Field notes on building and shipping AI that works — for the people deciding what to build. RSS
Everyone quotes '4-bit, ~0.6 GB per billion parameters' as if it were free. It isn't. How GPTQ, AWQ and QAT differ, why outlier channels are the whole problem, what NVFP4 changed, and what actually degrades — long context, multilingual and reasoning, none of which MMLU will show you.
ReadA collections call is a regulated conversation, not a chatbot session. Why RBI's data-localisation rules make self-hosted ASR, LLM and TTS the only viable architecture — plus the latency budget, the Indic-language problem, and the limits the system must never cross.
ReadA 12 GB iPhone does not give you 12 GB for a model. The real memory ceiling, honest tokens-per-second numbers, why the Neural Engine isn't where the gains went, and what still has to go to the cloud.
ReadA 3–9B model on your own hardware can handle most steps of an agent loop faster, cheaper and more privately than a frontier API. Here's how the router pattern works, what small models can and can't do, and how to build one without breaking quality.
ReadBuy, 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.
ReadA technical teardown of retrieval-augmented generation — ingestion, chunking strategies, embeddings, vector search, reranking and grounded generation — plus where RAG pipelines actually break.
ReadThe 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.
ReadThe 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.
ReadAI 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.
ReadA 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.
ReadBuild 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.
ReadMost 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.
ReadReady 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.
ReadA 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.
ReadComputer 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.
ReadWhat 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.
ReadAI 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.
ReadShould 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.
ReadA 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.
ReadRAG 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.
ReadCustom 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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