Data extraction: https://simplescraper.io A project that I launched on HN that became a business. Simplescraper rode the no-code wave of a few years back ('instant structured data without parsing html'). Now working on increasing the surface area for AI agents: MCP support, screenshots API, and (experimentally) x402^ ^ https://simplescraper.io/blog/x402-payment-protocol/. - Source: Hacker News / 5 months ago

1. Clicking the box programmatically – possible but inconsistent 2. Outsourcing the task to one of the many CAPTCHA-solving services (2Captcha etc) – better 3. Using a pool of reliable IP addresses so you don't encounter checkboxes or turnstiles – best I run a web scraping startup (https://simplescraper.io) and this is usually the approach. It has become more difficult, and I think a lot of the AI crawlers are... - Source: Hacker News / about 1 year ago

Making my data extraction Saas (https://simplescraper.io) more LLM friendly. Markdown extraction, improved Google search, workflows - search for this terms, visit the first N links, summarize etc. Big demand for (or rather, expectation of) this lately. - Source: Hacker News / almost 2 years ago

Things are much easier for one-person startups these days—it's a gift. I remember building a todo app as my first SaaS project, and choosing something called Stormpath for authentication. It subsequently shut down, forcing me to do a last-minute migration from a hostel in Japan using Nitrous Cloud IDE (which also shut down). Just pain upon pain.[1] Now, you can just pick a full-stack cloud service and run with it.... - Source: Hacker News / about 2 years ago

Simplescraper — Trigger your webhook after each operation. The free plan includes 100 cloud scrape credits. - Source: dev.to / over 2 years ago

If you build on the JVM and want to use Qdrant, the official client is io.qdrant:client — and it's built for Java. Every call returns a ListenableFuture, requests are assembled with protobuf builders, and it drags a gRPC/Netty stack onto your classpath. From Kotlin, that means fighting the language:. - Source: dev.to / 16 days ago

The stack runs on Qdrant for vector storage, Ollama for local embeddings, and optional Neo4j for a knowledge graph that I added later. I also set it up to route different operations to the best LLM for each task. It provides eleven tools for your Claude Code instance to manage long-term memory operations, and your memories data never leaves your machine. - Source: dev.to / 6 months ago

Qdrant: Open-source vector database optimized for hybrid search and easy integration with ML workflows. - Source: dev.to / 8 months ago

Yes, Java SDKs are critical. But you don't need to rebuild entire orchestration engines just to write agents in Java. The ecosystem already has platforms solving the hard problems: memory (Zep, Mem0, LangMem), tools (specialized platforms), vectors (Pinecone, Weaviate, Qdrant), observability (LangSmith, Helicone, Langfuse). Integrate, don't rebuild. - Source: dev.to / 9 months ago

James Allsopp adds, "LangChain or LlamaIndex for managing LLM workflows, especially if you're adding vector search or documents." These tools handle multi-step processes, essential for complex apps. - Source: dev.to / 12 months ago