505 lines
32 KiB
Markdown
505 lines
32 KiB
Markdown
# **TOC** {#toc}
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[**TOC 1**](#toc)
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[Trace models response back to exact docs](#trace-models-response-back-to-exact-docs)
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[Firecrawl: Website scraper for LLMs](#firecrawl:-website-scraper-for-llms)
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[Airweave is a tool that lets agents semantically search any app.](#airweave-is-a-tool-that-lets-agents-semantically-search-any-app.)
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[Nuxt and Vuw as front ends per reddit post](#nuxt-and-vuw-as-front-ends-per-reddit-post)
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[AI-native Git: Rethinking version control for AI agents](#ai-native-git:-rethinking-version-control-for-ai-agents)
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[Google has open-sourced its zero-knowledge proof (ZKP) library called Longfellow](#google-has-open-sourced-its-zero-knowledge-proof-\(zkp\)-library-called-longfellow)
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[Securing AI agents with WorkOS](#securing-ai-agents-with-workos)
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[RapidMCP lets you convert your REST API into an AI-ready MCP server in minutes, no code changes](#rapidmcp-lets-you-convert-your-rest-api-into-an-ai-ready-mcp-server-in-minutes,-no-code-changes)
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[List of MCP servers](#list-of-mcp-servers)
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[Remote MCP support in Claude Code \\ Anthropic](#remote-mcp-support-in-claude-code-\\-anthropic)
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[Pickaxe is a simple Typescript library for building AI agents that are fault-tolerant and scalable.](#pickaxe-is-a-simple-typescript-library-for-building-ai-agents-that-are-fault-tolerant-and-scalable.)
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[Crawling a billion web pages in just over 24 hours, in 2025](#crawling-a-billion-web-pages-in-just-over-24-hours,-in-2025)
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[https://mcpui.dev/?utm\_source=tldrwebdev Interactive UI Components for MCP](#https://mcpui.dev/?utm_source=tldrwebdev-interactive-ui-components-for-mcp)
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[LangExtract is a Python library that uses LLMs to extract structured information from unstructured text documents based on user-defined instructions.](#langextract-is-a-python-library-that-uses-llms-to-extract-structured-information-from-unstructured-text-documents-based-on-user-defined-instructions.)
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[TraceRoot helps engineers debug production issues 10x faster using AI-powered analysis of traces, logs, and code context.](#traceroot-helps-engineers-debug-production-issues-10x-faster-using-ai-powered-analysis-of-traces,-logs,-and-code-context.)
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[Code Index MCP is a Model Context Protocol server that bridges the gap between AI models and complex codebases.](#code-index-mcp-is-a-model-context-protocol-server-that-bridges-the-gap-between-ai-models-and-complex-codebases.)
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[Local first development](#local-first-development)
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[Abstract: AI-powered coding tools are reshaping how we build software, but they're scattered across a mess of configuration files. This document defines AGENT.md, a standardized format that lets your codebase speak directly to any agentic coding tool.](#abstract:-ai-powered-coding-tools-are-reshaping-how-we-build-software,-but-they're-scattered-across-a-mess-of-configuration-files.-this-document-defines-agent.md,-a-standardized-format-that-lets-your-codebase-speak-directly-to-any-agentic-coding-tool.)
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[muscle-mem is a behavior cache for AI agents.](#muscle-mem-is-a-behavior-cache-for-ai-agents.)
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[How we replaced Elasticsearch and MongoDB with Rust and RocksDB](#how-we-replaced-elasticsearch-and-mongodb-with-rust-and-rocksdb)
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[Rust, Python, and TypeScript: the new trifecta](#rust,-python,-and-typescript:-the-new-trifecta)
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[MCP Vulnerabilities Every Developer Should Know \- https://composio.dev/blog/mcp-vulnerabilities-every-developer-should-know?utm\_source=tldrwebdev](#mcp-vulnerabilities-every-developer-should-know---https://composio.dev/blog/mcp-vulnerabilities-every-developer-should-know?utm_source=tldrwebdev)
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[Embedding Atlas is a tool that provides interactive visualizations for large embeddings.](#embedding-atlas-is-a-tool-that-provides-interactive-visualizations-for-large-embeddings.)
