That 200k is a reasonable amount to start withdrawing from a 5M portfolio (exactly the 4% rule from the Trinity study [1]), but you’ll want to adjust it for inflation every year. My favorite tool for planning these strategies is TPAW Planner [2], which visualizes the distribution of withdrawals under various market outcomes. It’ll also suggest a portfolio of stocks and bonds that’ll be safer than just T-bills, which have a high risk of not beating inflation.
TUIs are great for low friction remote work. I do a lot of data processing work on remote VMs with a mix of interactive debugging/eyeballing and bulk jobs. TUIs are a great fit for the sorts of tools I build to support this work. The other UI paradigm I end up reaching for is locally hosted web UIs, as models are really good at one-shotting HTML reports with graphs and tables. Inside VS code those get automatically tunneled to the local machine.
You're exactly right. The llguidance library [1,2] seems to have emerged as the go-to solution for this by virtue of being >10X faster than its competition. It's work from some past colleagues of mine at Microsoft Research based on theory of (regex) derivatives, which we perviously used to ship a novel kind of regex engine for .NET. It's cool work and AFAIK should ensure full adherence to a JSON grammar.
llguidance is used in vLLM, SGLang, internally at OpenAI and elsewhere. At the same time, I also see a non-trivial JSON error rate from Gemini models in large scale synthetic generations, so perhaps Google hasn't seen the "llight" yet and are using something less principled.
I live half way across the world from my folks so I don’t see them often. I’d love something that gives me a greater sense of presence than a video call can give.
We don’t know that LLMs generating tokens for scenarios involving simulations of conscious don’t already involve such experience. Certainly such threads of consciousness would currently be much less coherent and fleeting than the human experience, but I see no reason to simply ignore the possibility. To whatever degree it is even coherent to talk about the conscious experience of others than yourself (p-zombies and such), I expect that as AIs’ long term coherency improves and AI minds become more tangible to us, people will settle into the same implicit assumption afforded to fellow humans that there is consciousness behind the cognition.
For some interesting context: this paper was a precursor to all the work on synthetic data at Microsoft Research that lead to the Phi series of SLMs. [1] It was an important demonstration of what carefully curated and clean data could do for language models.
Edge's Password Monitor feature uses homomorphic encryption to match passwords against a database of leaks without revealing anything about those passwords: https://www.microsoft.com/en-us/research/blog/password-monit... So not the first, but definitely cool to see more adoption!
1: https://www.bogleheads.org/wiki/Safe_withdrawal_rates
2: https://tpawplanner.com/