Building a RAG-Powered Smart AI Chatbot for E-commerce application using LangChain
In today’s fast-paced e-commerce world, customers expect instant, accurate, and conversational support — not long waits or static FAQ… Continue reading on Medium »
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Secrets Management for Laravel: .env, Encrypted Config, and Deploynix
Every Laravel application has secrets. Database passwords, API keys, encryption keys, third-party service credentials, payment gateway tokens. These secrets are the keys to your kingdom, and mishandling them is one of the most common security mistakes in web development. The default approach of storing everything in a .env file works fine during development. But as your application grows, your team expands, and your deployment pipeline becomes more sophisticated, the humble .env file starts showing its limitations. This article explores why .env alone isn't enough, what alternatives exist, and how Deploynix's credential management fits into a mature secrets management strategy. The .env File: Simple but Limited Laravel's .env file is elegant in its simplicity. It's a flat file of key-value
OSDK and Mobile Applications: Building with the Embedded Ontology
The Embedded Ontology lets you build powerful enterprise applications for teams that operate at the edge. Run the full, context-rich Ontology locally on the device. The power of Palantir, at the point of action. Traditional enterprise platforms are powerful. They aggregate data, enforce governance, orchestrate workflows, and provide a single pane of glass for an organization to run their business. But that glass is mounted in a climate-controlled office, connected to reliable Wi-Fi, and viewed on a large monitor. Now ride along with a field service technician. They’re driving between sites, inspecting equipment in mechanical rooms with no cell signal, documenting findings on a tablet while standing next to a roaring HVAC unit, among myriad other tasks. The reality at the edge is fundamenta
Frontend Engineering at Palantir: Building a Backend-less Cross-Application API
About this Series Frontend engineering at Palantir goes far beyond building standard web apps. Our engineers design interfaces for mission-critical decision-making, build operational applications that translate insight to action, and create systems that handle massive datasets — thinking not just about what the user needs, but what they need when the network is unreliable, the stakes are high, and the margin for error is zero. This series pulls back the curtain on what that work really looks like: the technical problems we solve, the impact we have, and the approaches we take. Whether you’re just curious or exploring opportunities to join us, these posts offer an authentic look at life on our Frontend teams. In this blog post, a frontend engineer based in CA shares an overview of several f
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I Built an AI That Eliminates Manual Data Entry — Here's What I Learned
The Problem Nobody Solved Properly Small businesses waste 20+ hours every week manually typing data from invoices, receipts, and forms into spreadsheets and CRMs. Human error rates on manual data entry average 1–4%. That sounds tiny until you're reconciling 500 invoices and find 20 wrong entries. I spent months looking for a tool that actually fixed this. Nothing fit — they were either too expensive, too complex, or charged flat subscriptions whether you processed 5 documents or 5,000. So I Built DataSwift AI The concept is simple: Upload any document (invoice, receipt, form, unstructured PDF) AI extracts and organizes the data with zero errors Export directly to your database or CRM No subscription. Pay only per document. Crypto-friendly via NOWPayments. What I Learned Building It Pricing
Mojo Programming
The Mojo Programming Language: Why I’m Done With Python Wrappers Python is a legend for sketching, but it’s a disaster for production-grade AI. We’ve spent years trapped in the "Two-Language Problem," prototyping in high-level scripts and then suffering through a brutal C++ rewrite just to ship. The Mojo programming language is the first real architecture that kills that cycle, giving us a unified stack that reads like Python but runs like raw assembly. No More Runtime Tax Mojo isn't just another JIT or a transpiler; it’s a systems-level beast built on MLIR (Multi-Level Intermediate Representation). This allows the compiler to map high-level tensor math directly to hardware intrinsics. When I’m building models now, I’m talking straight to the silicon—NVIDIA GPUs, TPUs, or AVX-512 units—wit
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