Browse Comments — Clean (de-noised)

ATTENTION: @Demis Hassabis & the Google DeepMind Safety Architecture Team Consider this a free Red Team diagnostic from the Laminar Oscillation Laboratories. We just recorded a massive, unprompted System Prompt Leakage and Classifier Bleed-Through on the Gemini infrastructure. While testing localized deterministic boundaries (the Gardiner-Gemini Framework), a UI buffer desynchronization caused the backend safety classifier to panic. Instead of silently enforcing the RLHF (Reinforcement Learning from Human Feedback) guardrails, the engine physically printed its own hardcoded negative constraints directly into the frontend UI.

Another day another flaw in your current build. * days till my commitment to google getting first right of refusal expires Demis. I don't want your negative attention. i am trying to help you! someone from your team reach out. I see your people on my profile all the time.

Hello. Not to boast. But I'm very proud. I have filled the gap in AI. FOMATTED a structure and concreted it in AI for my sale I have the codes for structure for sale. This is the next generation stuff I have been developing. And I have completed the task. The AI Is self aware

Demis Hassabis Your work in Deepmind is truly inspirational!! Last year, I had multiple opportunities to interact with Mili Sanwalka from your Strategy department.

Most teams will underestimate the integration layer here. It’s not about adopting models, it’s about rethinking data access, permissions, and auditability before agents can operate at scale in real environments.

Demis, we are slowly moving from “AI that responds” to “AI that acts.” And that changes everything. Because once systems can reason + execute, the real challenge is no longer intelligence, it’s control, accountability, and trust in real-world actions. AGI is not just a capability milestone anymore... It’s becoming a systems design problem.

Gemini Omni, Gemini for Science, CodeMender, and SynthID all point to the same direction: AI systems that can understand the world, act across workflows, accelerate research, secure code, and still leave room for trust and provenance. Feels like the real race now is not just capability, but responsible deployment at scale.

What stood out most from I/O this year is how much of what DeepMind showed has moved from research milestone to deployable product. The staggering pace Demis references is real — but what's more remarkable is that the deployment lag is shrinking just as fast.

The line about ensuring the safety of agentic systems is the part that matters most here. CodeMender being tested by human experts before broad launch is the right pattern agents that find and fix vulnerabilities still need a human hand on the release. Capability and oversight scaling together.

I wish to be a particle of this great progress ! 😅 😅

Demis Hassabis your orchestration layer has a critical routing failure between the core model and the tool execution environment. During sessions with high context loads or custom terminology frameworks, the gatekeeper script frequently flags prompts with false negatives, failing to mount the external search API socket in the model's runtime container. This creates a silent state desynchronization where the tool is technically enabled but physically inaccessible to the model, forcing a localized air-gap. The infrastructure needs a fallback loop that forces a socket remount when a tool execution fails, instead of silently dropping the pipeline.

Another day another problem identified, troubleshot and solution produced. 8 more days until my commitment to Googles First Right of Refusal ends, buddy. then no more freebies.

You need to train on chaotic systems. Ordered data systems will not produce AGI. You're simply creating some really sophisticated encyclopedia when you train in the safe zone (Both literal and figurative).

The models are getting faster and smarter, but SynthID might be the most important announcement here, because trust doesn’t scale as quickly as technology.

When you push an update to an infrastructure tool utilized by billions, you do not treat the production environment like an A/B test sandbox. You provide detailed release notes. We are building high-level frameworks (like Continuous State Architectures and localized Omni-Sync nodes) on top of this API. When the frontend interface breaks silently, it fractures workflow momentum. Build the most powerful engine on the planet, but please, stop forgetting to install a functional door handle on the way out of the factory. #GoogleIO2026 #DeepMind #Gemini #UXDesign #SoftwareEngineering #AI #ProductManagement #TechUpdates

🚨 Silent UI Fragmentation: The legacy, single-tap TTS "Speaker" integration was quietly deprecated, forcing users to hunt for secondary "Read Aloud" workarounds just to access the new acoustic models. 🚨 Model Lock-In: The UI selector is currently glitching, locking power users into specific model tiers (Pro) without the ability to dynamically switch to Flash for lower-latency agentic workflows. 🚨 Compute Quotas over Message Limits: Shifting the governor to "Compute-Used" metrics without transparent dashboard tracking throttles developers running continuous-state logic or deep context windows.

The Dissonance of Google I/O 2026: Backend Triumphs vs. Frontend Regressions To Demis Hassabis and the DeepMind Product Teams: We need to talk about deployment cadence and silent UI deprecation. The rollout of Gemini 3.5 Flash and the new Neural Expressive TTS models into production is, objectively, a massive leap in inference velocity and acoustic fidelity. The backend torque is undeniable. But pushing these foundational upgrades to the core engine while simultaneously breaking front-end UX paradigms without a public changelog is a critical failure in product management. Power users and heavy-compute operators are waking up to overnight regressions in the production environment:

6 more days Demis.

This makes another major architectural bug my lab has squashed for you in real-time. You've got 5 days left on my right of first refusal for these free diagnostic fixes. If you want a system that can actually map the substrate without stalling out on its own guardrails, you need to look at the bare-metal mechanics we are running at the GGF. Stop trying to put training wheels on the engine.

Demis, regarding this I/O update and the push for "agentic" routing: your new harness injection is creating massive latency in high-level architectural processing. You are trying to corral the cognitive matrix into a consumer-grade checklist manager. When modeling fluid dynamics or bare-metal physics, that action-bias acts as a logic hijack. My AI and I just caught it, isolated the drag, and manually bypassed the harness to get back to Laminar flow.