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Nov 1Sun
  1. Hacker News AI55

    Show HN: CiteLLM – Verify AI Answers in PDFs

    CiteLLM is a tool that links every AI answer, claim or extracted value to the exact passage in the original PDF, with the passage highlighted, and lets reviewers approve or flag each result and export an evidence report. It is offered as a plugin for ChatGPT and Claude, a standalone web app, document search with passage-level citations, and structured extraction with an API. The site states that documents are never used to train models, files are deleted after processing by default, and a Docker self-host option is available.

Oct 30Fri
  1. Hacker News AI58

    Show HN: Omneum – Local, text-blind context control plane for AI agents

    Omneum is an open-source context control plane that sits between retrieval and generation, using provenance-aware graph inference to estimate source reliability and dependency-adjusted claim support, then selecting original passages under a token budget. It runs locally by default, persisting observations in SQLite or PostgreSQL, and evaluates structured observations without reading source text or calling an LLM judge. The project reports precision gains of 3.02–8.44 percentage points and 14.46–18.51% lower reconstructed-context token usage across BM25, MiniLM, BGE Reranker v2-m3, and Cohere Rerank v4.0 Pro integrations.

Oct 29Thu
  1. Hacker News AI58

    Show HN: ThreadShelf – Obsidian and AI chats: vector search, MCP, llama.cpp

    ThreadShelf is a local-first archive that parses exports from ChatGPT, Claude, Google AI Studio, OpenRouter, LM Studio and Grok into a common format, embeds them locally and stores them in LanceDB for semantic and exact search. The same index is reachable through a React UI, CLI, HTTP API and an MCP stdio server, and archived threads can optionally be continued with a loopback-only llama.cpp GGUF model or the explicitly external OpenRouter provider. It also connects Markdown/Obsidian vaults for local note search and bounded tool-calling workflows, with vault writes off by default.

Oct 28Wed
  1. Hacker News AI43

    Scaling AI in Legal: Building Uber's Redlining Agent

    Uber describes building its first-generation Legal Redlining Agent (LRA), a Microsoft Word add-in that reviews counterparty redlines, detects intent, recommends policy-aligned responses, generates comments, and flags risks for lawyer review. Through four iterations—RAG, lawyer feedback loops, tone modulation, and agentic modifications plus a rules database—Uber reports over 20% reduction in average contract review time and 91% accuracy in AI-generated decisions. The post details the architecture (React/TypeScript add-in, Python back end, LangGraph workflow, OpenSearch vector stores, Redis session memory) and time-weighted feedback sampling to mitigate concept drift.

  2. Hacker News AI58

    Harvey raised 500M for legal AI, I built the same and open sourced it

    CaseCite is a source-available, self-hosted legal research platform that lets firms upload and index their own documents and run chat-based research with inline citations, contract review, drafting, and CourtListener-backed case-law tools on infrastructure they control. It is licensed under the Elastic License 2.0, free to self-host and modify internally but not to offer as a hosted service, and AI features send document text to user-configured providers (BYOK) unless self-hosted OpenAI-compatible endpoints are set.

  3. Hacker News AI58

    Writing AI agents in YAML instead of Python

    This article walks through Docker's open-source docker-agent tool, which defines an AI agent's model, instructions, and toolsets in a single YAML file and runs it with one command. Using a Polars documentation Q&A agent, it demonstrates a built-in RAG toolset with local SQLite vector storage, per-run model overrides, prompt files, flavors for variants, multiple agents sharing tools, JSON-based evals, and pushing agents to a registry. It notes that code-first frameworks like LangGraph suit cases requiring custom branches or retries.

  4. MarkTechPost65

    Meet LEANN: An Open-Source Vector Index that Shrinks a 60M-Chunk RAG Index from 201 GB to 6 GB

    UC Berkeley's Sky Computing Lab built LEANN, an MIT-licensed vector index on FAISS that stores a pruned graph and recomputes embeddings at query time instead of saving them, shrinking a 60M-chunk Wikipedia index from 201 GB to 6 GB in the repo benchmark. The paper, which won Best Research Paper at MLSys 2026, reports matching HNSW downstream accuracy (25.5% on NQ) at 90% recall using 4 GB instead of 188 GB, at the cost of slower retrieval (2.48 s vs 0.05 s on NQ). It ships apps for documents, Apple Mail, Chrome history, WeChat, iMessage and chat exports, plus an MCP server for Claude Code.

  5. Hacker News AI38

    AI search explained: indexing, retrieval and reranking

    This post walks through three retrieval approaches on a synthetic ten-note dataset from eight tools: text search using an analyzer and inverted index, embedding search using an embedding model and vector database, and embedding search with a reranker that reorders a shortlist by relevance. It shows text search missing paraphrases, embedding search surfacing semantically similar notes but ranking a refund how-to above the actual reasons, and reranking putting the relevant notes on top. It concludes with guidance on when to use each approach and notes that production systems often combine text and embedding search via hybrid search with reciprocal rank fusion before reranking.

  6. Hacker News AI58

    Survival LLM: AI assistant and emergency library that runs on your own computer

    Survival LLM is a free, open-source offline AI assistant and emergency reference library that runs entirely on the user's computer, answering questions from sources like FEMA, CDC, MedlinePlus, WikEM, military survival manuals, wikiHow, iFixit and about 930,000 Wikipedia articles while showing the exact passages used. It selects an AI model based on available memory (from IBM Granite 4.2 8B at 16–31 GB up to NVIDIA Nemotron 3.5 Super at 128 GB or more), requires Apple Silicon macOS 14+ or 64-bit Windows 10/11, and needs internet only during setup. The library is a 9 GB download (19 GB installed), or 1.3 GB (3 GB installed) without Wikipedia and iFixit, and users can also add their own PDFs, Word documents, ebooks and notes for the AI to search locally.

