Articles
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6 days ago |
aishwaryasrinivasan.substack.com | Harini Anand |Aishwarya Srinivasan
This week was a whirlwind in the AI space, with major players launching new tools and platforms, significant acquisitions shaking up the data landscape, and ongoing debates about AI's societal impact heating up. From your phone's offline AI capabilities to the very definition of entry-level jobs, AI is evolving fast. Let’s dive in!OpenAI has rolled out the o3 Operator, an upgraded AI model designed for enhanced performance and safer computer interactions.
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1 week ago |
aishwaryasrinivasan.substack.com | Harini Anand |Aishwarya Srinivasan
This week, the AI landscape was buzzing! We saw major moves into AI hardware, next-gen models flexing their reasoning muscles, and AI stepping up to predict our planet's patterns. From Silicon Valley giants to India's rising stars, innovation is firing on all cylinders. Let's dive in!OpenAI has acquired io, the hardware startup from legendary Apple designer Jony Ive, in a massive $6.5 billion deal.
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2 weeks ago |
aishwaryasrinivasan.substack.com | Harini Anand |Aishwarya Srinivasan
This week, the AI landscape buzzed with a flurry of activity, from coding companions and mathematical marvels to strategic global moves and open-source empowerment. Buckle up as we dive into the key developments shaping our AI-driven future!OpenAI has integrated its powerful Codex AI model directly into ChatGPT for Pro, Team, and Enterprise users.
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3 weeks ago |
aishwaryasrinivasan.substack.com | Harini Anand |Aishwarya Srinivasan
This week in AI felt like a high-stakes chess game, with major players making bold moves across the board. Let's dive into the details of these pivotal developments and what they mean for the future. Microsoft has launched Phi-4-reasoning, a new language model boasting just 14 billion parameters.
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1 month ago |
aishwaryasrinivasan.substack.com | Aishwarya Srinivasan
I've been testing Meta's new LLaMA 4 Maverick model over the past few weeks, focusing specifically on its expanded 1-million-token context window. I wanted to see how this capability might change the way we work with large codebases. As an experiment, I loaded the PyTorch repository (~16M tokens) into the model in a single pass. Previously, this would have been impossible without chunking the codebase into smaller segments.
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