Im so confused by this. Watching the demos it seems like they just do some OCR to fill in a form? Then grab an email and linkedin url? The demo takes ~10s to do that.
I was just looking through the Go example as well. For a first attempt its ok. I don't think its accurate to criticize that it doesn't make a case for using Go or teaching how to use it. It's attempting to be a more useful contributing.md. I think it does a decent job at that. Enough that you could find an area of interest and feel confident to start reading and understanding it yourself.
It just doesn't seem to be worth the effort though. I see myself using something like this for ~30 minutes to so I don't feel lost when getting started. After that it becomes significantly less useful.
Also, the video wasn't particularly helpful and if I have to here an AI voice say how fantastic something is again, Im going to unplug it (jk future overlords).
Orgmode has been on the back of my mind forever. Ive been working on implementing a system recently. My hesitation with orgmode is that I'd like to share calendar/todo list with my non technically minded spouse and I like Vim. Any suggestions?
This is exactly what I was looking for. Thanks! Im trying to give these tools a fair shot before I judge them. Ive had success with detailed prompts and letting the agent jump straight in when working on small/new projects. Ill give more planning prompts a shot.
Do you change models between planning and implementation? I've seen that recommended but it's been hard to judge if that's made a difference.
Whats your workflow? Ive been playing with Claude Code for personal use. Usually new projects for experimentation. We have Copilot licenses through work so I've been playing around with VS Code agent mode for the last week. Usually using 3.5, 3.7 Sonnet or 04-mini. This is in a large Go project. Its been abysmal at everything other than tests. I've been trying to figure out if I'm just using the tooling wrong but I feel like I've tried all the "best practices" currently. Contexts, switching models for planning and coding, rules, better prompting. Nothings worked so far.
I'd say for me, it depends on the task and the language. I find asking ChatGPT to generate some code that I copy and paste lines up with your experience. Same with using an agent in a new project. I find the error rate much higher though once I start asking it to write code using specific libraries. Or when using an agent in an established code base. It's also terrible with DSLs that probably don't have as much training data. Trying to get it to do anything with Azures KQL is borderline pointless.
Im fairly new to self hosting. I've been playing around with a raspberry pi running raspberry pi os. The documentation says the Debian package is only for amd64 architectures. Im assuming that has to do with one of the Node packages? Out of curiosity, if I wanted to get it to work on an arm architecture, where would I start? My first guess would be trying to install it on the pi and looking at the error messages.
How did you learn to build a BitTorrent client? I love the idea of codecrafters and books that walk you through building something but I always struggle if I don't have something to get me started.
While I do agree with this, I don't think assignments made by humans are immune to this either. There are assignments I had that contained bias and errors. Some I caught but I'm sure there's plenty I didn't.
The only assignments I remember spending extra time on were from comp sci courses. I think that's from a mixture of enjoying programming and some assignments providing a toy program. I'd always play around with what I built and take those ideas and build more. I found it difficult to do that with other subjects.
I think this is exciting because it gives students a chance to "play" around with their assignments. I can see students running through multiple simulations to compare results and thus going deeper on research beyond the scope of the assignment.
I think that's what the author is trying to accomplish, creating an engaging way to practice critical thinking and good research. Giving a student a paper and telling them to find the errors isn't terribly exciting. I think giving them a chance to create the story themselves helps with engagement. It's almost like a game, create a story and find the inaccuracies. To do that you're going to have to "see" the issues your story might have and do research to correct them.
I started learning Blender a few months ago and I agree, the donut tutorial is excellent. One tip that helped me was to recreate the tutorial from memory after each video. So I had two files, one for following the tutorial and one for practice.