K0IN

Claude I want to be a billionaire fast, make no mistakes.

I was a big fan of deno, so much that I migrated all my cf worker to demo, cause you can run the same engine in there cloud and local/selfhost - perfect no vender lockin(like with cloud flare workers), it's so sad to see it gets shelved.

I really really love deepseek v4(and 4.1), but for everything I thrown at it, it felt like gpt 5.6 + 6 luna or terra do it faster, in way less tokens and in a way I like more. So even tho the price is (very) cheap, i found myself using it less just cause I don't want to wait on something I have to itteratee on with the model.

TeamSpeak, git backup(I use gittea) to backup public repos, backups in general, my website/blog, game servers, ssh(obv) for local app port forward, litellm as ai bridge to monitor usage, some of my own apps: (notification broker to send messages to my devices using a simple http push), everything behind traefik (with let's e encrypt)

agree, but do you think every dev you know konws this?

also "i just want to edit a config file and i want a nice ui", is how you get there.

a hile ago (when big providers still provided logprobs) i created a VS Code highlighter that visualizes unsure tokens.

Since most chat models want to answer with a human-readable message i think their logprobs are not as meaningful. It would be interesting to see if one choice is like "correct" and if the model wants to choose it more often, cause it might not answer the question but to prose to the user.

Does reinterpret_cast works in c++ for this?

I saw a lot of ppl think about what jev could use under the hood and could someone explain why this can't just be an embedding model where we just embed all the input + decisions and give back the cosine (or whatever) similarities?

My day job is improving data quality in big enterprises (we build products for this), and I see where this article is going, it's a bit watered down, but I see this every day at our customers, most companies just don't have a simple way to query their own data, create metrics, get ai to actually do stuff cause data is spread between 3-10 systems where 4 are legacy and don't even have an API and even if they can access they have like zero context 100 custom fields and suffer from no first time right inputs from the last 30+ years.

(ofc. I see only the ppl who need this kind of service so I'm biased, and maybe it's not a wide spread issue)

now add a source tab and lets see, how many ppl. will fix it themselves and how many turns it needs.

I have used the flash model for over 3b tokens and ofc. I saw some hallucinations and premature termination (I also get this on Astra - way more often than with deepseek v4 flash), but I never had a infinite loop (using the copilot as harness).