Hi everyone,

Over the past couple of months I've tried bootstrapping applications a few times with AI. I would typically spar with chatGPT a few times and then get it to output text documents that give information about the product or business, and another that contains a plan. I then get a coding agent to implement the plan phase by phase.

My most recent attempt is something I will probably end up releasing for real. I tried even harder to keep some guardrails in place, to really come up with a structured approach for building out various features for an MVP - all while still trying to get AI to do most of the work autonomously. The plan was to review the product first, and then once the product is acceptable, review the code behind it.

The other day I started that review, and oh boy this thing is quite the monstrosity.

There's a ton of taglines and copy all over the application that's unnecessary and sounds really try-hard, clearly AI written.

There's things on nearly every page that looks like a feature, however it turns out it's hardcoded. A user is displayed with a title behind their name, but there's no title field anywhere in the database (and we don't even need to display a title, it makes no sense). There's a "readiness check" somewhere that could be interesting at some point... but not for an MVP, and not if it's hardcoded in the markup.

The list goes on.

I'm just curious, how does everyone else start these projects from scratch and ensure that the things that get built are real, and necessary for an MVP to begin with?

My current plan is to move it all into a "legacy" folder or something, so it can be references... but otherwise just start over and hold it's hand. I'd go through it like a real user would, from registration and sign-in, through onboarding, and really using the application. Basically laying out the rails in front of a moving train, building the next thing a user would use but doesn't exist yet.

It's quite unfortunate because none of this stuff is really that mind blowing, but I just can't seem to stop AI from building mockups rather than proper services. I need better guardrails, or a better approach.

been using claude skills for a while now and a few things tripped me up that i didn't see mentioned anywhere so putting them here.

the description field is everything. i kept building skills that weren't triggering and every single time it came back to a vague description. claude reads that field to decide whether to load the skill or not. if it's too generic it never fires, if it's too broad it fires when you don't want it to. i spent way more time than i should have tweaking the actual instructions when the real problem was one sentence at the top.

there's also a 200 character limit on that field. roughly two sentences. if you don't know it exists you'll write something longer, it gets cut off silently, and the skill behaves unpredictably.

a few other things worth knowing:

if your skill isn't triggering after upload, check if code execution is enabled in settings. custom skills need it on. wasted time debugging a perfectly fine skill because of this.

disable-model-invocation in the frontmatter does nothing on Claude AI web interface. it's claude code only. if you add it thinking it'll stop auto-triggering on the web it just silently ignores it.

when zipping the skill, zip the folder not the contents. loose Skill MD at the zip root doesn't work. the folder needs to wrap it.

and skills vs projects, worth being clear on before you start building. skills load automatically across every conversation. projects are scoped to one ongoing context. people mix these up and then wonder why behavior is inconsistent.

The problem was simple and well known by every coder / vibecoder: every new Codex / Claude / Copilot session kept rediscovering the same repo structure, files, decisions, failed commands, current task state, and validation steps, wasting context and tokens.

I have been trying to work around that problem with different approaches: small handoffs, heavy memory systems, context engines...

I finally found it: Operational continuity for AI coding agents. I built an open-source (Python CLI) continuity runtime so agents don’t restart from zero every session, and it already made my own AI coding workflow feel much less like restarting from scratch every time.

The continuity idea is not to add more hidden memory or dump more context into the prompt. AICTX keeps operational continuity inside the repo:

• active Work State;
• next actions;
• decisions and handoffs;
• failure memory;
• validation evidence;
• execution summaries;
• repo context relevant to the current task.

The next agent should resume from what actually happened, not infer everything again from README + chat history.

A few parts I’m currently focusing on:

Execution Contracts

Each resume can include a compact contract for the next agent: first action, edit scope, canonical validation command, expected evidence, and finalize instruction. The goal is not only “remember context”, but guide the next execution safely.

Continuity View

I’m experimenting with a deterministic Mermaid continuity view generated from repo-local AICTX artifacts. It shows the current operational state of the repo visually: Work State, open handoffs, relevant failures, execution contracts, summaries, RepoMap hints, and portable continuity status.

Here you can see what it looks like. The link to this view can be returned after each task, so the next session has an inspectable continuity map. I’m still working on making it easier to read.

Portability

The continuity lives with the repository. The idea is that useful operational state should not be locked inside one chat, one vendor, one local machine, or one agent tool. If the repo moves, the continuity can move with it ... if you want it to!

Easy to use

pip install aictx
aictx install
aictx init

# Then keep using your coding agent normally, they will take care of use it

The medium-term benefit

Agent-based development starts to feel less like a sequence of isolated chats and more like an ongoing engineering process:

• less rediscovery;
• fewer repeated failed commands;
• clearer handoffs;
• better validation discipline;
• easier switching between Codex, Claude, Copilot or other agents;
• and a repo that can explain its current state to the next session.

