pgvector

pgvector AI

pgvector AI

pgvector AI

pgvector AI

pgvector AI

pgvector AI

pgvector AI

pgvector AI

pgvector AI

pgvector AI

I used Discord as the mobile UI for my local notes search

tl;dr: before you build a whole mobile app for notes search, check if a chat command in an app you already use gets you most of the way there. I had searchable notes working on my PC with a pgvector index behind it, which was nice until I wasn't at my desk. The annoying case kept coming up: I'd remember that I wrote down some policy detail, a planning note, a renewal date, or wording from an old document, but I had no clean way to search any of it from my phone. My first instinct was to make it way bigger than it needed to be. Mobile app. Hosted dashboard. Auth. Maybe a small web UI. I looked at a few options and every one of them added another thing I'd have to maintain. What I ended up doing was dumber, and honestly better. I already had Discord on my phone, and I already had a private bot running for other parts of my stack, so I had Claude Code wire the existing notes search into one Discord command. Now I just type the question in a private Discord channel. The bot runs the same semantic search my PC uses and replies in the thread with the relevant match. So if I vaguely remember writing something six weeks ago and have no idea what file it was in, I can search by meaning instead of trying to guess the filename or exact keyword. The build took an afternoon. Most of that was me fighting the urge to make a "proper" interface. Claude handled the technical wiring. The useful decision was admitting that a chat command was enough. So now I have mobile notes search without another app, another login, another bill, or another tiny system sitting around waiting to break. That was kind of the whole point. submitted by /u/myLifeintheStack [link] [comments]

I built MOS (MemoryOS) – a lightweight, self-hosted memory microservice for LLMs using Node.js, pgvector, and local embeddings.

Hey everyone, I’ve been experimenting with LLM applications and found that managing long-term context windows efficiently can get messy fast. A lot of existing RAG/memory solutions felt too heavy for my needs, so I built a decoupled, lightweight infrastructure service called MOS (MemoryOS). 🔗 Repo:https://github.com/dhiraj2105/mos The Architecture: I wanted to keep the I/O-heavy API operations separate from the CPU-heavy ML tasks. Backend: Node.js + TypeScript (Express). Database: PostgreSQL utilizing the pgvector extension for 384-dimensional embeddings. Embedding Microservice: A separate Python/Flask app running sentence-transformers (all-MiniLM-L6-v2) locally to avoid external API costs and protect privacy. How it works under the hood: Instead of just relying on pure vector similarity, I wanted the memory to feel a bit more dynamic. Ranking Algorithm: The system calculates a similarity_score (1 / (1 + similarity_distance)) and adds a user-defined importance_score to get a combined_score for ranking the retrieved context. Memory Expiration: Memories can be created with an expires_at timestamp. The SQL queries automatically filter out expired records from the similarity search and context building endpoints. Prompt Compression: It has a basic /compress endpoint to merge memory text blocks and reduce prompt bloat. Deployment: It is fully containerized. A single docker compose up --build spins up the Postgres database (with auto-schema initialization), the Python embedding service, and the Node backend. I am planning to expand on the text compression algorithms and potentially add an external authentication layer (since it currently lacks default auth). I would genuinely love some brutally honest feedback on the architecture, my TypeScript implementation, or the ranking formula. If anyone finds this useful for their own LLM projects, feel free to use it or drop a star! PRs are also very welcome. Let me know what you think! submitted by /u/Dhiraj0 [link] [comments]

Megathread Summary: I Asked Multiple Reddit Communities How to Build a Living Memory /Context Engine for Business. Here's what everyone had to say.

