lumost

TLA+ doesnt prove the code. if they never read the proof, then how do we k ow that it proved the right things?

I think the biggest shift is in predictability. Will something be done today, next week, or next month - nobody knows. Will we converge on a bug free state? not sure.

So far AI works great at fast moving work which you will probably recode or throw away in a month or two anyway (e.g. a startup) but is weak at maintaining a large code base that you rely on. At least part of this is cultural, as the cost of misalignments between team members can be quickly translated into broken software.

What happens if AI is a net loss for the tech industry? It's entirely plausible that AI kills the SaaS business, along with the web and mobile business, alongside large categories of software engineers/tech professionals.

While there might be new stuff, these business's are currently the ones funding AI - if they all get wiped or substantially reduced, then there will be less ai revenue then there is current tech revenue - potentially at reduced margins.

If I understand this clearly enough, what they really did was

A) add a post-processor which accepted a decoded tool call specifying what the processor should invoke.

B) had the post-processor perform the computation in tensor space.

The kv-cache/activations were not shared between qwen/the processor which is how they avoided fine tuning etc. I'm not clear how the tensor representation benefited the computation in this pipeline.

I'd imagine all of the vendors have an incentive to keep their main customer a float, while there might be penalties etc. for missed targets - it doesn't make sense for any of these vendors to ride the contract into chapter 11.

I’m somewhat dubious of the idea that there will be a market for skipped ai operators.

Ai doesn't really give us a new “tool” in as many cases as it “performs tasks.” there isn't any real reason the ai can't perform the supervision task itself. Without a viable market for task doers or supervisors we may observe the equivalent of the 1980s US manufacturing apocalypse in technical white collar fields.

I work in code generation, and often feel the fallacy of “because I can build something that looks 80% like your product in a day, you should pay me N millions.”

It's roughly the same pitch that the ruby on rails crowd had 15 years ago, and the no code crowd 5 years ago.

Very few companies need to rebuild their product every week, or actually care what tech stack it runs on. They are trying to build new things, or reduce how often those things break, or figuring out what if anything is worth building next.

There may be quite minimal reasons for an AI civilization to engage in expansion, with the ability for it to intelligently change and enforce new objectives on itself it could simply choose not to expand beyond resource constraints. Or alternatively invest continuously in greater efficiency improvements.

How much compute could you really pack into a Dyson swarm if that was your only objective? what are the ultimate theoretical bounds for compute density/efficiency? perhaps ai civilizations ride a Moore's law of compute gains indefinitely and simply have no need for expansion.

There are rumors that the employees did not get paid out. Such a deal could be done by, for instance buying the equity from the investors at a current fair market value.

That the new "investor" choses to let their investment die may not matter, it would also be cheaper to selectively buy out investors than buy out all equity in the company.

was it a run around of anti-trust or character's liabilities? It will be interesting if there are creditor lawsuits / trustee clawbacks in the future around these deals.

The suggestion is that this person can get a mortgage to pay their property taxes over the next 20 or 30 years as part of their retirement - or they can sell their property and live well in a cheaper location.

Claiming that the retiree has a right to both the home value and the ability to live there is the friction point. It reads as hollow in the face of rising homelessness and the inability for young people and families to access housing.

I had a thought the other day that you could probably divide up the world into cubic miles and with a few thousand codex subscriptions make a high fidelity blender model of the entire world based on google maps, street view, and tagged location photos.

What would you do with a whole model of the world? I don't know, but it would be fun to walk a national park while I'm on the treadmill or basejump off of a realistic el capitan.

I think this up-skilling sentiment misses that as a worker who thought a seasonal service job on the waterfront was a good deal, up-skilling means taking on 6 figures in debt - after which they will only have a ~1/5 chance of getting a job which compensates for the opportunity cost and debt. For many, this is actually a bad deal and growing their own seasonal business would have been a stronger career path.

The issue is ownership, we have no means of distributing the knowledge from the AI or rewarding those who could help.

We are quickly moving to a world where all symbolic and numeric reasoning for economic purposes is performed by AI.

There is a perceived opportunity cost from someone using a lower-tier model on their task. What if the better model did a "better" job? what if my trials and tribulations are due to model quality?

If you are used to talking to opus5.5 medium, going to GPT6.1 luna low will feel like a step down. Why would any employee take the (personal) risk?