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Search Startup Unveils AI-Powered Enterprise Retrieval Engine
Article discussing search and retrieval improvements for enterprise data.
AI Search Tool wesearch.press Launched on Hacker News
AI Search Tool, Wesearch.press, Debuts on Hacker News A new player has emerged in the realm of AI powered search tools. The recently launched wesearch.press aim…
SigMap AI Tool Achieves 81.1% Retrieval Hit Rate, 96.9% Token Reductio
SigMap AI Tool Delivers Impressive 81.1% Retrieval Accuracy and 96.9% Data Compression The SigMap AI tool has recently garnered attention for its exceptional pe…
Explore Agentic AI with Free Interactive Curriculum on AgentSwarms
Hey Everyone, Over the last few months, I noticed a massive gap in how we learn about Agentic AI. There are a million theoretical blog posts and dense whitepapers on RAG, tool calling, and swarms, but almost nowhere to just sit down, run an agent, break it, and see how the prompt and tools interact under the hood. So, I built **AgentSwarms**.fyi It’s a free, interactive curriculum for Agentic AI. Instead of just reading, you run live agents alongside the lessons. **What it covers:** * Prompt engineering & system messages (seeing how temperature and persona change behavior). * RAG (Retrieval-Augmented Generation) vs. Fine-tuning. * Tool / Function Calling (OpenAI schemas, MCP servers). * Guardrails & HITL (Human-in-the-Loop) for safe deployments. * Multi-Agent Swarms (orchestrators vs. peer-to-peer handoffs). **The Tech/Setup:** You don't need to install anything or provide API keys to start. The "Learn Mode" is completely free and sandboxed. If you want to mess around with your own models, there's a "Build Mode" where you can plug in your own keys (OpenAI, Anthropic, Gemini, local models, etc.). I’d love for this community to tear it apart. What agent patterns am I missing? Is the observability dashboard actually useful for debugging your traces? Let me know what you think.
Google's Deep Research Max: Autonomous Research Agent for Expert Repor
Google quietly dropped something interesting last week. They updated their Deep Research agent (available via Gemini API) and introduced a "Max" tier built on Gemini 3.1 Pro. What it actually does: you give it a topic, it autonomously searches the web (and your private data via MCP), reasons over the sources, and produces a fully cited, professional-grade report — including native charts and infographics. Two modes: Deep Research — faster, lower latency, good for real-time user-facing apps Deep Research Max — uses extended compute, iterates more, designed for background/async jobs (think: nightly cron that generates due diligence reports for analysts by morning) The MCP support is the most interesting part to me. You can point it at proprietary data sources — financial feeds, internal databases — and it treats them as just another searchable context. They're already working with FactSet, S&P Global and PitchBook on this. Benchmarks show a significant jump in retrieval and reasoning vs. the December preview. They also claim it now draws from SEC filings and peer-reviewed journals and handles conflicting evidence better. So what do you think, is it another trying or game changer 😅
Andi: AI-Powered Search Engine for Direct Answers
Andi is a generative AI-powered search engine that provides direct answers instead of just links.
Mdlens: Optimize Token Use in Markdown Repos
Optimize Token Use in Markdown Repos with Mdlens Managing large Git repositories can be challenging, especially when dealing with token limits and large Markdow…
AI-Powered Tool Asklandd.dk Revolutionizes Information Access
AI Powered Tool Asklandd.dk Revolutionizes Information Access In the digital age, accessing accurate and reliable information quickly is more critical than ever…