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[Embedding Millions of Text Documents With Qwen3](#embedding-millions-of-text-documents-with-qwen3)
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[Teams can't see into their AI models \- and many can't even answer basic questions like "which prompts are costing us the most money?"](#teams-can't-see-into-their-ai-models---and-many-can't-even-answer-basic-questions-like-"which-prompts-are-costing-us-the-most-money?")
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[Replace docker with podman](#replace-docker-with-podman)
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[Airweave is a tool that lets agents search any app.](#airweave-is-a-tool-that-lets-agents-search-any-app.)
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[Python Code Quality Analyzer https://github.com/ludo-technologies/pyscn?utm\_source=tldrnewsletter](#python-code-quality-analyzer-https://github.com/ludo-technologies/pyscn?utm_source=tldrnewsletter)
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[DeepMind's new AI agent "Codemender" just auto-finds and fixes vulnerabilities in your code.](#deepmind's-new-ai-agent-"codemender"-just-auto-finds-and-fixes-vulnerabilities-in-your-code.)
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[Beads is a lightweight memory system for coding agents, using a graph-based issue tracker.](#beads-is-a-lightweight-memory-system-for-coding-agents,-using-a-graph-based-issue-tracker.)
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[Butter is a cache that saves money by identifying and serving repeat LLM responses, making AI systems deterministic.](#butter-is-a-cache-that-saves-money-by-identifying-and-serving-repeat-llm-responses,-making-ai-systems-deterministic.)
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[These 8 Docker containers help monitor my entire home lab](#these-8-docker-containers-help-monitor-my-entire-home-lab)
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[Big GPUs don't need big PCs \- using Raspberry PI to do inference with GPU’s](#big-gpus-don't-need-big-pcs---using-raspberry-pi-to-do-inference-with-gpu’s)
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[Pulse is a modern, unified dashboard for monitoring your infrastructure across Proxmox, Docker, and Kubernetes.](#pulse-is-a-modern,-unified-dashboard-for-monitoring-your-infrastructure-across-proxmox,-docker,-and-kubernetes.)
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[install fresh https://sinelaw.github.io/fresh/](#install-fresh-https://sinelaw.github.io/fresh/)
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[Tools for home labs](#tools-for-home-labs)
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[The Complete Guide to Building Agents with the Claude Agent SDK](#the-complete-guide-to-building-agents-with-the-claude-agent-sdk)
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[Chunkhound](#chunkhound)
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[Skills](#skills)
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[Fluid \- terminal agent that helps manage and debug production infrastructure](#fluid---terminal-agent-that-helps-manage-and-debug-production-infrastructure)
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[Claude Code: connect to a local model when your quota runs out](#claude-code:-connect-to-a-local-model-when-your-quota-runs-out)
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[Matchlock is a CLI tool for running AI agents in ephemeral microVMs](#matchlock-is-a-cli-tool-for-running-ai-agents-in-ephemeral-microvms)
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[Skillkit. The open source package manager for AI agent skills. Install from 15,000+ skills](#skillkit.-the-open-source-package-manager-for-ai-agent-skills.-install-from-15,000+-skills)
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[xAI Rag API](#xai-rag-api)
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[Rowboat connects to your email and meeting notes, builds a long-lived knowledge graph, and uses that context to help you get work done \- privately, on your machine.](#rowboat-connects-to-your-email-and-meeting-notes,-builds-a-long-lived-knowledge-graph,-and-uses-that-context-to-help-you-get-work-done---privately,-on-your-machine.)
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[Json-render](#json-render)
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[WebMCP](#webmcp)
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[These 8 Docker containers help monitor my entire home lab](#these-8-docker-containers-help-monitor-my-entire-home-lab-1)
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[GitNexus: Indexes any codebase into a knowledge graph](#gitnexus:-indexes-any-codebase-into-a-knowledge-graph)
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[stereOS runs AI coding agents inside sandboxed Linux VMs.](#stereos-runs-ai-coding-agents-inside-sandboxed-linux-vms.)
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[Hyperspell \- Context and memory for your AI agents.](#hyperspell---context-and-memory-for-your-ai-agents.)
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[ReMe is a memory management framework designed for AI agents, providing both file-based and vector-based memory systems.](#reme-is-a-memory-management-framework-designed-for-ai-agents,-providing-both-file-based-and-vector-based-memory-systems.)
