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How to Build an End-to-End OCR Pipeline with Baidu’s Unlimited-OCR for High-Resolution Images and Multi-Page PDF Parsing

ai, AI (Artificial Intelligence), AI Infrastructure, Applications, Artificial Intelligence, Editors Pick, Machine Learning, OCR, Staff, Technology, Tutorials

In this tutorial, we build a complete workflow for running Baidu’s Unlimited-OCR model on document images and multi-page PDFs. We configure the GPU environment, install the required dependencies, load the 3B-parameter vision-language model with automatic selection of bfloat16 or float16, and generate structured sample documents for testing. We then evaluate both the tiled Gundam inference […]

How to Build an End-to-End OCR Pipeline with Baidu’s Unlimited-OCR for High-Resolution Images and Multi-Page PDF Parsing Read More »

Andrew Ng Just Released OpenWorker: An Open-Source, Local-First Desktop AI Coworker That Returns Finished Deliverables Instead of Chat

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Andrew Ng has announced OpenWorker, an open-source desktop agent that produces finished work rather than conversation. OpenWorker asks the user for an outcome, not a prompt: a polished document, a Slack reply containing the actual numbers, an updated calendar, a triaged inbox. It then breaks that outcome into steps, works across local files and connected

Andrew Ng Just Released OpenWorker: An Open-Source, Local-First Desktop AI Coworker That Returns Finished Deliverables Instead of Chat Read More »

You Didn’t Get the AI Model You Paid For

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The line in the response object You call the API. You pass model: “claude-fable-5”. You get back a completion, a token count, and a field that reads “model”: “claude-opus-4-8”. Nothing errored. Nothing retried. The request was classified before generation began, matched a sensitive category, and was handed to a different set of weights entirely. Anthropic

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Best Open Speech Recognition (ASR) Models in 2026: WER, Languages, Latency, and License Compared

ai, AI (Artificial Intelligence), Applications, Artificial Intelligence, Audio Language Model, Editors Pick, Language Model, Large Language Model, Staff, Tech News, Technology, Voice AI

Open speech recognition stopped being a Whisper monoculture some time in the last twelve months. In March 2026 Cohere released Transcribe, a 2B Apache 2.0 model that took the top of the Hugging Face Open ASR Leaderboard at 5.42% average word error rate. Five weeks later IBM shipped Granite Speech 4.1 2B at 5.33%. Since

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Anthropic Releases Claude Security Plugin for Claude Code in Beta: A Multi-Agent Vulnerability Scanner That Runs in Your Terminal

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Anthropic has released the Claude Security plugin for Claude Code in beta. The plugin runs a multi-agent vulnerability scan of a repository from inside an existing Claude Code session, then turns the findings you select into patch files that you review and apply yourself. Anthropic emphasized the tool’s versatility upon announcement, highlighting its capability to

Anthropic Releases Claude Security Plugin for Claude Code in Beta: A Multi-Agent Vulnerability Scanner That Runs in Your Terminal Read More »

Research-Grade EdgeBench Analysis: AI Agent Benchmarking, Leaderboard Analytics, Scaling Laws, and Evaluation Metrics

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In this tutorial, we explore EdgeBench as a practical benchmark for evaluating advanced AI agents across diverse task categories, runtime environments, and interaction-time budgets. We begin by downloading the dataset snapshot from Hugging Face, parsing the released task specifications, and examining the benchmark taxonomy, execution settings, internet requirements, judging logic, and scoring metadata. We then

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Unsloth vs Axolotl vs TRL vs LLaMA-Factory: A Fine-Tuning Framework Comparison on Speed, VRAM, and Multi-GPU

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Four open source projects dominate LLM fine-tuning today. Unsloth, Axolotl, TRL, and LLaMA-Factory all wrap the same underlying PyTorch and Hugging Face stack. They diverge on where they spend engineering effort. Unsloth rewrites kernels. Axolotl composes parallelism strategies. TRL defines the trainer APIs the others build on. LLaMA-Factory optimizes for breadth of model coverage and

Unsloth vs Axolotl vs TRL vs LLaMA-Factory: A Fine-Tuning Framework Comparison on Speed, VRAM, and Multi-GPU Read More »

Cisco Foundation AI Releases Antares: 350M and 1B Open-Weight Models That Localize Known Vulnerabilities Inside Real Codebases

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Cisco Foundation AI has released Antares, a family of security small language models (SLMs) built for one narrow security task. The task is vulnerability localization. Given a vulnerability description and a repository, find the files containing the flaw. Two models are open-weight and available now on Hugging Face, Antares-350M and Antares-1B. Both are Apache 2.0.

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Poolside releases Laguna S 2.1, a 118B open-weight coding model that matches rivals many times its size

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Poolside has released Laguna S 2.1, a 118B-parameter open-weight model built for agentic coding. It is a Mixture-of-Experts (MoE) model with 8B activated parameters per token. It supports a context window of up to 1M tokens in both thinking and no-thinking modes. The weights are on Hugging Face under an OpenMDW-1.1 license, and the model

Poolside releases Laguna S 2.1, a 118B open-weight coding model that matches rivals many times its size Read More »

Poolside Releases Laguna S 2.1, an Open-Weight Agentic Coding Model Punching Above Its Weight Class on SWE-Bench Multilingual

Poolside Releases Laguna S 2.1, an Open-Weight Agentic Coding Model Punching Above Its Weight Class on SWE-Bench Multilingual Read More »