What are some of your strategies to stay in the loop?
Let's say that AI is useful, and is improving rather rapidly. And let's say that I only look around every six months. So my AI tools are somewhere between current and six months old at any time. If AI is actually useful, I miss out on some productivity.
But I also miss out on six months of trying to keep up, which is a huge productivity gain. So on net, am I ahead or behind?
How do I stay on top of AI tech? I let other people (like simonw) do most of the leg work for me.
For useful tools I basically wait until something starts appearing in multiple places like HN comments, YouTube tech videos/comments, or on Reddit. This is what led to me hearing about Claude Code and trying it out before convincing other engineers at my company to try it. Now we're considering it over Cursor not so much because of Cursor pricing change but just because Claude Code is doing so much better.
That then leads me to an even more specific topic which is how I stay on top of info about the tools I'm using and for that I tend to stick to subreddits i.e. /r/ClaudeAI
Some YouTube sources would be any of the "tech-influencers" like ThePrimeagen[0], Internet of Bugs[1], or dare I say Fireship[2]
0 - https://www.youtube.com/@ThePrimeTimeagen 1 - https://www.youtube.com/@InternetOfBugs 2 - https://www.youtube.com/@Fireship
In terms of the technology adoption curve, HN is in Early Majority. Show HN is a notch ahead, and that might be the place to watch.
Like asking whalers for their opinion on electricity.
I’m more on the applied/practical side, not really qualified nor interested in the theoretical side (I’m not a AI PhD). Beyond a high level understanding of how these tools work, there’s not much for me in this arena.
As such I just read articles here or in my everyday workflow I’ll see some new thing AWS added and try it out, and so on.
Or if I’m facing a problem that requires some tools to solve, I see what’s out there to fill my toolbox.
Otherwise it is just a ton of noise and most of it is not relevant to me.
The only thing I really pay attention to is stuff like Gemma QAT, where companies train smaller models with quantized aware training that gives them really good performance.