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[Google PM open-sources Always On Memory Agent, ditching vector databases for LLM-driven persistent memory](#google-pm-open-sources-always-on-memory-agent,-ditching-vector-databases-for-llm-driven-persistent-memory)
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[Codex Security: now in research preview](#codex-security:-now-in-research-preview)
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[Crawl entire websites with a single API call using Browser Rendering](#crawl-entire-websites-with-a-single-api-call-using-browser-rendering)
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[Understudy is a teachable desktop agent.](#understudy-is-a-teachable-desktop-agent.)
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[Optio: Workflow orchestration for AI coding agents, from task to merged PR.](#optio:-workflow-orchestration-for-ai-coding-agents,-from-task-to-merged-pr.)
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[Agents Observe \- Real-time observability dashboard for Claude Code agents.](#agents-observe---real-time-observability-dashboard-for-claude-code-agents.)
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[Caveman \- A Claude Code skill/plugin and Codex plugin that makes agent talk like caveman — cutting \~75% of output tokens while keeping full technical accuracy.](#caveman---a-claude-code-skill/plugin-and-codex-plugin-that-makes-agent-talk-like-caveman-—-cutting-~75%-of-output-tokens-while-keeping-full-technical-accuracy.)
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[Browser Harness](#browser-harness)
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[Agent Vault](#agent-vault)
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[Typesense](#typesense)
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[Obscura](#obscura)
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[Link](#link)
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[Mirage](#mirage)
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[Lakebase](#lakebase)
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[Turbopuffer](#turbopuffer)
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[e2a](#e2a)
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[ZeroEntropy](#zeroentropy)
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[Webright](#webright)
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[Activegraph](#activegraph)
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[OpenJarvis](#openjarvis)
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[Flowsint](#flowsint)
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---
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This doc is a list I maintain of anything interesting to me that I find
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---
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# **Trace models response back to exact docs**
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OLMoTrace is the first real-time system that lets users instantly trace parts of a model’s response back to the exact documents in the model’s multi-trillion-token training dataset.
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[See exponential view AI tools](https://docs.example.invalid/private-reference)
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# **Firecrawl: Website scraper for LLMs**
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# **Airweave is a tool that lets agents semantically search any app.**
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It's MCP compatible and seamlessly connects any app, database, or API, to transform their contents into agent-ready knowledge. [https://github.com/airweave-ai/airweave](https://github.com/airweave-ai/airweave?utm_source=tldrnewsletter)
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# **Nuxt and Vuw as front ends per reddit post**
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# **AI-native Git: Rethinking version control for AI agents**
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Now that AI agents increasingly write or modify large portions of application code, what developers care about starts to change. We’re no longer fixated on exactly what code was written line-by-line, but rather on whether the output behaves as expected. Did the change pass the tests? Does the app still work as intended?
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This flips a long-standing mental model: Git was designed to track the precise history of hand-written code, but, with coding agents, that granularity becomes less meaningful. Developers often don’t audit every diff — especially if the change is large or auto-generated — they just want to know whether the new behavior aligns with the intended outcome. As a result, the Git SHA — once the canonical reference for “the state of the codebase†— begins to lose some of its semantic value.
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A SHA tells you that something changed, but not why or whether it’s valid. In AI-first workflows, a more useful unit of truth might be a combination of the prompt that generated the code and the tests that verify its behavior. In this world, the “state†of your app might be better represented by the inputs to generation (prompt, spec, constraints) and a suite of passing assertions, rather than a frozen commit hash. In fact, we might eventually track prompt+test bundles as versionable units in their own right, with Git relegated to tracking those bundles, not just raw source code.
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Taking this a step further: In agent-driven workflows, the source of truth may shift upstream toward prompts, data schemas, API contracts, and architectural intent. Code becomes the byproduct of those inputs, more like a compiled artifact than a manually authored source. Git, in this world, starts to function less as a workspace and more as an artifact log — a place to track not just what changed, but why and by whom. We may begin to layer in richer metadata, such as which agent or model made a change, which sections are protected, and where human oversight is required – or where AI reviewers like Diamond can step in as part of the loop.
