AI
Interactive AI experiences are different from traditional RAG pipelines. They exist inside tight user-facing latency boundaries where even a 300–500 ms delay can change how users feel about the system. A chat interface that responds instantly feels intelligent; one that hesitates feels broken. When retrieval sits on the hot
Software
Ask any pet parent about their anxious dog, and they'll give you a novel. Ask an AI assistant the same question without proper context, and you'll get generic advice that could apply to any animal, anywhere. Here's what we learned building Hoomanely's
AI
Introduction On edge devices, every millisecond and every megabyte matters. Audio models must run reliably on low-power CPUs, handle noisy real-world environments, and deliver consistent predictions without draining battery or storage. Traditional deep audio models—large transformers or multi-million-parameter networks—struggle here. They are powerful, but far too heavy for
Software
Imagine this: You're chatting with an AI vet about your anxious rescue dog, Luna. Over hours, you've discussed her separation anxiety, dietary restrictions, medication schedules, and behavioural patterns. Then the next day, you ask a follow-up question about her progress—and the AI responds as if
AI
Introduction Running Python applications on edge devices is fundamentally different from deploying them on servers or developer laptops. These devices - whether embedded compute modules, IoT gateways, or sensor hubs - operate under strict storage ceilings and must deploy updates reliably without exhausting disk space. At Hoomanely, our edge systems
AI
Introduction Most computer vision models begin with a clean, carefully curated dataset. But real-world systems rarely stay that way. They evolve. New environments appear, edge cases emerge, and model failures surface in ways that the original training data could never fully capture. This is especially true when deploying lightweight models
AI
Noise cancellation sounds glamorous - until you try doing it in the real world. Dog sounds in homes are messy. Fans hum. TV dialogue spills into the mic. Steel bowls clink. A dog sniffs, licks, barks, scratches, and eats… all at the same time. A clean dataset of isolated dog
AI
The gap between a generic AI interaction and a truly intelligent one often comes down to context. While most applications treat prompts as static templates, the real power emerges when your system can dynamically construct prompts based on runtime conditions—user history, application state, retrieved data, and environmental factors. This
AI
Retrieval-Augmented Generation (RAG) systems rarely fail loudly. They fail in subtler, stranger ways: a support answer sounds different today than yesterday; a troubleshooting step the assistant always got right suddenly drifts; or the bot becomes strangely confident about something no one remembers teaching it. When you dig deeper, the underlying
AI
Introduction Choosing the right language model architecture for production is one of the most underrated engineering decisions teams make. Behind every AI feature - from a chatbot response to a search ranking - sits a strategic decision: Should we use an encoder-only model? A decoder-only model? Or a full encoder–