Alternatives
Products that do what GROOVY does
Universal Search and Signaling across LLMs
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Get Cited in Google AI search, ChatGPT, Perplexity & Copilot
Dec 2025
- 15NH
Hey HN! When I started looking into LLMs and agents for software development and introducing them at work, I quickly realised that a person new to the topic faces a real barrage: - all the hype (AGI, engineers getting replaced by AI etc.) - conflicting opinions in virtually every discussion—for every person saying they’ve 10x-ed their productivity, there is a comment decrying LLMs as an utter failure - a lot of jargon (MoE, MCP, RAG, distillation, quantisation etc. etc.) - a profusion of models, IDEs/IDE extensions, CLI agents, other tools etc. Sorting through all of this can be quite…
2025 · nohypeai.dev
- 16AA
Looking for feedback on how Props can make your life easier as an LLM application developer.
2024 · wwww.getprops.ai
- 17IM
AI search results are quickly becoming more important than SEO, but as businesses, we have no visibility over it! That's why I'm building "Ahrefs for AI search results". Track keyword performance on AI tools like ChatGPT, Claude, Perplexity & more
2025 · linrush.com
- 18AG
I’ve been building LLM tooling for a small VC fund and found myself explaining the same mental model over and over to non-technical people around me: how a stateless LLM becomes a chatbot, how tool use works, what an agent is mechanically, and why context windows shape all of it. I never found a guide that covered that full chain at the level I wanted, so I wrote one. It’s nine short chapters, each building on the last. Deliberately simplified: the goal is a useful mental model, not a textbook. Feedback, corrections, and contributions welcome: github.com/ymyke/aiaiai
Apr 2026 · aiaiai.guide
- 198B
Hey all, Justin here. I previously built Phind, the AI search engine for developers. One of the biggest problems we had there was figuring out what went wrong with bad searches. We had tons of searches per day, but less than 1% of users gave any explicit feedback. So we were either manually digging through searches or making general system improvements and hoping they helped. This problem gets harder with agents. Traces are longer and more complex. It takes more effort to review them, so I'm building a tool that lets you analyze LLM outputs directly to help developers of LLM apps and agents…
Jan 2026 · trails-red.vercel.app
- 20HL
At testup.io we have been working for a while to bring artificial intelligence to the field of test automation. Just a few years ago, the primary challenge laid in accurately identifying UI elements following minor structural changes, such as updates to IDs or paths. The emergence of Large Language Models (LLMs) raised the bar for what it meant to be smart. Now, we anticipate the robot to do lots of things autonomously, such as retry in cases of unresponsiveness or handle minor error reports. A more challenging, but soon expected feature, would involve the test robot navigating your web shop…
2024 · github.com
- 21IB
I built a tool to roast landing pages with AI agents. I was gathering feedback from watching landing page roast videos, and figured out I could prompt LLMs to analyse a screenshot and roast based on the same criteria. It's not 100% accurate yet, but it has been really insightful when I've tested it on my own websites. Let me know what you think!
2024 · roastmylandingpage.io
- 22AS
Hi HN! When I was the Product Management lead for HR Tech at Google, I thought there were several opportunities to integrate LLMs into our tools to make employees more efficient. Managers could get summaries of peer reviews, recruiters could quickly generate personalized outreach letters to candidates, etc. But I had 3 big problems: 1. Third Party Systems: A lot of work happens in Salesforce, Workday, SAP, etc. And we had no way of modifying that code to integrate what we wanted. 2. Legacy Systems: A lot of the tools in our portfolio were oooold, and nobody really wanted to go in and mess…
2024 · asksteve.to
- 23IM
Every time I wanted to use LLMs in my existing pipelines the integration was very bloated, complex, and too slow. This is why I created a lightweight library that works just like scikit-learn, the flow generally follows a pipeline-like structure where you “fit” (learn) a skill from sample data or an instruction set, then “predict” (apply the skill) to new data, returning structured results. High-Level Concept Flow Your Data --> Load Skill / Learn Skill --> Create Tasks --> Run Tasks --> Structured Results --> Downstream Steps And the bast part: Every step can be saved and reused as…
2025 · github.com
- 24GA
Hi HN, I’ve been working on Spiderseek, a platform to help track and grow website visibility in AI-powered search engines (e.g. Perplexity, ChatGPT, and other agents). Traditional SEO tools are expensive and focused on Google-style search. I wanted something lightweight and AI-first, so I built Spiderseek: AI Research – Explore domains and keywords to uncover new opportunities. AI Analytics – See traffic, crawl activity, and page metrics, plus insights from AI agents. Content Submission – Get content indexed instantly in major AI agents. Rankings – Browse the top 1000 domains sorted by…
Sep 2025 · spiderseek.com
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