AI insights, industry analysis, and technical guides
From OpenAI's hardware security partnership to IBM's efficiency breakthrough and a concerning malware discovery in a popular AI training library, the past 48 hours have reshaped the conversation around AI infrastructure, cost, and trust. Here's everything technical decision-makers need to know.
From a landmark study exposing how finetuning unlocks copyrighted content in LLMs to IBM's surprisingly capable 8B model, the past 48 hours have been dense with consequential AI developments. A data-exfiltration incident involving an AI spreadsheet tool rounds out a week that raises urgent questions about safety, efficiency, and trust.
Amazon's AI-powered interview automation raises fresh questions about algorithmic hiring, while the debate over who owns code generated by Claude Code heats up. Plus: ChatGPT's ad strategy and open-source voice AI make waves in the developer community.
Microsoft and OpenAI dissolve their exclusive partnership, China blocks Meta's Manus acquisition, and the industry confronts a growing vendor lock-in crisis. Here's what the past 48 hours mean for the future of AI infrastructure.
Learn how to build a fully functional semantic search engine using free, open-source embedding models and a lightweight vector store — no expensive APIs required. This hands-on tutorial walks you through every step, from encoding documents to querying results in milliseconds.
From a rogue AI agent wiping a production database to NVIDIA and Google slashing inference costs, the past 48 hours have delivered a masterclass in both the promise and peril of modern AI infrastructure. Here is everything technical decision-makers need to know right now.
From real-time content personalisation to AI-assisted production pipelines, media and entertainment organisations are deploying inference at unprecedented scale. Here is what the adoption landscape looks like today and why inference efficiency has become a boardroom priority.
Google is set to pour up to $40 billion into Anthropic, reshaping the AI investment landscape. Meanwhile, new research and developer tools are surfacing critical questions about LLM quality, benchmarking, and how language models represent knowledge internally.
Learn how to compile llama.cpp, download a quantized model, and expose it through a local REST API — all without a GPU. This tutorial walks you through every step so you can run production-grade language model inference on any Linux or macOS machine.