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Plurai — you describe what your AI agent should and shouldn't do in plain language, and it generates the eval test suite, runs tests at sub-100ms latency, and deploys a custom guardrails model from your specs — at 8x lower cost than GPT-as-judge.
Wonder — an AI design agent in public alpha that works directly on your canvas, generating UI, graphics, and pitch decks, with native integrations into Cursor and Claude Code so your designs stay connected to the codebase.
KarmaBox — runs AI agents including Claude Code from your phone, routing tasks to Claude, Codex, or Gemini and using your existing devices as a private compute pool, no separate infrastructure required.
Cloud Computer by Manus — gives your bots a dedicated persistent machine in the cloud so automated workflows keep running without tying up your laptop, from the team behind the Manus general AI agent platform.
Gemini Deep Research Agent — now available in the Gemini API in two variants: standard for fast synchronous queries and Max for longer async research runs, with MCP support to connect it to your own data sources beyond the web.
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Clients often start with the same request: "Let's build an AI agent."
My counter-question is always: which manual process actually costs you time, errors, or money right now?
My selection rule: remove it first. If it can't be removed, simplify it. If it's still needed, automate it with a plain script. Only then add an integration or an agent. Often the best result is the process that no longer exists.
Safe automation means real scenarios, error handling, and alerts. Without alerts, a workflow can silently lose data for weeks. I always define what stays with a human and what goes to the system.
Question for you: which process would you automate first, and why is it still manual?
Service for audit and implementation: https://contra.com/s/BmWLbgFU-ai-automation-from-your-dream-to-working-implementation?r=auto_booster
Two concepts. Two different first impressions.
Exploring two visual directions for a finance and AI platform.
Option A | Fundely
Warm gradients, soft curves, and a clean layout. Designed to make finance feel simple, approachable, and easy to trust.
Option B | Proxyma
Bold typography, a structured grid, and a striking 3D visual. Designed to make AI feel confident, modern, and forward-thinking.
Both share the same goal: create a strong first impression through design.
But which direction communicates better?
A or B? I'd love to hear your perspective.
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Shopify Abandoned Cart Recovery Automation 🛒
Ever wondered how many potential orders an online store could miss when customers add products to their cart but leave without completing checkout?
I built an abandoned cart automation workflow using Activepieces, Shopify, Google Gemini, Gmail, and WhatsApp Business.
⚙️ How It Works
🛒 Cart Trigger: Detects an abandoned checkout event from Shopify.
🔀 Conditional Routing: Routes the workflow based on configured conditions.
🤖 AI-Powered Messaging: Gemini generates a personalized reminder message.
💬 WhatsApp Reminder: Sends a cart reminder through WhatsApp Business.
📧 Email Reminder: Sends a follow-up email to encourage the customer to complete checkout.
💡 How It Can Help E-commerce Businesses
Remind customers about products left in their carts.
Reduce repetitive manual follow-up work.
Make customer communication more consistent.
Create an opportunity to recover potentially lost sales.
Tools Used: Activepieces | Shopify | Google Gemini | Gmail | WhatsApp Business
Project Type: Personal Automation Project
I built this workflow to demonstrate how automation can support abandoned cart recovery for Shopify and D2C businesses.
Interested in automating your store's customer follow-ups? Let's connect.
The 30–60 minute delay then 24-hour follow-up is a sensible cadence. The part I would harden next is the stop conditions: once the cart converts or the customer replies, every channel should cancel, and WhatsApp especially needs a clear opt-in trail before the first send....