GitHub: https://github.com/oldskultxo/aictx

Docs: https://aictx.org

I would love technical feedback, especially from people using coding agents across multiple sessions.

Collaborators welcome! It is still evolving!

Quick PSA for anyone who built an AI app with Lovable, Bolt, or V0:

Your AI endpoint probably has no rate limiting. The AI builder generated something like this:

const { prompt } = req.body;
const response = await openai.chat.completions.create({ ... });
res.json({ result });

Notice what's missing? No auth check. No per-user limit. No per-IP throttle. No cap on prompt size. No max_tokens on the response.

Now imagine someone finds your endpoint URL and points a script at it:

while true; do
  curl -X POST https://yourapp.com/api/chat \
    -H 'Content-Type: application/json' \
    -d '{"prompt": "Write a 5000-word essay"}'
done

That script runs all night. By morning, your OpenAI bill is $1k-$5k depending on the model. The charges have already cleared. Sometimes you can get credits refunded, sometimes not.

The fix is layered rate limiting:

• Per-IP (5 requests/minute, blocks anonymous abuse)
• Per-authenticated-user (100 requests/day, prevents legitimate user runaway)
• Global cost cap (2000 requests/day across everyone, caps total damage)

Plus: cap your prompt.length (reject anything over 2000 chars), set max_tokens on the response, and set up usage alerts in your provider dashboard.

For serverless deployments, Upstash Ratelimit is the standard. Free tier handles 10k commands/day, plenty for most starting apps.

I wrote a longer guide with full code examples for Vercel/Node. Happy to drop the link in the comments if anyone wants it.

Anyone here actually been hit by this? Curious what the real bill numbers look like in the wild.

Here's a pattern I keep seeing across SaaS landing pages:

"Revolutionize your workflow with our powerful, intuitive platform. Seamlessly integrate with your existing tools."

Your buyers have read this sentence 200 times this year. On 200 different products. They don't read it anymore — they just leave.

The founders who actually convert have one thing in common: their copy sounds like a real person built something to solve a real problem. Specific. Honest. Sometimes rough around the edges.

The fix isn't "use less AI." It's making AI write in your voice specifically — not in the voice of every generic SaaS that came before you.

I've been doing this manually for founders this week as research for something I'm building.

I was surfing through founder interviews on YouTube and found a nice concept which I couldn’t build even if somebody told me the idea first. The reason was I found it super niche and seasonal. But this guy built a mobile app around it and hit 38k downloads in 6 months while doing $20k per month.

The app (CutCoach) is for combat sport athletes, wrestlers, MMA fighters, and boxers. It creates science-based weight-cutting protocols, tracks nutrition, logs meals, and tells you exactly what to eat to hit your fight weight without crashing. He found the idea from his hobby as a wrestler.

From most of the videos I watched I found a pattern that ideas comes from real pain you already feel every single day. Here, He fixed something that annoyed him as a wrestler. This is a reminder of the age-old advice: solve your own itch.

“Go broad so you have a bigger market (TAM).”

This proves the opposite: the narrower and more painful the problem, the more loyal and willing to pay your users become. Seasonal? Still worked because the audience cares deeply when it matters.

Ship as fast as possible.

I think this is the biggest advantage every founder has right now. He used AI tools to go from idea to working app in weeks instead of months.
(Tools like Pixarc for beautiful mobile UIs and Claude code to stitch everything together exists now)

Talk to your people

He marketed inside wrestling/MMA communities and with small influencers.

And the key lesson for aspiring founders here is that, going as niche as you can is worth it.

Share your app idea (or the annoying problem in your daily life that nobody has solved yet) here. Let’s see what useful ideas people drop and learn from each other.

What niche has the biggest SaaS opportunity rn?

Feels like every founder is building:

• another AI wrapper
• another productivity app
• another “tool for founders” 😭

But the more I look around… the more it feels like the real money is in boring industries nobody on X talks about.

Stuff like:

• local business workflows
• compliance headaches
• scheduling chaos
• invoicing/accounting
• healthcare admin
• inventory messes
• internal tools
• weird spreadsheet processes
• industry-specific automation

Basically problems where businesses already waste hours every week.

Not “cool” products…
Just painful repetitive work.

Curious what niche you think still has massive SaaS opportunity rn?

And which niches are completely overcrowded now?

Anyone else feel like distribution became the real startup now? 😭

Lowkey feels like building the actual product is the “easy” part now compared to getting people to notice it.

AI can help people ship apps crazy fast now...

But getting:

• attention
• trust
• retention
• real paying users

still feels brutal.

Feels like a lot of founders can build now... but very few know how to consistently get distribution.

