The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
Endpoint health monitoring system that runs concurrent checks against 10 services (HTTP APIs, webhooks, database connections) using asyncio. Each check measures response time at p50/p95/p99 percentiles, tracks uptime over 24h windows, and triggers alerts when thresholds are crossed. Failed checks retry with exponential backoff (1s, 2s, 4s) before marking an endpoint degraded. Supports HTTP, TCP, and DNS protocols. The dashboard shows a real-time status board with per-endpoint heatmaps, incident timeline, and aggregate uptime. Built with Python + FastAPI + asyncio/aiohttp. Designed to run as a lightweight self-hosted alternative to commercial monitoring tools.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
Building AI agents is easy. Building one you can trust with a real business workflow is harder.
I built a Freelance Admin Agent to explore that problem using Strands Agents SDK and Claude Sonnet 4.5 on Amazon Bedrock.
The working MVP takes manually provided client emails and turns them into a structured payment workflow:
→ Classifies invoice and payment emails
→ Extracts client, invoice, amount, currency, dates and contextual details using structured output
→ Stores and matches records in SQLite
→ Uses deterministic logic to identify full/short and on-time/late payments
→ Uses a second agent to draft a contextual follow-up only when needed
→ Keeps the final decision with the human through Approve / Edit / Reject
One of the most valuable parts wasn't adding another AI feature. It was finding a real reliability issue during testing: reprocessing the same email could duplicate records and inflate payment totals.
I reproduced it through repeat testing, traced the database behavior, and fixed it.
That reinforced something I'm increasingly applying to my AI engineering work:
A useful AI product isn't just an LLM call. It needs clear boundaries between AI reasoning, structured data, deterministic business logic, persistence, testing, and human control.
The next iteration will focus on stronger production safeguards, including source-grounded financial extraction, precise currency handling, stricter trust boundaries for untrusted input, and eventually live inbox integrations.
I'm interested in building more systems like this for startups and teams with repetitive workflows that can genuinely benefit from AI agents and automation.
Stack: Python · Strands Agents SDK · Amazon Bedrock · Claude Sonnet 4.5 · Pydantic · SQLite · Streamlit
turning client emails into admin tasks is such a real freelancer pain, nice build 🔥
Built a simple lead intake and follow-up workflow for Nightstack Labs.
The goal was to make handling new customer inquiries more organized without manually moving information between different tools.
The workflow:
Customer submits a form
→ lead information is organized
→ lead is logged
→ the business gets notified
→ the customer gets a confirmation
Built with Tally, Make, Google Sheets, and Gmail.
AI can also be used to summarize incoming requests when it actually improves the workflow.
I like how the form feeds straight into a sheet and triggers the email, cutting out the copy paste step.
A concept sales tracker for small businesses. Every lead and deal sits on one simple board, with each customer's full history and automatic follow-up reminders, so no lead is forgotten. Owners can see what's coming in this month at a glance. Concept project by Ascalify.
Seeing the board pull each lead's full history together makes the monthly outlook instantly clear, and those auto reminders take the grunt work out of follow ups.