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Nov 14Sat
  1. Hacker News AI55

    Show HN: Scatter – better way to learn new things with AI

    Scatter is a source-available, browser-based app that lets you explore topics through AI-generated mind maps, where marked terms open connected explanations on an infinite canvas. It supports bring-your-own API keys for cloud providers like OpenRouter, OpenAI and Anthropic or local servers such as Ollama, and keeps projects in your browser with no app server in between. Features include interactive nodes, source-grounded answers with citations, fact-checking, image generation and editing, and Obsidian Canvas export.

  2. MarkTechPost59

    Stanford Research Open-Sources Memorilla, a Latent Memory Module for Frozen LLMs

    Stanford researchers have released Memorilla, an open-source 194M-parameter memory module that condenses a whole document collection into 16 memory tokens for a frozen LLM, with code, training recipes and checkpoints public under Apache 2.0. On FactKG, a frozen Qwen3-8B scored 84.33% with Memorilla versus 80.43% for full context, using 108x fewer document tokens, and it reported the highest average (49.68) across 8 benchmarks while losing on PubMedQA (59.00% vs 69.00% for LLMLingua-2). The quickstart requires one 80 GB CUDA GPU, and the full training recipe takes about 19 hours on 4 H100 GPUs.

  3. Hacker News AI58

    Show HN: Gamma PDF – AI-native PDF reader, self-hosted

    Gamma PDF is an open-source, self-hosted PDF annotator with outliner notes, live Markdown and LaTeX, and an AI that answers questions about papers with cited answers using your own Anthropic or OpenAI key or a paid ChatGPT plan. It runs on Windows, macOS, Linux, iPad, Android and Docker, stores data as SQLite and files in one folder with no account or telemetry, and supports shared workspaces, Zotero/Obsidian/Logseq/Notion imports, and read-only MCP access for Codex and Claude Code.

Nov 13Fri
  1. Hacker News AI38

    Stanford CS 329Z: Engineering AI Agents

    Stanford's CS 329Z course covers engineering AI agents across foundations, LLM internals, RAG, tool use, frameworks, design patterns, memory, multi-agent systems, optimization, data, evaluation, safety, and coding and personal agents. It includes guest lectures on Terminal-Bench, SWE-bench, and a fireside chat with Andrew Ng, plus a final project demo day.

  2. Hacker News AI40

    AI Security Wiki

    The AI Security Wiki is a reference for securing enterprise AI adoption, covering governance, risk management, secure engineering, monitoring, vendor evaluation and assurance, with pages reusable as templates that specify the audit evidence each activity should produce. It positions itself as the implementation layer—control objectives, evidence, audit procedures and test cases—that works alongside frameworks such as FINOS AIGF, ISO/IEC 42001, NIST CSF, PCI DSS and CERT-In rather than replacing them. The repository includes a 682-case test library, regional compliance hubs for the GCC, EU and other jurisdictions, framework crosswalks, role-based playbooks and downloadable templates.

  3. OpenRouter Announcements60

    Reranking in RAG: How to Fix Bad Retrieval with a Reranker

    This guide explains that vector search often ranks near-miss chunks first because embeddings compress whole chunks into single vectors, and shows how adding a cross-encoder reranker over the top 30 candidates raised precision@1 from 81% to 97% on a 274-question synthetic benchmark at roughly $0.10 per 1,000 queries. It compares 11 rerankers (voyageai/rerank-3-lite and rerank-3 were the most accurate dedicated models and cheapest, while LLM rerankers like openai/gpt-6-luna hit 99.6% but took about 4 seconds per call), covers candidate-count tuning, input_type settings, and silent failures such as cohere/rerank-v3.5's 4,096-token limit. It includes a Python example using the embeddings and rerank APIs and notes that real-world gains depend on your own documents and evaluation set.

    Why it matters: Offers a measured, benchmark-backed walkthrough of adding reranking to a RAG pipeline, including cost, latency and failure-mode comparisons useful for teams tuning retrieval.

