So i create Polygram where we first design the website or mobile designs in HTML where users can iterate on the designs and then once complete just one button to get hosted website or mobile app
This saves lots of token and give more screen or iteration compared to lovable or emergent
If you are making something will you use this approach?
So i create Polygram where we first design the website or mobile designs in HTML where users can iterate on the designs and then once complete just one button to get hosted website or mobile app
This saves lots of token and give more screen or iteration compared to lovable or emergent
If you are making something will you use this approach?
So i have built Polygram an vibe coding platform it creates websites and mobile apps but now to market it or just create campaigns of it is a different headache
So i am planning to built a marketing and sales pipeline where after successfully completing your website your can connect your google ads or any Ads account and we will generate creatives in form of Images and Videos and create a campaign which later can be tracked
So if i give this feature will you use it?
I’m researching the current generation of AI website builders (Lovable, Bolt, Replit, v0, Cursor + templates, Wix AI, etc.).
If you’ve actually built a website with them, what’s the most frustrating thing you’ve faced?
Not the marketing promises.
The real pain points.
Examples:
• AI generates messy code that’s hard to maintain
• Changes in one section break another section
• Design consistency falls apart after multiple prompts
• Difficult to customize beyond what AI generates
• Poor mobile responsiveness
• Performance and SEO issues
• Vendor lock-in
• Context gets lost in longer projects
• Hard to hand off to developers
If you could fix ONE thing about AI website builders today, what would it be?
Would love to hear both founder and developer perspectives.
I’ve been using Cursor, Claude Code, and other coding agents extensively.
One thing that keeps bothering me is that they’re optimized for individual developers.
The moment you put 3–5 engineers on the same project, everyone starts creating their own AI conversations, context, decisions, and fixes.
The result?
The same questions get asked repeatedly
The same files get analyzed multiple times
Context gets lost between developers
Teams spend money re-generating knowledge that already exists
We’ve been building a coding agent at Polygram to tackle this differently.
https://polygram.dev/coding-agent
A couple of things we’re experimenting with:
1. Shared AI Conversations
Instead of AI chats living on one developer’s machine, conversations become workspace assets.
If a frontend engineer spends 30 minutes working with the agent to refactor authentication, another engineer can access that conversation and continue from the same context instead of starting over.
The AI knowledge becomes team knowledge.
2. Intelligent Model Routing
Most tools make you manually choose the model.
We route requests internally based on task complexity and requirements, so developers focus on solving problems rather than deciding whether a task should go to GPT, Claude, Gemini, or something else.
The goal is to make AI-assisted development work better for teams, not just individuals.
I’m curious:
For teams already using Cursor/Claude Code/Windsurf, what’s your biggest pain point when multiple developers are using AI on the same codebase?
Would love to hear what’s broken in your workflow today.
I’ve been using Cursor, Claude Code, and other coding agents extensively.
One thing that keeps bothering me is that they’re optimized for individual developers.
The moment you put 3–5 engineers on the same project, everyone starts creating their own AI conversations, context, decisions, and fixes.
The result?
The same questions get asked repeatedly
The same files get analyzed multiple times
Context gets lost between developers
Teams spend money re-generating knowledge that already exists
We’ve been building a coding agent at Polygram to tackle this differently.
https://polygram.dev/coding-agent
A couple of things we’re experimenting with:
1. Shared AI Conversations
Instead of AI chats living on one developer’s machine, conversations become workspace assets.
If a frontend engineer spends 30 minutes working with the agent to refactor authentication, another engineer can access that conversation and continue from the same context instead of starting over.
The AI knowledge becomes team knowledge.
2. Intelligent Model Routing
Most tools make you manually choose the model.
We route requests internally based on task complexity and requirements, so developers focus on solving problems rather than deciding whether a task should go to GPT, Claude, Gemini, or something else.
The goal is to make AI-assisted development work better for teams, not just individuals.
I’m curious:
For teams already using Cursor/Claude Code/Windsurf, what’s your biggest pain point when multiple developers are using AI on the same codebase?
Would love to hear what’s broken in your workflow today.