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Nicole HennigDeep Dive into Three AI Academic Search Tools <a href="https://katinamagazine.org/content/article/reviews/2025/deep-dive-into-three-ai-academic-search-tools" rel="nofollow noopener" translate="no" target="_blank">https://katinamagazine.org/content/article/reviews/2025/deep-dive-into-three-ai-academic-search-tools</a> <a href="https://techhub.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://techhub.social/tags/libraries" class="mention hashtag" rel="nofollow noopener" target="_blank">#libraries</a> <a href="https://techhub.social/tags/search" class="mention hashtag" rel="nofollow noopener" target="_blank">#search</a> <a href="https://techhub.social/tags/RAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#RAG</a>

Markus EiseleBuild an AI-Powered Document Assistant with Quarkus and LangChain4j From Docs to Insightful Answers in Milliseconds <a href="https://myfear.substack.com/p/quarkus-langchain4j-ai-document-assistant" rel="nofollow noopener" translate="no" target="_blank">https://myfear.substack.com/p/quarkus-langchain4j-ai-document-assistant</a> <a href="https://mastodon.online/tags/Java" class="mention hashtag" rel="nofollow noopener" target="_blank">#Java</a> <a href="https://mastodon.online/tags/LangChain4j" class="mention hashtag" rel="nofollow noopener" target="_blank">#LangChain4j</a> <a href="https://mastodon.online/tags/Quarkus" class="mention hashtag" rel="nofollow noopener" target="_blank">#Quarkus</a> <a href="https://mastodon.online/tags/RAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#RAG</a> <a href="https://mastodon.online/tags/pgVector" class="mention hashtag" rel="nofollow noopener" target="_blank">#pgVector</a>

Tiago F. R. RibeiroCopilotKit é um ‘framework’ open‑source em TypeScript para criar copilotos de IA em minutos. Oferece:• Abstrações genéricas para LLMs e adaptadores para OpenAI, Anthropic, Azure, etc. • Pipeline de RAG com gestão automático de contexto. • Suporte a CoAgents paralelos e orquestração de fluxos conversacionais. • Módulos de UI generativa e hooks para React/Vue.📎<a href="https://docs.copilotkit.ai/" rel="nofollow noopener" translate="no" target="_blank">https://docs.copilotkit.ai/</a><a href="https://mastodon.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://mastodon.social/tags/TypeScript" class="mention hashtag" rel="nofollow noopener" target="_blank">#TypeScript</a> <a href="https://mastodon.social/tags/RAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#RAG</a> <a href="https://mastodon.social/tags/OpenSource" class="mention hashtag" rel="nofollow noopener" target="_blank">#OpenSource</a>

Markus EiseleBuild an AI-powered document assistant with Quarkus and LangChain4j Cloud-native AI for enterprise Java: RAG, embeddings, and native compilation <a href="https://developer.ibm.com/tutorials/build-ai-assistant-quarkus-langchain/" rel="nofollow noopener" translate="no" target="_blank">https://developer.ibm.com/tutorials/build-ai-assistant-quarkus-langchain/</a> <a href="https://mastodon.online/tags/Java" class="mention hashtag" rel="nofollow noopener" target="_blank">#Java</a> <a href="https://mastodon.online/tags/Quarkus" class="mention hashtag" rel="nofollow noopener" target="_blank">#Quarkus</a> <a href="https://mastodon.online/tags/LangChain4j" class="mention hashtag" rel="nofollow noopener" target="_blank">#LangChain4j</a> <a href="https://mastodon.online/tags/RAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#RAG</a> <a href="https://mastodon.online/tags/IBMDeveloper" class="mention hashtag" rel="nofollow noopener" target="_blank">#IBMDeveloper</a>

