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Training a family of large language models (LLMs) has always come with a painful multiplier: every model variant in the family—whether 8B, 30B, or 70B—typically requires its own full training run, its own storage, and its own deployment stack. For a dev team running inference at scale, this means multiplying compute costs by the number […]
OpenAI has launched a Codex Chrome extension for Mac and PC to streamline browser-based workflows that were previously difficult to handle via APIs or plugins. This release follows a trend where most users preferred working in a browser after the launch of “Computer Use,” allowing Codex to operate more effectively across various web-based tasks. What
Feature engineering is the foundation of strong machine learning systems, but the traditional process is often manual, time-consuming, and dependent on domain expertise. While effective, it can miss deeper signals hidden in unstructured data such as text, logs, and user interactions. Large Language Models change this by helping machines understand language, extract meaning, and generate
Feature Engineering with LLMs: Techniques & Python Examples Read More »
Building AI systems in healthcare isn’t just a technical challenge. It’s a regulatory one. In most industries, data pipelines focus on: Scalability Performance Cost In US healthcare, everything revolves around: Compliance Privacy Traceability If your AI pipeline mishandles patient data, it’s not just a bug, it’s a legal risk. This is where ADLC (AI-driven software
AI Data Pipelines for US Healthcare: HIPAA, PHI Handling and Audit Logs Explained Read More »
Gabriele Farina grew up in a small town in a hilly winemaking region of northern Italy. Neither of his parents had college degrees, and although both were convinced they “didn’t understand math,” Farina says, they bought him the technical books he wanted and didn’t discourage him from attending the science-oriented, rather than the classical, high
Games people — and machines — play: Untangling strategic reasoning to advance AI Read More »
Most ML projects do not fail because of model choice. They fail in the messy middle: finding the right dataset, checking usability, writing training code, fixing errors, reading logs, debugging weak results, evaluating outputs, and packaging the model for others. This is where ML Intern fits. It is not just AutoML for model selection and
ML Intern in Practice: From Prompt to a Shipped Hugging Face Model Read More »
The human brain remains one of the most fascinating and perplexing mysteries in medicine. Scientists still struggle to match neurological activity with brain function and detect problems early, slowing efforts to treat neurological disorders and other diseases.Beacon Biosignals is working to make sense of the brain by monitoring its activity while people sleep. The company,
Beacon Biosignals is mapping the brain during sleep Read More »
Agentic artificial intelligence (AI) is set to fundamentally reshape the structure of enterprise work and commerce. Rather than simply responding to instructions, these agents actively participate in workflows by planning tasks, creating and using tools, correcting their own errors, and pursuing multistep goals autonomously. The result is faster, more adaptive workflows. The emergence of the
In today’s hospitals and clinics, a dermatologist may use an artificial intelligence model for classifying skin lesions to assess if the lesion is at risk of developing into a cancer or if it is benign. But if the model is biased toward certain skin tones, it could fail to identify a high-risk patient.Perhaps one of
Solving the “Whac-a-mole dilemma”: A smarter way to debias AI vision models Read More »
There can be some practical constraints when it comes to deploying the AI models for retail environments. Retail environments can include store-level systems, edge devices, and budget conscious setup, especially for small to medium-sized retail companies. One such major use case is demand forecasting for inventory management or shelf optimization. It requires the deployed model
Compressing LSTM Models for Retail Edge Deployment: A Practical Comparison Read More »
