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Get ready to embark on an exciting journey into the realm of AI and discover how machines can learn and make intelligent decisions. Our blog articles are crafted with simplicity and clarity in mind, making complex machine learning concepts easy to understand for everyone. Whether you’re a beginner or an experienced practitioner, we’ve got you covered with informative and insightful content. Explore the fascinating world of algorithms, models, and data as we delve into supervised and unsupervised learning, reinforcement learning, and more. Discover practical applications in various domains like healthcare, finance, and autonomous vehicles. From introductory guides to advanced techniques, we’re here to help you demystify machine learning and unlock its potential. Join us on this journey as we unravel the secrets of machine learning and empower you to build intelligent systems that can analyze data, make predictions, and drive innovation.
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Introduction Running a small language model locally is straightforward. Running one productively — inside a real development workflow, with proper context, tool access, and iteration speed — is a different problem entirely. The gap between “I got a model responding in my terminal” and “I have a local AI setup that actually improves how I …
Today, we’re introducing Gemini 3.5 Transcribe, our most precise speech-to-text model yet, designed for intelligent voice interactions. Unlike conventional speech recognition models that struggle with background noise, complex jargon, and disfluency cleanup, Gemini 3.5 Transcribe converts raw audio directly into accurate, polished, formatted text. Across our products like the Gemini app and on Android, we’ve …
When RAG retrieval disappoints, the advice AI engineers hear today is almost always “add a reranker”. Ask why a reranker works, and the answer usually stays at the architecture level: it is a cross-encoder, it applies attention over the query and the passage together, it is fine-tuned on relevance labels. All of that is true, …
Introduction Most developers see Python dataclasses as a shortcut for avoiding repetitive dunder methods like __init__ and __repr__. So at first look, it seems like a simple way to write less code and move faster. In practice, dataclasses are designed to reduce boilerplate in data-focused classes while keeping behavior clear and under your control. Instead …
addresses the fundamental challenge of slow, sequential token generation in language model inference. DFlash speculative decoding support for CPUs was recently enabled in vLLM v0.25.0. In our testing with Qwen3.5-9B on an r8i AWS instance, powered by Intel® Xeon® 6 processors with Performance-cores, DFlash increased average token generation throughput to 3.92x that of the autoregressive …
Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash Read More »
Muse Glimmer is gaining attention in the local AI community and is being compared with Qwen’s 27B-class models. In many cases, it is performing better, especially for local coding and agentic workflows. Meta looks strong in the open-model space, and with a few more iterations, models like this could start competing closely with proprietary systems. …
Run Muse Glimmer for Local Vibe Coding with llama.cpp, DFlash, and Pi Read More »
This post was co-authored with Max Silfverberg (Data Scientist, AI Solutions Lead), Antti Hallavo (Lead AI Software Engineer), and Pontus Huotari (Lead Data Scientist). We work at Alma Media, a Finnish digital services, marketplaces and media company. One of our focus areas is developing AI/ML solutions for real estate listing services, where understanding image content plays an important role. services handle hundreds of thousands of listings a year. Most of those come with …
Why We Fine-Tuned SigLip (And Why That’s Not Always the Right Call) Read More »
Agentic AI has officially moved from the research lab into enterprise production. In 2026, the AI narrative has shifted dramatically from conversational chatbots — systems that wait for human prompts to generate text — to autonomous AI agents. These systems can plan, execute, and adapt multi-step tasks across external tools, databases, and APIs without …
5 Real-World Use Cases for AI Agents Transforming Industries Read More »
? Let me provide a real, personal example. We fine-tuned a 7B parameter model which completely blows foundation models out of the water, but just for this very narrow subtask: Filling out synoptic reporting templates for breast cancer. This is a hellishly difficult task with complex input formats, branching logic, and fields that must appear in …
Building an agent that works in a notebook takes an afternoon. Getting that same agent to survive real traffic, recover from a crash at 3 am, and not leak someone else’s data while it runs large language model (LLM)-generated code is a different job entirely, and it’s the job most teams underestimate. Only a …
5 Tools for Building and Deploying AI Agents in Production Read More »
