As Claude and ChatGPT has gotten better, I've found myself enjoying using Co-Work to make presentations at work. Sharing the HTML files on Slack and elsewhere was cumbersome and trying to host it somewhere public (even if unlisted) wasn't much of an option for my work stuff.
Then I saw Shopify's blog post about Quick (https://shopify.engineering/quick), an internal intranet with simple HTML page hosting and was inspired. I wasn't sure I could get buy-in to host it at my day job so I spent my own time coming up with Quickish. Now I can share all my beautiful presentations.
Originally I wanted it to be tied to Google Drive / Workspaces, you share the folder with quickish and put your HTML in, quickish hosts it while respecting the privacy of the folder (workspace only, etc). However, as I worked through building I realized I could make it easier to use and add that part in. Actually, it already works behind the scenes I just need to get the app verified.
And now, you have what you see. Everyone gets 1 free live site at a time (you can push multiple, just your latest one via CLI or whichever you choose one the web UI is active at a time unless you opt for the cheap unlimited plan). Just run `npm i -g quickish && quickish` in a directory with your HTML file and that's it, one Google OAuth away from the page being live. You can keep them private and only invite other users (only google for now) as well.
If you use a work e-mail sites you publish are auto-gated to only people within your org. Again, only Google Accounts for now (more coming, OneDrive, Dropbox to start).
Pages do get access to JS DB scoped to your account / workplace which is fun (imo) - I am in the process of touching up the live pub-sub (think claude made live dashboard fed by API posts to your quickish db).
Hope you enjoy!
by WhyIsItAlwaysHN ·
I've had a lot of discussions with peers about the possibility of us being replaced via ai agents and how to stay relevant, given a future where AI intelligence keeps improving to the point of being capable of replacing potentially any job.
My personal strategy is specialization, because I've noticed that I cannot work with AI in hard domains that I haven't got a background in. For example, I've tried to use claude opus to understand a phd thesis in quantum physics from a friend. My background is in engineering and later compilers/static analysis. Despite opus helping a lot to give me the high level idea behind it, I could see a few problems with my lack of background: - I couldn't verify if opus was right or wrong in its explanations - Even when asking the AI to simplify explanations, there was so much prerequisite knowledge I needed to absorb that there was little point to bother with its output - It was very hard to collaborate with the AI to understand what predictions can be made from the PhD. The model could produce a lot of output but I didn't really understand its answers. Asking follow-up questions did not really solve the issue because it felt like I was missing a mental model.
My thinking is that, no matter how much LLM intelligence grows, for sectors with inherent complexity, people will still need specialized expertise to understand, evaluate and use their outputs. I also doubt that the most efficient future is one where humans don't understand the outputs and delegate everything, because it will be both be hard to understand if the machines are aligned to the benefit of their users.
So specialization sounds like a good strategy for the future.
I'd like to hear some opinions around this topic. Have you observed the same? Do you disagree based on other experiences/data?
by WhyIsItAlwaysHN ·
I've had a lot of discussions with peers about the possibility of us being replaced via ai agents and how to stay relevant, given a future where AI intelligence keeps improving to the point of being capable of replacing potentially any job.
My personal strategy is specialization, because I've noticed that I cannot work with AI in hard domains that I haven't got a background in. For example, I've tried to use claude opus to understand a phd thesis in quantum physics from a friend. My background is in engineering and later compilers/static analysis. Despite opus helping a lot to give me the high level idea behind it, I could see a few problems with my lack of background: - I couldn't verify if opus was right or wrong in its explanations - Even when asking the AI to simplify explanations, there was so much prerequisite knowledge I needed to absorb that there was little point to bother with its output - It was very hard to collaborate with the AI to understand what predictions can be made from the PhD. The model could produce a lot of output but I didn't really understand its answers. Asking follow-up questions did not really solve the issue because it felt like I was missing a mental model.
My thinking is that, no matter how much LLM intelligence grows, for sectors with inherent complexity, people will still need specialized expertise to understand, evaluate and use their outputs. I also doubt that the most efficient future is one where humans don't understand the outputs and delegate everything, because it will be both be hard to understand if the machines are aligned to the benefit of their users.
So specialization sounds like a good strategy for the future.
I'd like to hear some opinions around this topic. Have you observed the same? Do you disagree based on other experiences/data?
I spent the past year building Micro Coach with two partners.
It creates personalized 4-week training blocks based on your goals, schedule, experience level, equipment, and preferences. Users can also generate individual workouts, track progress, and view training analytics.
I built it using my background in both software engineering and personal training.
I’d appreciate feedback on the onboarding, workout generation, and overall product experience: