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Engineered a voice-based AI agent for natural language appointment booking with Twillio integration. A user can attach the AI agent to their Twillio's Phone number, so they dont miss any lead.
Tech Stack: LangChain, LangGraph, LiveKit, FastAPI
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Agentic AI — The Biggest Shift in Tech Right Now 🤖⚡
Most people still think AI = ChatGPT.
Ask a question. Get an answer. Done.
That's old news.
🧠 Here's What's Actually Changed
Old AI:
You ask → It answers → You do the rest.
Agentic AI:
You give a goal → It plans → It acts → It delivers.
No hand-holding. Just results. 🎯
🔥 Real Example
You say: "Research my competitors and write a report."
The agent:
🔍 Searches the web
📄 Reads the pages
✍️ Writes the report
📬 Delivers it
You touched nothing.
⚙️ What Powers an AI Agent
🧠 LLM — the brain (GPT-4, Claude)
🛠️ Tools — web search, APIs, code
💾 Memory — remembers context
📋 Planning — breaks goals into steps
🔁 Self-correction — fixes its own mistakes
💼 What This Means For You
Still manually handling leads, reports, support, research?
AI Agents can replace all of that. Right now. 🚀
👨💻 I Build These
Custom AI Agent workflows using Next.js + OpenAI + Claude API + Supabase.
Thinking about adding AI to your product?
Comment below or DM me — I'll tell you exactly what's possible. No pitch. Just a straight answer.
Early adopters won't just save time — they'll operate like a team twice their size. 💡
Already using AI Agents? Still exploring? Drop your thoughts below 👇
#AgenticAI #AIForBusiness #OpenAI #ClaudeAPI #AIAgents #GenerativeAI #GPT4 #LLM #FreelanceDeveloper #BuildWithAI #FullStackDeveloper #NextJS #SoftwareDeveloper #ReactDeveloper #NodeJS #SaaS
True. The real shift is when AI stops being just a chat window and starts becoming part of the workflow.
For businesses, the value is not “AI answered me.”
It is “AI checked the data, made a decision, updated the CRM, alerted the team, and logged the result.”
Since Friday (May 15, 2026), I have been building a local AI chat app of my own with the help of generative AI🚀 The main reason is to use Gemma 4 on my laptop in an environment where I can expect what will happen. In addition, this project has been helping me learn a lot about how vibecoding tools work under the hood!
This chat app is built under the following setup:
• Next.js
• Two API Routes, separating LLM inference from the back-end part of the app
• Transformers.js (running on the server side)
• The ONNX q4 version of gemma-4-e2b-it
Note that this chat app is still in development and, despite its name, has not got any built-in tools yet. However, a first step is very important in app building, isn't it? I hope that this post inspires anyone to build their own local AI chat app!
See the comment section for additional notes (if any).
Post author notes will be added to this thread of replies.
I run lokerdollar.com alone.
No team. No content writers. No ops staff.
Just me — and 8 AI agents running 24/7 in the background.
This week they:
→ Ingested 1,700+ new jobs from multiple sources
→ Scored and filtered every single one
→ Dispatched 1,500+ personalised alert emails
→ Ghostwrote Indonesian blog posts from live job data
→ Ran market audits
80% of it happened without me touching anything.
I used to do all of this manually. Scraping, curating, writing, emailing. It was slow and it capped how far the product could go.
Now I've built what I call an "agent roster" — eight named agents, each with one job. The Scout finds jobs. The Curator scores them. The Ghostwriter writes content from the data. The Notifier sends the right alert to the right person. The Analyst watches the market.
They don't replace the product vision. They execute it.
This is what excites me about the moment we're in. Solo founders can now run operations that used to require a whole team. The leverage is real — I'm watching it in my own dashboard.
A few things I've learned building this:
Give every agent exactly one job. The second you let an agent do "a few things," it becomes unreliable.
Autonomy rate matters more than cost. $0.00 per run means nothing if a human still has to review everything.
Start with the highest-volume, most repetitive task first. For me that was job ingestion. The ROI was immediate.
I'm now thinking about how to help other founders and small businesses build their own agent operations — whether that's a product, a pipeline, or just a conversation.
If you're running any kind of data-heavy operation and you're doing too much of it by hand — I'd love to talk.
Drop a comment or DM me if you want to explore what this could look like for your business.
Cool 🔥 How does it manage token usage?
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