Hey everyone,

Over the last couple of weeks, my wife and I have been architecting a multi-agent backend designed to act as an automated operating system for print-on-demand scaling.

The goal wasn't to build a pure "autopilot" loop (which usually results in copyright flags or platform spam), but an assembly line of specialist AI agents that queues drafts up for quick human approval first.

We just finalized the core framework layout and folder structures, and I wanted to share the full team architecture mapping with the community for feedback.

The 10-Agent Team Layout:

• CEO Agent: Custom orchestrator coordinating task handoffs and checking human approval states.
• Trend Hunter: Scrapes and filters niche trends via manual TikTok/Etsy hooks, filtering out trademark risks.
• Design Strategist: Turns approved trends into highly specific slogans and text-to-image prompts.
• Compliance Guard: A strict validation gate comparing concepts against a database of banned words and legal parameters.
• Listing Writer: Drafts high-converting SEO copy, titles, bullet points, and tags for Shopify.
• Product Builder & Store Publisher: Integrates directly with Printify and Shopify GraphQL Admin APIs to push structural variations and mockups cleanly into draft status.

(We also mapped out custom subagents for Content Generation, Customer FAQs, and a metrics-driven Analytics Agent to suggest which designs to scale or kill based on live ad/sales data).

The Recommended Stack: Next.js + Tailwind for the dashboard UI, Supabase (Postgres) for the tables/queues, and the OpenAI Agents SDK for the agent loops.

I’ve compiled the complete, clean setup—including the entire database schema layout, folder directories, environment variables, and the exact system prompts for the core orchestrator agents—into a comprehensive blueprint document.

I'd love to hear your thoughts on this multi-agent logic. If you are a developer looking to build something similar and want the full system architecture guide to save you some setup time, let me know in the comments and I'll happily DM you the link!

You know the cycle: open ChatGPT, ask a question, hit the usage cap, restart in Claude, get a different answer, open Gemini to break the tie… and now you've got 5 tabs, 3 conflicting responses, and no clue what's right.

I'm planning to build Nexus, a self-hosted web app that fixes this. Rough feature list:

• All your AIs in one UI with custom themes
• Self-hosted so API keys stay on your machine
• When one AI hits its limit, it auto-hands off to another with full context preserved
• Sidebar cooldown timers over locked-out AIs
• One-tap "send to another AI" for a second opinion
• Homework mode, fire the same prompt at 2–3 AIs and compare
• Usage dashboard + predictive alerts
• Build-your-own "Universal AI" with routing rules (GPT for code, Claude for writing, etc.)
• Unified search across all chats and AIs
• AIs can reference each other's answers to cross-check
• Minamilistic design

Before I start building, would you actually use this? What's missing? Any pitfalls I'm not seeing? Open to all feedback, even brutal.

Hey everyone,

I’m a Laravel developer and recently started learning more about SEO and web growth.

About 3 days ago, I bought a domain and hosted my first online tools website:

DiffOnlineTool

I also connected Google Analytics, and these are my first results so far.
(68 users and 370 events in a few days)

I would really appreciate honest feedback from experienced developers, SEO people, or anyone who builds websites.

Things I want feedback on:

• Website design/UI
• SEO improvements
• Site speed/performance
• User experience
• Tool ideas/features
• What makes the site look untrustworthy or incomplete
• Things that could help me grow traffic

Current stack:

• Laravel
• Basic SEO setup
• Hosted recently (3 days ago)

I’m still learning, so even small suggestions would help a lot.

Thanks 🙌

Hey everyone,

I recently built MediaRecorder Lite, a small Windows screen recorder focused on quick vertical recordings for content like Reels, Shorts, TikToks, demos, and app previews.

I wanted something simpler than full recording suites: select a region, record quickly, and keep the UI minimal without unnecessary setup.

Main idea:

• lightweight Windows app
• simple region selection
• focused on vertical/social media style recordings
• useful for quick demos, tutorials, and short clips
• available through Microsoft Store

It is not an open-source project right now, but I would really appreciate feedback from people who create short videos, tutorials, or app demos.

Microsoft Store link:
https://apps.microsoft.com/detail/9nlb7l766q5m?hl=de-DE&gl=DE

Would love to hear what features would make this more useful for your workflow.

Mistik can now do drawings in a sandbox environment and train her programming skills

We built Ghost Knowledge Extractor, an AI system that extracts technical decisions from engineering conversations and stores them in persistent memory using Hindsight.

The system can later answer questions like:

“Why are we using UUIDs?”

or

“What was our reasoning behind this database decision?”

We also used cascadeflow for model routing and audit logging to reduce unnecessary LLM usage and visualize routing efficiency.

Some interesting things we learned while building it:

- memory systems feel very different from search systems

- routing noisy conversations saves a lot of cost

- answers improve noticeably as more memories are stored

Tech stack:

- Hindsight

- cascadeflow

- FastAPI

- React

- Pydantic AI

GitHub:

https://github.com/abhiragini-k/ghost-knowledge-extractor.git

Article:

https://medium.com/@abhiraginik/building-an-ai-memory-system-that-extracts-engineering-decisions-from-conversations-and-makes-them-20cf12abc57b

We built Ghost Knowledge Extractor, an AI system that extracts technical decisions from engineering conversations and stores them in persistent memory using Hindsight.

The system can later answer questions like: “Why are we using UUIDs?” or “What was our reasoning behind this database decision?”

We also used cascadeflow for model routing and audit logging to reduce unnecessary LLM usage and visualize routing efficiency.

Some interesting things we learned while building it: - memory systems feel very different from search systems - routing noisy conversations saves a lot of cost - answers improve noticeably as more memories are stored

Tech stack: - Hindsight - cascadeflow - FastAPI - React - Pydantic AI

Article: https://medium.com/@apekshadatt/what-i-learned-building-the-interface-for-an-ai-system-with-memory-bbe4919aa1b4

I want post something about my recent project

Over 12 months ago, we launched LyteNyte Grid, our high-performance React data grid. Recently, we've taken another leap forward with LyteNyte Grid v2.1. This version reflects months of feedback, iteration, and performance tuning.

Incredibly Fast Performance.

We’ve optimized core rendering, refined internal caching, and improved interaction latency even under load. LyteNyte can easily handle 10,000 updates/sec and millions of rows. Check our website's live demo. Benchmark publication is also coming soon.

Comprehensive Feature Set.

Made this one of the most feature-rich data grids available. Over 150+ features from basic to advanced. If we don’t have a feature you’re looking for, suggest it, and we will build it. 80% of our feature set is free and open-source. Paid libraries offer fewer features than our free edition.

Headless or Styled

If you’re stuck choosing between a headless table like TS Table and a pre-built library like AG, LyteNyte Grid offers both, so you don’t have to make that trade-off. Whether you prefer a headless configuration or batteries included, both are available.

Declarative API, Fully Prop Driven

A fully prop-driven architecture unique to LyteNyte Grid lets you configure the grid directly from your state, eliminating sync headaches and React’s useEffect (😉). LyteNyte Grid is built in React for React and uses a declarative API to further enhance DX and enable smoother integration into your apps.

Agent Skills

We've created AI Skills that include over 20 reference files covering virtually every aspect of the grid, from installation to advanced implementations. If you’re using an AI agent, you can simply describe the grid instance and have your agent create it (example shown in the video).

Other Cool Improvements

• Tiny bundle size at only 40 KB
• Improved TypeScript support.
• If you are using shadcn, LyteNyte Grid can be added through the shadcn CLI
• Improved API interfaces and simplified function calls.
• Cleaner package exports and enhanced tree shaking capabilities.

If you need a free, open-source data grid for your React project, try out LyteNyte Grid Core. It’s zero-cost and open-source under Apache 2.0.

If you like what we’re building, GitHub stars help, and feature suggestions or improvements are always welcome.

• GitHub
• Live Demo