Fun use case for OpenAI Agent · AI Developer Accelerator

Fun use case for OpenAI Agent

I asked OpenAI Agent the following question:

"Based at everything you know about me, browse all the thread in the community "https://www.skool.com/ai-developer-accelerator" and list the top 10 I could be able to participate something interesting and propose what I would reply (but DO NOT POST ANYTHING YOURSELF) just propose what I could post."

After 10 minutes here is the result:

"Understood. I’ll explore the community at https://www.skool.com/ai-developer-accelerator to identify the top 10 discussion threads where your expertise might add value and prepare proposed responses for you. I’ll not post anything without your confirmation. I’ll begin by accessing the site and reviewing the threads.

Here are ten discussion threads from the AI Developer Accelerator community that align well with your interests in AI architecture, new tools and pragmatic development. For each, I’ve included a short description and a suggested angle for your reply.

# Thread (with context) Why it might interest you & proposed contribution

1 Gemini CLI: Free Coding Agent – Real World Coding Test & First Impressions – Brandon Hancock reviews Google’s new Gemini CLI, a free coding agent you can run entirely in the terminal and puts it through several real‑world coding challenges.

As someone who experiments with new AI tooling, you could share your own impressions of Gemini CLI. Perhaps mention how you measure its productivity gains versus tools like Cursor or Claude, any integration challenges you faced, and whether it lives up to the hype in a professional workflow.

2 Kimi K2 is on par with OpenAI $20 models, but free – a community member shares that the new Kimi K2 model (with both chat and API versions) appears competitive with paid OpenAI models.

Given your experience evaluating LLMs, you could discuss your tests of Kimi K2, highlighting strengths (e.g., long‑context handling) and weaknesses compared with paid models. You might also mention latency or API reliability and how pricing factors into tool selection for proof‑of‑concept work.

3 Deploying to CrewAI Enterprise – a user’s Flow works locally but, after deployment to CrewAI Enterprise, calls to retrieve inputs return an empty array and trigger HTTP 500 errors.

Offer troubleshooting advice: verify environment variables and secrets are correctly set in the deployment, ensure the input dictionary schema matches production expectations, and check logging in CrewAI for error details. You could also mention best practices for packaging flows for different environments.

4 Voice ADK agent deployment in Vertex AI – someone has built a voice agent using the ADK and asks whether it can be deployed to Vertex AI and still use WebSockets.

Drawing on your system‑architecture experience, explain that Vertex AI Agent Engine supports HTTP endpoints rather than long‑lived WebSockets, so a common pattern is to deploy the voice agent’s backend (which uses WebSockets) to Cloud Run or Cloud Functions and have the Agent Engine call it. Highlight latency considerations and mention any alternatives you’ve tried (e.g., LiveKit).

5 Best Deployment Strategy for Vertex AI Agent with Persistent Memory and FastAPI Backend? – the author wants to use Vertex Memory Bank and wonders whether to deploy their AI agent separately from the FastAPI backend or bundle them together.

Explain the pros and cons: separate deployments let you scale the agent and API independently and leverage Vertex AI’s managed runtime, while bundling simplifies deployment but may limit scalability. Suggest starting with separate services connected via authenticated calls, and share how you handle persistent memory (e.g., external database vs. Vertex Memory Bank).

6 Do I Need RAG? – the poster has large CSVs in a SQL database accessed via an agent that converts queries to SQL and asks what RAG would add.

Clarify that retrieval‑augmented generation is most beneficial for unstructured or semi‑structured content (documents, logs) rather than relational data. Point out that if SQL queries already provide the needed information, adding RAG may not help, but RAG can enhance question‑answering over messy text or combine relational results with knowledge from other sources.

7 Leveraging AI to process log files – a newcomer wants to identify patterns and issues in large log data without high cost or latency.

Suggest combining traditional log processing tools (e.g., ELK/Opensearch) with lightweight AI models. For pattern detection, anomaly‑detection algorithms can pre‑filter data before passing interesting snippets to an LLM for summarization. Emphasize using streaming models or batch processing to control costs.

8 KIRO – a post asking if anyone has tried Amazon’s Kiro IDE, which claims to offer “spec‑driven AI development,” drawing comparisons to Brandon’s ShipKit.

If you’ve explored Kiro or similar AI‑assisted IDEs, share how its spec‑driven approach compares to frameworks like ShipKit. Discuss whether Kiro’s spec format integrates well with existing codebases and what trade‑offs you see between using an IDE with built‑in AI versus building your own pipelines.

9 FastStripe – A better Stripe payments Python library – a member links to a detailed write‑up about a new payment library that could help developers make money.

You might thank them for sharing and discuss whether you’ve used FastStripe or similar wrappers. Mention any considerations around security and PCI compliance when choosing third‑party payment libraries, and suggest evaluating community support and maintenance before adopting it in commercial projects.

10 Building an app – someone asks for examples of AI‑powered apps that generate at least $500/month and wants advice on marketing, hosting and service costs.

Share your pragmatic approach: start with a niche problem where AI adds clear value, validate with a minimum viable product, and reinvest earnings into marketing. Offer rough cost estimates for hosting (e.g., using serverless services), LLM API usage and databases, and stress the importance of optimizing queries to keep inference costs down. If you have examples of profitable micro‑SaaS projects, summarize them without revealing confidential details.

These threads span tool evaluations, deployment questions and business discussions, giving you ample opportunity to share insights from your system‑architecture and AI‑development background. You could provide thoughtful guidance, compare tools, and help newcomers navigate the rapidly evolving AI landscape."

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