SShortSingh.
Back to feed

VIGÍA ML Brings In-Browser AI Monitoring to Gas Wells Without Cloud Dependency

0
·23 views

VIGÍA ML is an open-source predictive monitoring console for gas wells that runs entirely in the browser using TensorFlow.js and React, with no backend or cloud infrastructure required. The tool trains four machine learning models — an LSTM production forecaster, an autoencoder anomaly detector, a neural fault classifier, and a recommendation layer — directly on user-loaded well data within each session. It was built to address a key challenge in the oil and gas industry: proprietary well data such as tubing pressure and flow rates cannot easily be sent to cloud pipelines due to security, cost, and connectivity constraints. Designed as a Progressive Web App, VIGÍA ML works fully offline, making it suitable for remote field sites with unreliable internet access. The Spanish-first interface targets Latin American operations teams, and the project is available under a source-available license on GitHub.

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)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

How to Build a Full-Stack RAG Pipeline Using React, Node.js, and MongoDB

A technical walkthrough published on DEV Community explains how to extend a standard React-Express-MongoDB stack with a Retrieval-Augmented Generation (RAG) pipeline for AI-powered applications. The architecture works by splitting uploaded documents into smaller chunks, converting each chunk into vector embeddings, and storing them in MongoDB Atlas for semantic search. When a user submits a question, it is also converted into an embedding and compared against stored vectors to retrieve the most relevant document sections. Those sections are then assembled into a prompt and passed to a large language model, which generates a context-aware response returned to the React frontend. The article also highlights production considerations such as authentication, rate limiting, caching, and hybrid search to move beyond a basic demo.

0
ProgrammingDEV Community ·

Workflow Separates AI-Generated Release Notes from Human-Owned Upgrade Promises

A proposed developer workflow addresses the risk of AI-generated release notes presenting unverified migration claims as reliable facts. The approach splits release communication into two distinct files: a machine-compiled inventory of git commits, file changes, and public symbols, and a separate reviewer-owned contract covering upgrade commands, compatibility guarantees, and breaking changes. Models are permitted to draft summary prose only from the facts file, while the contract must be filled exclusively by a human reviewer who can verify each claim. If any cell in the reviewer contract remains blank, the changelog gate is designed to fail, preventing unverified promises from reaching users. The goal is to ensure that support tickets are not the first real test of an upgrade path by making the distinction between compiled inventory and human warranty visible before release.

0
ProgrammingDEV Community ·

SMTP 250 OK Is Not Enough: Developer Logs 24% Bounce Rate on Verified Emails

A developer discovered on July 15 that 12 out of 50 email addresses bounced despite all returning a 250 OK response during prior SMTP handshake verification. The 24% failure rate exposed a key limitation of relying solely on SMTP checks, which cannot detect role accounts, catch-all domains, or greylisting behavior. To investigate, the developer ran the same list through a third-party API that checks MX records, breach history, role-account flags, and provider identity. The deeper validation revealed metadata that standard SMTP handshakes do not surface, such as whether an address is a role account or has been flagged in known data breaches. The findings highlight why multi-layered email validation is more reliable than SMTP verification alone for production mailing campaigns.

0
ProgrammingDEV Community ·

How to prevent AI coding agents from making destructive changes without human approval

AI coding agents can edit files, run commands, and alter configurations in seconds, raising questions about authority rather than capability. A proposed governance framework classifies agent actions into low-impact reversible tasks, high-risk destructive operations, and undefined-authority situations that require a human approval gate. Before executing any hard-to-reverse action, the agent must document the impact, a rollback path, the justification, and explicit human sign-off — otherwise execution halts. The framework also enforces evidence-based success claims, meaning an agent can only mark a change complete when observable proof exists, not merely the absence of visible errors. A free Portuguese-language starter kit with four policy and checklist files has been published on GitHub, while a more comprehensive paid governance kit is available for R$39.90.