People are trying it evaluate how good AI agents are by giving them a variety of tasks. Ars Technica decided to test different agents by telling them to make Minesweeper.
While now dated, how people are testing AI solutions is an ongoing problem. I do remember these early tests being a clear demonstration that AI was now capable of making working solutions from just vibe-coded slop.
Most keypad additions are largely gimmicks – but the Codex Micro AI keyboard has some interesting features (though I’m not sure it’s worth the $199 price tag).
The lighting portion is probably the most interesting. As you link AI agents to individual keys, each agent key has a multicolor LED that indicates the status as it works on your tasks. Blue is for thinking, green is for a completed task, amber is for items needing additional input or feedback, red is for errors, and solid white is for an idle agent.
There’s even a microphone button for voice prompts. The catch is the macro pad doesn’t actually have a microphone, so it triggers your current system default mic.
The clear polycarbonate border of the macro pad lights up to signal voice/mic status, with a sea-green color to show that the mic is live and recording, a white snaking effect to show that Codex is processing your voice prompt, or a simple solid-white glow to show that the prompt has processed fully.
Software development is converging in a way never before seen and Jaymin West gives us some of his observations from the bleeding edge.
AI quickly changed how software that used to take a whole team of software developers into something that could be autonomously generated with a single person using agents. This however, is just the first part.
What’s likely next is what some are calling software factories. Almost all major cutting edge developers such as Anthropic, Cognition, Cursor, Factory, Google, Github, OpenAI, and Ramp all seem to be working towards a factory system that continuously delivers and updates software.
Dark factories that run completely autonomously have been proposed, but experience shows things are not there just yet. West makes some observations from actual working systems people are trying and describes what people are doing – and gives a demo of his own factory.
This emerging software factory system , as supported by the foundation models, is made up of six components:
There’s a lot of pretty heavy AI rhetoric these days. Some of the strongest anti-AI stances are coming from content creators/artists and from game developers. The software developer community is more muted. It is also seeing that AI is completely disrupting their industry and likely to kill thousands of developer jobs forever, but they aren’t quite as vocally anti-AI. Why?
Maybe its because most of us have been changing and adapting to big effort reducing technologies on a regular basis for years (or decades in my case). First there were optimizing compilers, then there were developer tools and studios, then there were technology frameworks. Instead of trying to fight against technology – most developers have learned to survive one must be constantly adapting. To be a developer is to be constantly learning and growing. It’s one of the core traits needed by developers. Sticking your head in the mud or ignoring advancement rarely turns out well.
AI is very much looking to be the biggest change of them all. The industry will not be the same again. But the choice is to either embrace it so you can be part of the conversation that shapes it – or likely fight a losing battle against it with nothing really to offer the world in return to the conversation.
Open source has been also in a love-hate relationship with AI as well. What started off as legitimate complaints that AI was generating slop code and bug reports has rapidly and completely changed in the last 8 months. Now, open-source’s arguably most important person has spoken: Linus Torvalds.
His stance: the Linux kernel is not anti-AI. AI is a tool and they will use whatever tools make it better – or you’re free to fork it and walk away.
He’s also said they’re no longer interested in supporting obsolete hardware and that he’s largely now a development lead and most of the developers have started embracing LLM’s to assist day-to-day coding/bug fixing and work.
Straight from the horse’s mouth:
On Tue, 14 Jul 2026 at 19:01, Roman Gushchin <roman.gushchin@linux.dev> wrote:
>
> I think it makes the point of sashiko - helping maintainers - unachievable. If the point to not use
> LLMs in general, let’s discuss this, not how to make each use case more complex.
>
> It seems like [1] expresses a very anti-LLM position in general
Yes.
And no, that's not the position of the Linux kernel.
I realize that some people really dislike AI, but this is an area
where I'm willing to absolutely put my foot down as the top-level
maintainer.
Linux is not one of those anti-AI projects, and if somebody has issues
with that, they can do the open-source thing and fork it.
Or just walk away.
AI is a tool, just like other tools we use. And it's clearly a useful one.
It may not have been that "clearly" even just a year ago, but it's no
longer in question today.
There are other questions around AI (like what the economy of it will
actually look like in the end), but "is it useful" is no longer one of
those questions. Anybody who doubts that clearly hasn't actually used
it.
Yes, it can also be a somewhat painful tool, both for maintainer
workloads and just from a "it keeps finding embarrassing bugs"
standpoint.
But the solution is not to put your head in the sand and sing "La La
La, I can't hear you" at the top of your voice like some people seem
to do.
The solution is to make sure those LLM tools _help_ maintainers
instead of just causing them pain. There's no question on that side.
We're not forcing anybody to use it, but I will very loudly ignore
people who try to argue against other people from using it.
And no, AI isn't perfect. But Christ, anybody who points to the
problems at AI had better be looking in the mirror and pointing at
themselves at the same time.