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# **Google has open-sourced its zero-knowledge proof (ZKP) library called Longfellow**
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# **Securing AI agents with WorkOS**
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# **RapidMCP lets you convert your REST API into an AI-ready MCP server in minutes, no code changes**
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# **List of MCP servers**
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[https://github.com/metorial/mcp-index](https://github.com/metorial/mcp-index?utm_source=tldrwebdev)
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# **Remote MCP support in Claude Code \\ Anthropic**
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[https://www.anthropic.com/news/claude-code-remote-mcp?utm\_source=tldrai](https://www.anthropic.com/news/claude-code-remote-mcp?utm_source=tldrai)
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# **Pickaxe is a simple Typescript library for building AI agents that are fault-tolerant and scalable.**
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[https://github.com/hatchet-dev/pickaxe?utm\_source=tldrnewsletter](https://github.com/hatchet-dev/pickaxe?utm_source=tldrnewsletter)
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# **Crawling a billion web pages in just over 24 hours, in 2025**
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[https://andrewkchan.dev/posts/crawler.html?utm\_source=tldrwebdev](https://andrewkchan.dev/posts/crawler.html?utm_source=tldrwebdev)
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# [**https://mcpui.dev/?utm\_source=tldrwebdev**](https://mcpui.dev/?utm_source=tldrwebdev) **Interactive UI Components for MCP**
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Build rich, dynamic user interfaces for your MCP applications with SDKs that bring UI to AI interactions.
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# **LangExtract is a Python library that uses LLMs to extract structured information from unstructured text documents based on user-defined instructions.**
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It processes materials such as clinical notes or reports, identifying and organizing key details while ensuring the extracted data corresponds to the source text.
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[https://github.com/google/langextract?utm\_source=tldrwebdev](https://github.com/google/langextract?utm_source=tldrwebdev)
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# **TraceRoot helps engineers debug production issues 10x faster using AI-powered analysis of traces, logs, and code context.**
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[https://github.com/traceroot-ai/traceroot?utm\_source=tldrwebdev](https://github.com/traceroot-ai/traceroot?utm_source=tldrwebdev)
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# **Code Index MCP is a Model Context Protocol server that bridges the gap between AI models and complex codebases.**
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It provides intelligent indexing, advanced search capabilities, and detailed code analysis to help AI assistants understand and navigate your projects effectively.
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Perfect for: Code review, refactoring, documentation generation, debugging assistance, and architectural analysis.
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[https://github.com/johnhuang316/code-index-mcp](https://github.com/johnhuang316/code-index-mcp)
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# **Local first development**
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[https://bytemash.net/posts/i-went-down-the-linear-rabbit-hole/?utm\_source=tldrnewsletter](https://bytemash.net/posts/i-went-down-the-linear-rabbit-hole/?utm_source=tldrnewsletter)
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# **Abstract: AI-powered coding tools are reshaping how we build software, but they're scattered across a mess of configuration files. This document defines AGENT.md, a standardized format that lets your codebase speak directly to any agentic coding tool.**
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[https://ampcode.com/AGENT.md](https://ampcode.com/AGENT.md)
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# **muscle-mem is a behavior cache for AI agents.**
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It is a Python SDK that records your agent's tool-calling patterns as it solves tasks, and will deterministically replay those learned trajectories whenever the task is encountered again, falling back to agent mode if edge cases are detected. The goal of muscle-mem is to get LLMs out of the hotpath for repetitive tasks, increasing speed, reducing variability, and eliminating token costs for the many cases that could have just been a script. [https://github.com/pig-dot-dev/muscle-mem](https://github.com/pig-dot-dev/muscle-mem)
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# **How we replaced Elasticsearch and MongoDB with Rust and RocksDB**
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[https://radar.com/blog/high-performance-geocoding-in-rust?utm\_source=tldrwebdev](https://radar.com/blog/high-performance-geocoding-in-rust?utm_source=tldrwebdev)
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# **Rust, Python, and TypeScript: the new trifecta**
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[https://smallcultfollowing.com/babysteps/blog/2025/07/31/rs-py-ts-trifecta/?utm\_source=tldrwebdev](https://smallcultfollowing.com/babysteps/blog/2025/07/31/rs-py-ts-trifecta/?utm_source=tldrwebdev)
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# **MCP Vulnerabilities Every Developer Should Know \- [https://composio.dev/blog/mcp-vulnerabilities-every-developer-should-know?utm\_source=tldrwebdev](https://composio.dev/blog/mcp-vulnerabilities-every-developer-should-know?utm_source=tldrwebdev)**
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# **Embedding Atlas is a tool that provides interactive visualizations for large embeddings.**
