Hey there,

Ever wish you could explain a workflow once and have someone else wire it up across all your tools?

Zapier’s AI Copilot lets you describe the job in plain English, then turns that into working automations, chatbots, and internal tools that plug straight into apps like HubSpot, Slack, and Google Sheets.

Take a moment to see how handing off the busywork like this can free you to focus on decisions instead of clicking through tabs.

Zapier AI Copilot

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Zapier’s AI Copilot lets you describe what you want in plain English, then it builds automations, chatbots, and AI-powered workflows across thousands of apps.

Best for

• Operators and founders who live in HubSpot, Slack, Google Sheets, and similar tools

• Anyone drowning in repetitive, multi-step workflows

How to use it

• Start a Copilot chat and describe a workflow (“When a lead fills this form, score them, add to CRM, then send me a Slack DM”).

• Let Copilot auto-build the Zap, then tweak steps instead of building from scratch.

• Use it to spin up internal tools like simple CRMs, request forms, and intake bots.

When not to use it
If your process is still messy or unclear, map it on paper first. AI is great at building workflows, not deciding what your business rules should be.

Pro tip
Ask Copilot to explain the workflow it built in plain language and share that summary with your team so everyone knows what's automated.

🧠 Hybrid Reasoning Tops Neural Nets, Slashing Data Needs up to 90%

Researchers are blending rule-based logic with neural nets to boost reliability and common sense. In MIT robotics tests, adding symbols cut training from 700,000 examples to 70,000, and 1% still hit 92% accuracy. The unlock pairs fast pattern spotting with explicit rules, trimming errors and making decisions easier to audit across tough tasks.

Read the full article here »

Key Takeaways:

• 📊 Consensus Shift: An AAAI poll found that most researchers doubt pure neural nets reach human-level reasoning without symbolic methods that improve logic and transparency.

• 📐 Math Wins: DeepMind’s AlphaGeometry learned from symbolically generated problems, then solved Olympiad-level geometry reliably, showing that rules curb mistakes and speed checking.

• 🧪 90% Less Data: Jiayuan Mao cut a robot’s training from 700,000 examples to 70,000 using object labels plus symbolic relations, still hitting 99% accuracy targets.

• ♟️ Battle Lines: Richard Sutton says scale wins, Gary Marcus backs hybrids, Yann LeCun calls them incompatible. Stockfish shows a practical mix in today’s top chess engine.

🏭 150,000-Chip ‘Factories’ Bring Cloud-Grade Compute into Your Data Center

Amazon will drop dedicated compute stacks inside customer data centers so teams can build and deploy models on day-one hardware. Systems use customers’ existing space, power, and networks, and run like a private AWS Region. A new Saudi partnership targets up to 150,000 chips, signaling multi-gigawatt scale and stricter sovereignty controls next.

Read the full article here » 

Key Takeaways:

• ⚙️ Private Region Feel: Dedicated racks run like a private AWS Region, with managed services for storage, databases, security, and model tooling.

• 🧠 Top-Shelf Silicon: NVIDIA Grace Blackwell and Vera Rubin platforms, plus Trainium chips, Nitro, and EFA networking, push training and inference at scale.

• 🛡️ Classified-Ready Stack: Built for government workloads across Unclassified through Top Secret, tightening control over where data lives and who touches it.

• 🌍 Saudi Scale Play: A new HUMAIN deal plans a compute zone with up to 150,000 chips, including GB300 GPUs, to meet global demand.

🩺 Hospitals Cut Diagnosis Waits With Smart Tools, but Bias Still Bites

Medical Daily spotlights how hospitals use smart diagnostic tools to flag disease earlier and faster, sometimes shrinking imaging wait times from days to hours. The lift comes from models that scan scans, labs, and charts at scale, while privacy tech like federated learning grows. The next fight is bias, clinical proof, and trust at the bedside.

Read the full article here » 

Key Takeaways:

• 🔬 Earlier Catches: Systems spot lung nodules and diabetic retinopathy sooner, matching specialist reads in studies and pushing treatment windows wider.

• ⏱️ Days to Hours: Hospitals report imaging reviews dropping from days to hours when tools pre-triage scans and flag urgent cases for clinician review.

• 🛡️ Privacy Built In: Federated learning, encryption, and data anonymization let models learn across sites without moving patient records.

• ⚖️ Proof, Not Hype: Regulators want validation and explainability. Teams must track errors, retrain on diverse data, and document decisions for clinicians.

Rapid Fire Resources

Until next time,