Insights

Practical AI, no hype

Field notes on building and shipping AI that works — for the people deciding what to build. RSS

Is It Time to Buy Your Own GPU for LLMs? The Honest Hardware Math

Buy, rent or API — the real break-even math on owning a GPU for LLM inference, which cards actually matter, what fits in how much VRAM, and the hidden costs that change the answer.

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How RAG Actually Works: Chunking, Embeddings and Retrieval, Explained Properly

A technical teardown of retrieval-augmented generation — ingestion, chunking strategies, embeddings, vector search, reranking and grounded generation — plus where RAG pipelines actually break.

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Running LLMs on Your Own Hardware: Ollama, llama.cpp and vLLM, Explained

The open-source local LLM stack, demystified — what llama.cpp, Ollama and vLLM each actually do, how quantization works, what fits in your VRAM, and when self-hosting beats an API.

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Why Your LLM App Feels Slow — and the Engineering That Fixes It

The anatomy of LLM latency: time-to-first-token vs tokens per second, KV caching, continuous batching, quantization, semantic caching and model routing — the real levers behind fast AI products.

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How Much Does an AI Chatbot Cost in 2026?

AI chatbot development cost, itemised: off-the-shelf vs custom builds, typical market ranges, monthly running costs, and the hidden line items vendors forget to mention.

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How to Choose an AI Development Company (Without Getting Burned)

A buyer's checklist for choosing an AI development company: the questions that expose weak vendors, red flags in proposals, and how to compare quotes that differ by 5x.

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In-House AI Team vs Agency: The Honest Math

Build an in-house AI team or hire an agency? The real costs, timelines and failure modes of each — and the hybrid pattern most successful companies actually use.

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Why Most AI Projects Fail — and How to Be in the Minority

Most AI projects never reach production — and it's rarely the technology's fault. The real reasons AI initiatives fail, and a practical playbook for being in the minority that succeed.

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Where to Start with AI: A Practical Adoption Roadmap

Ready to adopt AI but not sure where to begin? A no-hype roadmap — how to pick a first use case that matters, run a real pilot, and scale without burning six months.

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Anatomy of a Web Platform: How We Built WeddingBestie

A look under the hood of a real platform we built. WeddingBestie looks like a wedding website — but it's a working platform, and the difference is exactly where the value lives.

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Computer Vision for Business: What's Actually Possible Today

Computer vision lets software understand images and video. A practical tour of real business use cases, what accuracy to expect, what data you need, and how to build it.

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From Idea to Production: The Lifecycle of a Custom AI Project

What actually happens between 'we have an AI idea' and 'it's live and reliable'? A stage-by-stage walkthrough of a custom AI project — so you know what to expect and what to ask.

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AI Agents Explained: What They Actually Are and When Your Business Needs One

AI agents are the most hyped — and most misunderstood — idea in AI right now. A plain-English guide to what they are, where they shine, where they fail, and how to use them safely.

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Custom AI vs Off-the-Shelf AI: When to Build and When to Buy

Should you build custom AI or buy a ready-made tool? A clear decision framework — with a comparison table, total cost of ownership, and the wrapper trap to avoid.

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Platform vs Website: What's the Difference, and Which Does Your Business Need?

A website tells people about your business. A platform does work for it. Here's how to tell which one you actually need — with costs, timelines and real examples.

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RAG vs Fine-Tuning: Which Do You Actually Need?

RAG and fine-tuning solve different problems. A plain-English decision guide with a comparison matrix, costs, common mistakes — and why most teams should start with RAG.

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What Does It Cost to Build a Custom AI App? (2026 Guide)

Custom AI development cost in 2026: typical market ranges by project type, the drivers that move the number, ongoing costs, and how to spend less without cutting corners.

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