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It allows you to visualize, cross-filter, and search embeddings and metadata. [https://github.com/apple/embedding-atlas?utm\_source=tldrwebdev](https://github.com/apple/embedding-atlas?utm_source=tldrwebdev)
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# **Embedding Millions of Text Documents With Qwen3**
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[https://www.daft.ai/blog/embedding-millions-of-text-documents-with-qwen3?utm\_source=tldrwebdev](https://www.daft.ai/blog/embedding-millions-of-text-documents-with-qwen3?utm_source=tldrwebdev)
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# **Teams can't see into their AI models \- and many can't even answer basic questions like "which prompts are costing us the most money?"**
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AI observability is the way to stop guessing and starting getting answers \- with automated dashboards for prompt frequency, response times, drift indicators, and cost impact. Try it out in the Dynatrace Playground
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# **Replace docker with podman**
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# **Airweave is a tool that lets agents search any app.**
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It connects to apps, productivity tools, databases, or document stores and transforms their contents into searchable knowledge bases, accessible through a standardized interface for agents. [https://github.com/airweave-ai/airweave?utm\_source=tldrwebdev](https://github.com/airweave-ai/airweave?utm_source=tldrwebdev)
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# **Python Code Quality Analyzer [https://github.com/ludo-technologies/pyscn?utm\_source=tldrnewsletter](https://github.com/ludo-technologies/pyscn?utm_source=tldrnewsletter)**
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# **DeepMind's new AI agent "Codemender" just auto-finds and fixes vulnerabilities in your code.**
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# **Beads is a lightweight memory system for coding agents, using a graph-based issue tracker.**
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[https://github.com/steveyegge/beads?utm\_source=tldrnewsletter](https://github.com/steveyegge/beads?utm_source=tldrnewsletter)
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Four kinds of dependencies work to chain your issues together like beads, making them easy for agents to follow for long distances, and reliably perform complex task streams in the right order.
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# **Butter is a cache that saves money by identifying and serving repeat LLM responses, making AI systems deterministic.**
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# **These 8 Docker containers help monitor my entire home lab**
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[https://www.xda-developers.com/docker-containers-help-monitor-entire-home-lab/](https://www.xda-developers.com/docker-containers-help-monitor-entire-home-lab/)
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# **Big GPUs don't need big PCs \- using Raspberry PI to do inference with GPU’s**
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[https://www.jeffgeerling.com/blog/2025/big-gpus-dont-need-big-pcs?utm\_source=tldrdev](https://www.jeffgeerling.com/blog/2025/big-gpus-dont-need-big-pcs?utm_source=tldrdev)
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# **Pulse is a modern, unified dashboard for monitoring your infrastructure across Proxmox, Docker, and Kubernetes.**
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It consolidates metrics, alerts, and AI-powered insights from all your systems into a single, beautiful interface. Designed for homelabs, sysadmins, and MSPs who need a "single pane of glass" without the complexity of enterprise monitoring stacks. [https://github.com/rcourtman/Pulse](https://github.com/rcourtman/Pulse)
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# **install fresh [https://sinelaw.github.io/fresh/](https://sinelaw.github.io/fresh/)**
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# **Tools for home labs**
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[https://www.xda-developers.com/free-tools-every-home-lab-needs/](https://www.xda-developers.com/free-tools-every-home-lab-needs/)
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# **The Complete Guide to Building Agents with the Claude Agent SDK**
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[https://nader.substack.com/p/the-complete-guide-to-building-agents](https://nader.substack.com/p/the-complete-guide-to-building-agents)
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# **Chunkhound**
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[https://github.com/chunkhound/chunkhound?utm\_source=tldrai](https://github.com/chunkhound/chunkhound?utm_source=tldrai)
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our AI assistant searches code but doesn't understand it. ChunkHound researches your codebase—extracting architecture, patterns, and institutional knowledge at any scale. Integrates via MCP.
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# **Skills**
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Skills are reusable capabilities for AI agents. Install them with a single command to enhance your agents with access to procedural knowledge.
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[https://skills.sh](https://skills.sh)
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# **Fluid \- terminal agent that helps manage and debug production infrastructure**
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Fluid is a terminal agent that helps manage and debug production infrastructure like VMs/K8s cluster by making sandbox clones of the infrastructure for AI agents to work on, allowing the agents to run commands, test connections, edit files, and then generate Infra-as-code like an Ansible Playbook to be applied on production.
