Meilisearch’s Post

𝗛𝗼𝘄 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗮𝗻 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗢𝗖𝗥 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝘄𝗶𝘁𝗵 𝗕𝗮𝗶𝗱𝘂’𝘀 𝗨𝗻𝗹𝗶𝗺𝗶𝘁𝗲𝗱-𝗢𝗖𝗥 𝗳𝗼𝗿 𝗛𝗶𝗴𝗵-𝗥𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗜𝗺𝗮𝗴𝗲𝘀 𝗮𝗻𝗱 𝗠𝘂𝗹𝘁𝗶-𝗣𝗮𝗴𝗲 𝗣𝗗𝗙 𝗣𝗮𝗿𝘀𝗶𝗻𝗴 In this tutorial, we build a complete workflow for running Baidu’s Unlimited-OCR model on document images and multi-page PDFs. From configuring the GPU environment to comparing high-detail tiled Gundam inference and faster Base modes, you'll learn how to process dense layouts, tables, and cross-page content in a reproducible, end-to-end pipeline. The post How to Build an End-to-End OCR Pipeline with Baidu’s Unlimited-OCR for High-Resolution Images and Multi-Page PDF Parsing appeared first on MarkTechPost. https://lnkd.in/gWE_CVDk

Ahead of our short course at #IMPS2026 this Monday, I completed a substantial update cycle for eight #RStats packages and submitted new releases to CRAN: • qgraph 1.10.1 • psychonetrics 0.16.9 • bootnet 1.9.1 • mlVAR 0.7.2 • semPlot 1.2.0 • parSim 0.4.0 • graphicalVAR 0.4.1 • IsingSampler 0.5.0 Together, these releases include a wide range of new features, performance improvements, compatibility updates, and bug fixes. Some highlights: qgraph received a major overhaul of its argument names, while retaining backward compatibility. It now offers argument autocompletion, new visualization themes, support for Chinese, Japanese, and Korean labels, interactive HTML output, and bridge-centrality statistics. psychonetrics now includes faster panel VAR and panel GVAR implementations, improved lavaan integration, and several new or experimental methods for multilevel modeling, robust estimation, ordinal data, RI-CLPMs, sampling weights, and meta-analytic SEM and VAR. bootnet now integrates with the mantar package, thanks to Kai Nehler, and includes improvements to bootstrapping, parallel processing, missing-data handling, and plotting. semPlot now supports a much broader range of models and packages, including psychonetrics, PLS-SEM, lavaan.mi, piecewiseSEM, metaSEM, psych, and blavaan. The releases of parSim, IsingSampler, mlVAR, and graphicalVAR add improvements to reproducibility, multistate simulation, estimation speed, validation, compatibility, and general stability. Feedback, issue reports, and contributions are always welcome: https://lnkd.in/gc_vrvSj #OpenScience #Psychometrics #NetworkAnalysis #Statistics #DataScience #RProgramming

I assumed vector search captured both semantic and lexical meaning. It doesn't, and that gap is why production RAG systems fail on queries that matter most. Vector search is powerful at finding semantically similar content such as paraphrases, fuzzy matches, broader intent. But ask it to find "ERR_AUTH_401" in your documentation and it returns a neighbourhood of authentication concepts that are close, but not the exact match your user needed. BM25 finds it immediately. Because BM25 looks for the literal term. Neither retriever alone is complete. Real queries need both. That's what hybrid search solves, two retrieval signals running in parallel, merged into one candidate pool, then re-ranked by relevance. The two-stage logic that makes it work: retrieve broadly first to improve recall, rank carefully second to improve precision. Skipping the re-ranking step is the most common mistake, you get higher recall but also more noise, and noise sent to an LLM produces confident wrong answers. Swipe through for the full pipeline and the senior insight that separates a demo from a production system. Comment HYBRID if you want the full 26-lecture RAG course. #RAGSystems #LLMEngineering #AIEngineering #SoftwareEngineering #MachineLearning

𝐒𝐭𝐨𝐩 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐏𝐑𝐈𝐒𝐌𝐀 𝐝𝐢𝐚𝐠𝐫𝐚𝐦𝐬 𝐢𝐧 𝐖𝐨𝐫𝐝/𝐏𝐨𝐰𝐞𝐫𝐏𝐨𝐢𝐧𝐭. Every systematic review needs one. Every reviewer checks it. And somehow it's still 2026 and people are manually dragging boxes and arrows in Word, praying the numbers add up. 𝐕𝐞𝐫𝐟𝐥𝐮𝐱 𝐟𝐢𝐱𝐞𝐬 𝐭𝐡𝐢𝐬 𝐢𝐧 𝐟𝐨𝐮𝐫 𝐬𝐭𝐞𝐩𝐬: 1. Log in 2. Start a project 3. Enter your screening numbers 4. Export a 300 DPI PRISMA 2020 flow diagram That's it. No design skills, no mismatched arrows. Publication-ready. Paste it straight into your manuscript. If you're running a systematic review or meta-analysis, your time should go into the science, not the flowchart. ---> verflux.com #SystematicReview #MetaAnalysis #PRISMA #EvidenceSynthesis #Verflux #ResearchWorkflow

Turns out an image of your text can be cheaper than the text itself. 🖼️ pxpipe is a local proxy between Claude Code and Anthropic. It renders bulky context, including the system prompt, tool docs, and old chat, into PNGs before the request leaves your machine. The model reads pixels, not per-character tokens. The math: 48,000 chars = ~25,000 text tokens Same content as 1 PNG = ~2,700 tokens Roughly 3x cheaper ⚡ The result: a 59 to 70% lower bill, so a $100 bill becomes ~$41. 💸 The honest catch: images make the model forget exact strings like IDs, hashes, and secrets. The rule: → Image what it only needs to understand → Keep as text what it must reproduce MIT licensed. 30-second setup. What would you image, and what would you never risk? 👇 #pxpipe #ClaudeCode #LLMTokens #TokenCost #VisionTokens #AIEngineering #OpenSource #PromptEngineering #AnthropicAPI #MachineLearning Source + write-up: https://lnkd.in/g9bXyXuE Follow for more: https://gattyworks.com/ 

New preprint: WikiPlanner LLM agents in sequential planning often fail not because individual steps are wrong, but because cross-step temporal constraints are violated. Current single-step filtering methods are not sufficient to capture these global inconsistencies. This work introduces WikiPlanner, a framework that compiles domain wiki knowledge into executable runtime monitors for LLM agent plans. Key ideas: - Extract temporal constraints from domain wikis into a typed intermediate representation (TC-IR) - Compile constraints into deterministic LTLf-based runtime monitors - Enable parallel validation of full agent trajectories with source-level violation reporting Preliminary results (medical planning benchmark): -Strong detection performance in controlled settings (F1 = 0.92) -Clear degradation in natural plans (F1 = 0.58), highlighting action grounding as a key bottleneck -Sub-millisecond runtime with linear scalability The main takeaway is simple: wiki knowledge can be operationalised into executable temporal constraints, but robust grounding of action vocabularies remains the critical challenge for deployment. Preprint: https://lnkd.in/eyYgZkcy Figure 1 (pipeline overview) provides a good visual summary of the framework. Feedback and discussion are welcome.

When Accessibility Is About Structure, Not Just Description In PDF remediation, one of the most overlooked decisions is how mathematical content is tagged. A common practice is to place equations inside a Span tag and provide an Alt Text description. While this may seem sufficient, it isn't always the most appropriate semantic approach. Mathematical expressions are not images—they are structured content. In many cases, using the Formula tag helps communicate that semantic meaning within the PDF's tag structure, allowing assistive technologies and accessibility workflows to better recognize mathematical content. The goal isn't simply to make an equation readable. The goal is to preserve its meaning, structure, and intent. True accessibility isn't measured by the number of tags added to a document. It's measured by whether the content can be understood and experienced as intended by every user. Accessibility isn't about choosing the easiest solution. It's about choosing the right one. At Velvora Solutions, we believe accessible documents are built through thoughtful structure, semantic accuracy, and attention to detail. #PDFAccessibility #PDFUA #Accessibility #DigitalAccessibility #DocumentAccessibility #AssistiveTechnology #WCAG #InclusiveDesign #VelvoraSolutions

Here's something lighter for the timeline, TraceCode (finally) enters the light ☀️ Since TraceCode's inception, everything, including trace visualization, pointer highlights, workspaces, lessons, was designed against a dark canvas. Making everything read just as clearly on white meant re-tuning that whole visual language. The most surprising part, though, is how the visualizations actually look better in light mode. They pop out in a way the darker tones could've never let them, it has me questioning if I should switch off light mode. But that's a wrap on Ship Week. Five days, five launches: Mon — Breakpoints, live complexity analysis, shareable playgrounds Tue — Trace Mode, the new default way to learn Wed — TraceCode Pro: semantic captions, trace export, verified runs, sync Thu — The library grew from 83 to 200 problems Fri — ☀️ If you tried TraceCode a few months ago, it's not the same product. Come see for yourself: https://tracecode.app

"𝗬𝗼𝘂𝗿 𝗥𝗔𝗚 𝗱𝗲𝗺𝗼 𝗶𝘀 𝗹𝘆𝗶𝗻𝗴 𝘁𝗼 𝘆𝗼𝘂." So I wrote the version I wish I'd had — and benchmarked it until it told me something I didn't expect. I built a Retrieval-Augmented Generation system three different ways, behind one interface: → Vector — embed everything, similarity search → PageIndex — vectorless. The LLM reasons over a table-of-contents tree, the way you'd flip to the right chapter → Hybrid — run both, let the model arbitrate Then I ran all three on the same corpus and measured. The result the tutorials never mention: No single strategy wins. • PageIndex ranked the correct pages highest • Vector was ~3× faster and produced the most relevant answers • Hybrid never missed the right document Best isn't a property of the architecture. It's a function of what you're optimizing for — latency, citation precision, recall — and the only way to know is to measure each swap instead of trusting your gut. The full write-up walks through every component with real code: chunking, embeddings, the three retrievers, a cross-encoder reranker, structured output that survives flaky local endpoints, grounded answers with citations — and the evaluation harness (deterministic IR metrics + Ragas ) that turns "it feels good" into a decision you can defend. If you're moving RAG from notebook to product, this is the part most guides skip. #RAG #LLM #AIEngineering #MachineLearning #GenAI