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🚀 Exploring how LLMs work in real systems
Recently, I explored how to take a Large Language Model beyond simple testing and design a scalable, secure, and production-ready architecture around it.
Instead of focusing only on prompt → response, I worked on building a complete system flow:
• Multiple users interacting through applications
• A backend API layer handling requests, validation, and control
• A security layer ensuring proper access and safe communication
• An LLM inference layer generating responses in real time
• Monitoring and logging to track performance and reliability
Also explored how CI/CD pipelines can be used to automate deployment and testing for such systems.
This helped me understand that working with LLMs is not just about models — it’s about building reliable, scalable systems around them.
#AI #LLM #DevOps #Cloud #SystemDesign #LearningInPublic
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Would you let an AI write SQL against your production database?
I wouldn't either. So when I built Quanta — a workspace where you ask questions in plain English and get back a live dashboard — the model never touches your data directly.
Every query it generates is SELECT-only and runs read-only. If one fails, the system feeds the exact database error back to the model and lets it correct itself once before anything reaches you. Credentials stay encrypted with AES-256-GCM and never leave the vault.
Connect Postgres or MySQL, switch between Gemini, Claude, and GPT, and build dashboards from 12 chart types. It's multi-tenant now too. Orgs, roles, and per-workspace AI spend caps.
Curious how other devs handle the "LLM + real database" problem. What guardrail would you add?
• Jul 5
The self-correction loop is such a smart touch. Only thing I'd add is a statement_timeout + forced LIMIT so a valid-but-heavy query can't tank prod. Ideally run against a read replica. Great build.
I specialize in building modern, scalable web applications that combine clean design with powerful functionality. My work focuses on creating fast, responsive, and user-friendly digital products that help businesses streamline their operations and deliver better experiences to their customers.
I enjoy developing full-stack applications, AI-powered platforms, SaaS products, automation systems, e-commerce solutions, and custom business tools using modern technologies like React, Next.js, Node.js, TypeScript, Python, and AI APIs. Every project is built with performance, security, and maintainability in mind.
I believe great software is more than just writing code. It's about understanding the problem, creating intuitive user experiences, and delivering solutions that are reliable, scalable, and easy to use. I pay close attention to clean architecture, responsive design, and long-term maintainability to ensure every project provides real value.
Whether I'm building an AI automation platform, a business dashboard, or a complete web application from the ground up, my goal is always the same: deliver high-quality work, communicate clearly throughout the process, and create products that people genuinely enjoy using.
I'm always excited to collaborate on new ideas and turn ambitious concepts into polished, production-ready applications.
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