Hey there,

AI is improving from single-use tools to full stacks. Perplexity included blending live search, deep research, and frontier models.

The question now is, how will you use that to your advantage without losing control?

Perplexity

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Perplexity is an AI-powered answer engine that blends live web search with top models to give fast and updated answers with credible sources. Use it to explore topics, summarize content, write code, and handle complex questions.

Best for

• Users who need reliable, current answers for research, planning, and daily decisions.

• Teams that want an AI “partner” to consolidate tasks and reduce tool-switching.

How to use it

• Search & Research: Ask questions, get quick cited answers, and generate structured multisource reports in minutes.​

• Build & Create: Use Labs to turn ideas into dashboards, apps, and long file documents powered by advanced models.​

• Automate in Browser: Use Comet to let the AI execute tasks, navigate pages, and work across your tabs with ease.

When not to use it
For critical legal, medical, or compliance decisions without expert human review.

Pro tip
Create a shared ‘Perplexity prompts’ library for recurring tasks like reports, memos, and briefs so your team gets consistent, high quality outputs faster.

💼 Perplexity Locks In $750M Microsoft Cloud Deal for Frontier AI Scale

Perplexity contracted a three-year, $750 million deal with Microsoft to run on Azure via the Foundry program, unlocking access to frontier models from OpenAI, Anthropic, & xAI while keeping AWS as its primary cloud.

Read the full article here » 

Key Takeaways:

• 🏗️ What It Does: Establishes a long-term Azure foundation so Perplexity can run a broad range of advanced AI models via Microsoft’s Foundry.

• 🧠 Model Sourcing: Secures streamlined access to frontier systems from OpenAI, Anthropic, and xAI under one strategic agreement.

• ⚙️ Cloud Strategy: Adding Microsoft alongside AWS, creating a dual cloud setup for greater scalability, redundancy, and deployment.

• 🤝 Strategic Signal: Positions Perplexity as a key AI partner for Microsoft in the competitive race for high quality AI search platforms.

🧠 OIST Self-Talk AI: Learns Faster with Inner Mumbling & Memory

OIST researchers unlocked faster, smarter AI via internal "mumbling" self-talk paired with working memory. Models now generalize across tasks, multitask efficiently, and adapt to new challenges using few data.

Read the full article here » 

Key Takeaways:

• 🗣️ Self-Talk Boost: The internal mumbling program organizes its thoughts, weighs options, and improves adaptation during training.

• 🧩 Working Memory Slots: Multiple slots hold/manipulate info for sequence reversal, pattern recreation, and multi-step challenges.

• 🔥 Sparse Data Wins: This generalizes with less training data, outperforming strict memory based systems on unfamiliar & new tasks.

• 🌍 Real World Path: Targets noisy environments for robots in households/agriculture, blending neuroscience with ML.

🏭 Noise Works Like a Tiny Fridge, Gently Cooling Quantum Computers

Scientists at Chalmers University of Technology have built a tiny “quantum refrigerator” that uses controlled microwave noise to cool quantum circuits instead of fighting it. By steering heat on-chip at a very minimized scale, it could make future quantum computers more stable and easier to expand.

Read the full article here »

Key Takeaways:

• 🔧Noise as a Tool: The build uses injected microwave noise to guide and control heat flow inside a tiny superconducting quantum circuit.

• 🧊 Inside the Device: Eases a core quantum hurdle by cooling chips directly on device, where standard refrigeration methods often struggle.

• 🔗 Inside the Circuit: Connects AI “molecule” to two microwave channels adding a narrow band noise as a fridge, heat engine, or amplifier. 

• 🌐 Bigger Picture: Lifts Brownian-style refrigeration from theory into hardware, enabling more stable and scalable quantum computers.

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Until next time,