AgentOne – AI Dev Assistant for Enterprise Code Gen

Hacker News

My team and I have developed AgentOne, a VSCode extension designed to be your personal AI development assistant. As a startup, we constantly balance new features with customer requests, all while managing a limited budget and team. This pushed us to focus on writing code quickly, efficiently, and securely. Using AgentOne you will be able to develop your entire code base automatically. We found existing code-building AI tools often fell short due to issues like generating "garbage code," struggling with large codebases (millions of lines), and incurring high costs. AgentOne was built to overcome these limitations. AgentOne is optimized for developing enterprise-level code, offering transparency by displaying the cost of each query. With AgentOne, adding a powerful dev assistant to your workflow costs only a few dollars and comes with almost 0 limitations. Simply insert your Anthropic key to get started. We're eager to gather initial feedback and conduct testing to identify areas for improvement. Let us know what you think!

Visit product View on Hacker NewsJun 26, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, new, using · Missing: mac, agents, macos

90%90% 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, efficiently · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

65%65% 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: personal · Missing: mobile apps, ios, entrepreneurs

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% 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.

Incorrect prediction on native model

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