I am trying to build a living memory/context engine for my business, something that can remember projects, decisions, timelines, risks, and conversations across emails, documents, notes, chats, and meetings. Since this is new territory for me, I asked several Reddit communities for advice. The responses were incredibly thoughtful, and many people shared architectures, engineering trade-offs, tools, and lessons learned from building similar systems. I consolidated the best ideas into a single summary. If you're exploring the same problem, especially if you're just getting started like me, I hope this will help. Core Philosophies & Perspectives Query-First Design: Do not build the storage layer first. Write out 20 real-world queries you will ask tomorrow and architect backward, because the retrieval interface shapes the system more than the storage layer. Chief of Staff vs. Search Engine: The goal is not just retrieving raw data, but synthesis. Like Microsoft Clarity’s bulk insights, the system should process updates and proactively tell you what projects need attention, what changed, and what the blockers are. The "Daily Mirror" Briefing: Focus on what the user needs to know at the start of the next session to continue without context loss, rather than striving for perfect archival completeness. Four Separate Problems: Treating user queries as a single search issue will fail; "latest status" is a retrieval problem, "unresolved issues" is state tracking, "decisions made" is entity extraction, and "important updates" requires significance scoring. Architecture & Strategies Append-Only Event Logs First: Avoid starting with a massive knowledge graph or vector database. Ingest everything as a timestamped, append-only event log, and build the knowledge graph later as a derived query layer on top. Artifact-Mediated Continuity: To prevent identity collapse over long timelines, separate retrieval (facts) from reconstruction (identity and working context). Use a "Principal-owned Artifact System" with files like MEMORY.md for project state, "Texture Packs" for behavior descriptions, and "Lane Files" structured around the Five W's. Parallel Retrieval Paths: Pure vector search fails at scale. Run vector search (for semantic similarity) alongside a graph/relational lookup (for exact entities) in parallel, because neither covers the query surface alone. Hybrid search (semantic + BM25 keyword) is heavily recommended. Split Memory by Lifespan & Namespace: Sector your memory from day one. Split durable facts (stable preferences, user info) from working context (recent events), applying different decay rates and routing queries to the appropriate layer. Continuous Summarization: Instead of treating everything as unstructured documents, use an LLM pipeline to continuously extract structured facts from new inputs to update project briefs, decision logs, and risk trackers automatically. The Hardest Engineering Challenges Entity Resolution (The Silent Killer): Different sources will refer to the same thing differently (e.g., "Project X" vs "the X pilot"). Without an entity registry mapping aliases to canonical IDs before writing, your graph will become a mess of duplicates. Ontology & Classification: The hardest part is often getting the system to universally understand the difference between a "decision", a "discussion", or a "risk" across varying data structures like emails versus meeting transcripts. Temporal Relevance & Stale Context: A "decision" stays load-bearing for months, whereas a "status update" decays in days. If you don't encode decay rates and version records, stale facts will outrank fresh ones and confidently contradict recent updates. Significance Scoring: Standard retrieval returns everything recent, not everything important. Write-time scoring fails because significance is retrospective; a better approach is "adaptive salience," where chunks gain weight when retrieved and decay when ignored. Context Moodiness: Especially in greenfield projects, meaningful status updates can be muddied by confounding, irrelevant, or noisy data. Tools & Tech Stack Recommendations Storage / Databases: Vector stores like pgvector for semantic search, paired with key-value or relational databases for exact lookup. Airtable, Databricks, Notion, and Obsidian were also noted as strong foundational or single-source-of-truth layers. AI Models & Agents: Claude Code, OpenAI Codex, Hermes-agent (by Nous Research), AsanaAI, and ClickUp Brain. Injecting local LLMs where appropriate can help cut down on continuous API costs. Middleware & Pipelines: Kapex: Memory middleware built specifically to score node significance, governing lifecycle so resolved stuff fades and unresolved stuff persists. Sauna.ai: An engine built out of Wordware that fits this use case. Automation: Make.com or n8n for routing deterministic logic and LLM reasoning. The "Party Model": A CRM data integration framework

Caught up with @openclaw maintainer @BradGroux to see what he’s been cooking up with the GitHub Copilot app 🦞 https://t.co/W5Jkt6E652

Caught up with @openclaw maintainer @BradGroux to see what he’s been cooking up with the GitHub Copilot app 🦞 https://t.co/W5Jkt6E652

Check out everything we launched today 👇 https://t.co/Mck7gcPr4c

Check out everything we launched today 👇 https://t.co/Mck7gcPr4c

As agents take on more of your workflow, you need a stronger guarantee that your code is secure, isolated, and controlled. Cloud and local sandboxes for GitHub Copilot allow you to safely experiment

As agents take on more of your workflow, you need a stronger guarantee that your code is secure, isolated, and controlled. Cloud and local sandboxes for GitHub Copilot allow you to safely experiment with agentic workflows and gives enterprise teams a way to centrally manage https://t.co/HOdaUogIEi

Introducing updates to the GitHub Copilot CLI experience. With voice mode, you can speak to Copilot using on-device speech-to-text models. Rubber duck is a built-in CLI agent that acts as a construct