  4. AWS Machine Learning Blog47

    How Natera designed an agentic AI system for lab ops with Amazon Bedrock AgentCore

    Natera's lab engineering team built SWARM (Systematic Workflow Analytics and Resolutions Manager) on Amazon Bedrock AgentCore to capture and deploy institutional knowledge for lab instrument support, using two runtimes (swarm-agent and swarm-hive) that share a versioned library of 33 skills. The system reduced average first response time from 17.4 hours to roughly 8 minutes (about 130x faster) and delivered an automated first response on 96.2 percent of tickets, measured over a 90-day production evaluation period. The post details the architecture, including a custom document pipeline with Amazon Titan Text Embeddings and Amazon Bedrock Knowledge Bases, keyless OIDC-based deployment, Jira integration via EventBridge and Amazon SQS, and a continuous evaluation loop.

  5. AWS Machine Learning Blog34

    Migrate to Amazon Bedrock Managed Knowledge Base with your coding agent

    AWS added a knowledge base migration guide to the amazon-bedrock skill in the Agent Toolkit for AWS, which lets a coding agent plan a move from a customer-managed knowledge base to Amazon Bedrock Managed Knowledge Base, starting with read-only discovery and a plan you review line by line. On approval, the agent creates the new knowledge base and data sources alongside the original or writes a migrated infrastructure as code template, while ingestion and updating the application's Retrieve calls remain with your team. The post covers why AWS recommends the move, prerequisites, installation, planning, updating retrieval calls, and validating retrieval before cutover.

Nov 12Thu
  1. Hacker News AI60

    Show HN: A free course from engineer to AI engineer, one Git tag per episode

    A free YouTube-based course, "From engineer to AI engineer," teaches experienced software engineers with no generative-AI background to build a retrieval-augmented generation (RAG) pipeline in eight episodes, with a second seven-episode series, "Prove it works," building an evaluation harness on the same repository. Each episode is one commit and one Git tag (s1e1–s1e8 and s2e1–s2e7), so checking out a tag reproduces the repository exactly as that episode leaves it. The series uses TypeScript, SQLite, curl and an HTTP server, answering with claude-sonnet-5-5 over a fictional 26,000-word corpus (Lantern Supply Co.) that includes deliberate traps such as near-identical product names and a policy changed in later release notes.

  2. Hacker News AI20

    Show HN: A free CRM with AI tools for independent insurance agencies

    HarborIQ offers a free CRM for independent insurance agencies covering clients, pipeline, renewals and follow-ups, designed to sit alongside an existing AMS, with CSV imports from systems such as Epic, EZLynx, HawkSoft, NowCerts and AMS360. Its paid Intelligence tier is $49 a month for the whole agency and answers carrier questions from the agency's own carrier guides, citing the source page, with 300 shared checks a month. The free CRM requires no card and has no per-seat fee.

  3. AWS Machine Learning Blog42

    Accelerating generative AI delivery with reusable assets

    AWS's Generative AI Innovation Center, now part of AWS Frontier AI Engineering & Services, offers a catalog of 23 production-ready open source reusable assets such as RAG pipelines, document processors, and agent scaffolding. The post cites DoorDash and Lyft using the Contact Center GenAI Agent and Ricoh using the GenAI Intelligent Document Processing accelerator, reporting results including a 50x increase in testing capacity and a 7-fold throughput increase. It also walks through deploying the Contact Center GenAI Agent and lists the full asset catalog on GitHub and AWS Labs.

  4. Databricks Blog38

    Air gapped, not data dark: how Bright Machines gives customers live factory dashboards without opening the plant

    Bright Machines captures PostgreSQL changes at the factory edge with Debezium, writes them as Apache Iceberg, pushes them out over a single auditable outbound path, and reads them in place in Databricks via Unity Catalog federation, with zero inbound ports to the plant. A row committed on the line is queryable in Databricks in one to two minutes, dashboards refresh every ten minutes, and a heartbeat check detects a broken pipeline in under thirty minutes. The first customer-visible dashboard went live about two months after kickoff, replacing nine loosely coupled services with one governed platform plus one edge path.

