ygouzerh
It's a bit procedural and admin focus... but if you execute all of it, at least it's easier to defend yourself during an hack towards your clients or third parties.
I would have prefer something a bit more technical, but it's a good entrypoint, at least to help hire or consult a security professional.
On one side it's interesting, because it means that if you buy a machine, next years you can buy more, and more and more things get automated.
The bad side, is that we doesn't have a way for labor to reap the benefits of it as well, except from lower purchasing costs... which is counter-balanced by the need of the system to push for more and more consumption every year, to keep it working. (E.g price of electronics/performance decreasing, but need every few years to buy new phones with more performance to keep up with the more and more hungry apps)
We keep struggling with disk capacity on servers being filled, which will then need cleaning. Currently, we got an alert when it's reaching 80%. It's reactive, we need to work on an unplanned task, and it disrupt our sprint and delivery commitments.
Instead, we could pipe the disk capacity data points to Timestral, and we could approximate the date when we will need to work on it, way earlier, so that we can plan our work accordingly.
I guess it might be integrated later directly in tools like Datadog, or Cloudflare straight away, rather than being used by corporates as it is.
And if LLM give us already what we need... why ask her for more complicated things? And so why would the AI improve or be improved?
I would say... a lot.
After indeed, maybe without capitalism we would have not got this amount of wealth already, and nothing stops us to build socialism inside a capitalistic system, by using more cooperative, governement venture capitals, sovereign funds.
The machines are operated by the labor forces, the real humans. However, the exponential compounding benefits of theses feedbacks loop us are mostly redirected to the ones owning the machines, the owner of the means of production.
Of course, the labor forces operating the machines/means of production benefits it, because they have either cheaper, or more goods. However they doesn't get this compounding effect.
AI is the god of the machines, the ultimate means of production. The amount of labor forces needed to operate it is very few. When the owner of capital invest a specific amount of money for their company, they get X amount of tokens, enabling them to work on Y amount of tasks. It's reward with low-risk.
The drift between capital owners and labor forces will become wider and wider. Until the labor forces doesn't have any more capacity of buying the goods produced. And then the system will implode.
If I remember well, in the background, it does the multi-choice question: "What is the component requested ? --> Calendar, Slider, ..."
The others colleagues all stopped school very early, got the same irrational political views that the ones you mentioned, etc.
However, they were beating us totally, students coming from fancy universities, for everything else related to machinery, gardening, repair, etc... Even on new problems, they were able to quickly make these logical connections, that we would miss.
For example, someone doing system engineering will be able to understand how the networking of a system works, while someone being a movie critics will find the hidden meanings and connections in the movies they watch. Similar for a musician.
Probably indeed the semantic is important. Someone can say something stupid in a specific occasion or all the time in a specific domain, but almost every time there are areas of life where they will be considered "smart".
As a DevOps Engineer, I never once saw before the advantage of using a classifier. Now I see multiple parts of the stack where a better level of expressiveness will be useful (PR validations, Blue/Green validation, notification router for alerts, quick smoke tests, etc).
Nobody will give us the time and budget to build a custom classifier for these use cases, but a simple API call yes.
It's the killer feature from me personally, often when wanting to troubleshoot for example things like Kubernetes workloads. LLMs are now really good at it, but we doesn't want them to like delete a pod.
Other harnesses like Codex have often on static rules, like the allow/deny of claude code, that can filter out based on regex. It's quite good already, but sometimes the model can find a way to write something that wasn't anticipated, or in a convoluted way.
After, I guess it's something that can be added in an open-source harness like Pi, and add like this new Jev model or something else equivalent
If the money could flow back to the real economy instead of the AI economy, it might be healthier for the whole system.
I didn't knew about that rationale. I thought Defense-in-Depth was all about that, preventing security incidents even when an attacker start to get some elevated privileges on the system?
If not, that's quite a practical reference for future CyberSec meetings.