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LightOnOCR — the fastest open-source OCR model right now. It outperforms DeepSeek OCR, PaddleOCR-VL, and dots.ocr — and it’s fully end-to-end. Why it’s impressive: ✅ True end-to-end OCR — no text-box detection, no segmentation, no messy pipelines. ✅ Processes full pages in a single pass → outputs clean, structured Markdown. ✅ 5.71 pages/sec on a single H100 → extremely fast + ultra-cheap (about $0.01 per 1,000 pages). ✅ Trained on 17.6M synthetic pages generated by Qwen2-VL-72B. ✅ Smaller tokenizer (32k/16k) → faster inference without losing accuracy. ✅ Handles native resolution (up to 1540px) for high-density documents. ✅ Fully open-source: model weights + complete dataset available. If your workflow involves document processing, automation, or AI agents, this model is worth following. 🔗 Model (Hugging Face) https://lnkd.in/eDYjt-sz 🔗 Demo (Hugging Face Spaces) https://lnkd.in/dz6Ukhk4 #AI #OCR #LightOnOCR #DeepSeekOCR #PaddleOCR #ComputerVision #LLM #Automation #Python #OpenSource #AItools #DocumentAI #MachineLearning #Datasets #AIEngineering #ProductivityTools

GPT-5 is changing the way we write code. It’s not just “better answers” – it’s a completely different collaboration model between developer and AI. After testing a lot of workflows, one thing is clear: GPT-5 requires more precision and intentional prompting than previous generations. But once you adapt, code quality jumps significantly. Here are 6 rules that make the biggest difference in daily coding: - Be precise and avoid conflicting instructions. GPT-5 is far less tolerant of vague or messy prompts. The cleaner your intent, the better the output, especially in files like cursor.rules or AGENTS.md. - Use the right level of reasoning. GPT-5 thinks deeper by default Simple tasks = shallow reasoning. Complex tasks = deeper, intentional reasoning. Too much leads to overthinking, too little leads to guessing. Use structured prompts (XML-like blocks). GPT-5 performs better when the instructions have hierarchy and clear context. XML-style structure reduces hallucinations and creates more predictable code. <code_rules>  <principles>   Keep components modular and reusable.  </principles>  <tasks>   <frontend_defaults>   </frontend_defaults>  </tasks> </code_rules> -Avoid overly firm language. Commands like “Be extremely thorough” or “Make sure you have the full picture before replying” often backfire. GPT-5 becomes too verbose, too careful, and too slow. Neutral, calm instructions work better. - Give the model space for planning and self-reflection. Ask GPT-5 to outline the architecture, consider alternatives, simplify the plan, and review the code after writing it. This leads to cleaner, more maintainable solutions. <self_reflection>  Before coding, outline all components, state, types and edge cases.  Create a simple roadmap.  Consider alternative, simpler approaches.  Rewrite the plan until minimal.  After coding, review the code carefully.  Simplify if needed.  Keep comments up to date. </self_reflection> Control the model’s eagerness. GPT-5 tends to rewrite or extend code even when not asked. Setting behavioral rules like “ask for clarification when unsure” or “don’t change existing code unnecessarily” makes the model more reliable and predictable. <persistence>  Ask for clarification when needed.  Don’t assume unless interruption has high cost.  Avoid invalidating existing code.  Double check quality before finalizing.  Review work after completion. </persistence> The bottom line: GPT-5 works best with clarity, structure, minimalism, and explicit control over reasoning depth. Have you tested GPT-5 for coding yet? #GPT5 #Coding #AIForDevelopers #SoftwareEngineering #PromptEngineering #AIAgents #LLM #TechTrends #Programmers #AIWorkflow

Chandra — the new open-source OCR leader by Datalab Chandra, the new OCR model from Datalab, has just taken the top spot in the olmocr benchmark, outperforming dots-ocr, DeepSeek OCR, and Gemini Flash 2. It delivers state-of-the-art text recognition for scanned documents, tables, math formulas, diagrams, and even handwritten notes, supporting 40+ languages. Key features Supports 40+ languages Converts images and PDFs to HTML, Markdown, or JSON while preserving layout Recognizes text, tables, handwriting, forms, diagrams, and math Outstanding accuracy on handwritten and historical documents Two inference modes: local (HuggingFace) or vLLM server for scalable deployment Open license for startups under $2M revenue or funding olmocr benchmark results: ModelOverall ScoreDatalab Chandra v0.1.083.1 ± 0.9dots.ocr79.1 ± 1.0DeepSeek OCR75.4 ± 1.0Gemini Flash 263.8 ± 1.2 Chandra leads in: Mathematical formula recognition (+5.4 advantage over next best) Table extraction (88.0 pts – highest precision) Fine text recognition (92.3 pts) Official sources GitHub repo → https://lnkd.in/dDGrWgZn Official blog → https://lnkd.in/dNZSXiMh Documentation & demo → https://lnkd.in/df7spG-h Why it matters Chandra sets a new standard for open-source document intelligence — combining high accuracy, strong multilingual support, and enterprise-ready scalability. Perfect for research labs, startups, and AI developers working with OCR pipelines, document parsing, and data extraction. #OCR #ArtificialIntelligence #ComputerVision #DocumentAI #DeepLearning #MachineLearning #HandwritingRecognition #OpenSource #Startup #ChandraOCR #Datalab

