david1542

Thanks for sharing, I'll check it out!

I actually think there's a very big market for it tbh. A lot of teams building agents are struggling with this today, especially in legal, finance, pharma and government. It's also a much deeper problem than most people realize, even for tool builders, and it really requires working backwards from the experience an agent should have.

Vespper is our take on what that should look like.

Either way, it's exciting to see more people building in this space. Agents definitely need better ways to work with Office docs.

I think it's a valid point. A few reasons we went this way. - The teams we're building for don't want to touch DOCX at all. They just want to plug in their agents and move on, so we went for a smooth hosted experience instead of an open-source lib they'd have to set up and tune themselves. - We run a fine-tuned model under the hood, and it needs a GPU, so running it locally isn't really practical for most folks.

I agree privacy matters a lot. Because of that, we offer zero-data retention for teams, and there's also a self-hosted option if you want everything running in your own cloud.

Yep, a real run with DOCX skill would pay an extra tool call (e.g load_skill). We pre-loaded its context because we wanted to neutralize setup differences between tools and just focus on editing.

Good question. Vespper is meant to replace the python-docx work for your agent and simplify things. That said, it can definitely run alongside your agent (either as additional tools to your agent or as a subagent that uses our tools).

For example, you could keep python-docx for the easy stuff and fall back to a Vespper-based approach when your agent hits errors filling the document. However, the end goal is to completely free your agent from python-docx and low-level work, so it can just focus on what goes into the template.

EigenPal is great! I actually love their product.

Where I think it can break (and we show some of that in the benchmark post, though not on EigenPal specifically) is on tricky document edits, like multi-turn track changes, implicit style understanding (respecting surrounding styles without being told to), advanced list manipulation, etc. We also saw these problems get amplified on long-horizon, challenging document workloads.

Eventually we realized no product out there truly handles the wide variety of cases agents run into, so we set out to build one.

So to answer your question, it depends. If your documents are simple and you're happy with the results, stick with what works. But if you have tricky documents and find yourself chasing the N-th edge case, I think delegating that to something like our product makes sense.

Legit question :) so we've worked on a few ideas since we've been accepted to YC, one of them was the document editor for pharma.

After working on that for a while, we've decided to shift focus and work on the DOCX angle. We felt there's a big opportunity here since .docx is such a widely used format.

As to the feature complete point - I agree. We already cover the most common features today (paragraphs, lists, tables, tracked changes, header/footer) and we're working hard on implementing more. Our main focus right now is to be the most accurate, cheapest and fastest solution out there for common scenarios.

Looks great! What would you say are the main differentiators compared to SVGator?

Submitted, thanks!

Hey everyone, I worked on a fun weekend project.

I tried to build an OAuth layer that can extract memories from ChatGPT in a scoped way and offer those memories to 3rd party for personalization.

Would love to hear what you think!