How to Build an Automated AI Cold-Calling System for B2B Leads Using Vapi and OpenAI

·1 views

A developer tutorial published on DEV Community outlines how to build a fully automated outbound calling pipeline for B2B lead generation using Vapi, OpenAI, ElevenLabs, and Twilio. The system allows a voice AI agent to greet prospects, handle objections, and log call outcomes directly into a CRM without human sales representatives. The stack runs on self-hosted Docker containers using n8n for workflow orchestration and PostgreSQL for storing call transcripts and scoring data. The pipeline is designed to handle 50 to 100 calls per hour, feeding qualified leads into a sales funnel automatically. The guide estimates a build time of 8 to 12 hours for a developer familiar with Docker and REST APIs.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Freelancer loses long-term client to AI, learns which work is truly irreplaceable

A freelancer lost a client of over a year not due to poor performance, but because the client chose to replace routine tasks with an AI tool at a lower cost. The writer reflects that the work lost was simple, repetitive, and required little creative judgment — making it an easy target for automation. The experience revealed that predictable, low-effort work carries the highest risk of being displaced by AI tools. Clients who continued working with the freelancer were those who needed complex, judgment-driven problem-solving that AI cannot reliably handle. The writer now uses this as a lens to reconsider how freelancers should position their skills in an AI-augmented market.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How barometer.today builds its animated global isobar maps using open weather data

Barometer.today, a web tool showing air pressure forecasts for cities worldwide, also features an animated global isobar map spanning one day back to four days ahead. The map is generated from ECMWF forecast data sourced via Open-Meteo, delivered as a pressure grid at 0.25-degree resolution and updated four times daily. To prevent jagged contour lines, the team applies a Gaussian blur, but uses a normalized convolution technique to stop inflated sea-level pressure values over high plateaus — such as the Himalayas and the Andes — from distorting surrounding data. Elevated terrain above 2,500 metres is masked using a digital elevation model, and isobars crossing these regions are rendered as dashed lines rather than omitted, keeping the map visually continuous while signalling extrapolated values. The underlying code is written in Python using numpy, scipy, contourpy, and shapely, and the approach is applicable to anyone drawing contour maps from gridded geospatial data.

0 comments Read more at DEV Community

ProgrammingHacker News ·

CIA Declassifies 71 President's Daily Briefs Tied to 9/11 Anniversary

The CIA has released 71 declassified President's Daily Brief documents related to the September 11 attacks. The release was made in commemoration of the 25th anniversary of 9/11. President's Daily Briefs are highly classified intelligence summaries prepared for the sitting U.S. president. The declassification provides the public with a rare look at the intelligence landscape surrounding the 2001 terrorist attacks. The documents are available through the CIA's official website.

0 comments Read more at Hacker News

ProgrammingGitHub Blog ·

GitHub Engineer Automates APAC Marketing Event Ops Using Code-Based Workflows

A GitHub engineer developed an automation system to streamline the full lifecycle of marketing events for the company's Asia-Pacific team. The approach is built on the principle that any documented, repeatable workflow can be automated. The system covers event operations from initial planning through to post-event follow-up. By treating marketing processes as code, the solution reduces manual effort and improves consistency across campaigns. The project was shared via the official GitHub Blog as a practical example of applying engineering thinking to marketing operations.

0 comments Read more at GitHub Blog