Welcome to this week's Top 7, where the DEV editorial team handpicks their favorite posts from the previous week (Saturday-Friday).
Congrats to all the authors that made it onto the list 👏
@jasmin uses four GIFs to break down what actually happens when you make an LLM API call, from the raw request shape to token billing. It's a visual-first approach to understanding concepts that SDKs typically hide, covering everything from statelessness and stop reasons to why output tokens cost so much more than input ones.
@dannwaneri built a RAG-powered bot trained on 50,000 personal bookmarks and came away with a sharper, more skeptical understanding of what generative AI actually does under the hood. Seeing the retrieval logs up close reframed not just AI, but the nature of thinking itself.
@shubhradev shares four silent breaking changes encountered after upgrading to Next.js 16, none of which surfaced during a clean build or CI run. Each issue gets a before-and-after code fix and a checklist of what to verify before shipping an upgrade.
@viktor_koves makes the case that AI coding tools have lowered the floor for building software without raising the ceiling for shipping it responsibly. Real examples, from open-source pull requests to a company-wide database wipe, make the argument that velocity and production-readiness are not the same thing.
@dayvster takes a look at Bun's AI-assisted rewrite from Zig into Rust, pushing back on the narrative that this is a loss for Zig. Four detailed outcome scenarios build toward a conclusion that most of the discourse around this story has completely missed.
@msulaimanmisri shares a hybrid architecture experiment born out of a real performance problem: image uploads in a Laravel app that were slowing down requests and bogging down the server under load. The fix came from asking a different question entirely about which parts of the stack should actually own the work.
@gabrielanhaia breaks down the four distinct caching layers between a user and a database, explaining what each one is designed to do and why collapsing them into a single Redis tier leads to problems. A decision matrix and production-ready code examples make it easy to figure out where each type of data actually belongs.
And that's a wrap for this week's Top 7 roundup! 🎬 We hope you enjoyed this eclectic mix of insights, stories, and tips from our talented authors. Keep coding, keep learning, and stay tuned to DEV for more captivating content and make sure you’re opted in to our Weekly Newsletter 📩 for all the best articles, discussions, and updates.
Top comments (10)
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 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! 🙂
Ha, 20 minutes — still counts. Thanks Francis.
Thank you so much @jess ☺️
This makes me so happy. Congrats to all authors! 🎉
Thank you for the feature, @jess! That article took quite a while to write, so I'm glad folks appreciate it 😁
This is a useful roundup of Mac productivity tools. One thing I'd add to the clipboard management side: TextStow takes a slightly different approach by combining history with reusable favorites and prompt templates ({{variable}} syntax). It's local-first with SQLite storage, so your data never leaves the Mac. The built-in text cleanup for PDF line breaks, JSON formatting, and URL extraction is handy if you copy-paste a lot of messy content. Worth a look: textstow.com
Big congrats to everyone featured this week 🙌