HVHarsha V Khurdulainblogdata.jigsawstack.com·Sep 17, 2025 · 5 min readEmbedding v2: One Vector, Every Modality2025 has been great for the embedding model space, with Google’s Gemini-Embedding-001-Model and Alibaba team releasing their own series of Qwen3 embedding models. Both models outperform their predecessors in quality on various tasks for text embeddin...00
HVHarsha V Khurdulainblogdata.jigsawstack.com·Sep 16, 2025 · 4 min readA Practical Zero-Shot Multimodal ClassifierMost classifiers ship with a fixed label list. They perform well inside that box and fail silently outside it. So we took this as a challenge. We introduce a classifier that starts from the opposite assumption: the world is open. New product names ap...00
HVHarsha V Khurdulainblogdata.jigsawstack.com·Jul 22, 2025 · 5 min readContext Engineering: Yet Another Branch of "Engineering" Involving Language ModelsHave we just rebranded prompt engineering? Like Apple did with “Glass“? Well, turns out that there is more to context-engineering than writing lengthy prompts. If prompt engineering was about cleverly wording a single query to coax a good answer, con...00
HVHarsha V Khurdulainblogdata.jigsawstack.com·Jul 9, 2025 · 6 min readBeyond the Box: The Future of Object DetectionIt’s 2025, and object detection is a solved problem. Or is it? Object detection is like giving machines eyes. It’s the computer vision task of finding and localizing people, cars, cups, and other objects in images or videos. Unlike plain image classi...00
HVHarsha V Khurdulainblogdata.jigsawstack.com·Mar 14, 2025 · 9 min readJigsawStack MCP Servers: Bridging LLMs with Context and ToolsLarge Language Models (LLMs) are incredibly powerful, but they don't know everything out of the box. They rely on the context we provide, which can be limited by prompt sizes or outdated training data. Imagine if your AI assistant could plug into ext...00
