Capacity
Create Fullstack Web Applications in seconds using AI
387 followers
Create Fullstack Web Applications in seconds using AI
387 followers
Create Full Stack Web Apps in Seconds with AI and natural language.
This is the 2nd launch from Capacity. View more
Spec Coding by Capacity
Launched this week
Most AI app builders jump straight into building.
That feels fast until your app gets messy, inconsistent, and nothing like what you imagined.
Today, we’re introducing Spec Coding in Capacity.
Instead of building immediately, Capacity now lets you define your app first with the help of an AI co-founder that asks the right questions before any code is generated.
More structure upfront.
Far less refactoring later.
Much better results.
Payment Required
Launch Team / Built With
Capacity
@samuel_rondot Hi Samuel, seems like you're on the right track with this. How do you deal with testing and code qual?
Capacity
Hey @zolani_matebese !
Great question.
The main thing is we don’t let the AI guess.
Capacity starts with a spec (expected behavior, flows, data models), then generates code and tests from that spec.
That makes tests more reliable and code more predictable.
We also keep changes small, reviewable, and human-approved so you keep the speed of vibe coding without the usual quality drop.
@zolani_matebese @samuel_rondot thanks for sharing the insight! I wonder if you ever met corner cases that are not covered by the spec? How would those cases be handled?
Raycast
@samuel_rondot pretty cool!
Also, did you know that you video goes black from ~2:28-2:56?
TimeTuna.com
Hey @samuel_rondot ,
I like the design of @Capacity . I like the name as well - brilliant.
Which tool / stack did you use to create @Capacity ?
What is your GTM?
I would like to meet with you and chat: https://timetuna.com/pavel
Capacity
@pavelk2 Hey Pavel !
Thank you so much !
We are using Nextjs, Nodejs. Our GTM strategy is simple : building an ai website builder that works. Currently most vibe coded app fail and we are fixing that with Spec coding.
TimeTuna.com
@samuel_rondot Thanks! Good luck with the PH launch :)
Capacity
@pavelk2 Thank you !
Triforce Todos
Slowing down at the start to save time later feels obvious now, but I never thought of it like this. Do you guide users on what “matters” vs “doesn’t matter”?
Capacity
Hey @abod_rehman
On Capacity it all starts with a project brief you can co-write with an AI agent we developed. The document describes the problem you noticed and the solution you want to develop to kill this pain. The agent helps you refine your business idea, target market and MVP scope. Guiding the user on what matters and what doesn't as you mentioned is its purpose.
And it works the same for the design specs of your platform and the features to develop. Dedicated AI agents focused on these specific tasks do the heavy lifting to prepare the prompts that will be sent to the coding agent.
It starts slower but development is faster and app scales better
I really appreciate the focus on reducing refactoring because I usually spend more time fixing AI-generated messes than actually building the features I want. 🛠️
Capacity
@navin_kumar_singh clearly! That's definitely what motivated us about going hard on spec coding
does the co-founder assistant allow me to export the technical documentation separately if I want to keep a record of the app structure?
Capacity
@rahul_manjhi1 you have access and can export all the documents of your project: codebase, project brief, design specs, coding task... data is yours
Adventory
Hey @samuel_rondot ,
Been following you from far on X, liked the Starter Story video too
Cool launch and very nice product you guys cooked here, congrats for top 3 :)
Capacity
@samuel_rondot @ugo_builds we appreciate the support 🙏
Nice, 2 questions:
1. How much it differs from planners such as the one that Cursor and other tools have (when they set a long todo list) + Planning mode
2. How consistent the agent stays on the created initial plan? and how flexible it can be to adjust the plan and micro-steps based on progress it makes?
Capacity
@khashayar_mansourizadeh there are noticeable differences:
Our planning system operates not only at the task level, but at the project level as well. You start co-writing your project brief (pain point + solution) with a specialised AI agent, then your design specifications and finally coding tasks (=user stories with technical details). On the contrary, planners usually focus on coding tasks only
Honestly, pretty good. Since coding tasks are co-writed with a specialised AI agent that has access to the complete project documentation, drafts are on-point and refined fast. And if you project evolves, you update you project brief, adjust your design specs if needed etc
@baboo77777 very nice, I'm an Ultra user of Cursor, typically spending ~$400 per month on it, since I do the job of 5 engineers myself (backend + frontend + IaC), I'd be interested to know which parts of it I can potentially replace with your solution?
Honestly I feel like at least 5-15% of my expense is due to the fact that agents don't know the high level stuff and they make tons of mistakes, Opus 4.5 (Thinking) is much better than other models, can be more consistent and thoughtful, but it's super expensive, I had spent $100 in 1 day only running Opus 4.5, so it's very painful :D
If your solution can be super consistent on high level architecture and know what must be done and what should be avoided, that can be super smooth and also maybe we can use much lighter/cheaper models like Gemini 3 Flash to perform even better than Opus 4.5 on tools like Cursor.
Have you done any comparisons? I think this might be a very good case study that if your solution wins in it, can draw massive users (including me) towards your solution (high level planning + consistent memory + specific skills/instructions + small/cheap model == HIGH performance > huge/expensive model + dumb planner + less instructed agent)
Capacity
@khashayar_mansourizadeh definitely something we are looking into 😁
I'm benchmarking how cheaper models perform when it comes to guided implementation (like Kimi K2, GLM 4.7 or Minimax 2.1). Atm I feel uncomfortable to integrate them on Capacity because I find they lack stability (implementing only half a feature, smelly architecture or generating code that needs fixes). But their improvement over the past months is very promising. So I keep an eye on them.
Currently, we use SOTA models such as Claude Sonnet 4.5 or Gemini 3 Pro for planning and implementation. They are costly, but they are the smartest models by far and produce quality work.
As for replacing part of your work with Capacity, our planning system enables implementing frontend and backend features (relying on supabase). I would love to handle IaC as well but it's off limits today.