I built 5 Gemini Gems that stop me repeating myself to AI — here’s how to make them
THE SO WHAT
Reusable Gemini Gems are an early proof that users will invest in configuring task-specific agents once and then expect that behavior everywhere. If you’re shipping AI features, assume a future where users bring their own prompts/agents—design for import, sharing, and composition, not one-off chats.
READ THE SOURCE
MORE FROM THE WIRE
Applied AIIntroducing Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber
Google is segmenting its model line by latency, cost, and security specialization—3.6 Flash for speed, Flash-Lite for ultra-cheap, Flash Cyber for vuln-hunting. If you’re standardizing on Gemini, you now have a clearer menu to right-size models per workflow instead of overpaying for a single default.
Applied AICan AI Ruin Something as Innocent as Birdwatching?
Even birdwatching is now a battleground between assisted and “pure” experiences—AI IDs vs. human observation. For consumer products, expect a split market: one segment wants AI everywhere, another will pay to opt out.
Applied AIGoogle launches Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber, and says it has started its "most ambitious pre-training run yet" for Gemini 4
Google is pushing a barbell strategy—cheap, fast Flash variants for mass deployment now, while quietly spinning up a huge Gemini 4 pre-train for the next capability jump. If you’re building on Gemini, optimize around Flash economics this year but keep your high-stakes roadmap modular enough to swap in Gemini 4 without a full rewrite.
Google doubles down on cheaper, faster AI — but says Gemini 3.5 Pro still isn't ready
Google is prioritizing cost and latency with Flash and Lite while the higher-end 3.5 Pro slips—suggesting near-term demand is skewed toward “good enough” at scale rather than frontier performance. If you’re cost-sensitive, this is a green light to design around mid-tier models and bank the unit economics now.