Keenable SELECT is an MCP server that lets agents query the web directly using SQL, combining web search and semantic extraction into single queries instead of reading through individual pages. It handles the filtering and data extraction server-side, reducing the LLM token cost of web research.

• Instead of returning ten links for an agent to read, SELECT runs one query across 1,000+ pages, filters with exact WHERE clauses, and extracts fields with a single LLM call per matching row.
• The system uses semantic operators (WEB_SEARCH, WEB_FETCH, SEM_EXTRACT, SEM_MATCH) embedded directly in SQL statements, which the server parses and executes before returning results as plain columns.
• Research agents use the select tool to iteratively write and run queries until they answer a question, then generate shareable HTML reports via a second agent that builds the page from structured result sets.

Lovable is shifting from a tool for building traditional applications to a platform for creating "capabilities" that AI agents can call directly, enabling companies to consolidate their software sprawl into a single "company brain" interface. The move reflects how fast AI infrastructure companies are evolving — Lovable went from open-source prototyping tool to a $13.3 billion company in three years, now hitting $500 million annualized revenue.

• Lovable turns apps into agent-accessible capabilities by exposing functions through an MCP server, letting ChatGPT, Claude, and other AI clients call them without humans opening the app — essentially giving each app two interfaces, one for humans and one for agents.
• The company has grown from a prototyping tool to a full software platform where users build production apps, internal tools, and operational software (CRMs, admin panels, support consoles), competing directly with Vercel and Cloudflare.
• Lovable's security model separates external system connections from application code using encrypted, server-side credential storage and short-lived keys, preventing apps from exposing sensitive data to the company brain.
• The vision consolidates how employees interact with software — instead of switching between multiple SaaS tabs, they'll use one agent interface accessing capabilities from many tools, forcing traditional SaaS companies to adapt or become irrelevant.

Stash lets your AI agent keep memory across conversations, so you don’t have to repeat context every time. It’s an open-source, self-hosted layer that uses Postgres, pgvector, and an eight-stage consolidation pipeline to turn raw observations into structured knowledge. It plugs into any MCP-compatible agent with a single Docker command.

• Stash gives AI agents persistent memory across sessions using Postgres + pgvector, run via a self-hosted Docker stack (no cloud dependency).
• A background service runs an eight-stage consolidation pipeline (fact extraction, relationship mapping, causal links, goal tracking, failure patterns, hypothesis checks, confidence decay) that only processes new entries, keeping the knowledge base efficient as it grows.
• It works with any MCP-compatible agent (OpenAI Agents, Claude Desktop, Continue, Cursor, Windsurf, Ollama, OpenRouter) via a single Docker command setup.
• It's open source (Apache 2.0), letting users customize consolidation rules, swap models, or plug in custom retrieval logic.

This article explains how to set up a Telegram bot to work with Claude Code using an MCP server. It covers the steps from creating a bot with BotFather to configuring the server and pairing it with Claude for direct messaging. Key commands and configurations are detailed for effective integration.

• An MCP server bridges Telegram and Claude Code, letting bot messages flow directly into a Claude Code session, with replies, reactions, and edits supported.
• Setup requires creating a bot via BotFather for a token, installing the plugin, and running the server with a channel flag; each bot needs its own directory for multi-instance use.
• Pairing happens by DMing the bot for a code entered into Claude Code, with no invite needed (unlike Discord), and allowlist mode should replace pairing mode afterward for security.
• The bot can't search or access message history, so it downloads photos automatically but relies on the user to manually supply earlier context when needed.

This guide explains how to set up a Telegram bot with Claude Code using an MCP server. It covers creating a bot through BotFather, installing the necessary plugin, and the steps to configure and pair the bot with your Claude session.

• Setting up requires creating a bot via BotFather, installing the plugin, and running `/telegram:configure` with the token to write it into the environment file.
• Multiple bot instances need separate `TELEGRAM_STATE_DIR` values to avoid conflicts.
• Pairing works by DMing the bot for a 6-character code, then running `/telegram:access pair <code>` in Claude Code, avoiding the need for numeric user IDs.
• The bot can reply, react with emoji, and edit its own messages, but has no access to message history—only real-time interaction.