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New concept for AI agent workspaces for complex workflows

Hacker News

New concept for AI agent workspaces for complex workflows

Hey folks! Wanted to share the project I've been designing and building for several months to try to make AI development more centralized and accessible for anyone to start getting value, especially with new advancements in models or workflows. The core framework is a drag and drop builder for multi-agent workflows where you can chain LLM calls and tools together with logic similar to other automation builders. The part that's a bit more exciting is the workspace threads that will detect and choose tools to use (web search, knowledge base retrieval, etc.). From trying different tools you can directly save and start a workflow that runs on different triggers (chat, tickets, emails, etc.). Today it's completely free with multiple premium models (gpt-4o, claude sonnet, deepseek r1) and I believe it's a good start for experimenting as well as building early concept agents. Thanks for taking a look and would love to discuss more on the concept!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor
96%96% 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
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: builder, calls · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
30%30% 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
17%17% 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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