Vi

Visual Workspace for Agents Based on Unix

Hacker News

Visual Workspace for Agents Based on Unix

Hey HN, Thijs here! I'm the founder of Prototyper and today we're launching the first visual canvas built for agents. Couple of interesting lessons from building the product that I think are worth sharing: For the agents, everything is a file. In Prototyper, everything from plans, to apps, and diagrams, can be read as a file. We found that a filesystem is the most natural way for an agent to navigate: it discovers new content and functionality just by traversing the tree. We kept this layer deliberately thin and unopinionated, because it's the substrate, not the experience. It's the foundation that makes everything work on top. It essentially operates as a distributed, web-based unix kernel. Because of that it's built for the agents you already use. People run all kinds of models and harnesses, often several at once. With this architecture, we don't try to lock you into one, it's built to work with any agent that you like. That means that any agent can read from and write to your workspace. It's fast. File writes land in under a millisecond. That's not a bogus metric, it's what makes the whole thing feel seamless and like a real extension of your thinking. Thanks to our custom unix kernel. Visual first. Every file in your workspace can be opened on the canvas. Whether you're an engineer working on a new frontend or a PM building a product roadmap. It's a real visual workspace for the actual work, not a description of it. As said, it's not a blank canvas with a box of primitives: the canvas represents a real unix system, which is the kind of purpose-built, opinionated experience that makes a product. In essence, the substrate is generic so the things you build on it don't have to be. What's most interesting is that by this architecture we found that we can decouple system prompt length from agent capability. I'm happy to get feedback from the community and see what you all think of it :).

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
98%98% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: filesystem, io · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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