Technical deep-dives into AI development: model architectures, training approaches, API integrations, and developer tools. Stay current with the rapidly evolving AI development landscape.
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With GPT-5.2-Codex, OpenAI releases a specialized model that understands logical relationships across complete repositories for the first time. It removes previous context limits and allows you to safely refactor entire legacy applications in a single run.
The new Ironwood TPU (7th generation) from Google revolutionizes AI infrastructure with exceptional performance and efficiency. This hardware innovation addresses the growing requirements of complex AI models and offers significant advantages for your development and inference processes.
Azure AI Foundry is now Microsoft Foundry. Microsoft is thus positioning itself more broadly, as Foundry can also manage AI models outside of Azure and Azure models can be used in all environments.
Cursor is an IDE for developers that fully supports AI. The Cursor 2.0 update brings autonomous AI agents for coding and lightning-fast execution using the proprietary AI model Composer.
With Azure AI Foundry, companies can operate modern AI models such as GPT-4.1 in their own Azure region – fully GDPR-compliant and with full control over data residency, security and governance. A step-by-step guide with Azure AI Foundry
With Data Tables, n8n brings directly integrated, extremely high-performance storage to automation workflows – independent of external services such as Google Sheets or traditional databases. The feature offers advantages in terms of speed, flexibility and simple management for team and AI processes.
In this tutorial, we show you how to start automation workflows from n8n or Zapier easily and directly from Google Sheets. It’s easier than many people think.
The popular AI platform Lovable from Sweden makes it possible to quickly create functioning web applications, tools or prototypes without the need for coding skills. Instead of writing code by hand, Lovable helps you turn ideas into interactive projects – from websites and landing pages to tools and dashboards.
Anyone who automates in n8n or Zapier must consider how to set up their workflows efficiently and maintainably. The article shows how classic workflows, agents with tool use and MCP-based workflows differ – clearly, compactly and with an evaluation.
Vector databases store data as high-dimensional vectors (embeddings), enabling semantic search — finding results by meaning, not just keywords.
Bottom Line: If you’re building any AI application that needs to search or retrieve information, you need a vector database.
If you want to create AI applications such as a semantic search, you need a suitable vector database. These make embeddings available at lightning speed and scale better than classic SQL or NoSQL stores. We present popular options from SaaS to open source – including strengths, costs and typical use cases.