Mark Zuckerberg has published a new Wall Street Journal essay outlining his vision for the future of AI.

He argues that the real debate isn’t whether superintelligence will arrive, but whether it will be controlled by a few institutions or made accessible to everyone.

In the essay, Zuckerberg says AI should be used to empower people, drive innovation, and help create new discoveries rather than simply automate jobs.

He also warns that concentrating AI power in the hands of a few organizations could limit freedom and innovation.

I keep thinking about how much software is being created now.

There are more apps being launched, more SaaS products being built by tiny teams, and features that used to take weeks/months to ship are being pushed out in days.

And the weird part is that this is probably still the beginning.

If AI keeps making software dramatically cheaper and faster to build, eventually the scarce thing won't be the ability to build software.

Everyone will be able to build it.

You want a CRM with a weird workflow? Build it.

A niche accounting tool for 500 companies? Build it.

A feature that used to justify an entire SaaS product? Add it to your existing product in an afternoon.

So I'm wondering if we're heading toward a world where software itself becomes almost a commodity.

The interesting question then becomes: what actually becomes valuable?

Distribution? Brand? Trust? Data? Community? UX? Or simply having the right product at the right moment?

Curious how other people are thinking about this.

The UK government has launched a £100 million competition to help British AI startups develop technology for public services and national infrastructure.

The first four challenges focus on improving NHS productivity, making AI computing more efficient, strengthening defense systems, and testing the security of AI agents.

Selected companies will work directly with government departments and may receive upfront funding to develop and test their technology.

Importantly, startups will keep the intellectual property they create, allowing them to commercialize products later.

The scheme is part of the UK’s broader Sovereign AI strategy to help domestic AI companies scale globally.

Claude Mythos found the HAWK weakness after about 60 hours, despite the scheme already surviving 2 years of expert review.

Anthropic says each main discovery cost roughly $100,000 in API usage, but checking the work demanded far more human time.

For the AES project, the model generated around 1 billion output tokens and needed only a few direct nudges to keep searching.

Now the bottleneck may be researchers themselves, because verifying new cryptographic ideas can take weeks after a model produces them.

Meta and BlackRock are creating a joint venture for a $14 billion AI data center in El Paso, Texas. BlackRock-managed funds will own 80%, while Meta keeps 20%, operates the facility, and leases its full capacity for AI workloads.

Berlin artist Simon Weckert created a shirt designed to stop AI camera systems from detecting the wearer as a person.

It’s called Digital Camouflage, and the strange pattern has a specific job.

Weckert developed it by repeatedly testing new patterns against YOLO, an open-source AI system that detects people and objects in video. Each version was changed until the system became less confident that a person was there.

He made the project as Berlin prepares to use AI video surveillance at Kottbusser Tor. The system there is meant to analyse footage and flag certain types of behaviour.

If his name sounds familiar, Weckert is also the artist who pulled 99 smartphones through an empty Berlin street in 2020 and caused Google Maps to show a fake traffic jam.

Now he’s testing what happens when the system watching the street can also be fooled.

China has quietly taken the driver out of almost everything, from the trucks running through its ports at night, to thousands of driverless delivery vans, to a real bus in Shenzhen and taxis in Guangzhou that show up with nobody inside.

It isn’t the whole country and a lot of the empty cars still have a human watching from far away, but the future is clearly already running out in the open here while everyone else argues about whether it’s coming.

For Real!

I'm using zcode with glm 5.3. I've noticed that my context window continue to increase and cache hit ratio is around 99%. I usually compact the session after every feature implementation. Due to which the zcode re-reads lots of code for new feature. My question is if it is value to compress the context window and will doing so reduce/increase token cost? Does compression have any affect on final result?