🧿🪬🍄🌈🎮💻🚲🥓🎃💀🏴🛻🇺🇸<a href="https://mastodon.social/tags/ContextEngineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#ContextEngineering</a> - Unlocking <a href="https://mastodon.social/tags/AgenticAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AgenticAI</a>’s True Potential> Today's <a href="https://mastodon.social/tags/LLMs" class="mention hashtag" rel="nofollow noopener" target="_blank">#LLMs</a> are far more complex with context size of millions of tokens and the ability for calling external systems, tools, and even <a href="https://mastodon.social/tags/agentic" class="mention hashtag" rel="nofollow noopener" target="_blank">#agentic</a> orchestration with multi-agent <a href="https://mastodon.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> systems. <a href="https://mastodon.social/tags/Context" class="mention hashtag" rel="nofollow noopener" target="_blank">#Context</a> has therefore evolved beyond the prompt to include System Prompt, User Input/Prompt, Memory, Retrieved Information (<a href="https://mastodon.social/tags/RAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#RAG</a> etc.), information on tools (<a href="https://mastodon.social/tags/MCP" class="mention hashtag" rel="nofollow noopener" target="_blank">#MCP</a>), responses from tools, and structured output format<a href="https://deepgains.substack.com/p/context-engineering-unlocking-agentic" rel="nofollow noopener" translate="no" target="_blank">https://deepgains.substack.com/p/context-engineering-unlocking-agentic</a>

WulfyHere is something you can add to your <a href="https://infosec.exchange/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> LLM vocab.<a href="https://infosec.exchange/tags/RAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#RAG</a> Retrieval Augmented Generation. Basically well defined, structured dataset with a LLM front end.One of the (valid) critiques of <a href="https://infosec.exchange/tags/LLM" class="mention hashtag" rel="nofollow noopener" target="_blank">#LLM</a> is that they do not have accurate data. This new technique allows natural language orchestration of your company/SME data with an AI on the front end.Use case; Your KB + Local model = RAG

Miguel Afonso Caetano"As frontier model context windows continue to grow, with many supporting up to 1 million tokens, I see many excited discussions about how long context windows will unlock the agents of our dreams. After all, with a large enough window, you can simply throw everything into a prompt you might need – tools, documents, instructions, and more – and let the model take care of the rest.Long contexts kneecapped RAG enthusiasm (no need to find the best doc when you can fit it all in the prompt!), enabled MCP hype (connect to every tool and models can do any job!), and fueled enthusiasm for agents.But in reality, longer contexts do not generate better responses. Overloading your context can cause your agents and applications to fail in suprising ways. Contexts can become poisoned, distracting, confusing, or conflicting. This is especially problematic for agents, which rely on context to gather information, synthesize findings, and coordinate actions.Let’s run through the ways contexts can get out of hand, then review methods to mitigate or entirely avoid context fails."<a href="https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-how-to-fix-them.html" rel="nofollow noopener" translate="no" target="_blank">https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-how-to-fix-them.html</a><a href="https://tldr.nettime.org/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://tldr.nettime.org/tags/GenerativeAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#GenerativeAI</a> <a href="https://tldr.nettime.org/tags/ContextEngineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#ContextEngineering</a> <a href="https://tldr.nettime.org/tags/PromptEngineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#PromptEngineering</a> <a href="https://tldr.nettime.org/tags/RAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#RAG</a> <a href="https://tldr.nettime.org/tags/MCP" class="mention hashtag" rel="nofollow noopener" target="_blank">#MCP</a> <a href="https://tldr.nettime.org/tags/LLMs" class="mention hashtag" rel="nofollow noopener" target="_blank">#LLMs</a> <a href="https://tldr.nettime.org/tags/AIAgents" class="mention hashtag" rel="nofollow noopener" target="_blank">#AIAgents</a>

infoDOCKETNEW Journal Article: "Retrieval-Augmented Generation of Event Collections from Web Archives and the Live Web" <a href="https://newsie.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://newsie.social/tags/RAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#RAG</a> <a href="https://newsie.social/tags/webarchives" class="mention hashtag" rel="nofollow noopener" target="_blank">#webarchives</a> <a href="https://mastodon.archive.org/@internetarchive" class="u-url mention" rel="nofollow noopener" target="_blank">@internetarchive</a> <a href="https://link.springer.com/article/10.1007/s00799-025-00419-7" rel="nofollow noopener" translate="no" target="_blank">https://link.springer.com/article/10.1007/s00799-025-00419-7</a>

