"🚀 Take Your AI Agent Live with Amazon Bedrock AgentCore" by Swarali Chine
#amazon-bedrock-agentcore #strands-agents #amazon-bedrock #rag #guardrails

"🚀 Take Your AI Agent Live with Amazon Bedrock AgentCore" by Swarali Chine
#amazon-bedrock-agentcore #strands-agents #amazon-bedrock #rag #guardrails
EmbeddingGemma2 vs. Gemini Embedding 2:実用的な多言語テキスト・画像検索の比較検証
RATED is a new retrieval framework for AI agents that combines semantic matching with a contextual bandit module to adapt to runtime tool failures. The authors also release EcoAgent, a benchmark of 5,172 real-world tools and…
#AIagents #RAG #MLresearch #Tooling
https://arxiv.org/abs/2610.13889
Finding a source paper is not the same as writing an accurate report. Explore a physics RAG proposal using titles and abstracts: semantic search, BM25, rank fusion and reranking. Recall@10 does not verify generated claims.
https://note.com/hydezero/n/na3ae20fd2064?hl=en
#Physics #RAG
I created this animated flow to represent that progression, from the first Python programs to RAG architecture.
🔗 My GitHub repository:
github.com/Disha-pro/CH...
KnowForge separates document QA into a Locator agent, a deep Reader, and a typed evidence pool, instead of letting one model handle both retrieval and reading on its own. Reported F1 of 47.5 on M3DocVQA suggests the role-specialized…
#arXiv #AIagents #RAG #devtools
https://arxiv.org/abs/2610.13128
Building an AI agent is easy. Engineering one you can trust in production is the real challenge.
Leanpub: leanpub.com/staai
Google Play Books: play.google.com/store/books/...
#AgenticAI #SystemsThinking #AIEngineering #SoftwareArchitecture #AIAgents #RAG #MCP
DockerがAIエージェントを配れるようにした理由
https://papoo.work/doc/f17a959b559c8b17
#docker #aiエージェント #dockerdesktop #mcp #rag
DockerがAIエージェントを配れるようにした理由
https://papoo.work/doc/f17a959b559c8b17
#docker #aiエージェント #dockerdesktop #mcp #rag
DockerがAIエージェントを配れるようにした理由
https://papoo.work/doc/f17a959b559c8b17
#docker #aiエージェント #dockerdesktop #mcp #rag
🔍 AI search at millions of embeddings needs more than brute force.
Vector indexing methods like HNSW, IVF and PQ help balance search speed, accuracy and memory.
The goal: relevant results at scale.
New arXiv paper examines whether memorization in LLMs changes when prefixes appear with surrounding context like RAG inputs, using paired item-level measurements across three open-weight instruction-tuned models. The work asks…
#LLM #Memorization #RAG #AIResearch
https://arxiv.org/abs/2610.12085
#LangGraph #RAG #Kubernetes #OpenTelemetry #Observability
One of the biggest concerns many teams have with cloud-based LLMs is sending private data, even with agreements in place. Payslips, health records, and private notes are good examples. For many teams the answer is
MCP Tool Server Context7 Agent: Stop stuffing 100k tokens into system prompts. Context7 streams surgical RAG chunks via FastMCP, cutting token costs by 76% and dropping hallucinations to 1.1%. Full blueprint: https://botskillstack.com/bots/mcp-tool-server-context7-agent #RAG #
Antes de aceptar una mejora de RAG, pide qué se midió: documentos recuperados, fidelidad al contexto o respuestas válidas para el caso. Una cifra sin esa distinción puede responder a una pregunta distinta de la tuya.
RIT-RAG proposes combining content retrieval with structural navigation by building per-document trees offline and using chunk positions at query time to guide retrieval at corpus scope.