Posted by speckx 10/28/2025
Is the 1.5 years that I have left worth it? (I already have an Associate's Degree).
1. The degree is useful. Having a Bachelor's opens up a lot of career paths because it shows that you committed to the Data Analytics program for four years. It also helps HR check off the "has a bachelor's" item on their list.
2. What you learn is useful. At the end of the day, you will be responsible for the code that the AI produces. How will you understand, explain, and justify your code to your colleagues and managers? "SQL, Python, JavaScript" and "theoretical Data Analytics knowledge" are both tools that will help you.
3. So far, senior engineers tend to have the most productivity boosts with AI. These engineers became "senior" before AI coding agents became mainstream, which means they know how to program. So based on this pattern, if you know how to program, then you will benefit more from AI.
Maybe you have other factors you are considering (e.g. money). My response is primarily based on the "existence of AI coding agents in the industry" factor.
I think what you say makes sense. There's times when you hear advice and you just know it's true and on point. And that is exactly what I saw in your words of advice.
I'm going to stick it out and just finish. Aside from career, it is also helping me with random side interests that I have like making my house smart, setting up media servers, creating my own Raspberry Pi surveillance system, automating work tasks. So like you said, the things I'm learning are useful in and of themselves.
Thanks a bunch, friend! You made a real difference!
I love the feature set of Claude Code and my entire workflow has been fine tuned around it, but i had to to codex this month. Hopefully the Claude Code team spends some time to slow down and focus on bugs.
Everything Anthropic does from an engineering standpoint is bad, they're a decent research lab and that's it.
This may be true, but then I wonder why it is still the case that no other agentic coding tool comes close to Claude Code.
Take Gemini Pro: excellent model let down by a horrible Gemini CLI. Why are the major AI companies not investing heavily in tooling? So far all the efforts I've seen from them are laughable. Every few weeks there is an announcement of a new tool, I go to try it, and soon drop it.
It seems to me that the current models are as good as they are goingto be for a long time, and a lot of the value to be had from LLMs going forward lies in the tooling
Claude is a very good model for "vibe coding" and content creation. It's got a highly collapsed distribution that causes it to produce good output with poor prompts. The problem is that collapsed distribution means it also tends to disobey more detailed prompts, and it also has a hard time with stuff that's slightly off manifold. Think of it like the car that test drives great but has no end of problems under atypical circumstances. It's also a naturally very agentic, autonomous model, so it does well in low information scenarios where it has to discover task details.
I'd like to build an integration with Whisper Memos (https://whispermemos.com/)
Then I'd be able to dictate a note on my Apple Watch such as:
> Go into repository X and look at the screen Y, and fix bug Z.
That'd be so cool.
They're running an offer for 9€/quarter for the model, and the results are promising.
That specific part doesn't have anything to do with Claude Web though, does it? When I use Codex and Claude they repeatedly look up stuff in the local git history when working on things I've mentioned I've worked on a branch or similar. As long as you make any sort of mention that you've used git, directly or indirectly, they'll go looking for it, is my feeling.
- Time to start your container (or past project) is ~1 sec to 1 min. - Fully supported NixOS container with isolated, cloned agent layer. Most tools available locally to cut download times and ai web access risk. - Github connections are persistent. Agents do a reasonable job with clean local commits. - Very fast dev loops (plan/build/test/architect/fix/test/document/git commit / push to user layer) with adjustable user involvement. - Phone app is fully featured... I've never built apps on roadtrips before replit. - Uses claude code currently (has used chatgpt in the past).
Tips: - Consider tig to help manage git from cli before you push to github. - Gitlab can be connected but is clumsy with occasional server state refreshes. - Startups that haven't committed to an IDE yet and expect compatibility with NixOS would have strong reason to consider this. It should save them the need to build their own OS-local AI code through early builds.