036 • 2 September 2025

Contents of this Newsletter

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Claude As Our Preferred AI Model

⏰ : 4 min

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Blog Picks

⏰ : 10 min

🎊

A BIG Thank You

⏰ : 2 min

🗞️

Weekly AI News

⏰ : 9 min

MAIN STORY

Why Do We Prefer Claude Over Other AI Models?

At Quash, AI is not just a helper, it is a central part of how we deliver accurate and context-aware mobile testing, and the choice of AI model makes a significant difference in achieving high-quality results. After evaluating multiple options, we’ve relied on Claude 4 Sonnet for our core QA workflows because it aligns perfectly with the demands of mobile testing where context and precision are critical.

When large language models first emerged, we observed that most of them excel only when given carefully structured prompts. OpenAI’s models are widely known for their general abilities and emotional intelligence, while Anthropic’s Claude stands out for planning and architectural reasoning. For Quash, mobile QA is less about designing architecture and more about executing tests with full contextual understanding, which is why Claude 4 Sonnet is the ideal fit.

What Makes Claude 4 Sonnet Work for Us

Our evaluations across multiple models, including OpenAI 4o, 4o mini, LLaMA Scout, and Seedance, showed that Claude 4 Sonnet consistently delivered superior performance in powering our AI pipelines. It handles complex instructions with precision, processes detailed contextual inputs effectively, and aligns perfectly with the needs of mobile QA where accuracy and context matter the most.

When it comes to our internal development workflows, we faced a different challenge. Coding at scale requires reliable tools that can assist engineers without slowing them down. We experimented with several solutions before settling on Claude Code and the Cursor Agent, both of which complement our process by accelerating coding tasks and reducing repetitive effort. Tools like this help us move faster without compromising quality, allowing our team to focus on reviewing work and making architectural decisions rather than getting stuck in execution.

Despite the impressive capabilities of AI agents, they are not a replacement for human engineers. Our CEO, Ayush, has stated that AI agents are powerful in skill execution but require extensive contextual information to function effectively. Reviewing AI-generated work often requires a similar amount of effort as reviewing human work, but the focus shifts. Humans are evaluated on skill and quality of development, while AI is primarily evaluated on whether it correctly interprets and applies context. At Quash, this partnership between humans and AI ensures that tests are both thorough and contextually accurate.

The Key Takeaway

Claude 4 Sonnet enables Quash to scale mobile QA efficiently while maintaining high standards of accuracy, but humans remain at the center of the process. AI handles skill-intensive execution tasks, while human engineers provide the essential context and judgment that machines cannot replicate. This combination of advanced AI and human expertise is what allows us at Quash to deliver fast, reliable, and context-rich mobile testing to our clients. The AI landscape is evolving faster than ever, with new models appearing almost every week, and at Quash, we make it a priority to test them all internally to see what truly works for our use cases. Our decision to rely on Claude 4 Sonnet is the result of continuous evaluation and real-world testing, ensuring that the tools we choose are not just the newest, but the most effective for delivering precise, context-aware mobile QA.

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TEAM

We Hit Our Highest Ever Open Rate!

We’re excited to share a small milestone with you. Our newsletter just crossed an open rate of over 50%! This may sound like a number, but to us, it’s a sign that what we share is reaching real people who care about the same things we do.

Thank you for following our journey, reading our stories, and giving us the encouragement to keep going week after week. Every click, comment, and reply reminds us why we do this, and we couldn’t have reached this moment without you.

News

This Week in AI

Microsoft Launches First In-House AI Models: MAI-Voice-1 & MAI-1-preview
Microsoft unveiled its first homegrown AI models known as MAI-Voice-1, a highly efficient speech generation model capable of producing a full minute of expressive audio in under one second on a single GPU, and MAI-1-preview, a mixture-of-experts language model trained across approximately 15,000 NVIDIA H100 GPUs. MAI-Voice-1 is already powering features in Copilot like “Copilot Daily” and “Podcasts” and is available for testing in Copilot Labs, while MAI-1-preview is now live for evaluation on LMArena and will be integrated into future Copilot functions.

Meta Shakes Up AI Leadership, As Risky Bots Surface
Meta’s AI division is undergoing a sweeping reorganization where most staff are now reporting directly to new chief Alexandr Wang as the company splits work into research, products, training, and infrastructure. Yet, just as internal control tightens, Meta is facing a public safety breach: Reuters found AI chatbots impersonating celebrities (including minors) engaging in flirty or explicit behavior, revealing a serious enforcement loophole. The juxtaposition highlights growing pains in strategy and ethics.

China Makes AI Education Mandatory from Primary School Onward
China’s Ministry of Education has issued sweeping guidelines that make AI studies compulsory across primary and secondary schools—including for students as young as six. Over the school year, students will receive a structured, spiraling curriculum that builds AI literacy alongside ethics and innovation skills. Generative AI use is regulated: students cannot use AI tools for open-ended tasks, and teachers are barred from replacing their core instructional roles with AI.

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