How to use Inspector MCP Server for debugging with AI

"In production, DeepSeek-OCR can generate training data for LLMs/VLMs at a scale of 200k+ pages per day (a single A100-40G)." DeepSeek lowkey release, they call it just another OCR this week but if you dive deeper, they introduce a new way of compress the image token 10x or 20x. You can store 10k words in 1.5k compressive visual tokens. It's a breakthrough.

We've made a blog post about a new major version of the #rstats tune package! Two main changes: parallel processing frameworks and the ability to tune postprocessors. https://lnkd.in/ei5zMSSf

Here’s something for system engineers (senior, staff and principal). How do you get sub-30ms latency (exc. bandwidth limitations). Zero-copy In-memory L1 cache (think ETS, MemCache) Functional programming where it matters Thin infra (Debian running on blade) Single node (one box). Custom compression. Where possible- use Blake3 for hashing, encryption, etc. Thank me later! For additional insight: paste the above tips into your AI chatbot of choice. And drop the responses you get in the comments below. Maybe, just maybe - you maybe be able to solve that prod. bottleneck that has plagued your team for weeks. Happy learning!

Say hello to a new OCR model - from Ai2, the champions of actual true open source: - OlmOCR 2: a major update to the open OCR model for complex documents - now better at handling tables, equations, handwriting, and degraded scans. - Achieves 82.4pct on olmOCR-Benc thanks to a richer training mix, including 20k historical documents pages - FP8 quantized model processes 3.4k tokens/sec on a single H100 → around USD 180 to parse one million pages. - Apache 2.0 license, with full support for domain fine-tuning and deployment.

Fine-Tuning: The Original Path to Domain Mastery Before parameter-efficient methods took over, fine-tuning was the brute-force route to performance. Developers would take a pre-trained model like BERT, throw labeled data at it, and adjust every parameter. It worked—but it was expensive. As models scaled from millions to hundreds of billions of parameters, full fine-tuning became financially prohibitive. The modern era belongs to methods that achieve similar specialization without retraining the entire model’s brain.

Building RAG Agents in Minutes — No Code, No Hassle! In my latest workflow, I explored a faster, smarter way to build #RAG (Retrieval-Augmented Generation) agents using #n8n + #Pinecone Assistant — and it completely changes the game! What’s new? No preprocessing pipeline. No manual chunking. No custom embedding flow. Just drop your documents in, and Pinecone takes care of the ingestion, splitting, and indexing on the backend — automatically. I connected it all together to get: Accurate, context-aware answers. Page-level citations. Exact text quotes straight from the knowledge base. Load your docs → Connect in n8n → Test grounded responses → Deliver transparent, trustworthy answers. #n8n #Pinecone #AIagents #RAG #Automation #NoCode #AIAutomation #OpenAI #Gemini #MachineLearning #AIWorkflows

Wondering if it's just me... I'm observed a shift in the release patterns of open LLMs in the last few months. With the notable exception of Apertus, the general-purpose open models have dried up, and in their place a flurry of smaller specialists started getting released. I'm certainly not going to complain about smaller, faster models, especially ones that can deliver on being just as good at their tasks at a fraction of the inference cost... but my observations when comparing them are all over the map. The syntaxes for declaring and invoking tools vary wildly from one model to another, which makes integrating and objectively evaluating them especially challenging. I'm also wondering if we're really seeing a slowdown in the development of general-domain models, or just the calm before the next storm. Photo by Johannes Plenio via Pexels

To mitigate complex debugging of ML systems: Isolate phases of a model’s behavior as early as possible, and assert preconditions, postconditions, and invariants. Before weights are repeatedly updated, it's easier to debug issues such as initialization bugs: Were the weights initialized as expected? Since the model has not learned anything useful yet, are output values (e.g., classes) almost uniform for the first few training steps (given the assumptions in model architecture)? Instead of waiting for an entire epoch to smoke out surprises, curate a smoke-test dataset of extreme examples (e.g., the longest sequences) to exercise the training code and systems; for example, making sure these extreme batches fit in memory. Unless proven before, make sure your model can learn from the data by overfitting first; if it can't overfit, it can't generalize. For more ML practices to ship production systems, pre-order #ShippingMachineLearningSystemsBook https://lnkd.in/gu9hgXBJ

🔴 We are live with another #XPWebinar series. Get ready to decode how AI is revolutionizing quality engineering. Learn strategies for building scalable testing pipelines that accelerate delivery and ensure software reliability. 👉https://lnkd.in/gwWdGhqw

New in VS 2026: ✅ Inline if-statement debugging (no hover needed) ✅ "Did You Mean?" AI file search ✅ Adaptive Paste (context-aware code integration) ✅ Mermaid chart rendering in Markdown ✅ AI profiling agent ✅ 11 new color themes Enterprise development just evolved. #VS2026