Welcome to this week's Top 7, where the DEV editorial team handpicks their favorite posts from the previous week (Saturday-Friday).
Congrats to al...
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Thanks for publishing on DEV @jasmin, @dannwaneri, @shubhradev, @viktor_koves, @dayvster, @msulaimanmisri, @gabrielanhaia 💜
Yea I was 20 minutes late. My fault lol.
Congrats @jasmin, @dannwaneri, @shubhradev, @viktor_koves, @dayvster, @msulaimanmisri, @gabrielanhaia!!!
Thank you! 🙂
Thanks Francis, appreciate it.
Ha, 20 minutes — still counts. Thanks Francis.
Thank you so much @jess ☺️
This makes me so happy. Congrats to all authors! 🎉
Thanks for this @jess . The "reframed not just AI but the nature of thinking itself" line is more accurate than I expected from a summary . That's exactly where the essay landed for me and it surprised me while writing it.
The comment thread ended up going somewhere I didn't plan for either: someone created an account just to argue about John Stuart Mill and we ended up at AlphaFold and whether the gap between LLMs and genuine reasoning is architectural or just a training signal problem. Still no clean answer.
Congrats to the other 6. The Jasmin GIF piece and the Viktor Köves production-readiness argument both hit.
Congrats @dannwaneri
I enjoyed going through your post especially the Granta detector part.
Thanks Jasmin and congrats back, the GIF breakdown was sharp. The Granta section was the one I rewrote most. It kept wanting to become a rant and I had to pull it back to the actual point: that confidence in the tool is the problem, not the tool itself. Same energy as your statelessness section — the SDK hides the thing you most need to understand.
Thanks for including my post here, wasn’t expecting this.
That upgrade caught me off guard in a few places, so I’m glad the breakdown helped.
Went through the rest of the posts here as well, a lot of thoughtful work in this list. Congrats everyone.
Congratulations! 🎉 You are a Next.js 16 expert!
Haha, appreciate it 😄
Definitely not an expert yet, just ran into a bunch of these while upgrading and wrote them down. Glad it was helpful.
Thank you for the feature, @jess! That article took quite a while to write, so I'm glad folks appreciate it 😁
good week - the LLM-in-GIFs piece landed for me more than most explainers do.
Big congrats to everyone featured this week 🙌
The Nwaneri RAG piece and the Viktor Köves production-readiness argument belong in the
same conversation. One is saying: here's what AI actually does when you get close
enough to see it. The other is saying: building fast and building soundly are different
objectives, and the current tooling lets you confuse them until something breaks.
The open question Daniel mentioned in the thread — whether the gap between LLMs and
genuine reasoning is architectural or training signal — I'd push back on the framing.
The gap shows up most clearly not in single-prompt performance but in what a system
does when you make it maintain state across calls. Stateless inference handles well.
The moment you introduce memory — retrieval, context accumulation, multi-turn
dependencies — the failures appear in a specific and reproducible pattern. That's not a
training problem. That's a design assumption that was never stress-tested at the right
layer.
Hi, I want to collaborate
🚀🎉
Thanks for publishing on DEV @jess
This is so useful keep doing this 👍
The point about AI tools lowering the floor but not raising the ceiling really hits home. Velocity and production-readiness definitely aren't the same thing.
hhh，thank you