International JavaScript Conference’s Post

Gemini File Search capability is an impressive leap, it abstracts the RAG (Retrieval-Augmented Generation) pattern into a seamless, built-in experience. It acts like an in-built RAG, letting the model search, retrieve, and reason from your files automatically, reducing the need to build complex retrieval pipelines. This kind of RAG abstraction is not yet seen in open source LLMs as a native function though it’s only a matter of time before similar capabilities arrive to automate parts of the RAG process. RAG is not going away it is just becoming smarter and more effortless, letting us focus more on outcomes. Reference: https://lnkd.in/ddvHFca4 #AI #RAG

I’ve been studying how to build a RAG demo using LangChain…handling every step manually: loading files, chunking documents, generating embeddings, managing a vector database, and retrieval search. Then… boom 🚀 Google releases the File Search tool that does all of that automatically. Now, you can focus only on building amazing apps without the backend weight. This tool truly transforms how we integrate generative AI with knowledge. Learn more: https://lnkd.in/dQ3eW3j3 #AI #RAG #LangChain #GoogleAI #GenAI

AI Radar - 0110010 🚀 Latest in AI - Google launches the File Search tool in Gemini API—a new, fully managed system that allows developers to ask Gemini questions about their own files (PDFs, docs, and more) and instantly get grounded answers with citations, making AI integration much easier and more accurate for apps and agents. Source: https://lnkd.in/dNQeUj-j - Parallel debuts its Search API, a new web search engine built from scratch for AI agents. Unlike traditional search, Parallel’s API fetches the most information-rich pieces of content—called “high-signal tokens”—and delivers precise answers directly to LLMs with better speed, accuracy, and cost than before. Source: https://lnkd.in/dHqJYipA - Kimi K2 Thinking by Moonshot AI is an open-source “thinking agent” model built for deep reasoning and long tool use. It sets new records on open benchmarks (like SWE-Bench Verified and BrowseComp), can perform 300+ step tool chains, and runs efficiently thanks to native INT4 quantization and a 256k context window. Source: https://lnkd.in/dzWhxr2j - GigaML officially launches, empowering enterprises to deploy large language models on their own infrastructure, keeping data secure and boosting performance by providing 2.3x faster inference and full customization for each business use case. Source: https://lnkd.in/dH6z2CTj - OpenAI enhances GPT-5 with “context injection,” improving the model’s ability to remember and use relevant information from previous conversations and uploaded data, leading to smarter and more personalized interactions. Source: https://lnkd.in/d8bGZBd7 #AIRadar #GeminiAPI #ParallelSearch #KimiK2 #GigaML #GPT5 #AI #TechNews

Google just dropped something huge for AI builders — the new File Search Gemini API. It’s an end-to-end RAG framework baked right into Gemini: embeddings, chunking, vector search, citations — all handled natively. You bring your files; Gemini handles the retrieval intelligence. This could seriously reduce the friction in building AI products that reason over your own data — no custom pipelines, no maintenance overhead, just focused innovation. Excited to see how this changes the landscape for AI product teams. #AI #RAG #GeminiAPI #Google #ProductManagement #Innovation https://lnkd.in/gupHyWA2

Big news for devs: Google's just dropped File Search in the #Gemini API—a fully managed #RAG tool that supercharges your apps with semantic search over your docs! Upload files, get cited responses, and say goodbye to manual indexing hassles. Build smarter bots & assistants in minutes. Dive in: https://lnkd.in/gvia_Pry #GeminiAPI #AI #DevTools

Search API Market Map. The next search war isn't for your eyeballs. A new report from a16z details a fundamental shift in how the internet is being indexed. The web is being rebuilt from the ground up not for humans, but for AI search agents. This has ignited a battle between API providers like Exa, Parallel, and Tavily, who are creating a cleaner, AI-native data layer. They're moving beyond the ad-riddled, SEO-driven web to power a new generation of applications. The goal is to enable "deep research" agents, smarter CRMs, and coding tools with real-time information, fundamentally changing how we access and synthesize knowledge.

Google’s new File Search could simplify how we build RAG systems Google just launched File Search, a fully managed Retrieval-Augmented Generation (RAG) capability built into the Gemini API — and I think it’s a meaningful step forward. What stands out to me is how it removes so much of the operational friction. No vector databases to maintain, no chunking logic, no retriever pipelines. You just upload files and start querying — Gemini handles the rest. Embeddings and storage are free, and the system takes care of chunking, semantic retrieval, and even built-in citations for transparency. It’s still early days, but I see this as a move toward accessible, context-rich AI, where developers can focus less on infrastructure and more on crafting real intelligence and value for users. #AI #AgenticAI #RAG #Innovation #FutureOfAI P.S.: my personal analysis and views. https://lnkd.in/ezXdV2uP

The era of passive RAG is over. I just leveled up my Retrieval-Augmented Generation pipeline by transforming it into a true autonomous AI Agent. I built a custom Agentic RAG system using the ReAct framework that can intelligently reason, plan, and orchestrate multiple tools to answer complex, multi-source queries. This is a step-change from simple RAG: the Agent is no longer just executing a fixed chain. It autonomously decides the best steps to take, giving it capabilities like: Multi-Tool Orchestration: Uses Web Search for real-time, up-to-date context, alongside dedicated document tools for retrieval, section content, and page summarization.  Complex Reasoning: The ReAct core enables a true multi-step thought process (Reasoning + Acting), allowing the Agent to autonomously decide whether to use the PDF, web search, or other tools — and synthesize the right information before generating the final answer. Deep Document Interaction: Includes specific tools to not only retrieve but also summarize or pull content from specific sections, enabling highly targeted answers from the source document.

MCP is the new hype, so of course I had to try it out 😎 After wrestling with the challenges of building and testing an MCP server, I figured I’d share what MCP is and how to build one with JavaScript. Turns out Model Context Protocol (MCP) is exactly what makes AI agents tick -tools, schemas, structured calls -all the stuff that lets LLMs actually do things. I’m also working on a project where an MCP server was a perfect fit, so this deep dive was super practical for me. If you’re curious about building AI tools or just want to see how agents actually work, give it a read 👇 https://lnkd.in/dW_fPKP5

I stress test a lot of open source local AI generators. I’m going to start posting the results whether they are successful or not, and give a straight opinion on whether you should use it or skip it. So follow me if you want the opposite of AI hype and test workflow downloads. This is LongCat. It’s similar to WAN 2.1, running locally in ComfyUI. It’s an AI video generator that lets you chain multiple “shots” from a single prompt so you get longer clips, with overlaps designed to keep things cohesive. Technically it does blend the clips, but you can decide how well the continuity holds up. Prompt coherence is poor by Q4 2025 standards, and detail can fail at a distance. Even with careful prompting, it struggles to keep subjects, props, and environments consistent as the scene progresses. I recommend keeping your prompts simple and if needed increase the overlap. Have a look at these 8 samples. For my workflow, LongCat is in the “interesting experiment, but not production ready” bucket. If you need reliable, controllable shots, I’d skip it for now. Setup: RTX 5090, Threadripper, 128 GB RAM. Each sequence is 455 frames with a 16–24 frame overlap, about 15 minutes per run at 8–12 steps per clip. Huggingface Model Page https://lnkd.in/gE6PvZpC If you want to try it for yourself, maybe you can improve on my results. You'll find to workflow at the bottom of my website, no login required. https://lnkd.in/gCudxq6p If you do download the workflow or like that I posted non cherry picked results, please leave a thumbs up.