I use AI agents to build UI features daily. The thing that kept annoying me: the agent writes code but never sees what it actually looks like in the browser. It can’t tell if the layout is broken or if the console is throwing errors.So I built a CLI that lets the agent open a browser, interact with the page, record what happens, and collect any errors. Then it bundles everything — video, screenshots, logs — into a self-contained HTML file I can review in seconds. proofshot start --run "npm run dev" --port 3000 # agent navigates, clicks, takes screenshots proofshot stop It works with whatever agent you use (Claude Code, Cursor, Codex, etc.) — it’s just shell commands. It's packaged as a skill so your AI coding agent knows exactly how it works. It's built on agent-browser from Vercel Labs which is far better and faster than Playwright MCP.It’s not a testing framework. The agent doesn’t decide pass/fail. It just gives me the evidence so I don’t have to open the browser myself every time.Open source and completely free.Website: https://proofshot.argil.io/
FL score
out of 100
Verdict
high confidence
Competition
10
competitors found, emerging market, funded players
Trend
No signal yet
A clever open-source CLI tool providing visual feedback for AI coding agents, solving a real developer pain point, but lacking a clear path to monetization.
The pain
The gap
Build angle
Strengths
Questions about this idea?
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Risks
Next steps
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
A highly relevant tool for AI-assisted UI development, addressing a clear pain point with a clever technical angle and competitive differentiation. However, the 'open source and completely free' model fundamentally limits its business viability as a micro-SaaS.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
High value proposition and market timing, but the 'open source and free' model completely undermines its viability as a profit-generating venture under the Hormozi lens.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Excellent problem clarity, creator fit, and a strong niche, but completely fails on monetization due to its free, open-source nature, making it unsustainable for a solo builder.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Clear value and target audience with good early validation of the problem, but fundamentally lacks a viable business model as a free, open-source tool, creating significant risk for micro-SaaS.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Addresses a clear, growing pain point for developers using AI, with a simple, effective solution that leverages existing tools, but future growth for a *company* requires a monetization path beyond being free.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, emerging market
Applitools is an AI-powered visual testing platform that detects visual discrepancies in software applications by comparing screenshots to baseline images.
Pricing: Not explicitly stated, but alternatives mention high subscription costs.
Percy is an AI-powered visual testing platform that automates visual regression testing for web applications, integrating with CI/CD pipelines.
Pricing: Comparable plans to Chromatic can cost around $549/month when billed monthly.
Chromatic is a visual regression and UI review platform built around Storybook, automatically snapshotting components and highlighting visual changes.
Pricing: Free for open-source projects, with paid plans for private repos based on snapshot count. A plan comparable to Percy costs less, at $4,440 less per year for more snapshots.
Lost Pixel is an open-source visual regression testing tool that integrates with GitHub to catch visual bugs across Storybook, Next.js, and Playwright tests.
Pricing: Free in open-source mode, with a focus on offering better pricing for their platform.
Happo is a cross-browser visual regression testing tool focused on CI integration and speed, taking screenshots across real browsers.
Pricing: Not explicitly stated in search results, but it's a paid tool.
TestSprite is an autonomous AI testing agent purpose-built for AI-driven development, automating the validation loop for UIs and E2E tests.
Pricing: Not explicitly stated on the website, offers a free download and community discord.
Testsigma is a no-code, AI-powered test automation platform supporting visual regression testing alongside functional, API, and cross-browser tests for web and mobile apps.
Pricing: Not explicitly stated, but offers a cloud-based platform.
Mabl is a low-code, AI-driven test automation platform for web, mobile, and API testing with features like auto-healing to adapt to UI changes.
Pricing: Not explicitly stated, but generally positioned for enterprise-scale testing, so potentially higher cost for smaller teams.
Testim (now part of Tricentis) leverages machine learning to accelerate authoring and maintenance of UI tests with self-healing capabilities.
Pricing: Not explicitly stated, but offers paid plans.
