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OpenDocBot – bring your own model to Word, Excel and PowerPoint

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OpenDocBot – bring your own model to Word, Excel and PowerPoint

I got tired of the lock-in and black-box nature of AI inside MS Office. One vendor decides which models you can use, where your document data goes, and what the agent is actually allowed to do. And when it fails, you can't see the code or send a PR to add a feature you may need. Many people don't have that choice: they're forced to use these tools (MS Office) at work. For coding agents we already have open interfaces like OpenCode where you can bring your own key, swap providers and models, run local, inspect everything, etc. I thought "agentic Office" needs the same. So I built OpenDocBot: an agent that runs inside Word, Excel and PowerPoint. Bring your own provider (OpenAI, Anthropic, Gemini, DeepSeek, OpenRouter, Ollama, or your own endpoint / custom gateway). It's client-side, so document data goes straight to the provider you configure, and the source is public so you can always audit the code and self-host it. I added a few things I thought would be useful: human-in-the-loop approval before any action is applied; file uploads (e.g. building a slide deck from a PDF) with local OCR for scanned documents, and tunable model behavior. Caveat: it's early, and Microsoft Marketplace distribution isn't live yet, so today you have to sideload it. I know that's real friction, I'm fighting daily with the MS Support team to achieve MS Partner Center account validation. I wrote some scripts that automate the sideloading process, and recorded short videos to make it easier. If you use Office heavily, I'd love your feedback. Repo: https://github.com/opendocbot/opendocbot Site + docs: https://opendocbot.com

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
92%92% 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: gemini · Missing: supports, reddit linkedin, podcasting
91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, interface · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, way · Missing: mobile apps, ios, personal
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: llama, ide, io · Missing: https docs, excited, just released
32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
21%21% 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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