And honestly that's for a reason. GLM5.3 on max has in my experience far less hallucinations than any other open weights model and it feels it has some intuition to bring in the right information when it's in principle out of context but relevant to the topic. Like its goal is more to bring value and assist you than just solving the given task with the least token spent.
We will soon see some kind of self replicating prompts. Might be crafted by some malicious actors or just caught in some accidental loop. That doesnt assume any consciousness. Its still a program following its instructions like a biological virus or a computer worm do as well. Just in a more abstract form, where the script aka prompt gets interpreted by an interpreter aka llm.
why not just learn terraform and k8s and get all those features for free? The overhead of a simple k3s setup to bind a couple nodes into a cluster is similar to docker compose but you can manage and debug all your container and pods on a single interface, put everything in a git repository and enjoy life.
The funny thing about Revolut is, that they send you from the same "no-reply" address your payment receipts and a ton of spam. There is no link in the spam do stop it and no obvious scheme in the header which would allow to filter the spam from the relevant mails. Good luck recognizing this breach notification as an important one...
But those people who doesn't slop their app them self seem to let their llms install appslop from github en masse. At least that's what I conclude after looking at some recent 50k+ star repositories.
I'm somewhat confused. Its an interesting posting but I'm not sure if I understood exactly where he was going with his "policy as code". He concludes "policy-as-code produce enterprise coherence with no central architecture function" to which I would agree but argue that he misses that today there is a lack of infrastructure / company as code. At least if you want to have an agent being able to solve or at least aware of the problems he begun with. Like the policies define the boundaries of our working environment but doesnt the work itself. This "know how" is mostly implicit for people but invisible to llms and limits the context frame in which the agent operates to its given prompt (and maybe restrained by the policies if they're in the context).
Like if I take his example:
"Front-line staff may be skipping mandatory fields because the process adds fifteen minutes of friction to every customer interaction."
First it's unclear whats the policy for those fields are when they are mandatory and also can be skipped. Then why are those mandatory if skipping them seems only lower friction with no other consequences? How should a model decide if it should enforce the policy for those fields, code an automation or just make them voluntary?
At least they seem still provide results for my searxng instance. I mean sure, they are horrible but duckduckgo just blocks most queries (and I'm the only person using the ip / seraxng instance)...
Next i'll do is to implement tavilly, exa, tinyfish etc. as search engines for searxng. No agents, no mcp, just their search api endpoint.
In my opinion the biggest advantage of podman is that it uses pods with basically the same config and behavior as kubernetes does. As long as you just use podman pods instead (or possibly via) compose files you likely just notice that only the pod has one (and not any more) veth interface, that you reach other containers inside a pod via localhost:$port (instead of $service-name:$port) but when you switch later on to kubernetes you are already familiar with some basics.
I don't see much hope since I last explored some github repositories. There was a time when a successful repo had about 10 - 20k stars and usually those older repos stay around this level. But now there is a ton of vibe coded slop 50k + stars. Most of them have a "nice look", maybe even extensive docs but are usually build with no security considerations at all. One recommended to provide a "google app password" to the agent which has the same permissions as your regular login. Another was a browser plugin with permissions to read all cookies, inject js, open background tabs etc. You would probably assume the chrome store would at least put some visible warnings on the app store page or force the user to actively confirm those permissions. But because they are already stated in the manifest there is only a small footnote and it's even "recommended by google".
It's a good time to reduce the reliance on technology.
Throw out the IoT and "smart" stuff from your home. Remove apps from your phone and leave the absolute basics. Go through the password manager and close accounts for sites you are no longer using. Start migrating off Google. Print out your most precious photos on paper. And so on :-)
Since I bought early this year out of curiosity the z.AI pro plan for $30 / month its mainly glm-5.2. I prefer it over 5.3 and 5.3-flash because it feels more verbose and answers questions more detailed, where the newer ones feel to me benchmaxxed.
Sometimes I try other models but they always feel less concise and tend to not as strictly respect the instructions. Maybe the the top frontier models from openai / anthropic would not feel under this category but they are way to expensive on openrouter compared to my coding plan.
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