TunerLabs Editorial
Practical insights on AI engineering, LLM integration, AI agents, and enterprise AI strategy. Written by engineers who build production AI systems, not just write about them.
The complete architecture guide — Skills, Agents, Memory & the full Tools reference. Everything in one beautiful page.
The complete interactive architecture reference for Claude Code. Configure the 7-level memory hierarchy, build custom Skills and Agents, set up MCP servers, and master every tool.
A complete beginner's guide to making Claude Code remember everything across sessions. Set up CLAUDE.md, primer.md, memory.sh, and git hooks in 20 minutes.
Set up Claude Code in 60 seconds. Auto-detects your stack and generates CLAUDE.md, permissions, skills, agents, and MCP configs - all tailored to your project.
One curl command installs 5 curated marketing skill packs for Claude Code - 70+ skills covering SEO, CRO, copywriting, content strategy, and frontend design. Auto-updates, selective installs, and zero config.
Most teams overspend 10x on LLM APIs by sending every request to GPT-4. Here's the exact routing framework that uses small language models for 80% of traffic and reserves LLMs for the 20% that actually needs them - same user experience, 80%+ cost reduction.
A definitive guide to the companies driving enterprise AI transformation in 2025. From specialist AI engineering firms to global consultancies, here is how to choose the right partner for your AI journey.
Not all AI consulting firms are equal. This buyer's guide breaks down what to look for, what to avoid, and which types of firms deliver the highest ROI for enterprise AI transformation projects.
AI agents are moving from research labs into production systems. This deep dive explains how autonomous AI agents work, where they deliver the highest ROI, and what organizations need to deploy them successfully.
A practical engineering guide to integrating large language models into enterprise applications. Covers architecture patterns, RAG vs fine-tuning, cost management, and production deployment considerations.
AI transformation is not only for large enterprises. This playbook gives small and mid-size businesses a practical framework for adopting AI effectively, affordably, and at the right pace.
The choice between retrieval-augmented generation and fine-tuning is one of the most consequential decisions in an LLM project. This guide explains both approaches with clear criteria for choosing the right one.
AI-powered workflow automation is delivering measurable cost reductions across industries. This article examines the highest-impact automation patterns and how to implement them without disrupting operations.
Should you build or buy AI? This guide provides a framework for deciding between custom AI development and off-the-shelf AI tools, with clear criteria based on your use case, budget, and competitive position.
Building an ML model is 10 percent of the work. Getting it to production reliably is the other 90 percent. This guide covers the engineering decisions that determine whether ML pipelines succeed at scale.
AI-first products are designed from the ground up with intelligence at their core. This guide explains how to approach product design, architecture, and development when AI is the primary value driver.
An honest assessment of generative AI business use cases: which applications are delivering real value, which are still experimental, and where organizations should focus their generative AI investments.
A practical framework for business leaders to build an AI transformation roadmap that delivers results, not just a strategy document. Covers use case prioritization, resource planning, and governance.
India, and Bengaluru in particular, is emerging as a global center for specialist AI engineering. This article examines the factors driving this development and what it means for organizations seeking AI transformation partners.
AI investments are difficult to measure but essential to justify. This guide provides a practical ROI framework for enterprise AI initiatives, covering measurement methodology, common pitfalls, and realistic expectations.
Choosing the right AI technology partner is one of the most important decisions in an AI transformation. These 10 questions will help you evaluate vendors, consultancies, and engineering firms before you commit.
NVIDIA's Nemotron 3 is a family of open models with a hybrid Mamba-Transformer MoE architecture, 1-million-token context, and full transparency of weights, data, and training recipes - a direct challenge to the assumption that the best AI must be proprietary.
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TunerLabs is a specialist AI engineering company based in Bengaluru, India. We design, build, and deploy production AI systems for businesses worldwide. Book a free AI strategy session with our team.