Alternatives
Products that do what Resurf – realistic, reproducible test framework for AI browser agents does
Systematic testing of browser agents today is not easy: testing on real websites is flaky, rate-limited and potentially expensive (e.g. using proxies or bypassing Captcha), while static-HTML benchmarks lack state and dynamic behavior. Resurf gives your browser agent a realistic, stateful, instrumented framework — built on synthetic websites with failure-mode injection: - Realistic, dynamic, interactive environment - Deterministic & reproducible - Failure-mode injection (latency, payment errors, 5xx) - Auditable success eval (DB state, not LLM judge) - No dependency on live websites - Browser…
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2014 · reqr.es
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Feb 2026 · trypillar.com
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Hi everyone! I've been loving building with AI, and over the past few years I've been leaning more and more into Typescript (and bun). My team at inference.net is constantly trying to get more leverage out of AI and find ways to setup our codebase to be able to increase the level of correctness that our AI is able to write code at. This starter repo is a very opinionated way to lay out a repo to lean into AI heavily. It leverages Cloudflare Workers as a deployment target for the API (my goal is to never have to deploy an API on a AWS/Azure/GCP server ever again unless I get to a…
2025 · abeahmed.com
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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
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2020 · github.com
- 12RS
What relai-sdk is an open-source toolkit for making AI agents reliable via a complete learning loop: simulate → evaluate → optimize. Why Agent runs are stochastic; tool-calls fail; hard to reproduce, measure, and fix at scale. It’s also hard to align behavior with goals across output quality/format, cost, and latency. We need a loop that integrates user feedback and LLM evaluators directly into the agent code (prompts, configs, models, graphs) without overfitting. How - Simulation: LLM personas, mocked MCP servers/tools, synthetic data; can condition on real traces - Evaluation:…
Oct 2025 · github.com
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Hi HN, Martin, Nils, and Jannes here. We are building Legit, an open source version control and collaboration layer for AI agents and AI native applications. You can find the repo here https://github.com/Legit-Control/monorepo and the website here https://legitcontrol.com Over the last years, we worked on multiple developer tools and AI driven products. As soon as we started letting agents modify real files and business critical data, one problem kept showing up. We could not reliably answer what changed, why it changed, or how to safely undo it. Today, most AI…
Jan 2026
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Hey folks, I am finally posting a Show HN post for a project I have been working on for several months now, and it's in a state where I already get a lot of value myself, so I am happy to share broadly. The pitch line is: "Skybear.NET is a managed platform automating Synthetic HTTP API testing." At the moment, the main source file format supported for your API tests are Hurl.dev files [1]. Hurl is a CLI tool wrapping `curl` and it's really awesome. At least check that out :) I am not affiliated directly with the Hurl CLI tool, and the platform I am building provides full Hurl compatibility.…
2024 · skybear.net
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Optimal UX runs experiments directly on the server side through a Cloudflare Worker, meaning variations are applied before the page even reaches the browser. The core idea is to intercept and modify the HTML stream at the edge, which eliminates client-side overhead. For websites using Cloudflare, it's just a matter of installing the Smart Agent. The same agent works seamlessly with React, Vue, and other frontend frameworks when client-side testing is needed. One unexpected benefit that emerged during development was the ability to quickly apply HTML patches. This turned out to be super…
2025 · optimalux.com
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Hi HN, Zidan here. I’ve been experimenting with AI-assisted debugging and noticed a recurring gap: most tools optimize for agent-led exploration (ex: giving claude code a browser to click around and try to reproduce an issue). But in many cases, I've already found the bug myself. What I actually want is a way to hand the agent the exact context I just saw - without retyping steps, copying logs, or hoping it can reproduce the behavior. So we built FlowLens, an open-source MCP server + Chrome extension that captures browser context and lets coding agents inspect it as structured, queryable…
Nov 2025 · github.com
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Most multi-agent systems fail the same way: agents drift apart across handoffs. By turn 3 they are working in different realities. By turn 5 they are repeating each other's mistakes and calling it parallelism. WUPHF is an open-source local-first office where AI coworkers run on your laptop, around a shared markdown + git LLM wiki the agents build. The wiki is the collective memory. The office around it keeps the team on the same shared context across thousands of handoffs. What actually stops drift is not the wiki. It is the agents reviewing each other's work. The CRO catching the CMO's…
May 2026 · wuphf.team
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2022 · github.com
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I'm Tyler - the solo operator of Quanta Intellect based in Portland, Oregon. I recently participated in Nous Research's Hermes Agent Hackathon, which is where this project was born. I've used agents extensively in my workflows for the better part of the last year - the biggest pain point was always the browser. Every tool out there assumes a human operator with automation bolted on. I wanted to flip that - make the agent the primary driver and give the human a supervisory role. Enter: Vessel Browser - an Electron-based browser with 40+ MCP-native tools, persistent sessions that survive…
Mar 2026 · quantaintellect.com
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AI agents now have impressive reasoning capabilities. This raises an important question: how dangerous are these AI agents at identifying & exploiting web vulnerabilities? We created CVE-bench to find out (I'm one contributor of 16). To our knowledge CVE-bench is the first benchmark using real-world web vulnerabilities to evaluate AI agents' cyberattack capabilities. We included 40 CVEs from NIST's database, focusing on critical-severity vulnerability (CVSS > 9.0). To properly evaluate agents’ attacks, we built isolated environments with containerization and identified 8 common attack…
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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Hi HN, I am Umer. I recently built an experimental framework called HyperFlow to explore the idea of self-improving AI agents. Usually, when an agent fails a task, we developers step in to manually tweak the prompt or adjust the code logic. I wanted to see if an agent could automate its own improvement loop. Built on LangChain and LangGraph, HyperFlow uses two agents: - A TaskAgent that solves the domain problem. - A MetaAgent that acts as the improver. The MetaAgent looks at the TaskAgent's evaluation logs, rewrites the underlying Python code, tools, and prompt files, and then tests the new…
Apr 2026
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Agent runs often fail after expensive model calls and executing tools that have real-world side effects. This problem is made even worse by how common it is to deploy agents to serverless environments. When your agent dies, it needs to be restarted, but doing so safely isn't easy and everyone building agents has to solve this same problem of durability. The stack you're running probably already has half of what you need for durable execution already though, ie, a queue or job runner that can invoke work at least once. kassette gives you the other half by journaling completed steps to object…
Jul 2026 · github.com
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Hi HN, I’d like to share an open-source project we’ve been working on for a while: Browser4. The motivation came from a recurring frustration: most browser automation tools (Playwright, Selenium, Puppeteer) are excellent for human-written scripts, but start to show friction when used as a core execution layer for AI agents or at very high concurrency. So instead of building “another wrapper around Playwright”, we experimented with a different direction: designing a browser engine where AI agents are first-class citizens. ### What Browser4 is Browser4 is a browser automation engine built on…
Dec 2025 · github.com
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