My one-person project Zigpoll [https://www.zigpoll.com] I've cracked the eCommerce market (1M ARR as of a couple days ago) but want to spread out more broadly into other verticals (SaaS, Hotels, Restaurants, Home Services, etc...) to reduce sector risk. If anyone is cooking something up please reach out and I'll be happy to hook you up with the service for free. [jason@zigpoll.com]
It’s literally all just context engineering. Just different ways of attempting to give the model the information it needs to complete your task. This is not a significant change to your interaction model with Claude
A lot of our customers use post purchase surveys and on-site surveys to help with this sort of thing. For example a really common use-case is an attribution survey which appears after a sale is made. The survey will ask something like "how did you hear about us?" which helps determine what actually drove the sale so they can get some clear insights outside of Google and Meta. It's not perfectly reliable but it's an additional data point that helps with the mess out there...
On-site surveys for eCommerce and SaaS. It's been an amazing ride leveling up back and forth between product, design, and marketing. Marketing is way more involved than most people on this site realize...
48 months solo is impressive. That marketing line is 100% true.
I'm building a tool that auto-generates ad creatives from a url (img-pt.com). Happy to run it on zigpoll.com and show you what it comes up with, if you think it can help you out.
I've seen people post this same advice and I agree with you that it works but you would think they would absorb this common strategy and integrate it as part of the underlying product at this point...
The people who build the models don't understand how to use the models. It's like asking people who design CPUs to build data-centers.
I've interviewed with three tier one AI labs and _no-one_ I talked to had any idea where the business value of their models came in.
Meanwhile Chinese labs are releasing open source models that do what you need. At this point I've build local agentic tools that are better than anything Claude and OAI have as paid offerings, including the $2,000 tier.
Of course they cost between a few dollars to a few hundred dollars per query so until hardware gets better they will stay happily behind corporate moats and be used by the people blessed to burn money like paper.
> The people who build the models don't understand how to use the models. It's like asking people who design CPUs to build data-centers.
This doesn't match the sentiment on hackernews and elsewhere that claude code is the superior agentic coding tool, as it's developed by one of the AI labs, instead of a developer tool company.
You don't see better ones from code tooling companies because the economics don't work out. No one is going to pay $1,000 for a two line change on a 500,000k line code base after waiting four hours.
LLMs today the equivalent of a 4bit ALU without memory being sold as a fully functional personal computer. And like ALUs today, you will need _thousands_ of LLMs to get anything useful done, also like ALUs in 1950 we're a long way off from a personal computer being possible.
That's $500k/yr, and I guarantee there's a non-zero amount of humans out there doing exactly that and getting paid that much, because of course we know that lines of code is a dumbass metric and the problem with large mature codebases is that because they're so large and mature, making changes is very difficult, especially when trying to fix hairy customer bugs in code that has a lot of interactions.
Doesn't specifically seem to jive with the claim Anthropic made where they were worried about Claude Code being their secret sauce, leaving them unsure whether to publicly release it. (I know some skeptical about that claim.)
A lot of it is integrated into the product at this point. If you have a particularly tricky bug, you can just tell Claude "I have this bug. I expected output 'foo' and got output 'bar'. What went wrong?" It will inspect the code and sometimes suggest a fix. If you run it and it still doesn't work, you can say "Nope, still not working", and Claude will add debug output to the whole program, tell you to run it again, and paste the debug output back into the console. Then it will use your example to write tests, and run against them.
I learned about JQBX and similar platforms through people that reached out as I've been sharing Jukebox around and they seem like they were beautiful corners of the internet.
We're building Zigpoll (https://www.zigpoll.com), a survey platform focused on zero-party data collection — think post-purchase attribution, customer feedback, and segmentation — all done directly on your site without relying on third-party cookies or offsite links.
We initially built it for Shopify, but now it’s fully embeddable, supports headless implementations, and integrates with tools like Klaviyo, Zapier, n8n, and Snowflake. One thing we’re especially proud of is how fast and unobtrusive it is: polls load async, don’t block rendering, and are optimized for mobile and low-latency responses.
From a tech angle:
Frontend is all React, optionally SSR-safe.
Backend is Node.js + Postgres, with a heavy focus on queueing + caching for real-time response pipelines.
API-first design (public API just launched: apidocs.zigpoll.com).
We recently open-sourced our n8n integration too.
If you're a dev working on ecom, SaaS, or even internal tooling and need a non-annoying way to collect structured feedback, happy to chat or get you set up. Feedback welcome — especially critical stuff. Always looking to improve.
[1] Surveys. Thinking about how to tighten up the onboarding experience, improve brand awareness, improve in-app data analysis, and how to integrate AI in new and exciting ways... and handling customer support tickets!
I've been working on Zigpoll as a one-man project for a while now: https://www.zigpoll.com/ it has traction and solid growth (~100% YoY for the past 3 years) but the larger the numbers the harder it gets to double each year.
In a past life I would have thought this would be the easy part given the product market fit but it's hard to figure out growth channels that are scalable and cost-effective at this stage. Burning what would otherwise be a large salary month on month in search of growth is mentally taxing when it doesn't deliver. Metrics across the board only seem to tell part of the story so it's tricky to figure out what needs changing and what's worth doubling down on.
If anyone has experience doing this sort of thing - please get in touch!
