Creating a RAG program has become very easy. Google’s File Search tool for Gemini API now handles the heavy lifting of connecting LLMs to your data. Chunking, embedding, targeting are all handled by you. And with the latest update, it’s gone multimodal. Now you can search both text and images in one way, with custom … Read more
Large language types are incredibly powerful, but let’s be honest—they are speed of thought it’s still a huge headache for anyone trying to use them in production. Google just launched Authors of Multi-Token Prediction (MTP). Of course Gemma 4 exemplary family. This is special speculative decoding architecture it can actually triple (3x) your speed time … Read more
Microsoft, Google DeepMind and Elon Musk’s xAI have offered to allow the US government to access new AI models before their general release, which sets up a new stage in the often fractured relationship in Silicon Valley and the US government’s fear of AI threats, based on a recent report of AI companies providing models … Read more
Voice AI has a dirty secret: most of it was never designed for conversation. The leading paradigm – text feed, output sound – tracks their list in audiobook narration and voice production, where the model is never heard by the person on the other end. It’s fine if you’re producing a podcast intro. It’s not … Read more
Optimizing LLMs has become much easier thanks to open source tools. You no longer need to build a full training stack from scratch. Whether you’re looking for low VRAM training, LoRA, QLoRA, RLHF, DPO, multi-GPU scaling, or a simple UI, there’s likely a library to fit your workflow. Here we are the best open source … Read more
PLOT_STEPS = 55 x_ = np.linspace(-5, 5, 500) y_ = np.linspace(-2.2, 2.2, 500) X, Y = np.meshgrid(x_, y_) Z = loss(X, Y) fig = plt.figure(figsize=(16, 10), facecolor=”#FAFAF8″) gs = GridSpec(2, 3, figure=fig, hspace=0.45, wspace=0.38, left=0.07, right=0.97, top=0.88, bottom=0.08) COLORS = { “gd”: “#E05C4B”, “mom_good”: “#3A7CA5”, “mom_large”: “#F4A536”, “contour”: “#D4C9B8”, “minima”: “#2A9D5C”, “start”: “#444444”, } PANEL_TITLES … Read more
fig, axes = plt.subplots(2, 2, figsize=(14, 10)) colors_a = [“gray”, “#1f77b4”, “#ff7f0e”, “#2ca02c”, “#d62728″][: len(asmd_means)] axes[0, 0].bar(list(asmd_means.keys()), list(asmd_means.values()), color=colors_a) axes[0, 0].axhline(0.1, ls=”–“, color=”red”, label=”0.10 imbalance threshold”) axes[0, 0].set_title(“Mean ASMD across covariates”) axes[0, 0].set_ylabel(“Mean ASMD”); axes[0, 0].legend() axes[0, 0].tick_params(axis=”x”, rotation=20) truth = target_df[“happiness”].mean() colors_b = [“#888”] + [“#1f77b4”, “#ff7f0e”, “#2ca02c”, “#d62728”][: len(methods)] + [“black”] axes[0, 1].bar(list(outcome_means.keys()), … Read more
The White House of President Donald Trump is considering whether the US government should be allowed to test the most powerful AI models before they are available to the public, which is a significant change from his previous approach to AI in the AI ​​industry. In a recent story about the White House AI model … Read more
Training and rendering large scale transformer models is a memory management problem. Every GPU in the collection has a fixed amount of VRAM, and as the model numbers and core length increase, developers always have to make changes in how to distribute the work across the hardware. A a new way from Zyphrait was called … Read more
Web search and content retrieval have quietly become the most important infrastructure decisions in AI agent development. An agent without reliable access to live web data is effectively working on outdated information – a serious limitation for any research to handle production deployment, lead enrichment, competitive intelligence, or real-time monitoring. In 2026, the ecosystem of … Read more