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The public narrative around Google has changed significantly over the past 1 year. (I say public, because people who were closely following google probably saw this coming). Since Google's revenue primarily comes from ads, LLMs eating up that market share questioned their future revenue potential. Then there was this whole saga of selling the Chrome browser. But they made a great comeback with the Gemini 3 and also TPUs being used for training it. Now the narrative is that Google is the best position company in the AI era. submitted by /u/No_Turnip_1023 [link] [comments]

Z.ai (creators of GLM) have released an open weight image generation model that is showing benchmark performance competitive with leading models like Nano Banana 2. "GLM-Image is an image generation model adopts a hybrid autoregressive + diffusion decoder architecture. In general image generation quality, GLM‑Image aligns with mainstream latent diffusion approaches, but it shows significant advantages in text-rendering and knowledge‑intensive generation scenarios. It performs especially well in tasks requiring precise semantic understanding and complex information expression, while maintaining strong capabilities in high‑fidelity and fine‑grained detail generation. In addition to text‑to‑image generation, GLM‑Image also supports a rich set of image‑to‑image tasks including image edit...

Jeff Bezos: financial bubbles like 2008 are just bad. Industrial bubbles, like biotech in the 90s, can actually benefit society. AI is an industrial bubble, not a financial bubble – and that's an important distinction. Investors may lose money, but when the dust settles, we still get the inventions. submitted by /u/SunAdvanced7940 [link] [comments]

Everyone is chasing larger context windows (1M+), but the retrieval accuracy (Needle In A Haystack) is still sub-optimal for professional use. I’m theorizing that we’re hitting a physical limit of the Transformer architecture. The future isn't a "bigger window," but a better "active memory" management at the infrastructure level. I’d love to hear some thoughts on RAG-Hybrid architectures vs. native long-context models. Which one actually scales for enterprise knowledge bases? submitted by /u/Foreign-Job-8717 [link] [comments]

submitted by /u/esporx [link] [comments]

Thrilled to be launching Plano today - delivery infrastructure for agentic apps: An edge and service proxy server with orchestration for AI agents. Plano's core purpose is to offload all the plumbing work required to deliver agents to production so that developers can stay focused on core product logic. Plano runs alongside your app servers (cloud, on-prem, or local dev) deployed as a side-car, and leaves GPUs where your models are hosted. The problem On the ground AI practitioners will tell you that calling an LLM is not the hard part. The really hard part is delivering agentic applications to production quickly and reliably, then iterating without rewriting system code every time. In practice, teams keep rebuilding the same concerns that sit outside any single agent’s core logic: T...

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