AskUI uses a Vision Agent that interacts with applications at a pixel-level, visually identifying and clicking UI elements to make tests more resilient across platforms.
Pricing: Not explicitly stated, but offers a platform with different features.
What they charge
Recent news
Show HN: ProofShot – Give AI coding agents eyes to verify the UI they build
Hacker News, March 24 2026
deepidv: AI-native verifications & checks at 10x less cost
Product Hunt, March 13 2026
The best AI code testing in 2026
Product Hunt, March 18 2026
Uxia: Validate your User flows UX & UI in seconds with AI
Product Hunt, Not specified, but listed as similar to other products launched around early 2023.
Checkman: The best tool for UI/UX Designers to verify mobile app UI
Product Hunt, Not specified, but listed as a tool for UI/UX Designers.
Market signals
The market for AI-powered UI verification is growing and is becoming a significant area within the broader software testing industry. Recent activity shows a clear trend towards AI agents that can not only write code but also visually validate the UI, moving beyond traditional pixel-level comparisons to more intelligent, human-like understanding of visual changes. This is evidenced by numerous established testing platforms integrating AI for visual regression and new startups emerging with AI-native solutions.
What frustrates people
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
AI
Hi HN!I recently switched from a Fedora/GNOME laptop to a MacBook Air. My old setup served me well as a portable workstation, but I’ve started traveling more while working remotely and needed something with similar performance but better battery life. The main thing I missed was a simple taskbar that shows the windows in the current workspace instead of a Dock that mixes everything together.I built boringBar so I would not have to use the Dock. It shows only the windows in the current Space, lets you switch Spaces by scrolling on the bar, and adds a desktop switcher so you can jump directly to any Space. You can also hide the system Dock, pin apps, preview windows with thumbnails, and launch apps from a searchable menu (I keep Spotlight disabled because for some reason it uses a lot of system resources on my machine).I’ve been dogfooding it for a few months now, and it finally felt polished enough to share.It’s for people who like macOS but want window management to feel a bit more like GNOME, Windows, or a traditional taskbar. It’s also for people like me who wanted an easier transition to macOS, especially now that Windows feels increasingly user-hostile.I’d love feedback on the UX, bugs, and whether this solves the same Dock/Spaces pain for anyone else.P.S. It might also appeal to people who feel nostalgic for the GNOME 2 desktop of yore. I started my Linux journey with it, and boringBar brings back some of that feeling for me.
AI
### Describe the project you are working on Godot C# bindings ### Describe the problem or limitation you are having in your project For the past weeks, I've been discussing with several Unity users intending to move to Godot C# regarding dealing with the C# garbage collector. The most common complaint I hear from users is that, in Unity, allocations can trigger unexpected GC spikes into the game. In Godot, we target to make all of the high performance APIs (those that intended to be called every frame) not allocate any memory, so theoretically the GC should not be a problem. Additionally, Godot starting from 4.0, uses the Microsoft CoreCLR version of .net, which also supposedly has a better garbage collector than Unity. But in all, after several discussions with Unity users, neither is enough reassurance for them, and they would really feel safer if Godot exposed a zero allocation API. ### Describe the feature / enhancement and how it helps to overcome the problem or limitation The idea of this proposal is that Godot exposes zero allocation versions of many functions in the C# API, that users can use if they desire. Technically, this could be done from the binding generator itself, without breaking compatibility, and without doing any modification to Godot itself. ### Describe how your proposal will work, with code, pseudo-code, mock-ups, and/or diagrams **WARNING** I am not familiar with C#, so take this as pseudocode. Imagine you have two functions exposed as to C#: ```C# void MyClass.SetArray( Vector2[] array); Vector2[] MyClass.GetArray(); ``` This works and is pretty and intuitive. However, it has two problems: * GC is allocated on return * Memory is copied to Godot native formats every time there is a call. The idea is to add NoAlloc versions, which can be generated directly by the binder automatically when required: ```C# void MyClass.SetArrayNoAlloc( Godot.Collections.PackedVector2Array array); void MyCl
AI