Snapchat makes its first open prompt AI Lens available for free in the US

In this Newsletter Today:

• Snapchat makes its first open prompt AI Lens available for free in the US

• NeoLogic: Cutting Power Use by 50% with AI CPUs

• Apple dismisses Elon Musk’s claims that App Store favors OpenAI over other AI apps

• AI Tutorial

• 5 Best AI Tools

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Snapchat makes its first open prompt AI Lens available for free in the US

Snapchat has launched a new AI Lens that lets users generate and edit images using open text prompts. This marks the app’s first “open-prompt” image-generation Lens. It is currently available for free to users in the U.S. (or in a U.S. rollout) and unlocks a new layer of user creativity on the app.

Key Points:

• The Lens, known as the “Imagine Lens,” lets users type prompts like “turn me into a four or five-panel comic” or “make me a funny caricature” to create images.

• It is the first time Snapchat has offered this kind of open prompt generative image feature embedded in its Lenses.

• Users can share the resulting images with friends, post to their Story, or save them outside the app.

• Now, these generative features are available to more users. Before, they were only for paid subscribers.

Why it matters:

• Generative AI is becoming common in social apps. Now, everyday users can create images easily, without needing extra apps.

• Snapchat is making this feature free in the U.S. This sets a new standard for other platforms. So, expect more competition in AI-driven content creation.

• As users make more custom content, questions will come up about copyright, moderation, and using AI-generated images for social sharing.

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OpenAI’s Atlas is more about ChatGPT than the web

OpenAI has launched ChatGPT Atlas. This new web browser centres on its main chatbot, ChatGPT. Atlas isn’t just about enhancing web browsing. It focuses on making ChatGPT the main way you access, search, and interact with the internet.

Key Points:

• The browser has a special ChatGPT sidebar. It understands the content of any website you visit. You can ask follow-up questions or interact without leaving the page.

• Atlas has a feature called Browser Memories. If you opt in, ChatGPT remembers your browsing context and history. This helps it give you more personalised answers. It shows a closer link between ChatGPT and your daily web use.

• The analysis shows that the browser is missing several traditional “superior browsing” features. These include advanced reading modes, a built-in ad blocker, and VPN support. The focus is clearly on ChatGPT, not on speeding up page loads or making views smoother.

• OpenAI seems focused on owning the "pipes" of distribution for ChatGPT. They want the browser to be the main way to access ChatGPT, not just a link to the web.

Why It Matters:

• If many users switch to Atlas, it gives OpenAI more power over how people access information. This change reduces reliance on traditional search engines and browsers.

• It raises questions about data privacy and control. The browser makes web browsing part of a ChatGPT-focused experience.

• In the browser market, a new competitive front has emerged. Now, the battle goes beyond just rendering speed and extensions. It’s also about AI integration and user behaviour.

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Tensormesh raises $4.5M to squeeze more inference out of AI server loads

Tensormesh, a new startup, has emerged from stealth this week. They secured $4.5 million in seed funding. This money will help them make AI model inference more efficient on current hardware.

Key Points:

• Laude Ventures led the funding round. Angel investors, like database pioneer Michael Franklin, also backed it.

• Tensormesh is developing a commercial version of LMCache. This tool was originally created by co-founder Yihua Cheng. LMCache helps reduce the cost of inference by up to 10× in some cases.

• The core innovation involves improving the “key-value (KV) cache” system used during AI model inference. Traditional systems throw away the KV cache after each query. In contrast, Tensormesh keeps and reuses this cache for better efficiency across queries.

• This is especially helpful for conversational AI and agent workflows. Here, models must reference earlier context, like chat logs and actions. Re-computing everything for each query can be costly.

• Tensormesh sees that many companies find this hard to do on their own. They mention cases where teams used “20 engineers + 3-4 months” to build effective KV cache reuse. In contrast, Tensormesh provides a ready-made solution.

Why It Matters:

• As AI models expand and workloads rise, efficient hardware and infrastructure are crucial cost factors. A 10× improvement can make large deployments much more viable.

• Startups like Tensormesh show a trend. They focus on optimising inference pipelines, not just on creating bigger models. This has become a key competitive area.

• For businesses using big AI systems like chatbots and real-time services, finding ways to save costs and speed up responses is key.

• If commercialization works, Tensormesh could encourage more companies to use AI widely. They won't need huge hardware budgets like the hyperscalers do.

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