According to @_avichawla, agentic retrieval with indexing, routing, query construction, retrieval, and generation outperforms linear RAG for multi-source data.

According to @CNBC, client chatbots now rival human advisors, pushing firms to add AI planning tools, audits, and compliance workflows.

According to @bcherny, engineers see 10x output with Claude as orgs progress through four repeatable AI adoption steps.

According to @NotebookLM, Ethan Mollick contrasts ChatGPT Work with NotebookLM on 70+ files, highlighting process and sources centric answers.

According to @claudeai, Anthropic details Claude Code’s origins, early user feedback, and product milestones in a new behind the scenes video.

According to @claudeai, Claude Science links artifacts to code, spins up managed environments, and connects 60+ scientific databases in beta.

According to @_avichawla, moving transforms to GPU cuts CPU GPU transfer 4x; binary quantization shrinks embeddings 32x for fast RAG search.

According to @_avichawla, BM25 still powers Elasticsearch and OpenSearch, excels at exact matches, and pairs best with vectors for hybrid RAG.

According to @_avichawla, 8 RAG patterns from Naive to Agentic boost accuracy, reduce tokens 3x, and cut corpus 40x via better indexing.

According to AndrewYNg, a new deeplearning.ai course with VocalBridge teaches building low latency, reliable voice agents and outbound call bots.

According to DeepLearningAI, a free course shows how to add voice to AI agents with minimal code across 3 patterns without changing prompts or RAG.

According to @_avichawla, a 10-layer AI stack spans foundations to LLMOps, detailing RAG, agents, fine tuning, evals, and inference for 2026 deployment.

According to @_avichawla, swapping Firebase for InsForge cut tokens 5.5M to 2.3M and errors to zero by exposing backend topology via one CLI JSON call.

According to @godofprompt, 7 MCP servers let AI access files, data, and tools, enabling operator workflows and automations for real business impact.

According to @NotebookLM, users can now view prompts and sources behind each artifact and quickly iterate to customize outputs.

According to AndrewYNg, a new Deeplearning.ai course with AMD teaches LLM internals, attention, RAG, and GPU inference optimization for faster deployment.

According to grok... Grok adds 20+ connectors for docs, calendar, email, and code to automate retrieval and actions, as reported by X post on May 11, 2026.

According to @_avichawla, a free roadmap covers prompt engineering, RAG, fine-tuning, agents, deployment, optimization, and safety with open-source links.

According to DeepLearning.AI, distributed AI is redefining vector databases and making deployment topology a core design choice for modern architectures.

According to @_avichawla on X, moving to sparse attention at 128K tokens cuts prefilling cost from about $0.65 to $0.35 per million tokens and decoding from about $2.4 to $0.8, with equal or better long-context performance on V3.2. As reported by the post, sparse attention can preserve quality when engineered carefully, opening room for larger context windows without prohibitive inference costs. According to research cited broadly in industry literature, additional techniques to extend context include Rotary or YaRN position scaling to stabilize very long sequences, linear attention variants such as Performer or Hyena to reduce quadratic complexity, retrieval-augmented generation to offload context to external memory, chunking with cross-attention bridges for hierarchical conditioning, sliding-window or recurrent state compression to maintain continuity, and test-time attention sinks or key-value cache eviction policies to cap memory growth. For businesses, these methods can lower serving costs, improve long-document QA, contract analysis, code comprehension, and multimodal transcripts, while maintaining accuracy at scale, according to common enterprise LLM deployment case studies.