Every interesting problem I ever had that I checked SO for was either off topic and modded down or the original poster said “I found a solution” and disappeared. Pure misery!
Also, some subset of developers on subscriptions are going to develop products that use the API, and some of those products are going to be successful.
Only a minor in mathematics here. That's interesting that it struggles with definitions. Maybe unsurprising given the different ways they're chosen and written (motivational, aesthetic, pedagogical, etc)? A big lever to keep hold of.
I like the pragmatic take on AI in mathematics. Running off and proving/formalizing things in incomprehensible ways is marginally useful, and probably harmful for any work that needs to move beyond it. But, there's value in what AI is capable of doing for working mathematicians, when used as a partner.
The genie is out of the bottle though. It's hard to see how there won't be weird and inscrutable branches of mathematics that LLMs build and use effectively _for themselves_, in the same way much of the slop LLMs write in spoken language is really for the consumption of LLMs.
Surely there will be a new kind of research mathematician that's investigating this output in the same way, say, academics in literature read.
> Any time I tried to use an LLM for a game-y thing, it would fall over. Maybe this is my own bias. I’ve been a game programmer for nearly 15 years, I think I know how things work. I can sniff out “bad” game code a mile away. But when it comes to Svelte web-apps, I’m decidedly less well versed and am inclined to think that whatever code the LLM produces is “good”.
It feels like the discovery of a specific form of gell-mann amnesia. I know when working with an agent through areas where I have domain expertise I catch bad assumptions, decisions, etc. But then, unfortunately, have to trust it much more where I have minimal expertise.
To expand a bit for my current use cases. Inline routing of work to heavy task specific models, and prompt/context generation (user is asking something, what and how much should we prompt the expensive LLM with). Latency or time to first token does matter for some applications.
You can easily use Jev without an LLM (consider Jev used to make truthy decision branches in a script). It's not meant to replace LLMs. The tech is not a competition. But OpenAI is certainly in competition with TypeSafe, they'll want to keep people in their own ecosystem!
These are two different layers in the stack that do two different jobs. Agent to agent communication across remote machines is definitely lubricated with tailscale, I’m not sure what value moving up the stack would bring at this stage?
My read here isn’t about connecting to remote machines in the sense of ssh, but presenting a unified view across herdr instances running on multiple remote machines.
This stinks of the same enshittification moves Docker made 10 years ago.
100% convinced their raw output is intended as further inputs, and my workflows have been comfortable and efficient treating it as such. If you really need to read slop, you ask your agent to give it to you in a style that works for you. I can imagine a world where the slop from others doesn’t hit us directly but gets personal mediation.
The only time I ever used it (as a turk?) was when Jim Gray went missing at sea and satellite imagery of vast regions of the pacific were fed through Mechanical Turk. Seems a trivial problem now, but was not then, and needed humans to take a look.
I've been running a desktop Mac in addition to MacBook and iPad for the past decade (currently a first gen Studio still holding quite strong). It's primarily a focus thing, secondarily it's a way to keep work happening locally while I'm elsewhere, and never have the frustrations of docking and screen geometries all re-arranging, etc. The iPad is at the opposite extreme, it's for recreation, burn out management. The MacBook exists for when I actually need to do "real work" outside of the house.
At this point we should be sharing prompts not code for solutions like OpenLogi. Reverse engineering many things can typically be one-shotted (ignoring things like.. capturing a pcap or whatever to aid the effort). Let others add their own taste to the solution.
The grey beards in the mid-90s cut their teeth on much more constrained systems! The web crawling these days probably has all the ones who have passed spinning rapidly in their graves.
Modifying those games were the first times I ever touched code! There was one that existed somewhere between DOS 2.0 and 3.2 that simply asked your name, then asked how the hell you got that name. It was maybe half a dozen lines, but easy enough to modify output and learn a bit about variables.
Wine certainly. Guinness was marketed as being good for you and not uncommon for pregnant women to drink it. Old liqueurs were often considered medicine, with Chartreuse from the article being one. Doctors used to prescribe alcohol during prohibition, but that probably doesn’t count here.
We stock Chartreuse at our bar, not easy, and we occasionally have to modulate the cocktails on the menu to slow customers down a bit. The Bay Area itself gets an allocation each month, which is then further allocated to individual establishments.
System Z is vaguely the read reply cards you would find in magazines. While I enjoyed the snail mail spammed materials, my parents were terrorized through the entire 90s by my abuse of those found in professional magazines like Computer Language.
You’d be looking at fuel level affecting choices of ride height, brake bias, etc. Possibly changes in line and distance travelled too. But, I’m curious how psychology can fit in here (or not. How do you measure it?). Driver concentration and confidence are important when trimming out aero or other adjustments for single lap flyers.
It's a fun scene, but dear beginners, just eat the ramen. Be curious as usual about all the things. But you do not need to poke at your food and get all contemplative over fast food. I defy anyone to find a salaryman in Tokyo at 7am before work plowing a bowl of ramen (and clearly enjoying it) and also being weird about it.
"Since nearly 80% of the population uses cars to get to work and their important medical appointments, we are taking a closer look at federal guidance to ensure American families have the infrastructure they need," the spokesperson said. "This is a common sense approach despite the Left's belief that everyone should ride a bike to their shoebox-sized apartment to eat crickets to achieve a phony climate agenda."
Jumping straight into the middle of the article you might believe you stumbled onto The Onion.