JaschaHello World! <a href="https://infosec.exchange/tags/introduction" class="mention hashtag" rel="nofollow noopener" target="_blank">#introduction</a> Work in cybersec for 25+ years. Big OSS proponent. Latest projects:VectorSmuggle is acomprehensive proof-of-concept demonstrating vector-based data exfiltration techniques in AI/ML environments. This project illustrates potential risks in RAG systems and provides tools and concepts for defensive analysis. <a href="https://github.com/jaschadub/VectorSmuggle" rel="nofollow noopener" translate="no" target="_blank">https://github.com/jaschadub/VectorSmuggle</a>SchemaPin protocol for cryptographically signing and verifying AI agent tool schemas to prevent supply-chain attacks (aka MCP Rug Pulls). <a href="https://github.com/ThirdKeyAI/SchemaPin" rel="nofollow noopener" translate="no" target="_blank">https://github.com/ThirdKeyAI/SchemaPin</a><a href="https://infosec.exchange/tags/ai" class="mention hashtag" rel="nofollow noopener" target="_blank">#ai</a> <a href="https://infosec.exchange/tags/AiResearch" class="mention hashtag" rel="nofollow noopener" target="_blank">#AiResearch</a> <a href="https://infosec.exchange/tags/aisecurity" class="mention hashtag" rel="nofollow noopener" target="_blank">#aisecurity</a> <a href="https://infosec.exchange/tags/rag" class="mention hashtag" rel="nofollow noopener" target="_blank">#rag</a> <a href="https://infosec.exchange/tags/mcp" class="mention hashtag" rel="nofollow noopener" target="_blank">#mcp</a> <a href="https://infosec.exchange/tags/mcpserver" class="mention hashtag" rel="nofollow noopener" target="_blank">#mcpserver</a>

Alvin Ashcraft 🐿️Smarter SK Agents with Contextual Function Selection.<a href="https://devblogs.microsoft.com/semantic-kernel/smarter-sk-agents-with-contextual-function-selection/" rel="nofollow noopener" translate="no" target="_blank">https://devblogs.microsoft.com/semantic-kernel/smarter-sk-agents-with-contextual-function-selection/</a> <a href="https://hachyderm.io/tags/semantickernel" class="mention hashtag" rel="nofollow noopener" target="_blank">#semantickernel</a> <a href="https://hachyderm.io/tags/ai" class="mention hashtag" rel="nofollow noopener" target="_blank">#ai</a> <a href="https://hachyderm.io/tags/aiagents" class="mention hashtag" rel="nofollow noopener" target="_blank">#aiagents</a> <a href="https://hachyderm.io/tags/dotnet" class="mention hashtag" rel="nofollow noopener" target="_blank">#dotnet</a> <a href="https://hachyderm.io/tags/csharp" class="mention hashtag" rel="nofollow noopener" target="_blank">#csharp</a> <a href="https://hachyderm.io/tags/rag" class="mention hashtag" rel="nofollow noopener" target="_blank">#rag</a>

DiSC_uibkIf you start using the Rankify toolkit, feel free sharing your experience with us or let us know if you have any feedback or questions! 🤓 <a href="https://social.uibk.ac.at/tags/RAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#RAG</a> <a href="https://social.uibk.ac.at/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataScience</a> <a href="https://social.uibk.ac.at/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://social.uibk.ac.at/tags/research" class="mention hashtag" rel="nofollow noopener" target="_blank">#research</a>

DiSC_uibkHave you ever struggled to find the best document retrieval model for your project? Or had to combine multiple frameworks just to get a basic <a href="https://social.uibk.ac.at/tags/InformationRetrieval" class="mention hashtag" rel="nofollow noopener" target="_blank">#InformationRetrieval</a> pipeline running?Check out Rankify, developed by Abdelrahman Abdallah from the <a href="https://social.uibk.ac.at/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataScience</a> Group <a href="https://social.uibk.ac.at/@uniinnsbruck" class="u-url mention" rel="nofollow noopener" target="_blank">@uniinnsbruck</a>, which provides an all-in-one retrieval, re-ranking, and retrieval-augmented generation toolkit: <a href="https://www.doi.org/10.48763/000013" rel="nofollow noopener" translate="no" target="_blank">https://www.doi.org/10.48763/000013</a><a href="https://social.uibk.ac.at/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://social.uibk.ac.at/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#MachineLearning</a> <a href="https://social.uibk.ac.at/tags/RAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#RAG</a> <a href="https://social.uibk.ac.at/tags/OpenSource" class="mention hashtag" rel="nofollow noopener" target="_blank">#OpenSource</a> <a href="https://social.uibk.ac.at/tags/FOSS" class="mention hashtag" rel="nofollow noopener" target="_blank">#FOSS</a> <a href="https://social.uibk.ac.at/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLP</a> <a href="https://social.uibk.ac.at/tags/research" class="mention hashtag" rel="nofollow noopener" target="_blank">#research</a>

