so exciting to see these ideas out in the world! i'm now imagining a Scratch-like playground for kids to explore end-user programming / AI in an accessible way, like some of the example apps you've shown
there's been a rising tide of academic HCI work in a similar space, wonder if there will be cross-pollination of ideas along these lines (many more papers i'm sure but some off the top of my head):
https://arxiv.org/abs/2305.11473https://arxiv.org/abs/2309.09128
also, while i have your attention here, since you wrote that related post on (not) vector db's ... what would you recommend for a newbie to get started with RAG? let's say i have a large collection of text files on my computer that i want to use for RAG. the options out there seem bewildering. is there something simple akin to Ollama for RAG?
Your work is an inspiration as always!! My n00b question is: what do you think is currently the most practical path to running a reasonably-sized (doesn't have to be the biggest) LLM on a commodity linux server for hooking up to a hobby web app ... i.e., one without a fancy GPU. (Renting instances with GPUs on, say, Linode, is significantly more expensive than standard servers that host web apps.) Is this totally out of reach, or are approaches like yours (or others you know of) a feasible path forward?
Cool work! You and your team may be interested in these two recent CHI papers from Microsoft Research, both on very relevant topics to what you've been doing:
1) “What It Wants Me To Say”: Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models (https://arxiv.org/abs/2304.06597) -- they try to tackle a similar problem as what you described above
2) On the Design of AI-powered Code Assistants for Notebooks (https://arxiv.org/abs/2301.11178) - uses Mito as part of their case study
ping me by email (see profile) if you want to brainstorm privately; my two cents is biased toward academia if you have an above-average setup (don't know until I see your CV!); startups come and go with each hype cycle.
my two cents: find something where the grants align with your interests, or find a brand-new faculty member who has startup money so isn't as bound by grants in the near term. and if things don't work out, simply leave and go to industry -- with a marketable skill set like what you get from being a CS major, there's no way for you to lose. go wherever has work that you like more.
totally agreed! things can change even from one year to another with the same advisor. for instance, i'm not the same advisor to my students that i was last year, or the year before that, or the year before that. (i've only been at this for 4 years, and each year is incredibly different from the prior one.) circumstances change, resources change, and constraints change.
you've inadvertently answered the question for me, to a first approximation :) i think that academia is a great launching point for a wide array of scholarly activities that the free market (i.e., industry) doesn't directly pay for: research, public policy, outreach, teaching, mentoring, industry collaborations, etc. i can work with whatever companies i want (even ones that are actively competing with each other at the moment!) and be seen as a "neutral" party; i can share knowledge via teaching and research again with a "neutral" voice without being seen as a spokesperson for a particular company or other special interest group. you're right, though -- there's a whole lot to the story. maybe someday i'll write something up!
if you can't get on someone's critical path, then you have to make it very easy for them to help you with very little time commitment. e.g.,: http://pgbovine.net/how-to-ask-for-help.htm
(no guarantees that i'll be able to answer via text, though; maybe i'll make a video later. been trying to minimize my computering time off-hours due to increasing wrist pains ... PSA: take care of your wrists, everyone!)
Lecturers are part- or full-time teaching faculty (usually not tenure-track, but some schools like UC's have tenure-track Lecturer tracks) e.g., https://profiles.stanford.edu/48960
oh noes, not that guy again! this was written over 4 years ago before i became a professor, so YMMV :) i haven't looked at this for years; this is a more updated (and complementary) take on some of these issues, how that I've advised ~25 research students so far:
http://www.pgbovine.net/managing-me-as-your-advisor.htm
If you're cranking out papers, then you should have enough papers to finish soon; in that case, just finish your Ph.D. and move on. Tell your advisor that you want to graduate within the next year, and that you have the papers to do so. Like it or not, the credential can open up doors for you in the future in unexpected ways, and you're so close to it already. (If you were, say, a 2nd-year student and felt this way, then leaving would be a much more feasible option.)
there's been a rising tide of academic HCI work in a similar space, wonder if there will be cross-pollination of ideas along these lines (many more papers i'm sure but some off the top of my head): https://arxiv.org/abs/2305.11473 https://arxiv.org/abs/2309.09128