Pulsedive API Documentation and Playground Now Live

Had this same thought for some time. Not only code, comercial llms also benefit from interacting with highly qualified and experienced engineers sharing their thoughts and correcting llms. In effect llms are now being trained by distilled knowledge of product engineers from all product companies. The winners at the end of speed run phase of comercial llms will be the those companies that created and operate the models.

Firecrawl Alternative: Same API, 5.5x Faster, Self-Hostable. The migration takes 5 minutes. Here's why. CRW implements a Firecrawl-compatible API. Same endpoints, same response format. You change one line — the base URL — and everything works. Before: const client = new FirecrawlApp({ apiUrl: "https://api.firecrawl.dev" }) After: const client = new FirecrawlApp({ apiUrl: "http://localhost:3002" }) That's it. No SDK changes. No schema migration. No rewrite. But the numbers change dramatically: Latency: - Firecrawl: 4,600ms avg - CRW: 833ms avg (5.5x faster) RAM: - Firecrawl: ~500MB - CRW: 6.6MB (75x less) Content coverage: - Firecrawl: 77.2% - CRW: 92% Docker image: - Firecrawl: 2GB+ - CRW: 8MB Cold start: - CRW: 85ms Plus: - Fully open source (AGPL-3.0) - Self-hostable on any infrastructure - Built-in MCP server for AI agent integration - No API keys, no rate limits, no vendor lock-in If you're hitting Firecrawl's rate limits, paying for usage you could self-host, or need lower latency — the switch is one line of code. github.com/us/crw | fastcrw.com #FirecrawlAlternative #WebScraping #OpenSource #SelfHosted #DevTools

Your Postman collections just got a lot more useful. Postman's MCP server is now listed in the Google Antigravity IDE MCP store. Install it, and Antigravity's AI agents get direct access to your collections, environments, and workspaces. No config required. What does that actually unlock? Google Antigravity can run your Postman collection as a test suite and get structured results back. It can read the real request definition from your collection when writing integration code, instead of guessing at the contract. It can scaffold a full collection from an OpenAPI spec and publish it to your workspace. If you're already on Postman, there's nothing to rebuild. Just install from the store and go. Read the full walkthrough: https://lnkd.in/gc7uFYCz

Why you shouldn't just wrap your API in an MCP server. Check out this new blog post to discover why traditional APIs and the new Model Context Protocol (MCP) are complementary tools and why modern applications need both to succeed. https://lnkd.in/g9vukA9n

This is the fifth post in a series on our context engine. The earlier posts covered hybrid search internals, the persistent inference server, and reasoning chain capture. This post covers the last piece: how search became a native tool instead of a shell command. https://lnkd.in/dmACz4tW

Let me share something I’ve been experimenting with lately: extending AI desktop apps with real, production‑grade data—without any model training. In my new article, I walk through how I built an MCP server that lets Claude desktop app understand spatial queries like “What parks are within 1 km of me?” and turn them into Swift‑powered API calls against my own backend. This approach opens the door to a powerful new pattern: ➡️ Bring your existing APIs and databases directly into your AI workflow ➡️ Keep your data exactly where it already lives ➡️ Let the AI handle the natural‑language interface If you’re curious about MCP, spatial data, or integrating AI with real systems, this one’s worth a look. https://lnkd.in/dmRdevfC #mcp #swift #anthropic #claude

As a reminder, Anthropic refused to cooperate with the US for *domestic* mass surveillance. They had no qualms about non-domestic mass surveillance.

I’m excited to release a new framework I’ve been working on: 40mcp. https://lnkd.in/eGNymbUQ As I’ve gone deeper into AI and agentic systems, I kept coming back to the same need: good middleware. Not just something that connects APIs to tools, but something that can evolve into a real bridge layer for systems. 40mcp is my attempt at that. At the surface, it’s a universal API-to-MCP bridge. But the real idea is more geometric than that, as Hawking once said, "I took a Euclidian approach." I build tesseracts and tesseract accessories. 40mcp is built as a four-dimensional bridge: Dimension 1: take a REST API and expose it as MCP tools. Dimension 2: ingest different kinds of API surfaces like OpenAPI, GraphQL, and HAR traffic into the same model. Dimension 3: compose those tools through chaining, mixing, and response shaping so they become more usable in real workflows. Dimension 4: fold the system back on itself through reverse bridging and self-reference. That last part is what makes it interesting to me. Each layer doesn’t just add features. It folds the previous layer into something new. I’m still learning a lot as I go — about software, AI, and what actually holds up in practice — but this is one of those pieces I kept needing in my journey, so I built it and decided to open source it. If you’re building with MCP, agent workflows, or tool infrastructure, I’d genuinely welcome feedback! #MCP #OpenSource #AIEngineering #AgenticAI

I developed a lightweight Node.js proxy with no external dependencies, designed to improve resilience when working with free or rate-limited AI models. The model-chain-proxy allows you to configure a fallback chain across multiple models. When a model fails (due to rate limits, instability, or timeouts), the proxy automatically forwards the request to the next one in the list, completely transparently to the client application. Main features: -> Automatic and intelligent fallback across multiple models -> Full streaming support -> Prometheus-compatible metrics -> Flexible API key resolution (environment variables, shell configuration, or custom headers) It is especially useful for those working with APIs like OpenRouter, where availability can vary significantly. Instead of implementing complex retry and fallback logic in your application, the proxy centralizes this responsibility, simplifying your code and improving reliability. It can also be used alongside OpenClaude, enabling transparent distribution of requests across different models and improving both performance and overall availability. For now, the proxy is optimized for OpenRouter, which has proven to be a very practical option. In the near future, I plan to generalize it with support for additional providers, including automatic translation between different API formats. Repository: https://lnkd.in/dbcv_DGR

I'm releasing a tool I've been using 24/7 lately - dclaude. Bare-bones, super-simple docker wrapper on Codex/Claude. So you never waste time approving prompts again. Personally, I have wasted way, way too many wall-clock minutes waiting on pressing "Yes" for some stupid command. 🚩 Sometimes a few commands would get chained and I keep forgetting to press Yes on them. (Claude sometimes chains 10 'sed' commands in a row, each 3s apart) I tried enabling notifications. I would still miss them, especially when running multiple in parallel. Not to mention the annoying context switching this requires. The solution is to give the AI full permissions, but there's a security and productivity spectrum of tradeoffs here: ❌ I don't want to give it full read/write access to my whole personal computer ❌ I don't want to deal with setting up a dev-only VM -- I want easy local file integration (so I can paste things to it). This setup also costs more & is work to set up/maintain. ❌ The native CLI permission modes are clunky (they don't seem to work well, are different per tool and change every week) Running locally on Docker with whitelisted read/write permissions is good enough for me. Maybe it is for you too :) You'd think a tool like this existed, but I wasn't able to find one. Most were way over-engineered. Others didn't have the killer features I wanted: 📸 Seamless paths. The container sees the same absolute repo path that you see on your computer, so pasting the agent an image path like ~/Desktop/image.png just works. 📉 Uses 'cx' for semantic code navigation for 50% less token spend on reads ✨ Configure read-only folders (e.g ~/Desktop). The container can never write to them and can only read. 👌 Auth/Caches persisted across runs dclaude & dcodex have been my daily drivers for a month now. I polished the project up and I am sharing it here with you now. Go try it out and let me know if it helps you 👇 ✅ https://lnkd.in/duDUBsia