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- Tinder to use AI analyse your photos
Tinder to use AI analyse your photos
Plus: Anthropic's free guide to build efficient agent
Poonam Soni
November 07, 2025
In partnership with
Welcome to AI Toast!
Here’s what’s on the menu today:
Tinder’s AI Chemistry
Amazon’s AI Kindle Translate
Google Maps just got smarter
Superhero tools (learning)
Building more efficient agents with MCPs
Quick Toasts: Apple’s next Siri will quietly run on Google’s Gemini
Total read time: About 5 minutes, perfect for a quick coffee break.
Image Source: Mashable
Tinder is testing Chemistry, an AI-powered matching flow. It asks questions and, optionally, reads your Camera Roll to infer interests.
Daily Drops: Tap the diamond icon to get curated match recommendations personalized from your profile, answers, and optional photo insights.
Insights Hub: See and delete the AI-generated tags that influence your matches. Total control, opt-in for photo tagging.
Rollout + Timing: Piloting in New Zealand and Australia now; Match says it’s a major 2026 pillar.
Privacy Trade-off: Better matching if you allow full photo access, but optional. Data usage clarified in their policy and help center.
Business Context: Tinder’s paying users and revenue dipped; AI nudges for offensive messages and photo picking already live to boost engagement.
Tinder is betting AI can revive discovery. The product is optional, and this works best for hobby signaling like hiking pics, travel, pets where shared interests are so obvious.
Personal Take: Interest-mapping will become table stakes. The apps that win won’t just “see” your photos; they’ll explain why a match was chosen, let you edit the logic, and keep it lightweight. If Tinder nails explainability and consent-first design, AI matching could finally feel human and less swipe fatigue.
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Image Source: Amazon Blog
Amazon just dropped an AI translation tool for self‑published authors. Which is free for now. It targets the biggest gap: most Kindle books exist in one language. This could change discovery and earnings.
Availability: Limited beta for KDP authors, managed directly in the KDP portal.
Languages: English ↔ Spanish and German → English at launch; more languages promised.
Cost: Free initially; authors set list prices per translation.
Quality: Automatically evaluated for accuracy; authors can preview; clearly labeled as Kindle Translate.
Distribution: Eligible for KDP Select and Kindle Unlimited; readers get samples to test translations.
Personal take: Less than 5% of Amazon titles are multilingual. Opening that bottleneck can revive backlists and expand global reach. But fiction nuance is fragile. Expect debates on accuracy and voice, especially in literary work. If Amazon nails quality assurance and adds community review loops, this becomes a flywheel for global publishing and not a gimmick.
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Image Source: Google Blog
Gemini-powered Maps in India are adding hands-free AI assistance, smarter navigation and India-specific road safety features designed for how people actually travel across the country.
Gemini AI is now built into Google Maps, offering voice-enabled assistance, contextual suggestions while navigating and quick info on nearby places of interest.
This wasn’t a plug-and-play rollout. Google says it required deep localization to match how Indians speak, search, navigate and identify places
At launch, Gemini in Maps will support nine Indian languages across Android and iOS, rolling out in the coming weeks.
New India-first safety alerts are being added to navigation. Drivers will get visual + audio warnings when passing accident-prone zones.
Google is working with local authorities to deploy these alerts, starting with Gurugram, Hyderabad, Chandigarh and Faridabad on Android first.
Personal Take: This isn’t “Maps with AI sprinkled on top” but it’s Google rebuilding the navigation experience to fit the reality of Indian roads, languages and travel behavior. If done right, this could evolve into AI that adapts to how people move and communicate. India might just become the blueprint for hyper-localized AI navigation worldwide.
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Superhero Tools (Learning)
ChatGPT Study Mode: Step-by-step guidance to learn concepts and solve problems interactively.
Grammarly: AI writing assistant that fixes grammar, clarity, and tone across your apps.
Perplexity: AI search that answers with citations and helps you explore topics faster.
Notion: Flexible workspace to capture notes, organize ideas, and find anything quickly.
Socratic by Google: Snap a question to get explanations and resources for homework.
AI agents connect to many tools through MCP. Most clients load huge tool lists into the model and push big results through the context window. That wastes tokens, slows replies, and causes copy‑paste errors. Think thousands of tools and giant documents clogging the model’s brain.
The fix is simple and clever. Treat MCP servers like code APIs. Let the agent write code that calls tools, filters data, and only returns what’s needed. Load tool definitions on demand by exploring a filesystem of tool files. This cuts token use dramatically. The data shows a drop from around 150,000 tokens to roughly 2,000. That’s real money and speed.
Why it matters? Code gives clean control flow: loops, conditionals, retries, error handling. The agent can join data across sources and aggregate without dumping everything into context. It preserves privacy too. Sensitive fields can be tokenized in the execution environment so the model never sees raw PII. You choose exactly what gets logged or returned.
It also adds state. With filesystem access, agents can save progress, resume work, and build reusable skills. A working script today becomes a faster building block tomorrow. Over time, your agent’s toolbox compounds.
There are trade‑offs. Code execution needs a secure sandbox, resource limits, and monitoring. That adds ops work. But the benefits are lower latency, fewer mistakes, lean context, cleaner composition across many tools, make it a practical path for real-world agents.
This guide takes classic software engineering habits and applies them to AI agents, so they think less in tokens and work more in code.
If you are looking to build your own agents, this is the way to go. Guide link
Quick AI News Bites
Apple’s next Siri will quietly run on Google’s Gemini, with Apple processing personal data on its own servers for privacy. The system uses a planner, knowledge search, and summarizer to finally move past “I found this on the web.” Expect rollout with iOS 26.4 in spring 2026.
Amazon says Perplexity’s Comet agent accessed customer accounts and masked bot activity as human browsing. Perplexity calls the complaint bullying and a threat to user choice, reflecting a bigger fight over AI agents on the web. Case filed in Northern District of California.
Perplexity will pay Snapchat $400M to embed its AI search in Snapchat’s My AI, reaching over 940M users. Integration lands early next year; Snap recognizes revenue starting in 2026. Big distribution for Perplexity’s engine inside consumer chat.
OpenAI’s Sora hit about 470,000 Android installs on launch day across seven markets. It’s far bigger than iOS day one, helped by dropping invites in top regions. The app’s AI video creation and Cameos keep it high in chart ranks.
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Cheers,
— Poonam Soni