iFeeling Daily
Daily curated AI insights you can't miss.
Claude on Google Cloud: Frontier AI for Enterprise Production
Claude on Google Cloud integrates seamlessly with existing GCP infrastructure, offering managed inference, global endpoints, and enterprise security. For teams building AI applications, this means less time on infrastructure and more on features, with compliance and performance built in — a practical path to production at scale.
RL with Verifiable Rewards, Where the Verifier Is a Wet Lab
Lila Sciences treats the wet lab as a verifier in an RL loop, generating 10 trillion experimental tokens to train a single generalist model across biology, chemistry, and materials. The lab becomes a data center, not a product factory — and the counterintuitive result is that breadth across sciences yields better model depth than narrow specialization.
Inkling: A 1T-Parameter Open Multimodal Model with 1M Context
Inkling by Thinking Machines is a 1-trillion-parameter open multimodal model that natively understands text, images, and audio with a 1M context window. Its MoE architecture, short convolution, and MTP drafters enable efficient reasoning across modalities, with day-0 support in major inference engines for scalable deployment.
Vint Cerf on a plan to give AI agents open internet identities
Vint Cerf is advising Innovation Labs on DNSid, a standard that ties AI agents to existing domain names so they can identify themselves across the open internet. Without this, agents stay locked inside proprietary systems and cannot interoperate. The real test will be whether users demand a shared standard, just as they did for TCP/IP.
PARE: A Stateful Benchmark for Proactive AI Assistants
This work exposes a core blind spot in proactive AI assistant research: benchmarks that flatten apps into stateless APIs can't test real-world anticipation and timing. The proposed PARE framework models applications as finite state machines, enabling stateful user simulation and a 143-task benchmark across communication, productivity, and scheduling domains.
2026 M-Trends: Public Sector Must Pivot to Machine-Speed Defense
Mandiant's 2026 M-Trends report reveals a 22-second hand-off from initial access to ransomware, making human-speed triage obsolete. The public sector must pivot to continuous verification, using identity as the new perimeter and agentic defense to detect adversaries at machine speed.
The Harness Is the New Battleground for Enterprise AI Data Trust
The core tension exposed here is that enterprise AI adoption forces customers to hand over their proprietary knowledge—not just pay for compute—because every user query generates a trajectory that can be fed back into the model to improve it. Unlike SaaS, where customer data stayed in isolated databases, AI vendors can legally and technically absorb that data into their own intellectual property. Satya Nadella and Alex Karp both warned publicly that companies are paying for intelligence twice: once with money, and again with the trade secrets they must reveal.
DeepMind CEO Proposes FINRA-Style AI Regulator for Frontier Models
DeepMind CEO Demis Hassabis proposes a self-regulatory organization modeled on FINRA to test frontier AI models before release, aiming to replace ad hoc government reviews with a technically rigorous, industry-funded, and politically feasible standards body.
Meta’s Mosseri: AI token budgets may be capped per engineer in 1-2 years
Adam Mosseri predicts that within a year or two, Meta will need to cap AI token budgets per engineer because the burn rate could equal their salary. The article details how companies like Meta, Uber, and Microsoft are already wrestling with runaway AI costs, forcing a shift from unlimited experimentation to managed resource allocation.