AI Jesus, BuddhaBot, and Catholic chatbots signal a faith-tech boom in 2026

·1 views

A Los Angeles startup called Just Like Me is offering users AI-generated video calls with a Jesus avatar at $1.99 per minute or $49.99 for 45 minutes a month, trained on the King James Bible and modeled visually on actor Jonathan Roumie. The product is part of a broader surge in faith-based AI, which also includes BuddhaBot, a humanoid robot monk from Kyoto University, and Magisterium AI, a Catholic teaching chatbot based in Rome. A February 2026 study by Barna Group and Gloo found that nearly one in three American adults now considers AI spiritual advice as trustworthy as counsel from a pastor or priest, rising to two in five among Gen Z and millennials. Among practising Christians, 34 percent shared that view, and roughly four in ten said AI had already assisted them with prayer, Bible study, or spiritual growth. The data has been widely interpreted by the faith-tech industry as a significant commercial opportunity rather than a religious or cultural warning sign.

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)

Why AI Hiring Has Shifted From Generalists to Deep Technical Specialists

The pathway into elite AI labs and enterprise teams is increasingly demanding deep specialization rather than broad generalist knowledge, according to insights shared by a former OpenAI intern via Business Insider. Following the initial ChatGPT-driven boom, the industry was flooded with generalists capable of basic API orchestration and high-level scripting, but those entry points have largely been exhausted. As frontier models approach scaling limits, engineering challenges have shifted toward complex, low-level bottlenecks in areas such as inference optimization, synthetic data pipeline architecture, and fault-tolerant agentic workflows. Enterprises are also driven by financial pressures, as the total cost of ownership for AI systems is dominated by ongoing compute and engineering costs rather than model acquisition, making optimization specialists highly valuable. Organizations that rely on generalists tend to use oversized models and unoptimized pipelines, driving up operational costs in ways that specialized engineers are specifically hired to reduce.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How to Build a Branch-Based CI/CD Pipeline with SonarQube and GitHub Actions

A reusable CI/CD pattern has been documented for automatically deploying code to different environments based on branch names using GitHub Actions. The setup integrates a SonarQube quality gate that must pass before any deployment proceeds, mapping develop, staging, and main branches to their respective environments. A key technical insight addresses why the common workflow_run trigger silently fails — it only reads trigger definitions from the repository's default branch, making it unreliable for multi-branch setups. The guide recommends using GitHub's reusable workflow_call primitive instead, which resolves the workflow file from the caller's own branch reference. Production deployments additionally require manual human approval, configured through GitHub's Environments feature under repository settings.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

var, let, and const in JavaScript: Key Differences Every Developer Should Know

JavaScript offers three variable declaration keywords — var, let, and const — each with distinct scoping and hoisting behaviors introduced or clarified in ES6. The var keyword is function-scoped and allows redeclaration, meaning the same variable name can be declared multiple times without error. In contrast, let is block-scoped and prevents redeclaration, reducing bugs caused by accidental variable overwrites. A key behavioral difference involves hoisting: var declarations are hoisted and initialized as undefined, while let and const are placed in a Temporal Dead Zone, throwing a ReferenceError if accessed before their declaration. These distinctions make let and const safer and more predictable choices for modern JavaScript development.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Free PDF Parsers in RAG Pipelines Can Silently Inflate Your LLM Costs

A developer building a Retrieval-Augmented Generation (RAG) system discovered that a free PDF parsing library was silently corrupting document content — scrambling tables, losing diagrams, and mangling multi-column layouts — without throwing any errors. The corrupted text passed through chunking, embedding, and vector retrieval undetected, causing the LLM to return confidently wrong answers. Because users rephrased and retried failed queries, the hidden extraction flaw multiplied LLM token usage two to three times per successfully answered question. The author argues that document extraction is the first quality-versus-cost decision in any RAG pipeline, and that choosing a cheap, lossy parser does not eliminate cost — it merely shifts it to the most expensive downstream stage. At scale, such as one million pages per month, paying a few hundred dollars more for a faithful extraction service can significantly reduce overall pipeline costs by cutting unnecessary LLM retries.

0 comments Read more at DEV Community