Because it's not like natural intelligence is always all that great either.
The kernel project has been and will continue to be about the technology.
Sure, the social angle of working on open source is important and
often a very motivating part of the project, but in the end that's a
side benefit, not the _point_ of the project.
This is *NOT* some kind of "social warrior" project, never has been,
and never will be.
In the kernel community we do open source because it results in better
technology, not because of religious reasons.
And so we make decisions primarily based on technical merit. Not fear
of new tools.
Linus (Torvalds)Kitboga does a lot of videos of scamming scammers. Now he ventures into scamming AI scammers…
Gui Wainwright was once a war correspondent that became a technology entrepreneur. While on paternity leave, he wondered if he could use his coding skills to do something for Ukraine. A former MI6 chief and relative Sir Alex Younger gave him the inspiration for how – over a cup of tea. What he did was to completely change modern warfare forever.
Eighteen months later, Wainwright created the world’s first known network of fully autonomous drones on a real battlefield: Terminator-style, kamikaze drones enabled with AI to find and kill targets independently without any human interaction. It allowed Ukraine to turn an asymmetric war of attrition that Russia could easily win into one in which it allowed one man in a bunker with a computer to potentially field 10,000 drones simultaneously and hold 1,000 miles of land. A war in which as early as March 2026 Russia was losing 30,000 troops a month – with almost 90% of them dying by drones.
Wainwright first focused on assault in which drones were strapped with explosives and given a clearly defined kill box. Drones were then turned loose, completely autonomously, into the kill box. Anything it identified was a valid target: vehicles, troops, or weapons. Being autonomous, they could not be jammed by most conventional jamming systems.
He is now are moving on to the next advancement of defensive and denial operations in which drones fly in, settle on the ground in cover, and await enemy encroachment to then pounce and attack invaders. “Think about it as a sophisticated minefield”.
The software is the magic. The drones already have extensive supply systems. To protect the software, he says they have taken measures to “protect code security and prevent them from falling into the wrong hands” – hinting they are boobytrapped.
While he’s giving the software to the Ukraine for free, the company has a development team already at work in Estonia and is in detailed talks with four other countries’ militaries. Many of whom want to start fielding solutions in the next 12 months.
This is a great write-up on how it happened – which is probably the most important change in warfare in 50 years.
The introduction of super-cheap, super-disposable attack drones has completely changed the nature of warfare. $500 off the shelf drones with a little C4 can destroy million dollar planes, ships, tanks, and refineries. They can completely cripple and cut off supply lines. Capital ships and million dollar smart missiles are being rendered useless. Warfare on land, sea, and air will now be fought with tons of small drones in brutal 1-on-1 fights.
It’s probably why everyone from the Navy to the capital are completely changing tactics from expensive, complex weapons systems that cannot last for a sustained war into small, modular, disposable attack drones and drone defense systems.
When we measure effort instead of value, we lose sight of what we are trying to achieve
Like lines of code produced per day and story points, AI token usage is now being tracked by most large tech companies. Even to the point performance reviews depend on high usage.
The natural response: tokenmaxing. Tokenmaxing is a way of using AI that absorbs tremendous amounts of AI tokens without regard to efficacy.
Bryan ONeill finds the better question is to ask does this reduce friction for a user, close a gap in a workflow, or improve reliability for a customer – or is it merely ‘activity’.
He argues, and I have also found, that spec-driven development is far better: a method where engineers write detailed specifications first and AI generates code against them. But the spec should now go further and be the place where engineering intent and business value get defined together. The spec is where you answer, “Why does this matter, and what problem are we solving?” before a single token gets spent.
Each generation inherits the task of shaping its own era, of guiding history to become a place where the dignity of every person is safeguarded, justice is promoted and fraternity is made possible. Yet every era also runs the risk of creating an inhumane and more unjust world. Whenever humanity is in danger of marring its true identity, we Christians lift our eyes to the Incarnate God, knowing that it is “only in the mystery of the Word made flesh that the mystery of humanity truly becomes clear.”
Pope Leo XIV has released his very large encyclical letter MAGNIFICA HUMANITAS – SAFEGUARDING THE HUMAN PERSON IN THE TIME OF ARTIFICIAL INTELLIGENCE.
Definitely give it a read.
Alex Eisenhart, inspired by Seth Bling who released MarI/O in 2015, he has started web series that shows how to make your own AI powered Mario game player. He is only up to the second part right now, but seems to be making decent progress.
Claude 4 Opus has received a level three rating on the company’s four point ‘AI responsibility’ scale – implying the model has ‘significantly higher risk’ than previous models.
While the Level 3 ranking is largely about the model’s capability to aid in the development of nuclear and biological weapons, the Opus also exhibited other troubling behaviors during testing – something already noted in other models.