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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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Distribird: Literature-Informed Prior Distribution Design for Bayesian Model Calibration
arXiv:2608.11210v1 Announce Type: new Abstract: Bayesian calibration of process-based models requires a prior distribution for each model parameter. Despite decades of methodological work, researchers almost always fall back on uniform priors. The main reason is that building informative priors from scientific literature is slow and needs both domain and statistical expertise. We present \textbf{Distribird}, an agentic web application that automates this process. Given a parameter name, physical description, and domain context, Distribird deploys a multi-agent pipeline that searches the literature, extracts and weights reported values by domain relevance, and fits a probability distribution via AIC model selection. When no literature is available, the system falls back to sensible uninformative alternatives, and clearly reports both the evidence behind and the confidence level of every prior it produces. It is designed for the problems where the models have physically interpretable par