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AI search is reshaping digital discoverability. Learn how Outset PR adapts crypto PR strategies for LLM visibility through editorial authority, syndication, and data-driven media selection.
In this tutorial, we explore how to use Repowise to build repository-level intelligence for the itsdangerous Python project in a practical and reproducible way. We start with an already cloned repository, configure Repowise using the available LLM credentials, and initialize its indexing pipeline. We then inspect the generated .repowise artifacts, analyze the repository graph with […] The post How to Build Repository-Level Code Intelligence with Repowise Using Graph Analysis, Dead-Code Detection, Decisions, and AI Context appeared first on MarkTechPost.
ArXiv, a popular platform for preprint academic research, is taking a new step to attempt to reduce the volume of papers that include AI slop. If a paper has "incontrovertible evidence that the authors did not check the results of LLM generation," such as hallucinated references or "meta-comments" left by an LLM, authors will be banned from ArXiv for a year, according to Thomas Dietterich, ArXiv's section chair of its computer science section. Future ArXiv submissions will also have to be accepted at "a reputable peer-reviewed venue." Here's what he said on X: Attention @arxiv authors: Our Code of Conduct states that by signing your name … Read the full story at The Verge.
Nous Research releases Token Superposition Training (TST), a two-phase pre-training method that cuts wall-clock training time by up to 2.5x at matched FLOPs by averaging contiguous token embeddings into bags during Phase 1 and reverting to standard next-token prediction in Phase 2 — without changing the model architecture, tokenizer, optimizer, or inference-time behavior. Validated at 270M, 600M, 3B dense, and 10B-A1B MoE scales. The post Nous Research Releases Token Superposition Training to Speed Up LLM Pre-Training by Up to 2.5x Across 270M to 10B Parameter Models appeared first on MarkTechPost.
I spent a weekend trying to convince a language model it was C-3PO. Here's what actually worked. The post What’s the Best Way to Brainwash an LLM? appeared first on Towards Data Science.
Alexa for Shopping is Amazon’s new AI-powered shopping assistant. | Image: Amazon Amazon is bringing Alexa Plus to Amazon.com, integrating its LLM-powered AI assistant directly into the company's shopping experience. Beginning today, when you type a query into Amazon, you'll be talking to Alexa for Shopping, the company's new shopping assistant, powered by Alexa Plus. So, while a search for "toilet paper" will still return the expected list of brands, typing "What's a good skincare routine for men" or "When did I last order AA batteries" will now trigger an answer from Alexa. Alexa for Shopping is replacing Amazon's Rufus AI shopping assistant and, unlike Rufus, it will be front and center in the Amazon app and on the … Read the full story at The Verge.
OpenAI – the LLM pioneer that had it all, but has more recently squandered some of its lead – is now going all-in on driving ...
Modern large language models are no longer trained only on raw internet text. Increasingly, companies are using powerful “teacher” models to help train smaller or more efficient “student” models. This process, broadly known as LLM distillation or model-to-model training, has become a key technique for building high-performing models at lower computational cost. Meta used its […] The post Understanding LLM Distillation Techniques appeared first on MarkTechPost.
In this tutorial, we implement how Memori serves as an agent-native memory infrastructure layer for building more persistent, context-aware LLM applications. We start by setting up Memori in a Google Colab environment and connecting it to both synchronous and asynchronous OpenAI clients, so that every model call can automatically pass through the memory layer. We […] The post A Coding Implementation to Build Agent-Native Memory Infrastructure with Memori for Persistent Multi-User and Multi-Session LLM Applications appeared first on MarkTechPost.
