I agree with this but for a different reason than what the author gives.
When you write in a notebook you can see where what you are writing is within the notebook (e.g. ~1/3 of the way through, etc). That seems like a small thing but I think it really helps with anchoring the memory when writing, as well as with referencing it later. And of course you can also add all sorts of tabs and page markers to help even more.
And airline stocks are typically a bad investment and Delta has been called a bank that operates an airline due the the amount of their revenue that comes from credit card fees.
I built a multi-agent framework and coding harness that I'm using as my primary coding tool now, mostly with DeepSeek and GLM. I've been having a lot of fun working on it and improving it in my spare time. I'm thinking about open-sourcing it but right now the deployment and sandboxing model is fairly dependent on nix.
A handful of super-rich families got together in the 90s, hired some people to put together a campaign to re-label the estate tax as the death tax and convince everyone it was causing families to lose their small farms, and we haven’t talked seriously about it since.
I just have a desktop at home that I run inference off of. It is a great setup and I don't find myself wanting to inference models directly on my laptop.
Yeah, I've realized that the things I don't like in tech have everything to do with the culture and politics. When I've been able to work with a small team of people I really like and respect, I've generally been quite content.
Huh, as a Lanaboote user I’m surprised to see this on the front page. I use this in combination with sbctl for key generation. I’m mostly using it because I wanted to set up full disk encryption with TPM2 auth.
This won't help you right now, but GrapheneOS did recently announce a partnership with Motorola, so presumably in a year or so support will start showing up for some Motorola devices.
Side note: I did get the 10a on launch from Google Fi for ~300.
Here's the most relevant section I could find from the original source:
"Chrome extensions can expose internal files to web pages through the web_accessible_resources field in their manifest.json. When an extension is installed and has exposed a resource, a fetch() request to chrome-extension://{id}/{file} will succeed. When the extension is not installed, Chrome blocks the request and the promise rejects.
LinkedIn tests every extension in the list this way."
"Chrome extensions can expose internal files to web pages through the web_accessible_resources field in their manifest.json. When an extension is installed and has exposed a resource, a fetch() request to chrome-extension://{id}/{file} will succeed. When the extension is not installed, Chrome blocks the request and the promise rejects.
LinkedIn tests every extension in the list this way."
Yeah, Gnome consolidates feature changes into major releases every six months, so it aligns well with fixed release distros like Ubuntu and Fedora that have release cycles that are aligned with Gnome's.
KDE drops a new point release with new features ~ every four months, and has a more flexible release schedule, so it is just to just get the changes when they are released.
I'm currently running KDE on NixOS unstable which is great, but if I weren't doing that I'd still be on OpenSUSE Tumbleweed.
Yeah, this is something I am thinking a lot about. Companies won't be able to sustain this level of spending forever, and one of two things will need to happen:
1. Models become commodities and immensely cheaper to operate for inference as a result of some future innovation. This would presumably be very bad for the handful of companies who have invested that $1T and want to recoup that, but great for those of us who love cheap inference.
2. #1 doesn't happen and the model providers start begin to feel empowered to pass the true cost of training + inference down to the model consumer. We start paying thousands of dollars per month for model usage and the price gate blocks out most people from reaping the benefits of bleeding-edge AI, instead being locked into cheaper models that are just there to extract cash by selling them things.
Personally I'm leaning toward #1. Future models near as good as the absolute best will get far cheaper to train, and new techniques and specialized inference chips will make them much cheaper to use. It isn't hard for me to imagine another Deepseek moment in the not-so-distant future. Perhaps Anthropic is thinking the same thing given the rumors that they are rumored to be pushing toward an IPO as early as this year.
Maybe. I don't think we yet have a good understanding of how many deaths he will have caused as a result of DOGE so abruptly cutting off assistance to so many vulnerable people around the world, but I've heard estimates hover around 600,000.
Assuming that number turns out to be close to reality, how do you weigh so many unnecessary deaths against VTL rockets and the electric cars?
Perhaps a practitioner of Effective Altruism could better answer that question.
Honestly my real fear is ICE agents at polling places on Election Day harassing would-be voters with citizenship checks and aggressive behavior, slowing things down and maybe causing some people to leave.
Regarding voter data though, if it becomes known that registering to vote as a minority will get you extra scrutiny from ICE, and perhaps a visit to your home, that would probably cause some citizens avoid voting altogether, especially if they are associated with people who are not her legally.
Either way, the federal government really has no right to that data or legitimate use for it, so hopefully they don't manage to get their hands on it.
> I recently used a coding agent on a project where I was using an unfamiliar language, framework, API, and protocol.
You didn’t find that to be a little too much unfamiliarity? With the couple of projects that I’ve worked on that were developed using an “agent first” approach I found that if I added too many new things at once it would put me in a difficult space where I didn’t feel confident enough to evaluate what the agent was doing, and when it seemed to go off the rails I would have to do a bunch of research to figure out how to steer it.
Now, none of that was bad, because I learned a lot, and I think it is a great way to familiarize oneself with a new stack, but if I want to move really fast, I still pick mostly familiar stuff.
Wondering if anyone here has a good answer to this:
what protection does user data typically have during legal discovery in a civil suit like this where the defendant is a service provider but relevant evidence is likely present in user data?
Does a judge have to weigh a users' expectation of privacy against the request? Do terms of service come into play here (who actually owns the data? what privacy guarantees does the company make?).
I'm assuming in this case that the request itself isn't overly broad and seems like a legitimate use of the discovery process.