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Most of the companies that have fully committed to building AI models are gobbling up every Nvidia AI accelerator they can get, but Google has taken a different approach. Most of its cloud AI infrastructure is based on its line of custom Tensor processing units (TPUs). After announcing the seventh-gen Ironwood TPU in 2025, the […]
Google unveils two new TPUs designed for the “agentic era” Read More »
How to make multiple agents work together like an elite team — autonomously dividing tasks, communicating efficiently, and collaborating seamlessly? The openJiuwen community released the latest version of JiuwenClaw, which adds support for AgentTeam — a multi-agent collaborative capability. It proposes that the next leap beyond Harness Engineering is Coordination Engineering. In in-depth tests, this team
Next Leap to Harness Engineering: JiuwenClaw Pioneers ‘Coordination Engineering’ Read More »
Turmoil has followed the launch of Claude’s new model. Opus 4.7, the younger sibling of Anthropic’s revolutionary Mythos, is the recent attempt by the company to go public with some of the capabilities of Mythos. Better agentic workflows, better memory, and better real-world tasks than the outgoing model, i.e., the Opus 4.6. That is what
Moonshot AI, the Chinese AI lab behind the Kimi assistant, today open-sourced Kimi K2.6 — a native multimodal agentic model that pushes the boundaries of what an AI system can do when left to run autonomously on hard software engineering problems. The release targets practical deployment scenarios: long-running coding agents, front-end generation from natural language,
Music streaming services like Spotify and YouTube Music have become the primary way people listen to music, which can be a lot more convenient than buying individual albums like we used to do. However, this also makes it easier for AI-created tracks to worm their way into your playlists. Most streamers don’t go out of
Deezer says 44% of new music uploads are AI-generated, most streams are fraudulent Read More »
Error tracking has evolved far beyond catching stack traces after something breaks. In modern software teams, the best error tracking tools for developers help identify crashes in real time, group similar issues intelligently, surface rich debugging context, connect failures to code changes, and reduce the time between detection and resolution. That matters even more now
Why it matters: Living with AI in everyday life. See how artificial intelligence powers apps, healthcare, society, and more.
Living With AI: How Artificial Intelligence Is Changing Everyday Life Read More »
Developers use Claude Code as an enhanced autocomplete system. They open a file, type a prompt, and hope for the best. The system produces decent output which sometimes reaches great quality. The output exhibits inconsistent results. The system loses track of context and repeats its initial errors. The solution needs a more organized project, not
How to Structure a Claude Code Project that Thinks Like an Engineer Read More »
Imagine asking your AI model, “What’s the weather in Tokyo right now?” and instead of hallucinating an answer, it calls your actual Python function, fetches live data, and responds correctly. That’s how empowering the tool call functions in the Gemma 4 from Google are. A truly exciting addition to open-weight AI: this function calling is
In this tutorial, we explore how to run OpenAI’s open-weight GPT-OSS models in Google Colab with a strong focus on their technical behavior, deployment requirements, and practical inference workflows. We begin by setting up the exact dependencies needed for Transformers-based execution, verifying GPU availability, and loading openai/gpt-oss-20b with the correct configuration using native MXFP4 quantization,