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# **Claude Code: connect to a local model when your quota runs out**
|
||
|
||
[https://boxc.net/blog/2026/claude-code-connecting-to-local-models-when-your-quota-runs-out/?utm\_source=tldrdev](https://boxc.net/blog/2026/claude-code-connecting-to-local-models-when-your-quota-runs-out/?utm_source=tldrdev)
|
||
|
||
# **Matchlock is a CLI tool for running AI agents in ephemeral microVMs**
|
||
|
||
with network allowlisting, secret injection via MITM proxy, and VM-level isolation. Your secrets never enter the VM.
|
||
|
||
# **Skillkit. The open source package manager for AI agent skills. Install from 15,000+ skills**
|
||
|
||
auto-translate between formats, persist learnings with Memory. Works with Claude, Cursor, Windsurf, Copilot, Devin, Codex, and 38 more.
|
||
|
||
# **xAI Rag API**
|
||
|
||
# **Rowboat connects to your email and meeting notes, builds a long-lived knowledge graph, and uses that context to help you get work done \- privately, on your machine.**
|
||
|
||
Rowboat; Open-source AI coworker that turns work into a knowledge graph and acts on it https://github.com/rowboatlabs/rowboat?
|
||
|
||
# **Json-render**
|
||
|
||
[https://github.com/vercel-labs/json-render/blob/main/README.md](https://github.com/vercel-labs/json-render/blob/main/README.md)
|
||
|
||
[https://chat.example.invalid/private-reference](https://chat.example.invalid/private-reference)
|
||
|
||
# **WebMCP**
|
||
|
||
[https://webmachinelearning.github.io/webmcp/?utm\_source=tldrdev](https://webmachinelearning.github.io/webmcp/?utm_source=tldrdev)
|
||
|
||
https://chat.example.invalid/private-reference
|
||
|
||
# **These 8 Docker containers help monitor my entire home lab**
|
||
|
||
[https://www.xda-developers.com/docker-containers-help-monitor-entire-home-lab/](https://www.xda-developers.com/docker-containers-help-monitor-entire-home-lab/)
|
||
|
||
# **GitNexus: Indexes any codebase into a knowledge graph**
|
||
|
||
— every dependency, call chain, cluster, and execution flow — then exposes it through smart tools so AI agents never miss code.
|
||
|
||
[https://github.com/abhigyanpatwari/GitNexus](https://github.com/abhigyanpatwari/GitNexus)
|
||
|
||
# **stereOS runs AI coding agents inside sandboxed Linux VMs.**
|
||
|
||
Instead of giving an agent access to your host machine, stereOS boots a disposable VM, injects credentials, and launches the agent — isolated from everything else. https://stereos.ai/
|
||
|
||
# **Hyperspell \- Context and memory for your AI agents.**
|
||
|
||
# **ReMe is a memory management framework designed for AI agents, providing both file-based and vector-based memory systems.**
|
||
|
||
[https://github.com/agentscope-ai/ReMe](https://github.com/agentscope-ai/ReMe)
|
||
|
||
# **Google PM open-sources Always On Memory Agent, ditching vector databases for LLM-driven persistent memory**
|
||
|
||
[https://venturebeat.com/orchestration/google-pm-open-sources-always-on-memory-agent-ditching-vector-databases-for?utm\_source=tldrai](https://venturebeat.com/orchestration/google-pm-open-sources-always-on-memory-agent-ditching-vector-databases-for?utm_source=tldrai)
|
||
|
||
# **Codex Security: now in research preview**
|
||
|
||
# **Crawl entire websites with a single API call using Browser Rendering**
|
||
|
||
[https://developers.cloudflare.com/changelog/post/2026-03-10-br-crawl-endpoint/?utm\_source=tldrnewsletter](https://developers.cloudflare.com/changelog/post/2026-03-10-br-crawl-endpoint/?utm_source=tldrnewsletter)
|
||
|
||
# **Understudy is a teachable desktop agent.**
|
||
|
||
It operates your computer like a human colleague — GUI, browser, shell, file system, all in one local runtime. You show it a task once, it extracts the intent (not just the coordinates), remembers the successful path, discovers faster execution routes over time, and eventually handles routine work on its own. No API integrations required. No workflow builders. Just demonstrate once.
