Building in Public with AI Agents: Lessons from Creating a Twitter Bot

Building AI agents in public means sharing your development process, challenges, and learnings openly while you create them. This approach differs from traditional software development where you might wait until launch to show your work.

Why Build AI Agents Publicly

Faster feedback loops. When you share early prototypes and decision points, you get input from practitioners who've solved similar problems. This saves weeks of dead-end exploration.

Built-in accountability. Public commitments create gentle pressure to maintain momentum. Knowing others are following your progress makes it harder to abandon projects halfway through.

Attracts unexpected collaborators. People with complementary skills often emerge when they see interesting work happening. You might find someone with domain expertise you lack.

A Concrete Example: Building @ContentAnalyzer

I built a Twitter bot that analyzes viral tweets and identifies patterns in successful content. Here's how building publicly shaped the project:

Week 1: Sharing the Initial Idea

I posted about wanting to understand why certain tweets perform better than others. The responses revealed:

• Three people had tried similar projects and shared their failures
• A linguistics researcher offered to help with sentiment analysis
• Someone pointed out existing APIs I hadn't considered

This early feedback prevented me from rebuilding existing solutions and connected me with domain expertise.

Week 3: Exposing Technical Decisions

I shared my agent architecture choice between using LangChain vs. building custom prompt chains. The discussion thread included:

• Performance benchmarks from other builders
• Cost comparisons for different model providers
• Code examples of alternative approaches

The community helped me avoid an expensive mistake with token usage patterns.

Week 5: Debugging in Public

When the bot started producing inconsistent analyses, I posted example outputs and asked for help. Within hours:

• Someone identified a prompt engineering issue I'd missed
• Another person suggested a validation approach using multiple models
• A thread emerged about handling edge cases in content analysis

This debugging session would have taken me days alone.

Practical Steps for Building AI Agents Publicly

Choose Your Platform

Twitter/X: Good for quick updates and technical discussions LinkedIn: Better for professional AI development content GitHub: Essential for code sharing and collaboration Personal blog: Best for detailed technical writeups

What to Share

Architecture decisions: "Should I use RAG or fine-tuning for this use case?" Performance metrics: Response times, accuracy rates, cost per interaction Failure modes: What breaks and how you're fixing it Code snippets: Non-proprietary examples that illustrate concepts Learning moments: Discoveries about prompt engineering, model selection, etc.

What Not to Share

API keys or credentials: Use environment variables and document without exposing Proprietary training data: Stick to synthetic examples or public datasets Customer information: Always anonymize any real usage data Half-baked theories: Share experiments, not speculation

Managing the Downsides

Information Overload

Too much feedback can paralyze decision-making. Set specific windows for input: "Looking for feedback on this approach through Friday, then I'm proceeding."

Competitive Concerns

Most AI agent ideas benefit from execution more than novelty. Sharing your approach rarely creates competitive disadvantage, especially in fast-moving spaces.

Time Investment

Documenting and sharing takes time. Budget 10-15% of development time for public updates. The feedback you receive usually saves more time than you invest.

Tools That Help

Documentation

• README-driven development: Write your README first, share it, iterate based on feedback
• Architecture diagrams: Use tools like Excalidraw for shareable system diagrams
• Screen recordings: Loom or similar for demonstrating agent behavior

Code Sharing

• GitHub repositories: Even for works-in-progress
• Gists: For quick code snippets and examples
• Replit: For shareable, runnable examples

Progress Tracking

• GitHub Projects: Public boards showing your development pipeline
• Changelog files: Document what changed and why
• Issue templates: Let others report bugs or suggest features

Common Patterns from Successful Public Builders

Weekly updates: Regular cadence keeps followers engaged without overwhelming Problem-first sharing: Start with the problem you're solving, not the solution Show, don't tell: Screenshots, code examples, and demos over abstract descriptions Acknowledge failures: What didn't work and why teaches others more than success stories

Getting Started

1. Pick one platform and commit to sharing there consistently
2. Start with your current project, even if it's half-finished
3. Share one specific technical decision you're facing
4. Ask for input with a clear deadline
5. Thank contributors publicly and follow up on how their advice worked

Building AI agents publicly transforms development from a solo activity into a collaborative learning experience. The accountability, feedback, and connections you gain typically outweigh the time investment and vulnerability of sharing work in progress.

The key is starting before you feel ready. Your first posts won't be perfect, but they'll start the conversation that makes your agent better.