Nov 11Wed
  1. Databricks Blog60

    From enterprise context to action: Genie Agents now analyze more data sources to transform your work

    Databricks announced that Genie Agents can now analyze unstructured files in Unity Catalog volumes, connect to enterprise tools via generally available native MCP connectors, and be shared outside an organization through OpenSharing and Genie Clean Rooms (both in beta). The post cites customers including Unilever, HelloFresh, S&P Global Energy, Atlassian, General Motors and ModMed, and describes how agents parse documents, cite specific pages, and combine file content with governed tables.

    Why it matters: Useful for readers tracking how enterprise AI agents are expanding from structured-data querying to document analysis, tool actions and cross-organization sharing.

  2. Databricks Blog69

    Announcing GA of Genie Ontology

    Databricks announced the General Availability of Genie Ontology, an agentic ontology layer that agents build by learning from dashboards, SQL queries, notebooks and BI semantic models, while people govern definitions through Unity Catalog semantics. The release adds Genie Search (GA), Ontology Snippets (GA), external knowledge sources (Beta, starting with Power BI), organizational directories (GA, starting with Workday), Domains and Pages (GA), and Ontology Manager (coming soon in Beta). In a benchmark built with Turing simulating a Databricks workspace for a fictional UK energy supplier, Genie answered correctly in 91% of runs versus 45% and 58% for two frontier agents, using less than half the tokens and finishing three to four times faster.

    Why it matters: It details a major enterprise context-layer release and includes benchmark numbers comparing agents with and without the ontology.

  3. Hugging Face Blog38

    Token Pooling-Aware Training for Multi-Vector Embedding Models

    This post explains token pooling for multi-vector embedding models, which merges similar document token vectors via Ward hierarchical clustering to cut storage, and introduces pooling-aware training in Sentence Transformers that applies the retrieval loss to both original and pooled representations. At pool_factor=4, the pooling-aware model retains 97.8% of its unpooled NanoBEIR nDCG@10 versus 94.6% for a normally trained model, with a 4x reduction in document vectors. It includes a reproducible training example, optional kernel acceleration, and evaluation results across pool factors, fixed caps, and calibrated adaptive thresholds.

  4. Hacker News AI22

    Try our new AI that was built with it's user in mind

    Olo is a Mac intelligent companion, free with in-app purchases, that answers questions from sources you can open and checks each sentence as supported, contradicted or not covered. It can be invoked with Option+Space in any app or via Services > Ask Olo on selected text, and it accepts PDFs, Word documents, notes and chosen folders. Olo Starter (£19.99) and Olo Growth (£49.99) are auto-renewable monthly subscriptions, and the app requires macOS 14.0 or later.

Nov 10Tue
  1. Hacker News AI20

    Show HN: We wanted to build an AI application, not maintain RAG pipelines

    Akropolys offers free integration of live voice, chat, and proactive personal agents directly into a website, with Kiku providing the user-facing experience. It connects to data either through batch ingestion of an array of records (Growth, Pro and Business plans) or single-record ingestion (all plans including Free), both building the same search index. A coding agent can be given the Akropolys integration skill via `npx skills add https://akropolys.cloud --skill akropolys-integration`.

  2. Hacker News AI31

    Almanzor – Ask AI and an MCP server for your docs, with cited answers

    Almanzor Ask turns your docs or website into an assistant that answers from your own content, links the page it used, and says "I don't know" when the answer isn't there. It offers a docs MCP for coding agents such as Cursor or Claude Code, and a floating Ask button added with one line of code. It is hosted in the EU, and the company says content and conversations are never used to train models.

  3. AWS Machine Learning Blog38

    Single-shot and agentic retrieval with Amazon Bedrock Managed Knowledge Base

    Amazon Bedrock Managed Knowledge Base now supports single-shot RAG and retrieval-only requests through AgenticRetrieveStream, alongside existing agentic retrieval, letting one API serve simple and complex questions. The maxAgentIteration range is now 0–5: 0 returns passages with no model calls, 1 performs one retrieval plus one generated answer, and 2–5 run up to n minus 1 planning rounds before answering. The post illustrates each mode with a fictional banking policy assistant and advises choosing the lowest iteration limit that meets each workflow's quality needs.