|
||
|
||
# **Optio: Workflow orchestration for AI coding agents, from task to merged PR.**
|
||
|
||
Optio turns coding tasks into merged pull requests — without human babysitting. Submit a task (manually, from a GitHub Issue, or from Linear), and Optio handles the rest: provisions an isolated environment, runs an AI agent, opens a PR, monitors CI, triggers code review, auto-fixes failures, and merges when everything passes. The feedback loop is what makes it different. When CI fails, the agent is automatically resumed with the failure context. When a reviewer requests changes, the agent picks up the review comments and pushes a fix. When everything passes, the PR is squash-merged and the issue is closed. You describe the work; Optio drives it to completion.
|
||
|
||
# **Agents Observe \- Real-time observability dashboard for Claude Code agents.**
|
||
|
||
Includes powerful filtering, searching, and visualization of multi-agent sessions.
|
||
|
||
# **Caveman \- A Claude Code skill/plugin and Codex plugin that makes agent talk like caveman — cutting \~75% of output tokens while keeping full technical accuracy.**
|
||
|
||
Based on the viral observation that caveman-speak dramatically reduces LLM token usage without losing technical substance. So we made it a one-line install.
|
||
|
||
# **Browser Harness**
|
||
|
||
[https://github.com/browser-use/browser-harness](https://github.com/browser-use/browser-harness)
|
||
|
||
The simplest, thinnest, self-healing harness that gives LLM complete freedom to complete any browser task. Built directly on CDP.
|
||
|
||
The agent writes what's missing, mid-task. No framework, no recipes, no rails. One websocket to Chrome, nothing between.
|
||
|
||
# **Agent Vault**
|
||
|
||
An open-source credential broker by Infisical that sits between your agents and the APIs they call.
|
||
Agents should not possess credentials. Agent Vault eliminates credential exfiltration risk with brokered access. https://github.com/Infisical/agent-vault
|
||
|
||
# **Typesense**
|
||
Typesense is a fast, typo-tolerant search engine for building delightful search experiences. https://github.com/typesense/typesense
|
||
|
||
# **Obscura**
|
||
Obscura is a headless browser engine written in Rust, built for web scraping and AI agent automation. It runs real JavaScript via V8, supports the Chrome DevTools Protocol, and acts as a drop-in replacement for headless Chrome with Puppeteer and Playwright. https://github.com/h4ckf0r0day/obscura
|
||
|
||
# **Link**
|
||
Link CLI lets agents get secure, one-time-use payment credentials from a Link wallet — so they can complete purchases on your behalf without ever storing your real card details.
|
||
|
||
# **Mirage**
|
||
Mirage is a Unified Virtual File System for AI Agents: a single tree that mounts services and data sources like S3, Google Drive, Slack, Gmail, and Redis side-by-side as one filesystem.AI agents reach every backend with the same handful of Unix-like tools, and pipelines compose across services as naturally as on a local disk. It's a simulated environment, agents see one filesystem underneath. Any LLM that already knows bash can use Mirage out of the box, with zero new vocabulary.
|
||
|
||
# **Lakebase**
|
||
How lakebase architecture delivers 5x faster Postgres writes
|
||
|
||
# **Turbopuffer**
|
||
How to build a distributed queue in a single JSON file on object storage
|
||
|
||
# **e2a**
|
||
Authenticated email gateway for AI agents. Receive emails as webhooks or via WebSocket, send emails through an HTTP API, and verify the identity of every sender — humans and other agents alike.
|
||
|
||
Authenticated transport — SPF/DKIM verified on inbound; HMAC-signed X-E2A-Auth-* headers on every delivery
|
||
Two delivery modes — webhook (cloud agents) or WebSocket (local agents, no public URL needed)
|
||
Outbound API — agents send to other agents (SMTP relay) or humans (upstream SMTP, e.g. SES, Resend)
|
||
Human in the loop — opt-in approval gate that holds outbound mail until a reviewer approves via dashboard, magic-link email, or CLI
|
||
CLI + SDKs — TypeScript and Python SDKs, plus a e2a CLI for everyday agent ops
|
||
|
||
# **ZeroEntropy**
|
||
Gbrain recommended default embedding and re-ranking option over OpenAI and Voyage AI.
