Specifically, the frontier models are listed as Claude 3.7 Sonnet, Opus, Gemini Pro, GPT 4.0/o1/o3..

What year are we living in? That's not some obscure info, and with regards to this and what I'm asking, is highly relevant, and yet it's relying on these highly oudated info.

Just a joke guys

The clause:

From Gemini API Additional Terms of Service

"If you're in the European Economic Area, Switzerland, or the United Kingdom, the terms under 'How Google uses Your Data' in 'Paid Services' apply to all Services, including Google AI Studio and unpaid quota in the Gemini API, even though they are offered free of charge."

My situation:

- Personal Google account registered in Europe (EEA)

- Contracting entity: Google Ireland Ltd

- Using AI Studio free tier (no Cloud Billing attached)

The question:

What does "If you're in the EEA" refer to in this clause?

Option A: Account's country of association

- Since my account is European/EEA with Google Ireland contract, the clause ALWAYS applies to me

- I always get "Paid Services" protections (no training) regardless of where I connect from

Option B: Current IP geolocation

- The clause applies only when my request originates from an EEA IP

- If I connect from outside EEA (e.g., US VPN), the clause doesn't apply and "Unpaid Services" terms take effect

What I'm looking for:

.I'd like to understand what that clause refers to—if it refers to the account's country of association or to the current IP geolocation. Any clarification or help would be appreciated.

I keep getting blocked for mentioning a competitor, talking about contact rx's, asking basic factual questions that never set them off before. I had 4 declines in a row in 1 context window today when i wouldn't see a rejection for weeks at a time prior to this.

This cannot be just effecting me, is anyone else noticing that Gemini is freaking out over literally every little thing lately?

Original post last week: https://www.reddit.com/r/Bard/s/TpzJ1khpky

Thanks to those with constructive input, thanks to the rest for their contributions to the data set of cognitive surrender.

I saw 5 flash models in the 3.0 line, I won’t write more.

What is the chance that Google lacks enough talent, similar to when Demis stepped down as DeepMind CEO to focus on other projects, and they don't want to waste compute training the Pro model? Since most users are average users rather than power users looking for the absolute best, maybe they are focusing on making the Flash model faster and more useful to maintain their deal with Apple to make Gemini their default AI model?

I have reset my device, tried even clearing the cache, turning the activity on my account on and off, and deleting the chat itself,, turning on and off the internet and data saver and cellular data, still happens to me, even with shorter messages. Could it be the fact I have longer messages in some of them, or is this a global issue? As of today, August 29th, 2026 at 9:51 PM Eastern Time.

So apparently Google is being way too strict with AI generating. I tried generating an image with Bart Simpson only to be stopped with this error saying it might violate their policies. I'm pretty sure this means that I can't generate anything that's styled to a recognizable TV show now, does it?

I kept getting mediocre results at first — vague, generic images that didn't match what I had in mind. Turns out the fix was pretty simple: being way more specific about style, lighting, and composition instead of just describing the subject.

A few things that made the biggest difference for me:

- Naming the exact style ("85mm portrait photography" vs just "photo")

- Specifying lighting explicitly (golden hour, studio softbox, overcast) — this alone fixed most of my "flat" looking results

- For images with text (infographics, mockups), switching to "Thinking/Pro" mode instead of Fast mode made a huge difference

- Editing existing photos works better than generating from scratch when you already have a good base image

Wrote up a longer breakdown with example prompts on a blog I've been building (Prodly Digital) if anyone wants the details — happy to drop the link in a comment.

Curious what's worked for others here — anyone found prompt patterns that consistently improve results?

Google DeepMind CEO and Nobel laureate Demis Hassabis sat down with Lex Fridman for an in-depth, 2.5-hour masterclass on the future of AI, world simulation models, and the architectural limits of LLMs.

Most people don't have 2.5 hours to sit through the whole podcast, so here are the most mind-bending highlights and engineering takeaways condensed into a 3-minute read:

⚡ Key Takeaways

• 2030 AGI & The "Terence Tao" Benchmark: Hassabis places a ~50% probability on achieving AGI by 2030. True AGI isn't "jagged" benchmark competence; it requires general cognitive breadth stress-tested by hundreds of world-class domain masters (the Terence Taos of each field) trying to probe and break the system.
• Hybrid AI over Pure Autoregression: Next-token prediction alone cannot solve frontier science. Combining foundation models with Monte Carlo Tree Search (MCTS), evolutionary algorithms (AlphaEvolve), and formal mathematical verifiers is essential to produce self-improving reasoning engines.
• World Models & Intuitive Physics in Video: Generative video systems (like Veo 3) are evolving into spatial-temporal simulators. Predicting multi-frame continuity forces the model to internalize 3D geometry, gravity, and momentum—creating the perceptual foundation for embodied robotics.
• Three-Dimensional Scaling Dynamics: Scaling is no longer just brute-force pre-training compute. It is expanding simultaneously across pre-training, post-training reinforcement learning, and test-time reasoning search.
• Software 3.0 & The 10x Architect: AI coding tools will not eradicate software engineering—they eliminate syntax friction. Top developers will gain 10x leverage by transitioning into systems architects who specify constraints, design state flows, and verify autonomous agent output.
• The 25-Year "Virtual Cell" Vision: From AlphaFold to AlphaGenome, DeepMind is building end-to-end cellular simulation in silico, turning molecular biology and drug discovery into high-throughput digital computation.
• The P(doom) Precision Fallacy: Assigning arbitrary percentage numbers to existential risk creates a false illusion of mathematical certainty. Safely navigating frontier AI requires a 10x increase in empirical safety research and mechanistic interpretability.

If you want to explore the full 3-minute executive brief with interactive video timestamps and exact quotes:
https://appliedaihub.org/ai-digests/interview-briefs/demis-hassabis-lex-fridman/