep. Having said that, what are the other options if not a tenured track position that your partner might be interested in? Would you and your partner be willing to relocate to another country/continent? Did you explore stable positions at universities that are not highly rated?
If it is VP of Engineering and he/she is reporting to you, one option would be to delegate the responsibility to manage the senior SWE to the VP and make both accountable to deliver the results. I think you also might need to reconsider your position as a CTO, if a VP could simply bypass you like that you have bigger issues at hand than managing the protege.
Is that a VP of Engineering? If that’s the case shouldn’t it be the VP’s responsibility to get the things done? How did that appointment happen without CTO’s approval give that it is a small company?
I think part of the design craft is to ensure rapid prototyping and at the same time making sure that manufacturing is scalable. My question is more on how to hit the sweet spot where prototyping is fast and at the same time production pipeline changes are cost effective.
This is solid advice, particularly #4 is the reason I have started building my own bots. I do have one question though, how to design the production pipeline such that it is easy to iterate on the bot design with minimal disruption?
I don’t think you would loose access to academia, actually it is the opposite. Academia would invite you with open arms once you have funds and/or the data :)
I did not say anything about making money in the current economy. Research requires funding and only way to have academic freedom is to have a steady source of recurrent income or funding. One way to get that is to please those who have money but give up part of your control on what you study. Other way is to monetize your knowledge and acquire a ton of money. You seem to misunderstand what I said or have very little clue on how the academia works.
I would say follow the money, create a product or service that generates recurrent income and channel that income to study the fundamental problems that would interest you. Owning a product or platform opens up for a lot of opportunities.
You can extend this argument to internal promotions as well, most companies expect you to first perform at the next level before you are considered for a promotion. Essentially it boggles down to being at the right place at the right time. The attributes you listed are not sufficient to run a company, you might require capital, you might require political support, you might require land or some natural resources and stuff that is not purely in your hands.
I completely agree with you. There are many issues in that field. Though this might not also be a great fit just putting it out: https://github.com/chand-lab/openEyeTrack
I think this analogy is not right. System 1 vs 2 in biology is in response to the need to preserve the life and run away from threats. For interactive question answer system it makes no sense to resort to system 1 kind of responses.
May be you should seek more information and understand the basis for his stance. What assumptions did he make while hiring you? What changed? What are the data points that demonstrated your department is making it cumbersome?
We have barely scratched the surface of the algorithms that can satisfy all the attributes of significant learning. https://www.buffalo.edu/catt/teach/develop/design/learning-o...
Although I believe, the much more serious issues are around the impact of deploying AI systems on the Human agency. These algorithms could potentially make any kind of change harder and could seriously cap human potential.
Have you experienced a pivotal moment in your startup journey where the promise of supervised machine learning fell short of expectations? Drawing inspiration from the challenges outlined by Starsky Robotics in their Medium post, "The End of Starsky Robotics," I am curious to hear your own encounters with ML in production. What hurdles did you confront, and how did you navigate them amidst the evolving landscape of machine learning technology?
How hard is it come up with community run maker spaces supported by the city for putting such things together? Not every thing has to be owned by large private companies.
More than the loans, publicly traded companies must be regulated when they are loosing money. A lot of promises about future monetization are made with zero accountability