DeepSeek-OCR on huggingface: https://lnkd.in/dQuG3apK #AI #OpenSource #LLM #MiniMaxM2 #MachineLearning #ArtificialIntelligence #AIAgents #DeepLearning #GenerativeAI #ModelRelease #Efficiency #TechNews #AIInnovation

https://lnkd.in/eYMNBQP4 #AI #OpenSource #LLM #MiniMaxM2 #MachineLearning #ArtificialIntelligence #AIAgents #DeepLearning #GenerativeAI #ModelRelease #Efficiency #TechNews #AIInnovation

MiniMax M2 — Chinese Open-Source Model Outperforms Google Gemini Chinese AI startup MiniMax has released its M2 language model, achieving the highest score among open-source systems on the Artificial Analysis Intelligence Index — ranking 5th globally, just behind GPT-5, Grok 4, and Claude Sonnet 4.5. M2 scored 61 points, surpassing Gemini 2.5 Pro (60 points), marking a major milestone for China’s open-source AI ecosystem. Efficient Mixture-of-Experts (MoE) Architecture MiniMax M2 uses a Mixture-of-Experts (MoE) design with 230 billion total parameters, but activates only 10 billion during inference, ensuring outstanding efficiency. It can run on just four NVIDIA H100 GPUs at FP8 precision, delivering inference speeds of ~100 tokens per second, roughly 2× faster than Claude Sonnet 4.5. For comparison: DeepSeek V3.2 uses 37 billion active parameters, and Moonshot Kimi K2 uses 32 billion. 💻 Strength in Coding and Agentic Tasks M2 excels in agent workflows and coding tasks, achieving: 69.4 on SWE-bench Verified (real-world coding) 77.2 on τ²-Bench (tool use) 44.0 on BrowseComp (web research) According to Artificial Analysis, M2 shows exceptional skill in tool use and instruction following. Independent developer testing reports ~95 % blended task accuracy, compared with 90 % for GPT-4o and 88–89 % for Claude 3.5. Florian Brand, PhD student at Trier University: “I am really impressed by their progress — M2 is a substantial leap from M1.” 💰 Pricing and Availability MiniMax M2 is priced at: $0.3 per million input tokens $1.2 per million output tokens That’s just 8 % of Claude Sonnet 4.5’s cost, while maintaining competitive performance. The model is released under the MIT License on Hugging Face and GitHub, with free API access for a limited time. https://lnkd.in/g3hYhD4R #AI #OpenSource #LLM #MiniMaxM2 #MachineLearning #ArtificialIntelligence #AIAgents #DeepLearning #GenerativeAI #ModelRelease #Efficiency #TechNews #AIInnovation

Has Vibe Coding Already Died? Remember those CEOs who were posting memes about firing developers? Now they’re quietly creating new job listings: “Senior Staff Engineer (AI Code-Review).” The hype is over. We’ve entered the correction phase. Companies finally realized that the “kid who codes 3x faster” is also the same kid who just pushed 10 critical bugs into production. I’m not talking about big corporations — they have filters, Leetcode tests, and long review pipelines. I’m talking about startups that ship like crazy. Many CEOs dreamed of replacing their dev teams with AI. But the truth? Half of all developers only use AI for autocomplete — basically what VS Code extensions have been doing for years. Here’s the simple truth: AI won’t replace a senior developer. It actually makes senior developers more essential. You don’t need a “prompt engineer.” You need an architect — someone who can look at 1,000 lines of AI-generated code and ask: “…but why?” More than 50% of tech work is maintenance, not greenfield building. The future isn’t vibe coding. The future is validated coding — where the vibe is just a suggestion, and you’re still the one who signs off on the PR. 💬 How about you? Do you use AI as your autopilot — or as a very fast intern? #AI #programming #developer #softwareengineering #tech #startups #cybersecurity #refactoring #copilot #AItools #validatedcoding