Nov 9Mon
  1. Hacker News AI38

    Show HN: ModelRefs – Interactive reference hub for AI models

    ModelRefs is a structured reference layer that helps builders choose, compare, and implement AI models, providers, benchmarks, workflows, and tools using canonical registries, Knowledge Graph relationships, transparent methodology, and governance-aware evidence. The reference layer is free to read without an account, while the Evidence API is a paid subscription starting at $19 a month that provides programmatic JSON access to the same governed evidence with citations and methodology limits.

Nov 7Sat
  1. AWS Machine Learning Blog40

    Serverless MCP server for AI agent data lake access on Amazon ECS Fargate

    Amazon's North America Sort Center team built a serverless Model Context Protocol (MCP) server on Amazon ECS Fargate that acts as a centralized, agent-agnostic gateway between AI agents and the enterprise data lake. Adding a dataset requires only a metadata JSON file, which the pipeline indexes into Amazon Bedrock Knowledge Bases, while Amazon Cognito authentication, SELECT-only Athena tools, Bedrock Guardrails, and CloudWatch logging provide centralized controls. The architecture separates the tool layer from the agent layer so any MCP-compatible client can share the same tools and access controls in a multi-tenant environment.

  2. AWS Machine Learning Blog36

    Taxbit Assist: A grounded, role-aware AI agent on Amazon Bedrock AgentCore

    Taxbit built Taxbit Assist, an embedded AI assistant for its digital-asset tax compliance dashboard, on Amazon Bedrock AgentCore using the Strands Agents SDK, with grounded and cited answers, role and organization awareness, no tax advice, and regional data residency. The post details the architecture, a curated knowledge base with semantic chunking and reranking, identity bound in code rather than by the model, guardrails-based PII handling, and continuous production quality measurement. Taxbit reports the assistant has run in the US and EU since May 2026, with model cost averaging under 20 cents per conversation and about three-quarters of input tokens served from prompt cache.

Nov 6Fri
  1. Apple Machine Learning Research38

    Faster Retrieval for Better, More Efficient RAG

    Apple researchers present amortized maximum inner product search (MIPS), training small neural networks offline on a fixed key database to learn the support function of the key set, which is convex and whose gradient returns the best-matching key. SupportNet learns per-cluster support functions to route queries more accurately than centroid-based routing, while KeyNet rewrites queries before a standard IVF index; on Natural Questions, SupportNet reached 95% routing recall about 1.9× faster than centroid routing, and KeyNet cut the time to 99% recall by up to ~8×. The work was presented at ICML 2026.

Nov 5Thu
  1. TechCrunch · AI42

    Box taps Nvidia’s Nemotron for new agent-first content retrieval system

    At its developer conference, Box announced Box Content Retrieval, a feature that builds Nvidia's Nemotron embedding system into its product so agents can answer queries using embedded metadata. Nvidia VP Kari Briski told TechCrunch she expects queries answered four times faster with 20% fewer errors and 90% fewer tokens used, and the feature will roll out to Box's Enterprise Advance customers early next year. Box CEO Aaron Levie said the system sends back the most relevant document chunks instead of having an agent read through each document.

  2. AWS Machine Learning Blog38

    How Indegene is modernizing and scaling enterprise data quality in life sciences

    Indegene built an LLM-powered Data Quality Agent within its NEXT Unified Data Platform on AWS, using Amazon Bedrock, Strands Agents, and Amazon Bedrock AgentCore to generate context-aware data quality rules, validate them, and produce SQL and Python code under human-in-the-loop governance. The agent draws on internal enterprise and external reference knowledge bases via RAG to recommend technical and life sciences-specific business rules. Indegene reports the agent reduced rule definition and implementation effort from about 30 hours to about 4 hours per dataset and cut new table onboarding from 2–5 working days to 2–4 hours.

