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How Baidu Unlimited-OCR Works: Solving Long-Document Transcription
About a month ago, Baidu (often called the “Google of China ) introduced Unlimited-OCR, an advancement over DeepSeek OCR. The model was designed to transcribe long, multi-page documents with high accuracy while delivering fast and stable inference. Unlike conventional vision-language OCR systems, Unlimited-OCR addresses a major bottleneck in long-document transcription: the rapidly growing Key-Value (KV) cache, [ ] The post How Baidu Unlimited-OCR Works: Solving Long-Document Transcription appeared first on Analytics Vidhya.
Why the Same Question Gives Your AI a Different Answer Every Time and What “Temperature” Actually…
It feels like randomness, or a glitch. It s neither. It s a dial someone chose to leave turned up and knowing where it sits changes what Continue reading on Medium »
Claude Code Productivity Tips I’d Actually Use in 2026
The commands matter less than better prompts, clean context, and checks Claude can run without you.Continue reading on Towards AI »
LangChain vs LangGraph: 4 Key Differences and When to Use Each
A practical guide to choose the proper tool for your agentic workflows and systems The post LangChain vs LangGraph: 4 Key Differences and When to Use Each appeared first on Towards Data Science.
Autonomous intelligence backbone of memorization systems: How AI agents recall information
While the massive language models are exceptionally effective consisting one of the potential disadvantage in recalling things. Each Continue reading on AI Engineering Collective »
Why Trusted Data Is Still the Biggest Competitive Advantage in AI
The AI conversation has changed dramatically in the last few years.Continue reading on Medium »
How I Actually Made Money on YouTube Using AI (And Why Most People Fail)
I remember sitting in front of my laptop a few years ago, staring at a viral video about making automated YouTube channels. The presenter Continue reading on Medium »
I Tested an AI Presentation Workflow From Raw Notes to Final Deck
A deck is finished when someone has argued with slide 4, changed a headline they hated, updated a number after a call with their team Continue reading on ILLUMINATION »
Your RAG Pipeline Can Find Facts. It Still Can’t Reason. Here’s the Fix.
Why knowledge graphs are quietly replacing vector search for the questions that actually matterContinue reading on Medium »
Your AI Agent Said It Sent the Refund. Can You Prove It?
An AI agent tells a customer:Continue reading on Medium »
ChatGPT Desktop (Codex Desktop) for Linux
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FarSky: Task-Aware Latent-Space Coupling for Generative Intra-Hour Solar Forecasting
arXiv:2608.11254v1 Announce Type: new Abstract: Accurate solar irradiance forecasting is essential for the reliable integration of photovoltaic power into modern electricity grids. All-sky imagers (ASI) provide high-resolution observations of clouds, making them well suited for intra-hour forecasting. Recent deep learning approaches have substantially improved forecast accuracy but are often limited by deterministic predictions and a reduced capability to anticipate ramp events. This work proposes FarSky, a generative forecasting framework that leverages latent-space coupling to learn task-aware representations of sky images. A multi-task autoencoder first learns a shared latent representation for image reconstruction and irradiance estimation. A latent diffusion model then generates future latent states conditioned on recent observations, from which irradiance forecasts are directly decoded. Probabilistic forecasts are inherently obtained through stochastic sampling. The framework is