https://lnkd.in/dE-gAjtJ

Apple quietly changed the game again — and no one noticed. While everyone’s busy chasing reasoning and agentic AI, Apple just dropped Pico-Banana-400K — a 400,000 real-image dataset for text-guided image editing. What is Pico-Banana-400K? Think of it as ImageNet for image editing, not recognition. You feed it a photo and a sentence like “Make the red car blue.” and the AI makes it happen — 400,000 times over. But the twist? Unlike most “open” datasets filled with AI-generated fakes, Pico-Banana-400K uses real photos edited and verified through Apple’s internal AI pipeline. The Pipeline — Three AIs Walk Into a Lab Gemini-2.5-Flash → writes natural edit prompts (“Turn the sky golden”) Nano-Banana → performs the actual edit Gemini-2.5-Pro → scores realism, accuracy, and quality If a result scores below 0.7, it’s marked as “fail” — still stored, because failed data is training gold. The outcome: ✅ 257K successful edits ⚖️ 56K preference pairs 🔁 72K multi-turn edits All generated autonomously by AIs judging each other. Why It Matters Current “edit-by-text” models often look uncanny because they’re trained on synthetic junk. Apple’s dataset teaches real visual logic — lighting, materials, object consistency — things humans instantly perceive as real. This could push image-editing AIs from “close enough” → to → “wait, did a human do that?” Multi-Turn Editing = Visual Conversations Imagine a model that remembers your edits: “Add a yellow umbrella.” “Make it golden hour.” “Now turn it into Pixar style.” That’s not editing anymore — that’s interactive storytelling. Why You Should Care Researchers: new high-quality data for alignment and preference learning Developers: fine-tune assistants that actually understand edits Creators: one step closer to language-driven Photoshop And yes — it’s open-sourced. https://lnkd.in/ddFVBrDT https://lnkd.in/dg4v3myH #AI #Apple #PicoBanana #AIGeneratedImages #MachineLearning #ComputerVision #ImageEditing #Innovation #DeepLearning #OpenSource #Dataset #AIResearch #VisualAI #GenerativeAI #ArchViz #ComfyUI #NanoBanana

DeepSeek-OCR — How Turning Text Into an Image Solves the Long-Context Problem Feed any large language model a 100K-token document, and you’ll feel it — latency, memory blow-up, token costs spiraling. It’s not their fault. Transformer attention scales quadratically with sequence length, so long documents quickly become computationally expensive. 💡 The Radical Idea What if, instead of feeding all that text, you showed it to the model — as an image? That’s the concept behind DeepSeek-OCR: a model that doesn’t treat vision as a side feature, but as a compression layer for text. They call it Context Optical Compression — representing long textual content optically, then decoding it back using vision-language understanding. An image can hold far more text per token: 📄 Text page → 2 000–5 000 tokens 🖼️ Image of that page → 200–400 vision tokens 🔟 ≈10× compression — while keeping 97% reconstruction accuracy ⚙️ Core Architecture Two stages power this system: 1️⃣ DeepEncoder (~380M params) SAM-base (80M): local window attention for fine details CLIP-large (300M): global dense attention for layout understanding 16× convolutional compressor: reduces vision tokens before global attention 💡 Example: A 1024×1024 image → 4096 patches → compressed to 256 tokens. That’s a 16× reduction in activation memory. 2️⃣ DeepSeek-3B-MoE Decoder (~570M active params) A lightweight Mixture-of-Experts LLM — 6 of 64 experts activate per step. It reconstructs the text from the compressed vision tokens. Flow: Image (document page) → DeepEncoder → Compressed Tokens → MoE Decoder → Text 🧪 Training Setup Hardware: 20 nodes × 8 × A100 (40 GB) GPUs Throughput: 70–90 billion tokens/day Data: 30 M+ document pages (real + synthetic, 100+ languages) Includes charts, formulas, geometry, chemical structures, and diagrams This isn’t just OCR — it’s semantic visual understanding. 📊 Benchmarks Compression Test (Fox Benchmark): 10× compression → 97 % precision 20× compression → ~60 % precision OmniDocBench (Practical OCR): State-of-the-art OCR accuracy Processes a full page with just 100–200 vision tokens Over 200 000 pages/day on a single A100 🧠 Why It Matters Instead of trying to stretch attention windows infinitely, DeepSeek takes a different stance: “Compress it visually.” For LLM developers, this means: ⚙️ Fewer tokens → lower compute + faster inference 💾 Natural memory decay (older context = blurrier snapshots) 🧩 Multimodal fusion built-in — text is already vision This could redefine how we handle context — moving from token-hungry attention to vision-based contextual memory. DeepSeek-OCR on Hugging Face https://lnkd.in/eiPAa4UM #DeepSeek #OCR #OpticalCompression #DocumentAI #LLM #VisionLanguage #AIResearch #Efficiency #OpenSource #MachineLearning