@itzKashan2912 · X

@degensing · X

| Your growth engine on Twitter and LinkedIn

u/Moontrepreneur · Reddit

Shipped usage-based pricing and immediately found a bug that only exists once generation costs money We added a model picker to our scheduling tool last week. Pick which AI writes your posts, see the cost per model up front, pay the provider's exact measured rate with no markup on top. Then we found a bug that could not have existed the week before. Our Generate button had always returned three drafts per press. It was a UX call from when generation was free and unmetered. More options, o

@Robhay3s · X

@helloukey · X

@itsclarkholden · X

@devvybrian · X

is my new kid on GitHub block

u/bundlesocial · Reddit

A Fiverr animator ghosted us, so we made this product animation with 3 prompts and one HTML file We originally hired someone on Fiverr to create a product animation for bundle.social , the social media API we’ve been building for the past few years. Then they stopped replying. So instead of waiting, we tried building the first version ourselves. The video attached to this post was created from a self-contained HTML file in roughly 40 minutes, including a tea break. FYI, R

@Attalos__Life · X

Find a meeting time without the back-and-forth. FindOurTime auto-detects time zones, checks calendar conflicts from an .ics upload, and lets you schedule 15, 30, or 60-minute slots. No account needed.

@awadh_kartik · X

We eradicated something inevitable -

@harryjwang · X

I've used many of the major conference systems. Plenty still have docs as an unsearchable static site — or worse, as PDFs that stopped tracking the product three versions ago. Somehow, still true in 2026. We took an AI-first path with instead. Our docs are generated from the codebase itself. A custom Claude Code skill writes each page and retakes the screenshots whenever a feature changes — so the docs don't drift from the product. Every page still gets reviewed by a human before it ships. Now you can talk to them, too. We just shipped "Ask AI" over our docs: This feature also turned out to be a nice teaching case for agentic RAG vs. classic RAG: • Classic RAG: chunk → embed → vector DB → retrieve once, by fixed rules • Agentic RAG: hand the model a search tool and let it drive — write a query, read the results, rewrite, retry At more than 100 pages, still no vector database needed. And every answer cites the exact page it came from. #Age

@Bilal_Developer · X

I finally launched my first SaaS. 😩🥳 Funny thing… This isn’t the first SaaS I’ve built. It’s the first one I actually had the courage to ship