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Hello everyone! I’ve been independently researching & developing small-but-powerful vision-language models (VLMs) and noticed a gap in visual datasets – none were teaching my model to simply ground text in imagery, but trying to get it to reason about the text or about the scene itself. This lead me down a 2 week side-side-project to create SGOCR, an open source dataset pipeline for generating spatially-grounded, OCR-focused VQA tuples with tons of rich metadata to support diverse VLM training […]

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Reinforcement learning (RL) has become a prevalent paradigm for training tool calling agents, which typically requires online interactive environments. Existing approaches either rely on training data with ground truth annotations or require advanced commercial language models (LMs) to synthesize environments that keep fixed once created. In this work, we propose TRUSTEE, a data-free method training tool calling agents with dynamic environments fully simulated by free open-source LMs that can be as small as 8B, including task generation, user […]

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TIL: SQL functions in Google Sheets to fetch data from Datasette I put together some notes on patterns for fetching data from a Datasette instance directly into Google Sheets – using the importdata() function, a “named function” that wraps it or a Google Apps Script if you need to send an API token in an HTTP header (not supported by importdata().) Here’s an example sheet demonstrating all three methods. Tags: spreadsheets, datasette, google

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Predictions from machine learning algorithms can vary across random seeds, inducing instability in downstream debiased machine learning estimators. We formalize random seed stability via a concentration condition and prove that subbagging guarantees stability for any bounded-outcome regression algorithm. We introduce a new cross-fitting procedure, adaptive cross-bagging, which simultaneously eliminates seed dependence from both nuisance estimation and sample splitting in debiased machine learning. Numerical experiments confirm that the method achieves the targeted level of stability whereas alternatives do not. […]

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Reconfigurable intelligent surfaces (RISs) modify signal reflections to enhance wireless communication capabilities. Classical RIS phase optimization is highly non convex and challenging in dynamic environments due to high interference and user mobility. Here we propose a hierarchical multi-objective quantum metalearning algorithm that switches among specific quantum paths based on historical success, energy cost, and current data rate. Candidate RIS control directions are arranged as switch paths between quantum neural network layers to minimize inference, and a scoring mechanism […]

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In this tutorial, we implement how to run the Bonsai 1-bit large language model efficiently using GPU acceleration and PrismML’s optimized GGUF deployment stack. We set up the environment, install the required dependencies, and download the prebuilt llama.cpp binaries, and load the Bonsai-1.7B model for fast inference on CUDA. As we progress, we examine how 1-bit quantization works under the hood, why the Q1_0_g128 format is so memory-efficient, and how this makes Bonsai practical for lightweight yet capable […]

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Elon Musk’s AI company xAI has launched two standalone audio APIs — a Speech-to-Text (STT) API and a Text-to-Speech (TTS) API — both built on the same infrastructure that powers Grok Voice on mobile apps, Tesla vehicles, and Starlink customer support. The release moves xAI squarely into the competitive speech API market currently occupied by ElevenLabs, Deepgram, and AssemblyAI. What Is the Grok Speech-to-Text API? Speech-to-Text is the technology that converts spoken audio into written text. For developers […]

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Quantum computing has spent years living in the future tense. Hardware has improved, research has compounded, and venture dollars have followed — but the gap between a quantum processor running in a lab and one running a real-world application remains stubbornly wide. NVIDIA moved to close that gap with the launch of NVIDIA Ising, the world’s first family of open quantum AI models specifically designed to help researchers and enterprises build quantum processors capable of running useful applications. […]

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Headless everything for personal AI Matt Webb thinks headless services are about to become much more common: Why? Because using personal AIs is a better experience for users than using services directly (honestly); and headless services are quicker and more dependable for the personal AIs than having them click round a GUI with a bot-controlled mouse. Evidently Marc Benioff thinks so too: Welcome Salesforce Headless 360: No Browser Required! Our API is the UI. Entire Salesforce & Agentforce […]

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