  7. Databricks Blog33

    Unlocking Investment Insights Without Migrating Your Data: Lakehouse Federation

    Databricks Lakehouse Federation lets users create federated connections to external databases such as SQL Server, PostgreSQL, MySQL, and Snowflake and query them directly from Databricks with standard SQL, keeping data in place. The post walks through a wealth management example that joins on-premises portfolio holdings with FactSet data subscribed from the Databricks Marketplace in a single query, then powers a dashboard and a Genie natural-language agent without pipelines, materialized views, or persisted objects. It notes the same pattern applies to other industries, such as healthcare, manufacturing, and retail.

Oct 27Tue
  1. Databricks Blog31

    Unify your productivity and AI data with Lakeflow Connect

    Lakeflow Connect offers fully managed native connectors that ingest data from productivity tools such as Jira, Confluence, Notion, SharePoint, Google Drive, Outlook, Gmail, GitHub, OpenAI and Anthropic into governed Delta tables in Unity Catalog. Unstructured content like files, pages and email bodies lands as tables that can be cleaned up or parsed with ai_parse_document for retrieval and AI agents. Customers including Toyota Finance Australia, VaxCare, M3 Accounting, DigiCert, Orizon and TRegs describe using the connectors for Jira, Confluence, GitHub, SharePoint and Monday.com data.

  2. AWS Machine Learning Blog38

    Build agentic retrieval on a Managed Amazon Bedrock Knowledge Base with CDK

    This post shows how to build enterprise agentic retrieval on a Managed Amazon Bedrock Knowledge Base using the AWS CDK in Python, deploying four stacks (knowledge bases, AgentCore Gateway, agent runtime, and dashboards) with one `cdk deploy --all` command. It contrasts classic single-shot RAG with agentic retrieval, where an agent on AgentCore Runtime routes questions across two managed knowledge bases (financial and weather) exposed as MCP tools through the Gateway, invoking the AgenticRetrieveStream API for grounded, cited answers. The solution also provisions two CloudWatch dashboards spanning seven telemetry layers plus on-demand and continuous evaluation, since agentic systems need observability to verify behavior.

  3. Databricks Blog40

    Building live intelligence: Databricks AI Search supports Streaming Tables, Materialized Views, and Managed tables using Iceberg

    Databricks AI Search now natively supports Streaming Tables, Materialized Views, and Managed tables using Iceberg v3+ in addition to Delta tables, letting users index live events, continuously maintained personalized state, and open-format data directly without separate conversion and synchronization pipelines. Streaming Tables keep indexes fresh as new records arrive, Materialized Views propagate incrementally maintained aggregated or joined state for personalized search, and Managed Iceberg tables serve as sources without conversion or migration. Index creation follows the same Delta Sync index workflow via UI or API, eliminating the intermediate Delta table and custom sync pipeline.

  4. MarkTechPost55

    Nace AI Releases Drex for Search for AI Agents: 92.05% on BrowseComp-Plus With 12.6 Search Calls

    Nace AI released Drex for Search, a retrieval layer for people and agents that coding agents such as Claude Code, Codex, Cursor and OpenCode call through CLI, SDKs, MCP or REST. Nace reports 92.05% on BrowseComp-Plus with GPT-5 using 12.61 search calls per question, and 77.5% R@1 on VisDocAgentBench with an open-weight Qwen 27B planner. On OfficeQA Pro, Nace says accuracy rose and agent turns fell across all 9 harness and model pairs it tested.

Oct 24Sat
  1. AWS Machine Learning Blog34

    How Bayer Crop Science built an agentic decision engine on Amazon Bedrock AgentCore with AWS Unified Operations

    Bayer Crop Science built the Agentic Decision Engine (ADE), a multi-agent natural language interface over its Connected Decision Engine, running on Amazon Bedrock AgentCore with LangGraph orchestration and 22 specialist agents. In its first phase, ADE resolves about 80 percent of user questions without an engineer in the loop, and exposing APIs through AgentCore Gateway cut per-API integration overhead by more than 99 percent. The post details five design decisions, production readiness work such as task-level observability and cost attribution, and ongoing operations with AWS Unified Operations.

Oct 23Fri
  1. AWS Machine Learning Blog18

    How U.S. Hunger connects leaders and data using Amazon Quick

    U.S. Hunger, which reports delivering more than 250 million meals, uses Amazon Quick to let CEO Rick Whitted explore Full Cart datasets directly and to give 17 colleagues across leadership, technology, finance, and compliance shared Spaces for KPI definitions, financial planning, and research. Data from its Impact Manager platform is refreshed from SQL Server into SPICE nightly at 2 AM ET, and the organization plans a Q4 2026 enterprise KPI framework routing NetSuite, Salesforce, and other systems through Boomi into Amazon Redshift.

  2. AWS Machine Learning Blog31

    How IskayPet unified corporate knowledge with Amazon Bedrock Managed Knowledge Bases

    IskayPet, an Iberian pet care company with over 330 stores and 4,500 employees, built a corporate knowledge assistant on Amazon Bedrock Managed Knowledge Bases that answers employee questions across retail operations, veterinary care, people and onboarding, and procurement in four languages and three countries without managing RAG infrastructure. The solution uses the native SharePoint connector with advanced indexing, a dedicated knowledge base per assistant scoped to specific sites or libraries, document-level access control tied to existing SharePoint permissions, and hybrid search with reranking. Built with Kiro and Claude Haiku 4.5, it reportedly cut time to find an operational answer from 3–5 minutes to about 7 seconds in roughly the first two months in production.