HERMES DatenkompetenzzentrumWir freuen uns ein weiteres der vier geförderten Projekte der zweiten Runde unseres <a href="https://fedihum.org/tags/Forschungsstudienprogramms" class="mention hashtag" rel="nofollow noopener" target="_blank">#Forschungsstudienprogramms</a> am Leibniz-Institut für Europäische Geschichte bekanntzugeben!🏆 Rainer Simon (<a href="https://vis.social/@aboutgeo" class="u-url mention" rel="nofollow noopener" target="_blank">@aboutgeo</a>) und Michela Vignoli für ihr Projekt „Digital Camerarius RAG: Multimodal Information Retrieval Prototype for CH and DH“. Digital Camerarius: <a href="https://furman-editions-in-progress.github.io/camerarius/" rel="nofollow noopener" translate="no" target="_blank">https://furman-editions-in-progress.github.io/camerarius/</a>Herzlichen Glückwunsch! Wir freuen uns auf die innovativen Erkenntnisse, die dieses Projekt hervorbringen wird 🎉 <a href="https://fedihum.org/tags/HERMES" class="mention hashtag" rel="nofollow noopener" target="_blank">#HERMES</a> <a href="https://fedihum.org/tags/DH" class="mention hashtag" rel="nofollow noopener" target="_blank">#DH</a> <a href="https://fedihum.org/tags/RAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#RAG</a>

Joanna BrysonSorry, serious question: is this paper real? <a href="https://arxiv.org/abs/2501.09136" rel="nofollow noopener" target="_blank">arxiv.org/abs/2501.09136</a> It's got 24 cites, but does the tech. even exist? How can vaporware be surveyed? The only citation in its bibliography with "agentic" in the title is from an undergraduate conference. Have I missed something? <a class="hashtag" rel="nofollow noopener" href="https://bsky.app/search?q=%23AI" target="_blank">#AI</a> <a class="hashtag" rel="nofollow noopener" href="https://bsky.app/search?q=%23RAG" target="_blank">#RAG</a> <a class="hashtag" rel="nofollow noopener" href="https://bsky.app/search?q=%23agentic" target="_blank">#agentic</a> <a href="https://arxiv.org/abs/2501.09136" rel="nofollow noopener" target="_blank">Agentic Retrieval-Augmented Ge...</a>

Nicole Hennigs3: The new RAG framework that trains search agents with minimal data <a href="https://venturebeat.com/ai/s3-the-new-rag-framework-that-trains-search-agents-with-minimal-data/" rel="nofollow noopener" translate="no" target="_blank">https://venturebeat.com/ai/s3-the-new-rag-framework-that-trains-search-agents-with-minimal-data/</a> <a href="https://techhub.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://techhub.social/tags/RAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#RAG</a>

Emory<a href="https://masto.hackers.town/@megabyteghost" class="u-url mention" rel="nofollow noopener" target="_blank">@megabyteghost</a> if i didn't have SetApp i probably wouldn't use it at all. i have weird mixed feelings about TypingMind too, also in SetApp!<a href="https://soc.kvet.ch/tags/Witsy" class="mention hashtag" rel="nofollow noopener" target="_blank">#Witsy</a> is a linux/macOS/windows swiss army <a href="https://soc.kvet.ch/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> tool that has image generation canvas, text generation pad/chatbots, and <a href="https://soc.kvet.ch/tags/RAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#RAG</a> via Document Repositories. and you can use local embeddings and still have cloud LLMs able to work with you. dope. the speech feature is normally great but not working for me at the moment.here's the clever workbench <a href="https://soc.kvet.ch/tags/editor" class="mention hashtag" rel="nofollow noopener" target="_blank">#editor</a>:

Nicole HennigWhy enterprise RAG systems fail: Google study introduces ‘sufficient context’ solution <a href="https://venturebeat.com/ai/why-enterprise-rag-systems-fail-google-study-introduces-sufficient-context-solution/" rel="nofollow noopener" translate="no" target="_blank">https://venturebeat.com/ai/why-enterprise-rag-systems-fail-google-study-introduces-sufficient-context-solution/</a> <a href="https://techhub.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://techhub.social/tags/RAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#RAG</a> <a href="https://techhub.social/tags/hallucination" class="mention hashtag" rel="nofollow noopener" target="_blank">#hallucination</a>

