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> All of this is built around the simple idea that real friendships happen when you actually meet in person.

I understand the sentiment - but this would make it useless for my closest friends - we live in different cities and countries now - and it would take years to fill in the social graph. We would all have to travel and meet everyone else.

I suppose this is alleviated by the talk to a friend of a friend feature - but does sound like it partially excludes friends with limited mobility.

Do you use a docking station and an external display?

Interesting - but how do I patch, upgrade and build my own iso?

The source repository isn't very enlightening?

> The actual repository here hosts the source code for Lightwhale, and is not of any interest for most people.

> https://bitbucket.org/asklandd/lightwhale/src/master/

> this is a great example of a driver that should have been running in userspace a long time ago, just like how Windows has been moving in that direction.

Hasn't windows (nt lineage) moved solidly in the opposite direction? Used to be you could reload/restart the video card ("GPU") driver if the driver crashed?

I think there's room for a distinction between "not using metrics" and "not using data".

Unthinkingly leaning on metrics is likely to help you build a faster, stronger horse, while at the same time avoiding building a car, a bus or a tractor.

Posting inspired by this tweet on x:

>> I was in Ukraine drone HQ last year and they were using Palantir tech to blow up Russian tanks dude

> And I am a Ukrainian drone pilot on the frontline. We use the Delta Battlefield Management System, fully developed in Ukraine. Not American Peter “Antichrist” Thiel bullshit.

https://x.com/laser_kiwi_ua/status/2046446354558251100

The post in question (also linked in TFA):

https://x.com/PalantirTech/status/2045574398573453312

Not really comparable perhaps - but I had a Ericsson t18s or similar that went through a full 60C cotton wash cycle (being on at the start of the wash) and was fine after drying off.

The thing is - if the battery had been destroyed, that could have been replaced...

I think it might have to do with how models work, and fundamental limits with them (yes, they're stochastic parrots, yes they confabulate).

Newer (past two years?) models have improved "in detail" - or as pragmatic tools - but they still don't deserve the anthropomorphism we subject them to because they appear to communicate like us (and therefore appear to think and reason, like us).

But the "holes" are painted over in contemporary models - via training, system prompts and various clever (useful!) techniques.

But I think this leads us to have great difficulty spotting the weak spots in a new, or slightly different model - but as we get to know each particular tool - each model - we get better at spotting the holes on that model.

Maybe it's poorly chosen variable names. A tendency to write plausible looking, plausibly named, e2e tests that turns out to not quite test what they appear to test at first glance. Maybe there's missing locking of resources, use of transactions, in sequencial code that appear sound - but end up storing invalid data when one or several steps fail...

In happy cases current LLMs function like well-intentioned junior coders enthusiasticly delivering features and fixing bugs.

But in the other cases, they are like patholically lying sociopaths telling you anything you want to hear, just so you keep paying them money.

When you catch them lying, it feels a bit like a betrayal. But the parrot is just tapping the bell, so you'll keep feeding it peanuts.

> for static API keys, the backend injects the credential directly into the agent's runtime environment.

What prevents the agent from presisering or leaking the API key - or reading it from the environment?

> Meta CEO Mark Zuckerberg could soon have an AI clone of himself to interact with and provide feedback to employees, according to a report from the Financial Times.

https://www.ft.com/content/02107c23-6c7a-4c19-b8e2-b45f4bb9c...

https://archive.is/mtVXJ

That is fine, but you give up any pretence of security - your agent can inspect your tool's process, environment variables etc - so can presumably leak API keys and other secrets.

Other comments have claimed that tools are/can be made "just as secure" - they can, but as the saying goes: "Security is not a convenience".

Tunnel vision? If your model can handle big context, why divide into lesser problems to conquer - even if such splitting might be quite trivial and obvious?

It's the difference of "achieve the goal", and "achieve the goal in this one particular way" (leverage large context).

Seperate process as a seperate os user, and/or namespace.

Somewhat on the side - but is there a wireguard that works well for ReactOS? Does the windows version just work fine?

Just curious how/if the version support might work out for ReactOS.

> It's also insane the lack of version control: version control was copying entire directories.

I can understand that - under dos - but as I recall quite a lot of gnu/nix tooling was workable on the Amiga - RCS harks back to 1984...

I wonder about the "video running in the background" during qna of the talk:

https://youtu.be/1sd26pWhfmg?is=XLJX9gg0Zm1BKl_5

Did he write an exploit for the NFS bug that runs via network over USB? Seems to be plugging in a SoC over USB...?

Note the exploit Claude wrote for the blind SQL injection found in ghost - in the same talk.

https://youtu.be/1sd26pWhfmg?is=XLJX9gg0Zm1BKl_5

One thing I like is the possibility to turn off notifications on other apps - and then dms become/remain push (beeper) other SoMe crap become pull (check when actively opening the app).

Discord is the only app I still run with notifications enabled along with beeper - beeper handle dms, discord handle the rest.

Allows me to check only beeper for dms across services - and keep discord for channels etc.

Not sure what this app does for Facebook - but I'm a quite happy user of:

https://www.beeper.com/

Allows me to use Instagram messages without the app - as well as (Facebook/meta) Messenger (and others).

I do wish they had a "support us" subscription tier, as I think the base price is a little steep - and I don't really need any of the paid features. Maybe something around the third or quarter the price.

I would hope that would lead to more users subscribing.

To be fair, ripgrep is approximately one decade old, would be tricky to have used it for decades.

http://blog.burntsushi.net/ripgrep/

https://news.ycombinator.com/item?id=12564442

Nice series! Really takes me back to the days of Linux 1.x kernel, Lilo and trying to fit a kernel and initrd on a single floppy disk.

So ending up at:

> From a 292MB initramfs, we now have a 6.1MB initramfs, smaller than almost every other distro's initramfs and made entirely to run busybox wget dd.

Is pretty great achievement today - but way bigger than something that can fit on a floppy.

Excel for Mac has gotten a lot better - but I'm not surprised if it is still its own thing - with edge cases in format, macros, visual basic, linking to databases and data sources etc. I imagine excel under wine is better than the port to Mac in some respects.

I think that Knoll’s law of media accuracy applies quite well to LLMs as well:

> “everything you read in the newspapers is absolutely true, except for the rare story of which you happen to have firsthand knowledge”.

Interesting article - but perhaps a bit light on details in some places, like:

> I generated a list of the most common interview tasks

How? I suppose they mean gathered, or searched for, not strictly generated?

Also a little light on details of the actual interview.

I'm also a little confused about the listing of "problems" - do they refer to some specific leet-code site's listing of problems?

It seems like half-way between naming an actual algorithm/problem and naming a concrete exercise.

As for:

> How is it that we do not use this "forgotten and forbidden" coding in our daily production code, even though all highly reusable, useful code is essentially an exploitation of the intersection between classical algorithmic thinking and real-world problems?

I'm not sure what to say - most of this stuff lives in library code and data structure implementations for any language in common use?

Indeed the one saving grace of leet code interview is arguably that it shows if the candidate can choose sane data structures (and algorithms) when implementing real-world code?

Of you enjoyed this, you might like "Gnomon" by Nick Harkaway.

Related:

> Ministry of Defence launches Brave1 Dataroom, a secure environment for training military AI solutions

https://mod.gov.ua/en/news/ministry-of-defence-launches-brav...

> Our proprietary AI systems have never seen the original source code.

For this to be plausible satire, they need to show how they've trained their models to code, without mit, apache, bsd or GPL/agpl code being in the training set...

Adonis is nice, but still young and lacking features. And in my experience very verbose compared to rails.

That said, absolutely worth a look.