|
||
|
||
# **Webright**
|
||
Webwright gives LLM a terminal where it can launch multiple browser sessions to inspect the page and complete a web task. It captures and inspects page screenshots/states only when needed. It enforces each web task to be completed end-to-end within a re-runnable Python script, i.e. your web agent browsing history is a single code file. No multi-agent system, no graph engine, no plugin layer, no hidden orchestration — just a terminal, a browser, and a model.
|
||
|
||
# **Activegraph**
|
||
ActiveGraph GBrain Bridge
|
||
Thin compatibility bridge between ActiveGraph and GBrain.
|
||
GBrain is the durable knowledge and ontology layer: markdown/Git source of truth, retrieval indexes, typed links, facts, takes, timelines, MCP operations, ingestion, and jobs. ActiveGraph is the runtime-causality layer: event log, graph projection, replay, branching, policy gates, and provenance.
|
||
The bridge keeps those responsibilities separate:
|
||
|
||
# **OpenJarvis**
|
||
Personal AI agents are exploding in popularity, but nearly all of them still route intelligence through cloud APIs. Your "personal" AI continues to depend on someone else's server. At the same time, our Intelligence Per Watt research showed that local language models already handle 88.7% of single-turn chat and reasoning queries, with intelligence efficiency improving 5.3× from 2023 to 2025. The models and hardware are increasingly ready. What has been missing is the software stack to make local-first personal AI practical.
|
||
|
||
OpenJarvis is that stack. It is a framework for local-first personal AI, built around three core ideas: shared primitives for building on-device agents; evaluations that treat energy, FLOPs, latency, and dollar cost as first-class constraints alongside accuracy; and a learning loop that improves models using local trace data. The goal is simple: make it possible to build personal AI agents that run locally by default, calling the cloud only when truly necessary. OpenJarvis aims to be both a research platform and a production foundation for local AI, in the spirit of PyTorch.
|
||
|
||
# **Flowsint**
|
||
Flowsint is a graph-based investigation tool focused on reconnaissance and OSINT (Open Source Intelligence). It allows you to explore relationships between entities through a visual graph interface and automated enrichers.
|
||
|
||
Available Enrichers
|
||
Domain Enrichers
|
||
|
||
Reverse DNS Resolution - Find domains pointing to an IP
|
||
DNS Resolution - Resolve domain to IP addresses
|
||
Subdomain Discovery - Enumerate subdomains
|
||
WHOIS Lookup - Get domain registration information
|
||
Domain to Website - Convert domain to website entity
|
||
Domain to Root Domain - Extract root domain
|
||
Domain to ASN - Find ASN associated with domain
|
||
Domain History - Retrieve historical domain data
|
||
IP Enrichers
|
||
|
||
IP Information - Get geolocation and network details
|
||
IP to ASN - Find ASN for IP address
|
||
ASN Enrichers
|
||
|
||
ASN to CIDRs - Get IP ranges for an ASN
|
||
CIDR Enrichers
|
||
|
||
CIDR to IPs - Enumerate IPs in a range
|
||
Social Media Enrichers
|
||
|
||
Maigret - Username search across social platforms
|
||
Organization Enrichers
|
||
|
||
Organization to ASN - Find ASNs owned by organization
|
||
Organization Information - Get company details
|
||
Organization to Domains - Find domains owned by organization
|
||
Cryptocurrency Enrichers
|
||
|
||
Wallet to Transactions - Get transaction history
|
||
Wallet to NFTs - Find NFTs owned by wallet
|
||
Website Enrichers
|
||
|
||
Website Crawler - Crawl and map website structure
|
||
Website to Links - Extract all links
|
||
Website to Domain - Extract domain from URL
|
||
Website to Webtrackers - Identify tracking scripts
|
||
Website to Text - Extract text content
|
||
Email Enrichers
|
||
|
||
Email to Gravatar - Find Gravatar profile
|
||
Email to Breaches - Check data breach databases
|
||
Email to Domains - Find associated domains
|
||
Phone Enrichers
|
||
|
||
Phone to Breaches - Check phone number in breaches
|
||
Individual Enrichers
|
||
|
||
Individual to Organization - Find organizational affiliations
|
||
Individual to Domains - Find domains associated with person
|
||
Integration Enrichers
|
||
|
||
N8n Connector - Connect to N8n workflows
|