Gary AckermanRetrieval-Augmented Generation (RAG) is a powerful technique to ground LLMs on external data. This enhances the relevance of responses and helps reduce hallucinations by providing context. <a href="https://qoto.org/tags/RAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#RAG</a> <a href="https://qoto.org/tags/LLMArchitecture" class="mention hashtag" rel="nofollow noopener" target="_blank">#LLMArchitecture</a>

Europe Says<a href="https://www.europesays.com/2092537/" rel="nofollow noopener" translate="no" target="_blank">https://www.europesays.com/2092537/</a> NetApp partners with NVIDIA to boost AI data storage in Australia <a href="https://pubeurope.com/tags/AIAdoption" class="mention hashtag" rel="nofollow noopener" target="_blank">#AIAdoption</a> <a href="https://pubeurope.com/tags/AIAgents" class="mention hashtag" rel="nofollow noopener" target="_blank">#AIAgents</a>(AgenticAI) <a href="https://pubeurope.com/tags/ArtificialIntelligenceAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#ArtificialIntelligenceAI</a> <a href="https://pubeurope.com/tags/australia" class="mention hashtag" rel="nofollow noopener" target="_blank">#australia</a> <a href="https://pubeurope.com/tags/CloudStorage" class="mention hashtag" rel="nofollow noopener" target="_blank">#CloudStorage</a> <a href="https://pubeurope.com/tags/DataGovernance" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataGovernance</a> <a href="https://pubeurope.com/tags/DataInfrastructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataInfrastructure</a> <a href="https://pubeurope.com/tags/DataSecurity" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataSecurity</a> <a href="https://pubeurope.com/tags/EnterpriseStorage" class="mention hashtag" rel="nofollow noopener" target="_blank">#EnterpriseStorage</a> <a href="https://pubeurope.com/tags/GenerativeAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#GenerativeAI</a>(GenAI) <a href="https://pubeurope.com/tags/NetApp" class="mention hashtag" rel="nofollow noopener" target="_blank">#NetApp</a> <a href="https://pubeurope.com/tags/Nvidia" class="mention hashtag" rel="nofollow noopener" target="_blank">#Nvidia</a> <a href="https://pubeurope.com/tags/RAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#RAG</a> <a href="https://pubeurope.com/tags/RetrievalAugmentedGeneration" class="mention hashtag" rel="nofollow noopener" target="_blank">#RetrievalAugmentedGeneration</a>(RAG)

HoldMyTypeIn order for an <a href="https://mathstodon.xyz/tags/LLM" class="mention hashtag" rel="nofollow noopener" target="_blank">#LLM</a> using <a href="https://mathstodon.xyz/tags/RAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#RAG</a> to come up with a good answer, it has to both retrieve the information correctly and generate the response correctly. A bad answer results when one or both parts of the process fail.In the case of <a href="https://mathstodon.xyz/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> Overviews’ recommendation of a pizza recipe that contains glue—drawing from a joke post on Reddit—it’s likely that the post appeared relevant to the user’s original query about cheese not sticking to pizza, but something went wrong in the retrieval process, says Shah. “Just because it’s relevant doesn’t mean it’s right, and the generation part of the process doesn’t question that,” he says.Similarly, if a RAG system comes across conflicting information, like a policy handbook and an updated version of the same handbook, it’s unable to work out which version to draw its response from. Instead, it may combine information from both to create a potentially misleading answer. “The large language model generates fluent language based on the provided sources, but fluent language is not the same as correct information,” says Suzan Verberne, a professor at Leiden University who specializes in natural-language processing.The more specific a topic is, the higher the chance of <a href="https://mathstodon.xyz/tags/misinformation" class="mention hashtag" rel="nofollow noopener" target="_blank">#misinformation</a> in a large language model’s output, she says, adding: “This is a problem in the medical domain, but also education and science.” <a href="https://www.technologyreview.com/2024/05/31/1093019/why-are-googles-ai-overviews-results-so-bad/" rel="nofollow noopener" translate="no" target="_blank">https://www.technologyreview.com/2024/05/31/1093019/why-are-googles-ai-overviews-results-so-bad/</a>

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