Hi guys,
I have been building many projects and also tried posting about it on social media but there is literally no response. I think the issue is the way I am marketing, Im still not talking about it much online. Anyone who got their first sale or users, can you guys suggest anything specific that I should do?

Hey everyone,

Builder here. I’ve spent the last few days wrestling with a serverless architecture problem for an automated AI PR summarizer I'm working on, and wanted to share the fixes since these traps apply to almost any AI-driven SaaS built on serverless tech.

The concept is straightforward: a developer opens a Pull Request on GitHub, a webhook triggers, an AI reads the code diff, and it spits out a structured Markdown summary to a custom terminal UI dashboard.

But taking this from a local script to a live serverless environment (Next.js + Vercel + Supabase) broke almost instantly. Here are the three main technical hurdles and how I patched them:

1. The Vercel Timeout Trap

By default, Vercel kills serverless functions after 10–15 seconds on the free tier. Reading a large code diff, sending it to an LLM, and waiting for the response regularly takes 20+ seconds. The pipeline kept dying mid-execution, leaving the dashboard spinning forever.

• The Fix: I had to explicitly scale the execution limit by adding a route segment config at the very top of the API route: export const maxDuration = 60;. This gave the background worker a full minute to process without being killed.

2. Unauthenticated Webhooks vs. Supabase RLS

GitHub webhooks are completely unauthenticated public requests when they hit your API. Since my doc_jobs table is locked down with strict Row Level Security (RLS) rules, the webhook couldn't write the incoming payload to the database.

• The Fix: Created a dedicated backend endpoint that handles the webhook parsing securely. It utilizes the Supabase Service Role (Admin) key strictly on the server side to safely bypass RLS and initialize the job queue, keeping sensitive credentials completely hidden from the client browser.

3. Upstream LLM Overloads (503 Errors)

During heavy traffic times, hitting mainstream flash models threw constant 503 Service Unavailable errors. Initially, an API crash meant the database row got stuck in a permanent [PROCESSING] state.

• The Fix: I refactored the worker logic to wrap the generation step in an isolated try/catch block. If the API flakes, it gracefully aborts, marks the status as failed, logs the exact error text to a dedicated database column, and reflects a clean error panel on the frontend. I also swapped the engine to gemini-2.5-flash-lite, which has significantly higher availability under heavy free-tier loads.

Now the background workers run flawlessly, failures fail gracefully, and the pipeline drops the completed markdown files right onto the UI.

For anyone else building AI features on Vercel: How are you managing upstream API downtime or long-running tasks? Do you stick to route extensions or have you shifted entirely to external queues?

I have a live terminal UI running the pipeline. Since Reddit's spam filters hate Vercel links, let me know in the comments if you want to check it out and I will drop the link below.

Hey everyone,

Builder here. I’ve spent the last few days wrestling with a serverless architecture problem for an automated AI PR summarizer I'm working on, and wanted to share the fixes since these traps apply to almost any AI-driven SaaS built on serverless tech.

The concept is straightforward: a developer opens a Pull Request on GitHub, a webhook triggers, an AI reads the code diff, and it spits out a structured Markdown summary to a custom terminal UI dashboard.

But taking this from a local script to a live serverless environment (Next.js + Vercel + Supabase) broke almost instantly. Here are the three main technical hurdles and how I patched them:

1. The Vercel Timeout Trap

By default, Vercel kills serverless functions after 10–15 seconds on the free tier. Reading a large code diff, sending it to an LLM, and waiting for the response regularly takes 20+ seconds. The pipeline kept dying mid-execution, leaving the dashboard spinning forever.

• The Fix: I had to explicitly scale the execution limit by adding a route segment config at the very top of the API route: export const maxDuration = 60;. This gave the background worker a full minute to process without being killed.

2. Unauthenticated Webhooks vs. Supabase RLS

GitHub webhooks are completely unauthenticated public requests when they hit your API. Since my doc_jobs table is locked down with strict Row Level Security (RLS) rules, the webhook couldn't write the incoming payload to the database.

• The Fix: Created a dedicated backend endpoint that handles the webhook parsing securely. It utilizes the Supabase Service Role (Admin) key strictly on the server side to safely bypass RLS and initialize the job queue, keeping sensitive credentials completely hidden from the client browser.

3. Upstream LLM Overloads (503 Errors)

During heavy traffic times, hitting mainstream flash models threw constant 503 Service Unavailable errors. Initially, an API crash meant the database row got stuck in a permanent [PROCESSING] state.

• The Fix: I refactored the worker logic to wrap the generation step in an isolated try/catch block. If the API flakes, it gracefully aborts, marks the status as failed, logs the exact error text to a dedicated database column, and reflects a clean error panel on the frontend. I also swapped the engine to gemini-2.5-flash-lite, which has significantly higher availability under heavy free-tier loads.

Now the background workers run flawlessly, failures fail gracefully, and the pipeline drops the completed markdown files right onto the UI.

For anyone else building AI features on Vercel: How are you managing upstream API downtime or long-running tasks? Do you stick to route extensions or have you shifted entirely to external queues?

I have a live terminal UI running the pipeline. Since Reddit's spam filters hate Vercel links, let me know in the comments if you want to check it out and I will drop the link below.