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Case by DaemonLabs

Stop dreading the desktop part of your AI agent.

API
Developer Tools
Artificial Intelligence
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Hunted byAbhinav GuptaAbhinav Gupta

Most AI agents drive desktop apps by clicking around and hoping. Case replaces that with typed, verified procedures: requires, ensures, structured failures, idempotency labels, and reliability stats pinned to a specific app version. 50+ procedures live for DaVinci Resolve today, with Photoshop and Logic shipping next. Drop into Claude Code, Anthropic Computer Use, or Hermes. The desktop part of your agent stops being what breaks.

Top comment

This sounds wonderful. I am going to try this right now. Congrats on the launch

Comment highlights

For the PH crowd: 50 free procedure runs on us. Drop your email at daemonlabs.run/ph, key lands within 24h, then point case-sdk at DaVinci (or any other macOS app) and drive it from Python or Claude Code.

Hey PH, Mayank from Daemon Labs.

With all the hype around computer-use agents and software automation, I started exploring the space myself. And almost everything I saw boiled down to agents taking screenshots and guessing what to click next.

That felt fundamentally wrong.

If a system is meant to automate software reliably, it shouldn’t just look at the screen. It should understand the software, know the available actions, and handle every workflow inside the app.

That led us to a simple idea:
software automation should run on procedures, not guesses.

Instead of treating every interaction like a vision problem, we started building structured procedures for software itself. Reliable flows that know how to operate an application, recover from failures, and execute deterministically.

That eventually became Case.

Hey PH, Pulkit from Daemon Labs.

Built Case because we kept hitting the same wall with AI agents. The thinking part was great. The doing part was a mess.

A button shifts. A click doesn't register. The agent retries forever. And the workflow that ran clean yesterday just... doesn't today. You end up babysitting the thing instead of trusting it.

I didn't take the normal route into this. Spent most of my time deep in systems, infra, and applied AI, the unsexy stuff. And the more I poked at agents, the more obvious it got. The models are smart enough. They've been smart enough for a while. What's missing is everything underneath them actually working when it has to.

So we started building Case.

Instead of agents fumbling around the screen guessing where to click, Case runs procedures that are structured, typed, and check themselves as they go. They know what app version they're on. They don't break when something moves. They actually finish.

Early days. But it's the thing we kept wishing someone would build, so we're building it. 🚀

Hey PH, Ishant from Daemon Labs.

I chose GPU compute over rent. At 20. In college. And I'd do it again.

While my batchmates were grinding interview prep, I was reading transformer papers at 2am in my hostel room.

No lab. No funding. Just a bet that AI was the most important thing happening in the world and I needed to be inside it, not watching.

We applied to Founders Inc's Canopy program. Got rejected. I didn't take it. DM'd partners on X. Showed up in their threads with real responses. Sent handwritten letters promising a specific deliverable by demo day. Hours before the program started, we were in.

Same year, my paper on procedural memory in language agents got into the ICLR 2026 MemAgents workshop. Building that benchmark made one thing obvious:

Agents aren't failing because they can't reason. They're failing because the execution layer is a complete disaster.

Silent crashes. Pixel-dependent clicks that break every software update. Zero structure in the failure signal. Every retry is a gamble. Every demo is a prayer.

That insight became Case.

A verified procedure runtime for the desktop apps your agent has to drive. Typed calls. Structured failures with retry flags and recovery docs instead of cryptic stack traces. Idempotency labels so retries don't compound bugs.

Today we're shipping two things:

  1. case-sdk (open source): the procedure runtime. Build your own procedures to operate any desktop software. → https://github.com/DaemonLabsInc/case-sdk

  2. case-api (managed): plug-and-play access to our verified, battle-tested procedures. DaVinci Resolve today, more apps shipping soon.

99.6% reliability measured across 51,000 real customer calls. A number you can actually ship to your own customers.

We started with DaVinci Resolve because that's where the pain is loudest. The whole desktop is next.

Everyone from PH gets 50 procedure runs per day, free, for 7 days.

If you're building agents that touch real software, what's the procedure that's killed your demo? 👇

About Case by DaemonLabs on Product Hunt

Stop dreading the desktop part of your AI agent.

Case by DaemonLabs was submitted on Product Hunt and earned 17 upvotes and 7 comments, placing #31 on the daily leaderboard. Most AI agents drive desktop apps by clicking around and hoping. Case replaces that with typed, verified procedures: requires, ensures, structured failures, idempotency labels, and reliability stats pinned to a specific app version. 50+ procedures live for DaVinci Resolve today, with Photoshop and Logic shipping next. Drop into Claude Code, Anthropic Computer Use, or Hermes. The desktop part of your agent stops being what breaks.

Case by DaemonLabs was featured in API (98.3k followers), Developer Tools (514.1k followers), Artificial Intelligence (471.1k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 205.8k products, making this a competitive space to launch in.

Who hunted Case by DaemonLabs?

Case by DaemonLabs was hunted by Abhinav Gupta. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.

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