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 ๐
@hemapriya_kanagala reflects on joining different tech communities as an international student looking for internships and mentors, only to discover that belonging mattered just as much as opportunity. The post traces how those connections transcended geography and eventually inspired the Dev Opportunity Radar series.
@sebs argues that even though markdown has become the go-to spec format for AI workflows, it was never actually built for the job. The post walks through how Gherkin, the plain-text language behind Cucumber, offers a structured and executable alternative that keeps specs honest.
@bebechien takes readers inside the making of AIventure, a retro dungeon crawler built with Angular, Phaser.JS, and Gemma 4 that teaches generative AI concepts through gameplay. The post covers the journey from early Gemini Canvas prototypes to running the model entirely in the browser with MediaPipe.
@cseeman set out to build a small TRMNL plugin to display her current reads from StoryGraph, and quickly learned that a web request carries properties far below the header level. The post follows the investigation from Cloudflare TLS fingerprinting to IP reputation checks, and why the code worked locally but failed in the cloud.
@erikch puts TanStack Start through its paces after hearing about it repeatedly at conferences, comparing its server functions, typed search params, and end-to-end type safety against Next.js and Nuxt. The post shares three specific features that nearly convinced a self-described Vue and Nuxt person to switch.
@ahikmah shares what happened when a straightforward batching optimization on an LLM translation pipeline made costs go up instead of down. The post breaks down how a flawed fallback strategy turned one missing JSON key into 100 retry calls, and what it took to actually make batching work.
@saptarshisarkar explains catastrophic forgetting, the phenomenon where a neural network trained on a new task nearly completely loses performance on what it learned before. The post walks through the math, the loss landscape visualization, and why simply retraining from scratch is rarely a practical solution.
And that's a wrap for this week's Top 7 roundup! ๐ฌ 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 youโre opted in to our Weekly Newsletter ๐ฉ for all the best articles, discussions, and updates.
Top comments (10)
Thanks for including me this week! โบ๏ธ
Thanks for publishing on DEV @hemapriya_kanagala, @sebs, @bebechien, @cseeman, @erikch, @ahikmah, @saptarshisarkar ๐
Congrats @hemapriya_kanagala, @sebs, @bebechien, @cseeman, @erikch, @ahikmah, @saptarshisarkar!!!
Jess is not only fast, but strategic when posting this in the morning (where I am not even online) e-e
Thank you so much, Francis ๐
Thank you so much, Jess ๐งก
Thank you to the DEV team for including my article in this week's Top 7 ๐
To be honest, I wasn't sure how this post would be received. I just wanted to share a lesson that took me a while to learn: the opportunities mattered, but the people mattered even more.
Seeing so many people share their own experiences and stories in the comments has honestly been the best part. Reading through all the conversations has made this article even more meaningful to me.
And to everyone who read the article, shared their story, offered feedback, or joined the conversation, thank you. ๐
And congratulations to all the other authors whose posts made it into the Top 7 this week ๐
Congratulations ๐@hemapriya_kanagala, @sebs, @bebechien, @cseeman, @erikch, @ahikmah, @saptarshisarkar!โค๏ธ
Thank you so much ๐
Welcome โค๏ธ so much
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