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Products that do what APIMaster.ai does
Detect fake LLM APIs & reveal the real model underneath
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I built this out of curiosity about what Claude Code was actually sending to the API. Turns out, watching your tokens tick up in real-time is oddly satisfying. Sherlock sits between your LLM tools and the API, showing you every request with a live dashboard, and auto-saved copies of every prompt as markdown and json.
Jan 2026 · github.com
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I started leaning in on AI heavily this year, as I wanted to get more done autonomously, but then my token usage climbed dramatically to the point where my weekly quota would run out before the end of the week, sometimes a couple of days into the week. I realised I had to do something about it else I'd have to double my spend. So I decided to start tracking my cost per task type. This revealed that a lot of my spend went to searches/scans or simple things like scouting tasks. I then decided to turn this into a simple CLI tool that can be used to read your OpenAI-style logs locally, and…
Jul 2026 · github.com
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I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…
2025 · llmapitest.com
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Hi HN! Stefan here from superglue and today I’d like to share a new benchmark we’ve just open sourced: an Agent-API Benchmark, in which we test how well LLMs handle APIs. We gave LLMs API documentation and asked them to write code that makes actual API calls. Things like "create a Stripe customer" or "send a Slack message". We're not testing if they can use SDKs; we're testing if they can write raw HTTP requests (with proper auth, headers, body formatting) that actually work when executed against real API endpoints and can extract relevant information from that response. tl:dr: LLMs suck at…
2025 · github.com
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Hi HN, I've been working with LLMs in production for a while both as a solo dev building apps for clients and working at an AI startup. The one thing that always was a pain was to pay OpenAI/Gemini/Anthropic a few dollars a month just for me to say "test" or have a CI runner validate some UI code. So I built this server called ChunkBack, that mocks the popular llm provider's functionality but allows you to type in a deterministic language: `SAY "cheese"` or `TOOLCALL "tool_name" {} "tool response"` I've had to work in some test environments and give good results for experimenting…
Nov 2025 · github.com
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Hey HN! We're Ayman and Dylan, co-founders of Nuanced (https://www.nuanced.dev/). We want to share a tool we’re working on to detect fake and real images: https://trial.nuanced.dev/demo/. The UI is bare-bones but you’ll get the idea. Drag or upload an image and our tool will display the probabilities with which it thinks that the image might be AI-generated or not. If you want, you can click “No, it’s AI” to confirm that the image was AI-generated, or “No, it’s real” to confirm that the image was not AI-generated. Why we’re working on this: as AI-generated…
2024 · trial.nuanced.dev
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Hi! My name is Herve Kom, a computer science student that is interested in learning new things everyday! As one of my graduation project, I have developed a Claude Code -like Coding CLI, but with enhancement for API Testing: - Auto-generate & run tests (unit, e2e, Playwright, CI/CD, etc.) - Say bye-bye to hallucinations with built-in MCP Server to let LLM directly read from API Docs - Adding Agent.md support for better context persistence across your whole codebase - Automatic bug & security scans (logic is kind of basic but works great!) - Vibes, I want it to feel less "enterprise" but…
2025 · github.com
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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
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Hey HN! For that last 8 months I've been trying to make agents that can hack web applications to find vulnerabilities in them - An AI Security Tester. The system has 29 agents in total, a custom LLM Orchestration framework which works on the task-subtask architecture (old-school but works amazingly for my use case, and is pretty reliable) with custom agent calling mechanism. No Auo-Gen, Langchain and Crew AI - Everything custom built for pentesting. Each test runs in an isolated Kali linux environment (on AWS Fargate), where the agents have full access to the environment to undertake any…
2025
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Find exact queries to track AI search visibility
Nov 2025 · wellows.com
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