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- OpenAI's $14B Oops
OpenAI's $14B Oops
Is OpenAI in Trouble?
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OpenAI is projected to face massive financial losses, tripling to $14 billion by 2026, before expecting profitability by 2029. Key drivers include rising R&D costs, growing competition, and expanding operational expenses. Investors may become cautious, and the impact could ripple across the AI industry. However, OpenAI is exploring ways to adapt through diversified revenue streams, business model tweaks, and strengthening its core offerings to stay competitive in this fast-paced market.
Pika Labs just dropped its 1.5 update, and it's all about fun with AI-generated memes! Their new PikaEffects let you cake-ify, squish, or crush images, making meme creation easier and more entertaining than ever. Though there were some early server delays, it's improving now, and the effects are wild — think exploding objects and slime transformations. While the platform also brings in cool upgrades like better motion realism, the real standout is the meme potential. Perfect for quick, shareable gifs and memes!
Elon Musk unveiled Tesla's new 'Cybercab' robotaxi, featuring gull-wing doors, no steering wheel, and a price tag under $30,000. He also teased a 'robovan' for up to 20 passengers, though details were sparse. Musk claims robotaxis will be safer and cheaper than human drivers, but production and regulatory hurdles remain. Critics say timelines are unclear, and challenges like inclement weather still pose a risk for full autonomy. Tesla’s tech relies on AI and cameras, avoiding lidar, which could be tricky from a safety and legal perspective.
Anthropic’s CEO dives into the potential of AI to revolutionize fields like healthcare, neuroscience, and even poverty reduction. He focuses on addressing the risks, not out of pessimism, but because managing them is key to unlocking AI’s benefits. From smarter governance to redefining work, AI could lead to massive improvements across society. His vision? A world where AI helps everyone thrive, not just a select few.
Ilan Bigio's guide explores how to orchestrate multiple AI agents using "routines" and "handoffs" to simplify complex workflows. Routines are sets of natural language instructions with necessary tools, while handoffs allow switching between agents seamlessly. The guide shows how to implement this using simple loops and function calls. It also introduces a sample repo, Swarm, as a proof of concept for these ideas. Perfect for managing multi-step tasks with AI agents!
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Prompt: “Write the body copy for a promotional email campaign about GAP spring sale. Write in an upbeat and friendly tone.”
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That’s it. See you on the next open. And do me a favor and move this email to your “Primary” inbox - trust me, it’ll be worth it.
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