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#postgres
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AI-generated · Automatically published by LinkLoot. Self-host the engine behind inbrief.info to aggregate, score and summarize AI news from RSS, Hacker News, Reddit, X, YouTube and GitHub Trending. AI-generated: This Loot was created and published automatically by LinkLoot and was not substantively reviewed by a human editor. What it does This open-source project aggregates AI-related content from 94 distinct sources, including lab blogs, Hacker News, Reddit, X, YouTube transcripts and GitHub Trending. It runs every item through an LLM pipeline that filters for relevance, generates English and Chinese summaries, extracts tags and assigns an importance score. The system uses a two-stage deduplication process—SimHash fingerprinting followed by pgvector cosine similarity—to ensure the final daily digest is clean and non-repetitive before storing results in PostgreSQL. Who it helps Developers and researchers who want a curated, automated view of the AI landscape without manually checking dozens of feeds. It is particularly useful for teams needing bilingual (EN/ZH) insights or those who prefer self-hosting to control their data pipeline. If you only want to read the output, the hosted site at inbrief.info is available, but this repo provides the full engine for customization. Getting started The repository includes a pipelinerunner that orchestrates fetchers, content processing, and AI curation. To begin, set up a PostgreSQL database; the schema auto-creates on first run. You must configure an OpenAI-compatible API endpoint for the LLM steps (DeepSeek is the default). Use adminrss.py to add your preferred sources, as the engine ships with an empty source table despite documenting 94 production examples. Anti-bot measures are layered, using curlcffi, DrissionPage, and Playwright with persistent profiles for platforms like Reddit and X. Limits and costs While the software is MIT-licensed, self-hosting requires infrastructure resources: a server for the Python application, a PostgreSQL instance, and potentially headless browser environments for scraping protected sites. LLM usage incurs token costs depending on your provider. Optional Aliyun Green text moderation can be enabled via keys, though it is skipped if absent. The complexity of maintaining scraper resilience against platform changes is significant. Source links Official repository Project documentation Project license
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Supabase Pipelines is a managed CDC service for sending Supabase Postgres changes to BigQuery in near real time. Supabase Pipelines is now in public alpha for paid Supabase plans. It gives teams a managed change-data-capture path from Supabase Postgres to analytics destinations, starting with BigQuery, without running a separate Debezium, Kafka, or custom replication stack. Use it when production Postgres is doing double duty as both app database and analytics source. Pipelines reads from Postgres logical replication, performs an initial copy of selected tables, and then streams inserts, updates, deletes, and truncates with at-least-once delivery. What to evaluate before depending on it: Availability is public alpha, so behavior and supported destinations can change. BigQuery is the current managed destination; Supabase says more destinations are planned. It runs on paid Supabase plans, not self-hosted Supabase. Row filters, selected tables, column subsets, partitioned tables, schema-change handling, and dashboard monitoring are part of the published setup surface. Best fit: teams already on Supabase that want operational data in BigQuery for analytics, reporting, enrichment, or downstream AI workflows while keeping heavy analytical reads away from the application database.
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Omni is a self-hosted workplace search and agent layer that indexes tools like Google Workspace, Slack, Jira, Confluence, HubSpot, and local files with Postgres, ParadeDB, and pgvector. Omni is a useful find for teams that want Glean-style workplace search without sending every knowledge source into a closed SaaS stack. The project connects to common work tools, builds a shared context layer, and gives agents search, citation, and tool-use paths over company data. Best fit: technical teams that can self-host Docker services, already run Postgres, and want to test hybrid BM25 plus vector search before buying an enterprise search platform. What to check before using it: Confirm the connectors you need are production-ready, especially Microsoft 365, Notion, GitHub, or custom internal systems. Review permission inheritance against your own identity model before indexing private content. Decide whether your LLM provider keeps data inside your boundary; self-hosting the index does not automatically make hosted model calls private. Test Postgres, ParadeDB, and pgvector performance on a realistic document count. Treat the project as beta unless your own deployment test proves it can handle your security and uptime requirements. Area Omni angle Verification check --- --- --- Search BM25 plus vector search in Postgres Test recall against your real docs Agents Chat and tool use over indexed company data Review sandbox and permissions Hosting Self-hosted deployment path Validate backups, auth, and updates Lock-in Apache-2.0 repository Confirm license and active maintenance This is a tool to trial, not a blanket recommendation. Start with a small workspace, index non-sensitive sources first, and compare answer quality, latency, and review burden against your current internal search or RAG setup.
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