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Cheevly – A natural language IDE to build collaborative AI agents

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Cheevly – A natural language IDE to build collaborative AI agents

Cheevly is a product to build service-agnostic 'GPTs and copilots' for the desktop. I started this project a couple years ago, and opted to quit my day job last year to focus all of my time on it. As a solo developer, this has completely exhausted my runway, but I've finally reached a point where I'm ready to show it to the world. Cheevly aims to be a full-featured natural language IDE. If you've visited the website ( https://www.cheevly.com ), you'll find that the branding is somewhat sensationalized, though it is meant to be tongue-in-cheek. My goal is to empower users to build vendor-agnostic AI assistants. Cheevly is not a cloud service; everything runs on the user's desktop and connects only to third-party services explicitly authorized. With local models, Cheevly is capable of performing no-code automation even without internet access. I wanted to build a solution with: - Easy install and setup that anyone can use - Intuitive UI with integrated tooling - Conditioning reliable outcomes with any LLM - Powerful no-code automation - Seamless vision for desktop, images and video - Operating system integration - Seamless code execution So how does Cheevly work? At its core, you create chat-based 'channels' (akin to Discord or Slack) with characters that are each powered by a language model. You can connect channels to one another, enabling them to exchange messages. Finally, you can use in-context learning to condition each channel on how to interact and perform actions. Here's a 30-second video demonstrating the flow: https://www.youtube.com/watch?v=G_sz126UAS8 By creating channels that are each conditioned on a single responsibility, you can produce reliable, interconnected agents which orchestrate tasks in natural language. This philosophy is embraced across all aspects of Cheevly. In fact, Cheevly transforms your computer, files and services into channels that you can chat with. For example, each of your monitors are an AI-powered channel (ask one of your monitors to take a screenshot of the top-right corner). Each file that you share with Cheevly can be queried, edited or used as a tool. For instance, you can use a text file as long-term memory by adding it to a channel. One remarkable example of how Cheevly unifies language model capabilities is its seamless video understanding for any vision-based models. This means you can drag a video into Cheevly and prompt against it using GPT-4v or even a local Llava model. I encourage everyone to explore the website, documentation (incomplete, but will be finished soon) and videos below. Basic tutorial: https://youtu.be/7EYifjGAbg0 Using Javascript to orchestrate: https://youtu.be/h9pX7guT8kI Automate .NET DLLs: https://youtu.be/OmPrkipm0Fc Here is a preview (not yet available) of real-time interactive narrative with AI generated voice, music, and sounds: https://www.youtube.com/watch?v=7r6k1ln7Sqg YouTube channel: https://www.youtube.com/@cheevly/videos Windows is currently the only operating system supported, but Mac and Linux will be available in October. For the sake of HN, I've made the free version of Cheevly completely unlocked so that it can be fully utilized without a license for the week (though you can still purchase and activate a license). Please offer any advice, criticism and questions! I can also be reached at josh@cheevly.com. I'm active on Discord (link is on the site), but may be slow to respond at times.

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, claude
97%97% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting
96%96% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: video, users, way · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: intuitive, soon, users · Missing: plus, platform, reviews
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, active · Missing: mrr, revenue, profit
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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