Early on I asked Claude Code to recommend an auth setup and just went with what it suggested since I wanted to move fast. It picked a provider that was fine for the first few hundred users, but their pricing tier jumps hard once you cross a certain MAU threshold, and we hit that wall faster than expected. Now I'm staring down a migration that touches every session and password hash in the system, during a period where I really cannot afford downtime or angry users getting logged out randomly. For anyone who's picked infrastructure based on an agent's recommendation rather than researching it themselves, how did it hold up a year later? Where do you draw the line between letting the agent decide and doing the research yourself first?
First off, I am a history major, so I don't really have any knowledge or background in LLMs or what you guys call an AI Agent. But I have always been interested in the field and have lots of free time this summer.
I am, though, very good at using AI chatbots, like chatgpt, or Gemini, becasue I relied on them a lot while writing papers. This time, I wanted to use them for programming instead. I wasn't sure which AI coding tool to use for someone like me, so I simply picked a random one. Now, jumping to my question(please check the image I attached below):
This term has been coined by someone and there are already more than 80 products that you could put in this category. Looking at the numbers, it's growing pretty fast.
Been building Consile almost entirely with Lovable, and the thing I've found matters most isn't the AI generating code fast, it's having a real, repeatable process for verifying what it generates before it ships. Things like actually reproducing security findings against live endpoints instead of trusting a policy read, or independently confirming a claimed fix rather than taking "done" at face value.
Curious what others here have landed on. Do you have an actual verification process, or is it more ad hoc? What's caught you off guard that a quick glance wouldn't have?
Recently our team concluded in our peer-reviewed research that that code health determines AI-performance. The study "Code for Machines, Not Just Humans: Quantifying AI-Friendliness with Code Health Metrics" concluded that when agents operate on unhealthy code, the defect risk increases by 60% (at least).
It s was a large-scale study of 5,000 real programs using six different LLMs to refactor code while keeping all tests
Looked at my Anthropic bill this month and it was almost 3x what I expected. Went digging and most of the overage was from long sessions where I kept feeding the same large files back into context because I forgot to trim the conversation, plus a few runs where an agent got stuck re-reading the same directory before I noticed. None of that was the model doing useful work, it was just me being sloppy with session hygiene. For people running this stuff daily, what's your actual ratio of necessary spend to spend you could have avoided with better habits? Has anyone found a workflow? such as starting fresh sessions more aggressively, summarizing before continuing, caching, that meaningfully cut this down without losing useful context?
A few people asked after launch how this actually got built, so here's the real picture.
7 months, solo, zero coding background before I started. Backend runs on Cloudflare Workers with automated AI evals and vector-based memory search. Live on 4 platforms: iOS, iPad, Mac, web.
Around 32,000 automated tests across the stack before anything ships. I write the scenario spec, what it's testing and why it matters. AI generates the test case and a gold reference reply from that spec. Nothing ships unreviewed.
Happy to answer anything about the process, the stack, or vibe coding in general.
I ve been using Pencil.dev for a few days and honestly it s a big paradigm shift for how fast you can explore UI. I'm loving it!!
One thing I bumped into: I still need to move some screens into Figma to polish details, collaborate, and keep everything in the same place as the rest of our design work.
