Kimi K2 Thinking is now default model on

The best way to test your idea is to launch it and start talking about it online. To convert your idea into a product in hours, we're launching a SaaS template on https://t.co/Wles8P2mUH. It includes a landing page, dashboard, pricing page, and Stripe integration—and it's

Distribution is not a marketing problem anymore. its engineering problem now. just engineer it

Building in silence? Use https://t.co/Wles8P2mUH to create content about your product so people actually discover what you're making.

Dear algo, show this to folks hustling in SF. I'll be there in 2 weeks.

Our AI agents just learned to tell jokes while you wait. They're now dropping lines like "Works on my machine ¯_(ツ)_/¯" and "Git happens" during load times. Because if you're going to wait, might as well laugh at our tech stack. We're not sure if this makes us more human or less

We just built a state machine that can guard AI agents outside the LLM calls. This is one of the best ways to make agents more reliable and bring them back on track even when they try to do anything outside what they're asked to do. In the next few days, we'll be making it even

surprisingly state machine improves the reliability of AI agents drastically. coming soon on

If you believe you could be a rockstar intern, send your resume to hire@justcopy.ai We've cracked how to make effective AI agents - try it yourself at https://t.co/Wles8P1P59. This is a genuine opportunity. While the market is flooded with "vibe coding" apps that struggle to

Pretty soon, software is going to be dirt cheap. If you're building PowerPoints, web apps, etc. from scratch and wish they could look better, don't waste time. We are adding collections of software you can just copy, customize, and deploy for the win.

https://t.co/GSmQPB9Jxr

10/10 Want to build production AI agents? Start with: 1. Mandatory todo lists 2. Validation gates 3. Clear completion criteria 4. Structured handoffs This isn't theory. We ship with these patterns every day. See it in action:

9/10 Key insight: Structure > Intelligence A mediocre LLM with good task structure beats GPT-5 with vague instructions. Architecture matters more than model size. Plan your agents like you plan your code.

8/10 The result? We deploy full-stack apps built entirely by AI agents. Not demos. Not prototypes. Production apps with: - Authentication - Databases - APIs - CI/CD - Monitoring All coordinated through task-driven architecture.

7/10 But here's the genius part: Each of our 8 specialized agents follows the SAME pattern: - Requirements Analyst - UX Designer - Data Architect - Frontend Engineer - Backend Engineer - Integration Engineer - DevOps Engineer Sequential pipeline. Clear handoffs. Zero

6/10 Setup Manager Agent todos: - Initialize cloud sandbox ✅ - Clone project template ✅ - Install dependencies ✅ - Start dev servers (ports 3000/3001) ✅ - Verify services respond ✅ Can't proceed to next agent until ALL are ✅ This simple pattern prevents 80% of failures.

5/10 Benefits of mandatory todos: ✅ Prevents half-finished work ✅ Clean handoffs between agents ✅ Audit trail of what was done ✅ Debugging becomes trivial ✅ Users see real-time progress Instead of "AI magic," you get "AI engineering." Real example:

4/10 The Task-Driven Loop: 1. Agent initializes with structured todos 2. Get next task → Execute → Mark complete with evidence 3. System validates ALL todos complete 4. Only then can agent finish its phase No exceptions. No shortcuts. Why this is powerful:

3/10 AI agents need the same thing human engineers need: A clear todo list. Not a vague prompt. Not a 10,000 token instruction manual. A simple, structured list of tasks with validation criteria. Here's how it works:

2/10 The problem with traditional AI agents: 1. They lose track of what they're doing 2. They can't hand off work cleanly 3. They never actually finish Sound familiar? We spent 6 months debugging these issues. Then we realized something obvious...