when asking AI tools to solve specific coding challenges, their default solution often involves suggesting new third-party libraries or npm packages rather than using existing utility functions in the repo.

over time, small utility packages add up, increasing bundle size and creating additional security maintenance tasks for the team.

how do you guide AI coding tools to prefer native project patterns over suggesting external packages?

A successful product is often seen as one that is well-commercialized, with users willing to pay for it. But is that always the case?

Today, I came across many products that are incredibly fun and creative. It made me wonder: are there products that don t fit the conventional definition of success? Maybe some exist just to bring joy, even if users simply visit, smile, and leave.

Some might argue that if something brings value, people will naturally be willing to pay. But is that always true?

With today s Vercel Day and its financial contribution, I thought of a topic we probably haven t talked about much here.

Money allocation once the funds are raised.

It s so easy to fall into the trap of forcing the exact same card or button component onto every single screen just to keep the design system clean.

I ve worked on projects where sticking blindly to the rulebook made a simple workflow feel super awkward for the user just because we didn't want to break the pattern. A tidy layout looks nice on a Figma board, but if it makes the actual task harder it defeats the purpose.

How do you guys decide when it's okay to break the system rules for a better user flow?

"I saw @busmark_w_nika mention in another thread that LinkedIn now scans AI-generated posts and may reduce reach for content that 'sounds the same.'

This got me curious I use AI for drafting but always rewrite the hook myself. Have any of you actually seen a drop in reach when using AI drafts vs. writing from scratch?

From listening to different people and their journeys, I ve realised that before they built their first profitable product, many of them failed multiple times.

Similar stories were shared by @marclou , @levelsio or @iuliia_sh to name a few.

$300 MRR is actually surprisingly hard to reach if you don't have an existing distribution built. It took me 6 (quite difficult) months to get here.

Here's how I did it:

Hey all. I'm really interested in how you evaluate your agents in production.

The standard approach is sample, LLM as judge and possibly a golden dataset. That seems to be the standard setup.