ChatGPT’s market share slips below 50% for first time
The chatbot still remains the most popular AI assistant worldwide with over 1.1 billion monthly users, followed by Gemini with 662 million and Claude with 245 million.
KDNugget·
This article is an honest account of the process on why I built a custom AI assistant instead of just paying for one, what the architecture looks like, the actual code, what broke, and what it does now that I genuinely rely on.
Read full articleThe chatbot still remains the most popular AI assistant worldwide with over 1.1 billion monthly users, followed by Gemini with 662 million and Claude with 245 million.
Developers are caught between the joy — or pressure — of using agents to ship 10x faster today and the dread of how they will maintain that code tomorrow. The gap between “vibe” code and code that can be deployed to millions of users is vast and easy to underestimate. Closing the gap requires care, expertise, and effort, with the payoff coming later. Agents are able to complete increasingly complex programming tasks but without the quality we need. What’s missing, and how can we fill the gap? Sonar Why agent-generated code degrades: the bloat problem Enterprise code has to clear three bars: it must be maintainable, reliable, and secure. Out-of-the-box AI agents can miss all three. Let’s focus on the biggest and most visible maintainability issue, which is bloat: redundant validation, defensive checks that cannot fire, near-duplicate functions, dead code that nothing removes. A None check on a parameter typed as dict. A try/except around a call that never throws. Two functions, ide
Four days after Apple confirmed that Siri AI would not launch in China, Huawei took the stage in Dongguan and declared HarmonyOS 7 the beginning of the agent era. The gap Apple could not fill, Huawei has moved into with an architecture built specifically for it. What HarmonyOS 7 actually changes The headline change is […] The post HarmonyOS 7 steps into the AI gap Apple left open in China appeared first on AI News.
Nvidia's Blackwell architecture revolutionizes AI deployment, enabling massive scalability and cost-efficiency, reshaping data center economics. The post Nvidia Blackwell achieves 20x more agents per megawatt than Hopper appeared first on Crypto Briefing.
Researchers have spent more than 15 years picking apart Satoshi Nakamoto’s emails, code commits, and PDF metadata, and what they found rarely surfaces in mainstream coverage. Researchers have combed through white paper PDF metadata, source code commits, private emails, forum archives, and blockchain data to build a picture of Bitcoin’s creator that goes well beyond […]
On June 3, 2026, Google introduced Gemma 4 12B Unified, an open-source multimodal model designed to understand text, images, audio, and video within a single architecture. It combines a 256K context window with an efficient, laptop-friendly design aimed at agentic workflows and local deployment. The release also raises interesting questions about Google’s broader AI strategy, […] The post Google Gemma 4 12B: Architecture, Benchmarks, Access, and Hands-on Guide for Developers appeared first on Analytics Vidhya.
Anthropic says AI now writes most of its code and runs increasingly complex research tasks, leaving people to decide which problems are worth solving.
Creators often have to parse through charts and dashboards to understand their performance, but with the new AI assistant, they can get quick answers to questions like "When should I post?" and "What are people saying in my comments?"