u/catalinnxt

any methods would be good

The founder experience in a lot of AI software still looks the same. The system gives a strong output, then quietly expects the founder to decide what happens next, move data where it belongs, preserve context, and restart the work later.

That means the founder is still the coordination layer.

We kept running into that problem after the product side of building got much easier. Claude and the rest of the vibecoding stack made it much faster to get software into the world, but the operational mess after launch stayed almost untouched.

That is what pushed us toward Ultron.

We wanted a product where the connected nature of growth work was part of the architecture. Research should feed prospecting. Prospecting should feed outreach. Outreach should feed pipeline movement. Pipeline patterns should feed positioning and content. The founder should not be manually acting as glue every time something crosses a boundary.

So the system is built around five specialists. Cortex covers research. Specter covers prospecting and enrichment. Striker covers sales execution. Pulse covers content. Sentinel covers system reliability and improvement. That split gives the product cleaner responsibilities and makes handoff much more natural.

We also designed for parallel execution because a lot of the underlying work should happen that way. Independent searches, external lookups, scraping passes, and data enrichment calls can often run together. When the system is forced into one long serial chain, the product feels slower and less capable than the actual job requires.

Skills were the other important piece. Founders repeat the same classes of work constantly. They need competitor analysis, qualification, outreach, follow-up, and content generation in stable forms. We wanted those motions to exist as reusable behavior rather than relying on the system to improvise the structure of the task every time.

That is why I think vibegrowing is a useful frame.

It points at a real shift. Founders already build in an AI-native way. The next layer is giving them an AI-native way to run the post-launch motion too.

That is what Ultron is trying to be.

https://reddit.com/link/1se618k/video/ui24w0xbyltg1/player

Vibecoding works because the environment is relatively clean. The system works inside a codebase, the loop is short, and the result can be inspected quickly. You can ask for a feature, see what changed, patch the result, and keep moving.

Growth is a much messier environment.

The work spans market research, competitor mapping, lead discovery, enrichment, outreach, follow-up, content, and state tracking. Some parts depend on each other. Some parts should happen at the same time. