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):

Building with Ai is scary and the internet has as much bad propaganda as it claims your work is slop.

Most of us don t have coder we can reach out to we think has enough time to scan our code and let s face by the time they were done we would have to ask them to scan a whole new section we finished in the meantime.

I've been building my own AI agent for the past few months, and the one problem I keep coming back to is hallucinations.

At first I thought the answer was better prompts. Then I added memory. Then I redesigned the architecture, split long-term and short-term memory, added retrieval, planning, and verification. Every time I felt like I'd solved it, another weird failure showed up everytime.

Sometimes the agent remembers things perfectly and other times it confidently ignores its own memory or invents an answer when the information is already available. The frustrating part is that these failures aren't always predictable they often happen after the system gets more capable, not less.

While vibe-coding, most bugs are easy to spot because the compiler throws an error or the UI breaks. But here s a subtle, deeply hidden issue that often slips past human review: Silent Context Leakage in AI-Generated State Management.

When prompting an AI agent to continuously iterate on asynchronous functions or complex state pipelines, it frequently re-uses existing variable names or merges outer-scope context into asynchronous closures without explicit cleanup.

Why it's hard to catch:

1. No syntax or runtime errors: The code runs smoothly without crashing.

2. Intermittent behavior: Race conditions occur only under heavy load or specific execution timings, making standard tests pass consistently.

3. Superficial logic check: To a human reading the code casually, the flow looks clean, modern, and perfectly idiomatic.

Walkthrough: How I put my roadmap on the Continue button

Hey vibe coders- I wanted to share how vibe code my roadmap when the project gets bigger and bigger and how I structured a loop behind it.

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?

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?

Over the last few months, AI-assisted coding and "vibecoding" have significantly changed how quickly I can prototype and ship ideas. Instead of spending hours setting up boilerplate code, I've been focusing more on product logic and user experience.

However, maintaining code quality and debugging complex workflows can still be tricky when relying heavily on AI agents.

I m curious about how other builders in this community are balancing speed and code quality:

What does your primary AI tool stack look like right now?

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.