Introducing updates to the GitHub Copilot CLI experience. With voice mode, you can speak to Copilot using on-device speech-to-text models. Rubber duck is a built-in CLI agent that acts as a constructive critic and provides feedback. 🗣️ https://t.co/fOCgJur7JV

The GitHub Copilot SDK is now generally available. Embed Copilot's agentic engine into your own applications, services, and developer tools with a stable API and production-ready support. 🤖 https://

The GitHub Copilot SDK is now generally available. Embed Copilot's agentic engine into your own applications, services, and developer tools with a stable API and production-ready support. 🤖 https://t.co/qNM8MRQFG1

The GitHub Copilot app, an agent-native desktop experience built on GitHub, is now in an expanded technical preview. 🧑‍🍳 You decide what agents tackle, how much autonomy each agent gets, and what s

The GitHub Copilot app, an agent-native desktop experience built on GitHub, is now in an expanded technical preview. 🧑‍🍳 You decide what agents tackle, how much autonomy each agent gets, and what ships. Go from issue to merged pull request without leaving the app. https://t.co/tBa34vqit4

The next frontier of agentic development is here, and you are in control.  Today we announced releases that will keep you in the flow with your agents, all on the GitHub platform.  ⬇️ https://t.co

The next frontier of agentic development is here, and you are in control.  Today we announced releases that will keep you in the flow with your agents, all on the GitHub platform.  ⬇️ https://t.co/4YveYzO6HP

How long would a project like this take realistically?

I’m trying to calibrate my expectations as a developer building with Claude / AI coding tools and managed services. How long would it realistically take to build a system with the following scope? * user authentication + onboarding * AI persona configuration (behavior, tone, constraints) * uploading and processing user knowledge (PDFs, text, YouTube video transcripts via links) * RAG-based chat system over that knowledge * voice cloning via third-party APIs * voice-based interaction with the AI (speech-to-speech flow) * integrations with external social media platforms where the AI can respond on behalf of users * background jobs + orchestration between components Assuming heavy use of Claude for coding assistance and existing APIs/services (e.g., ElevenLabs + Composio), what would be a realistic timeline for a single developer to bring something like this to a usable level? For context I'm a junior dev this is not a personal side project, it's a company work project I work full time + around 2 hours overtime They gave me a 2 days then extend it to 3 days deadline what they want it to see the most is a decent quality of voice cloning / voice chat, AI persona configuration and RAG-Based chat. it's typeScript monorepo with Next.js frontend, NestJS backend, Prisma/PostgreSQL + pgvector submitted by /u/AppropriateLeading6 [link] [comments]

GitHub is heading to Microsoft Build. Coding, AI, workflows, and more are on the docket. 💻 Join in person or virtually June 2-3. 👇 https://t.co/e4wtmkocpL https://t.co/J8K79sYatU

GitHub is heading to Microsoft Build. Coding, AI, workflows, and more are on the docket. 💻 Join in person or virtually June 2-3. 👇 https://t.co/e4wtmkocpL https://t.co/J8K79sYatU

Maintainers, have you checked out the 2026 Partner Pack? 👀 These exclusive discounts, freebies, and perks are waiting for you. 🎁 https://t.co/CTsZcAgIkb https://t.co/6WLa4sNGKA

Maintainers, have you checked out the 2026 Partner Pack? 👀 These exclusive discounts, freebies, and perks are waiting for you. 🎁 https://t.co/CTsZcAgIkb https://t.co/6WLa4sNGKA

The GitHub Innovation Graph is fun to watch (who doesn't love a bar race? 🏁) but it's also a source of compelling economic data. It's helping researchers reveal trends in GDP, inequality, and emissi

The GitHub Innovation Graph is fun to watch (who doesn't love a bar race? 🏁) but it's also a source of compelling economic data. It's helping researchers reveal trends in GDP, inequality, and emissions that traditional indicators miss. Find out how. 💡 https://t.co/JBlwiYIXMI https://t.co/o89jKBuBMh

Mona drink float, cabana set, that <body> tee you've all been asking for: it's all here. Plus, a tiny hoodie for your drink. You're welcome. The ESC Collection has landed in the GitHub Shop—be

Mona drink float, cabana set, that <body> tee you've all been asking for: it's all here. Plus, a tiny hoodie for your drink. You're welcome. The ESC Collection has landed in the GitHub Shop—because we know some of the best ideas happen when we escape the confines of our desk. https://t.co/rxrVniE2c4