  3. Mastra Blog58

    Introducing Knowledge for Mastra Agents

    Mastra introduces Knowledge, a shared store of markdown pages organized into org, resource, and thread scopes that agents can search, read, and write through five built-in tools, working alongside signals to keep context current. Agents receive a Knowledge instance via the knowledge option, and storage adds mastra_knowledge_* tables, with KNOWLEDGE_ROUTES enabling a Studio page view. Knowledge is in alpha and requires @mastra/[email protected] or later.

Nov 4Wed
  1. Lovable Blog50

    How we built Lovable, and why we keep rebuilding it

    Lovable's founder recounts how the platform evolved from the 2023 GPT Engineer project into a product where users have created more than 70 million projects, repeatedly reinventing its agent design—from complex async subagents back to a predictable tool chain, then to "knowledge files" with retrieval instead of fine-tuning. He describes stateful sandboxes that keep development servers running for faster edit-check cycles and a framework called Camille for forking and merging entire running computers. He closes with three open problems: automating the full software lifecycle, making all types of software seamless to build, and building a better software stack.

  2. Hacker News AI58

    A private AI box in one apply

    Conn3x now has circuits for Ollama and Qdrant, and its Ollama repo includes an example that builds a private retrieval-augmented generation box on a DigitalOcean droplet with a single conn3x apply. The setup runs Ollama and Qdrant on loopback only, with SSH as the sole open port, and the post walks through storing a note, querying it by meaning, and having qwen2.5:0.5b answer. The author reports 101 resources applied in 2 minutes 36 seconds, a clean second plan, and a 41-second destroy.

  3. Hacker News AI34

    An easy way to try local AI with braindead-local-intelligence

    Solène introduces braindead-local-intelligence, a project that runs podman containers for an OpenAI-compatible LLM server, an image generation server, SearXNG web search and Open WebUI behind a single token-protected address, loading only one model at a time and unloading it after a few minutes idle. It requires a Linux system with rootless podman 5.0 or newer, targets AMD GPUs (tested up to 12 GB VRAM), and offers a documented list of ready-to-use text, vision and image models. Setup involves adding the user to the render and video groups, running bin/ia install and ia doctor, then ia start, with the web interface at http://127.0.0.1:3000.

  4. AWS Machine Learning Blog34

    Build a semantic layer for agentic AI with EDB Postgres AI and Amazon Bedrock AgentCore

    This post shows how to build a semantic layer for agentic AI using EDB Postgres AI's AIDB extension, where business meaning lives in Postgres COMMENT statements, join paths come from foreign keys, and governance uses GRANT and row-level security through the built-in MCP endpoint. A Strands Agents agent on Amazon Bedrock AgentCore queries the layer via AgentCore Gateway and an ALB to answer customer 360 questions, with two Postgres roles receiving different results for the same prompt based on column-level grants. AIDB also captures OpenTelemetry traces for each SQL execution as rows in the aidb_otel schema, queryable with ordinary SQL.

  5. AWS Machine Learning Blog22

    How Gen Digital built Norton Revamp’s AI-curated News Engine with Amazon Bedrock AgentCore Web Search

    Gen Digital describes how its Norton Revamp professional reputation platform, launched in March 2026, uses Web Search on Amazon Bedrock AgentCore as a managed, MCP-compliant connector to ground personalized news feeds, content suggestions and goal recommendations in cited, real-time web results. The post details an event-driven architecture using Amazon Nova Lite, Bedrock and an AI Learning System for ranking, plus reported results including 99.9 percent uptime and 3x faster feature implementation.

  6. AWS Machine Learning Blog25

    Bring structured and unstructured data together with Amazon Quick

    This AWS post walks through connecting an Amazon S3 bucket as a knowledge base, enriching a Product_Sales.csv dataset with semantic descriptions, and combining both in a Space that a custom chat agent queries through a single conversational interface. The agent uses text-to-SQL for structured questions, cites sources across both data types, and can generate PowerPoint, Excel, and Word artifacts from chat prompts. It also covers prerequisites, sharing, best practices, security considerations, and cleanup.

  7. Hacker News AI45

    Show HN: I built Knemora – An Open-source AI Second brain

    Knemora is an open-source, local-first knowledge and agent platform with a native Second Brain graph, a portable Markdown Wiki, and local AI inference that runs without a mandatory cloud service, external LLM API, or vector database. The repository is prepared for its first public release, v0.1.0-alpha, and supports managed setup on macOS Apple Silicon with Node.js 24, local Qwen3-8B inference via llama.cpp, document ingestion, BM25 search, and backup/restore workflows.

Nov 3Tue
  1. Hacker News AI62

    AI Red Team Academy: Free, open-source course on breaking AI systems

    AI Red Team Academy is a free, open-source course on offensive security testing of AI systems, spanning 8 modules and 60+ hours of content with hands-on Docker labs covering topics from prompt injection to supply chain attacks. The curriculum covers frameworks such as MITRE ATLAS, OWASP Top 10 for LLM Applications, and NIST AI 100-2, and includes labs using tools like garak, PyRIT, and promptfoo. All labs run locally via Docker and require no cloud API keys or external services.

  2. AWS Machine Learning Blog38

    How Circle K accelerated DevOps incident triage to under 15 minutes with Amazon Bedrock

    Circle K built an agentic AI triage system on Amazon Bedrock that investigates Grafana alerts across AWS resources, OpenSearch logs, and GitHub code, then posts a structured root-cause summary to Slack. Initially built on Amazon Bedrock Flows, the system was migrated to a single agent on Amazon Bedrock AgentCore Runtime with AgentCore Gateway brokering data source access. Circle K reports mean triage time dropped from 45–60 minutes to under 15 minutes, a 67–75 percent reduction based on internal incident-response metrics.

  3. AWS Machine Learning Blog40

    Agentic engineering intelligence with Amazon Nova Multimodal Embeddings and Amazon Bedrock AgentCore

    This post describes a multi-agent engineering assistant built on Amazon Bedrock AgentCore, Amazon Nova Multimodal Embeddings, and the open source Strands Agents SDK that assembles evidence for aerospace part substitution decisions. A planner routes queries to Catalog, Evaluation, and Knowledge specialists, with deterministic Python tolerance-rule scoring, Nova Multimodal Embeddings for multimodal retrieval and photo cosine similarity, and a supervisor that synthesizes results only when multiple specialists contribute. The engineer remains the decision-maker, and the system drafts justification memos and Integration Impact Assessments with audit trail entries aligned with AS9100 sections 8.4 and 8.5.6.

  4. AWS Machine Learning Blog34

    Prepare web content for Amazon Bedrock Knowledge Bases with Crawl4AI

    This post explains when to use the Amazon Bedrock Web Crawler connector versus a custom Crawl4AI-based preparation step that writes Markdown and metadata sidecars to Amazon S3 for ingestion into Amazon Bedrock Knowledge Bases. It walks through the sample AWS CDK workflow—scheduled Fargate crawling, S3 upload, and ingestion job—and covers chunking strategies, metadata filtering for retrieval validation, and responsible AI and security considerations.

Nov 2Mon
  2. Hacker News AI42

    The AI Agent Memory Stack: Building Blocks, Patterns, and Frameworks

    This article explains how AI agent memory works, covering working, episodic, semantic, and procedural memory types along with design strategies for writing, retrieving, and maintaining memory reliably. It compares frameworks including Letta, Mem0, Graphiti, and Cognee on storage models, retrieval approaches, and handling of fact changes over time, and discusses multi-agent memory concerns and evaluation metrics such as retrieval recall, precision, faithfulness, and staleness.

  3. Hacker News AI58

    Learn how to design AI systems

    A first-draft, CI-checked primer on designing systems built on large language models, covering trade-offs, components, solved designs and reference numbers, modelled on Donne Martin's System Design Primer. It includes per-topic deep dives, Anki flashcards, runnable labs and practice questions, with unverified figures marked and listed in PLAN.md. The material is licensed CC BY 4.0 for content and MIT for code, and corrections are invited.