I think it’s a bit early to be locking down your local LLM setup. The history of computing suggests there is some cost reduction to come for the hardware. In the mean time there is time to find the sweet spot for performance of the small and medium models. There are plenty of cloud hosts which can run the open models and allow you to experiment while the models mature. Hopefully your not burning 6k worth of tokens anytime soon.

I personally have email integrated into my editor (mu4e) so I can apply patches and search code directly from the email thread. It handles threads and searching really well.

Issue triage, code exploration, extracting information from disparate sources, first pass code review. There are loads of use cases that it’s potentially useful.

For me it’s a lot better at extracting the requirements for a CPU feature from a 10,000 page architecture reference manual than I am.

I have API access at work because I don’t want to be tied to a UI. I’m very aware of the cost because I’m trying to see where it offers good value for money.

Of course things like the deep research and notebooklm are covered by the Google workplace fees which while including more than the personal plans are also a fair bit more expensive.

Your making a big assumption extrapolating from one particular study involving Java code and a static analyser.

How is that patch sloppy?

I feel the term slop is being overused to cover anything an LLM has touched. If I ask an agent to re-read a mail thread for me and apply the changes to my tree to review is that slop? Would you feel better about it if I copy and paste from email to code in my editor?

I’ve just been doing a bunch of bug triage which was mostly driven by the agent although I checked the issues where it had commented. Was that slop? Ironically a lot of the issues where AI generated although for the most part more complete than a lot of the purely human submissions we get. Are those bug reports slop? What about the poorly drafted human ones?

That’s not kernel policy but LF guidance. From the kernel’s point of view patches still have a high bar to pass to get merged and I don’t think we have enough data yet to see if LLM based submissions to the kernel have a higher or lower error rate than humans.

I certainly feel the uptick in LLM reports though - one of the projects I’m working on is seeing a deluge of them at the moment.

At 43 that’s probably a little earlier than the OP expected and if their daughter wasn’t planning on starting that early it’s going to affect school and job prospects.

That’s not too say it can’t work. One of my in-laws had their first at 18 and now as their last leaves for uni they are still fit and young enough to enjoy the empty nest experience.

Where you live maybe. The NHS is centrally funded through taxation.

If course you do - if the cost of treating the patient down the line is going to cost you more. Public health systems have a vested interest in healthier citizens.

The majority of my gaming is on the road too but I’ve found the Steam Deck hits that niche for me. I carry a thin Chromebook for work related things. Admittedly you don’t need as powerful a GPU for a small 720p display.

How big a niche is that - because when I think high end gaming a laptop has all sorts of trade offs to make anyway.

I’m going to take a swing at the moon reflects a relatively uniform spectrum of light from the sun but our varied atmospheric conditions can alter the refraction of that light.

If it’s mentioned up front and fixed then it’s fine. One way or another the restaurant needs to cover it’s costs and it’s either done via inflating the price of the food or a fixed service fee.

What I hate is a discretionary tip suggestion because suddenly I’m made to be responsible for how much the staff get.

On the potentially bright side maybe this will make people think harder about which model to use for which task. You don’t need to feed your entire code base into Opus when a Gemini Flash sub-agent can do a perfectly fine job running grep and compiling a summary for the main agent.

Even Debian has popcon as an opt in. I can see why collecting data about hardware and package choices is useful to Ubuntu. I didn’t think they collected any personally identifying information.

I also have a diverter which heats up my hot water tank which saves on gas, especially in the summer.

Export to the grid, for every kWh I export during the day I can afford two kWh overnight.

If it’s finding valid vulnerabilities then it’s just another tool like static analysis, fuzzers and sanitizers. There definitely seems to be a difference in quality compared to earlier generations that were behind the sloppy avalanch of reports.

I think the article is over complicating things. I work in a project which is heavily forked for a variety of reasons. While it’s academically interesting to look at the reasons for those downstream forks we have no interest in going to the considerable effort of tracking them all.

If you can take a project and use an LLM to enable your niche use case then more power to you. FLOSS was never about ensuring all patches flow upstream.