Lien Chueh

As someone that has experienced their design come out completely different when it gets implemented as code.... I love this idea. Are there certain differences that Visdiff have trouble detecting versus ones that it is best at?

Do you find a difference in data quality between the developers that are being paid to test a tool versus actual users of the dev tool? For instance, I know that my behaviour is different when I'm filling out a survey for a contest versus one that I genuinely am interested in.

The segregation of duties aspect is interesting. Are there ways that GitAgent helps enforce that or remind me to improve SOD when any one agent begins to take on too many steps within one process?

One of my biggest challenges with customer feedback is trying to filter out which ones were real feedback and which ones were from bots/fake. Are there ways that ProductBridge help with this?

Would one use Assembly to replace an existing tool (like Notion) or is it meant moreso to be complementary to other tools (like how one would use Jane App for billing and scheduling)?

Are there ways to customize the type of tone or specific messaging that's used within the ad?

Similar to how there could be internal tool sprawl, could there be an issue with AI agent sprawl? For instance, AI agents spun up that do very similar tasks being used within the same team when there should really only be one?

Is the goal to start with Netlify and then export to the IDE for further complex work or is the goal that I should be able to stay within Netlify throughout my whole development process?

Are there ways to set guardrails for each specialist? For instance, when it comes to marketing, are there ways to focus in on specific topics, avoid certain words, or adopt a certain writing style?

This feels extremely relevant for solo devs or small teams that are price sensitive and are actively thinking of ways to keep costs down. Are there ways to be able to isolate for what's driving cloud cost after getting set up?

What signals do you look for to identify customers that are likely to churn?

Having audit trails is so important, so having the ability to know who authorized which agent is really nifty. Does Permit.io flag when policies fall outside standard best practices? Or does the auto-generation capability fully manage this such that no manual configuration is required after set up?

How much human review is typically needed before sending out a campaign?

This could be a really interesting way to help companies better customize their own LLMs based upon their own company's context, processes, and culture. Is use of the Unsloth's Data Recipes the best way to tackle something like this?

For someone new to observability, what’s the best way to get started with OpenObserve? Is there a recommended order of operations when it comes to getting set up?

When using AI to personalize emails, I have trouble with the emails coming out too robotic like or overly formal. Are there ways to configure Lightfield to train it to maintain the writing in the same style that I write in?

I always like being able to track back where the data source is being referenced from when AI completes their analysis. How easy is it to do that using Genie? Does it automatically always include an audit trail so that I can see where it pulled data or when it combined/manipulated data to get to a conclusion?

How does multi-client sync work exactly? Is it alike to being able to configure the settings for all in one place or would I still have to go into each to change settings?

For the video generator, is it better to upload multiple photos of the same person to improve the video quality?

What ways could I validate that the training data is actually improving downstream model performance?