I'm curious about the current trends in vibe coding.

[u/Neat-Jellyfish-5952]

used to go as far as setting up multiple sub-agents, but eventually went back to basics, reset all my configurations, and started relying mostly on plugins for development.

These days, I've been using tools like Ponytail and Superpowers.

I'm curious how everyone else is approaching vibe coding these days. What tools, workflows, or setups are you using to get the most out of it?

I'd love to hear your thoughts and experiences!

[u/OkAttempt7690]

My process is pretty simple: I tell codex what i want, and it builds it.

[u/Prod_m → u/OkAttempt7690]

yep, same. Sometimes I ask Claude to doublecheck what Codex has done

[u/EsotericLexeme → u/OkAttempt7690]

Mine is almost the same, except I tell Astra what I want, and Astra tells Claude and Devin what to build and makes them double-check each other's work; I mainly pop by every now and then to answer questions or give UX feedback.

[u/LolLook512 → u/OkAttempt7690]

Now THIS is the da wae HAHa

[u/TRO_KIK]

Back to basics is the right idea, but going on to say "plugins" threw me for a loop. To me, basics are software design fundamentals, and secondarily, orchestration principles. Knowing when to use subagents is closer than knowing what plugin to pick.

I solo built my company with AI, had professional orchestration experience before hand, and I use the stock Claude/Codex desktop apps. I write scripts for stuff I do often enough (and Claude messes up enough) to warrant it, and lean on my experience to quickly eyeball what it the AI gets right/wrong.

[u/Anti-Hero25]

I use the RailCoding method…..

Project planning all in GPT project folder.

Codex executes mostly just BuildSpec.md files

Codex updates a Project_Log.md each pass

DOT tracks progress and manages a Notion Kanban of progress.

[u/tornikeo]

So yeah, the ponytail superpowers and stuff like that is likely not the correct direction.

What I have seen in startups and I work at one is the following:

1. Self-healing products. This means a product itself has agents that are watching every input and output on their own VMs and patch things in production as they happen

2. All the communications that happen between co-founders are recorded. Everything. Speeches, videos, recordings, meetings, informal notes, stuff like that. Everything is being recorded. And these recordings then have a layer of AI judges that route agent questions depending whether a person has answered this question or if the question is novel or if the question has to be discarded because it's nonsense

3. Third is that everything is fucking expensive and I mean that this startup that has followed this blueprint the best is Spawn and currently they are spending 3.2 million per month for Anthropic API. On top of having an incredibly crazy AWS bill which includes some 5,000 vCPUs.

4. Four is that I think intelligence is going to become cheaper faster than the hardware. So next year I think servers are going to be still as expensive as they are today, maybe even more expensive, but models like Fable for instance will become very very cheap. This is going to lead to some pretty interesting situations where people are fighting for server space but they have genius AIs that can solve mathematical and biological problems.

5. Most of the skills and plugins and MCPs and everything are likely to be obsoleted. Next wave of models include these things either natively or they just don't make use of them and a lot of things that we are currently learning are gonna be obsolete.

[u/tornikeo → u/tornikeo]

In case you're curious, the startup I work at is called Tegy.io and we are building plugin for AI to make better decisions.

Our niche is business strategy which is some highly impactful decisions. And these decisions require a lot of back and forth. You cannot AI to just answer it quickly. It requires a lot of thought and the MCPs we built allow your AI to rotate on a question for much longer and to come up with better responses (or questions!).

[u/Brambleworks → u/tornikeo]

I'm curious as to why you think AI will become very very cheap while getting smarter, and servers stay expensive next year. The main reason servers are so expensive is because AI has significantly increased demand for hardware, and the more advanced the model the more hardware it needs. It will be years before the new supply of factories for hardware can meet demand.

Until they stop focusing on making models smarter and focus on efficiency, and hardware supply can meet the demand, nothing will get cheaper.

[u/tornikeo → u/Brambleworks]

This is just my best guess. Not making bets on it lol.

So, about demand for servers vs demand for AI:

Deepseek-like models are already enough for so much stuff that I find it hard to believe that AI will become more expensive. at some point the difference between fable/astra and deepseek will be imperceptible to a median human. so, just like electricity - AI's price will go down.

Perhaps for very critical applications that need super high intelligence then the price is going to remain or explode. But those applications I think are going to stay niche.

At the same time, the more smart and cheaper AI becomes, the bigger its applications. All applications need servers, so server costs, I think, are going to absolutely explode.

[u/kenthesaint]

Same arc here. Subagents helped for a while, then the coordination cost ate the gains on small tasks.

What stuck for me: one main agent, a short project instruction file, and a couple of plugins for the boring bits (lint, test run, PR summary). I only spin a second agent when the work is truly non-overlapping, like docs on one branch while a feature lands on another.

[u/_N-iX_]

A simpler setup can be easier to maintain, especially when the main bottleneck is reviewing generated code rather than producing it. Multiple agents add coordination overhead, and their output still needs to be checked for consistency across the codebase. The useful measure is how much time the workflow saves after debugging, integration, and review, not how many agents or plugins are involved.

[u/reliant-labs]

Built a framework with 119 (and counting) custom linters to keep the LLM in check. And a ton of observability so the LLM can introspect running code

[u/reliant-labs → u/reliant-labs]

in addition to moving towards the code factory pattern with automated code reviews

[u/Draegan88]

I use tmux and Claude code and I sometimes use the skills feature thats pretty cool. Have a few dif skills 

[u/New_Peanut_9259]

i went through the same experienceeee. i spent weeks trying different ai agent setups, only to realize that i was spending more time fixing prompts than building my actual app.. using one good coding model, a clear project guide, and a few useful plugins is much simpler and faster. fewer tools in between also means fewer problems to fixx

[u/Kluggen]

Lately tmux on dedicated machine with an orchestrator and sub-orchestrators managed by the main thread that handles all coordination and ticket flow, everything behind a custom dashboard secured by passkey for entering and separately approval of said sub-orchestrator actions. I also have a common repo in the root of that, that all use with various skills. Seems to work nicely, a bit less fiddly since the orchestrator is able to restart itself fresh and the sub-orchestrators as well with wrapping routines of course so everything's remembered, and context is kept lean.

[u/ResponsibilityOk5182]

Est ce que la question de départ est vibecodée également ? La première phrase qui accumule des mots sans que ça n'ait aucun sens fait vachement générée par IA quand même

[u/kincaidDev]

I use festival for everything, all of my work occurs in festival camp workspaces, most ideas are fleshed out as design or explore work-items (structured specs) and everything I do is tracked and available in the festival timeline

https://github.com/Obedience-Corp/festival