Sometimes I wonder if the real moat now is:

• audience
• community
• brand
• distribution systems
• SEO
• content
• partnerships

...more than the product itself 👀

Curious if other founders feel this too or maybe I’m just deep in startup brain rot lol

I’ve been thinking about this lately

with all the tools we have now, building something is honestly the easiest it’s ever been. frameworks are better, hosting is easier, you can spin up a pretty solid product in days

but getting people to actually use it feels just as hard

like you can build something genuinely useful and still have it sit there with zero traction just because no one sees it

it almost feels like distribution is the real bottleneck now, not development

I’ve been experimenting with different ways to get things in front of people early, and it made me realize how little structure there is around discovering web apps compared to building them

curious if other devs feel the same or if it’s just me being bad at the getting users part

Hey everyone, student here trying to build projects without spending money on expensive software  
Currently using:
ChatGPT free
VS Code
WPS Office
GitHub student tools
Trying to avoid sketchy office crack or random microsoft office download sites now after bad experiences. Would love recommendations for genuinely useful free dev/productivity tools.

https://preview.redd.it/1ammyooxa32h1.png?width=1920&format=png&auto=webp&s=809bd025454a87575755b494c74cb3f060c2207d

👋 Hey VibeCoders Community,

You might remember Mike – friend of mine who runs a small company. Most of the automations I've built over the last months have been for him and his finance colleague Sarah (duplicate invoice detector, Slack approvals, invoice classification, the whole thing).

This time it wasn't a finance problem. Mike called me last week because the company's going through a bigger hiring phase right now, and he has exactly one recruiter – let's call her Lisa – who's drowning.

The problem:

Lisa goes through dozens of CVs every day. The annoying part isn't reading them – it's that every single CV looks different. Different layouts, different sections, different orders. Some are PDFs, some are screenshots, some are exports from LinkedIn. Lisa already knows exactly what she cares about for the first-pass screen:

• Last 3 roles
• Last 3 educations
• Top 3 skills
• Location
• Years of experience
• LinkedIn link (if she wants to dig deeper)

But to extract those 6 things from a CV that's structured however the candidate felt like structuring it, she has to read the whole thing every time. Multiply by 30 a day. That's hours.

Mike asked: "Can you build her something that just shows her those 6 things, in the same format, every time?"

The build:

I wanted Lisa to be able to use this from where she already works – which is mostly Slack. So the flow ended up being:

1. Lisa drops a CV (PDF, PNG, or JPG) into a dedicated Slack channel
2. Bot picks it up, runs it through the easybits Extractor with 8 structured fields
3. Bot posts a clean summary back in the thread within ~5 seconds: name, location, years of experience, top 3 skills, last 3 roles with dates, education, salary expectations (if mentioned), LinkedIn
4. Below the summary, an action card with two buttons: 💾 Save to Sheet or Dismiss
5. Save → appends a row to a Google Sheet with all the fields split into discrete columns (sortable, filterable). Dismiss → just removes the card

The Sheet is a placeholder for now – Mike's actually still in talks with different ATS providers, so once they pick one I'll just swap the Sheets node for whatever API the ATS exposes. Same workflow shape, different endpoint.

What ended up being harder than I expected:

I had to split this into two workflows. Workflow A handles the trigger and posts the summary + card. Workflow B catches button clicks from the card and does the Sheet append. Same pattern as the Slack invoice approval I shared a while back — Slack interactivity needs its own webhook endpoint, and you can't have both a trigger and a webhook listener in the same workflow without conflicts.

The other annoying gotcha: the n8n Slack node has a bug right now where Block Kit payloads sometimes just don't render – you get the fallback text only ("Action menu") with no buttons. Worked around it by posting the action card via a direct HTTP call to chat.postMessage instead of using the Slack node. Documented this in the sticky notes so anyone running into it knows the fix.

Why CV screening fits extraction so well:

The interesting thing here is that this is exactly the kind of task that has historically been done with custom CV parsers or expensive ATS resume-parsing add-ons. But once you treat a CV as just another structured document, the easybits Extractor handles it the same way it handles invoices or contracts. 8 fields, description per field, return null when not present – same pattern I've been using all year.

The whole thing runs on the free plan since it's under 10 fields.

The templates:

• Workflow A (lookup): https://github.com/felix-sattler-easybits/n8n-workflows/blob/a8138f54ec6b225b7e90e2a66b4491c746767214/easybits-cv-slack-assistant/easybits_cv_slack_assistant_instant_summary.json
• Workflow B (save action): https://github.com/felix-sattler-easybits/n8n-workflows/blob/a8138f54ec6b225b7e90e2a66b4491c746767214/easybits-cv-slack-assistant/easybits_cv_slack_assistant_save_to_sheet_and_card_actions.json

Sticky notes cover the full setup – Slack app config, scopes, credentials, Sheet schema. Both workflows together take about 20 min to wire up if you've done a Slack integration before.

Curious if anyone else has built recruiter tooling in n8n – would love to hear what fields you found most useful, or if you've gone deeper into ATS integration than I have so far.

Best,
Felix

I developed an LSP, VS-Code extension and NPM package, please try it out and give me your thoughts!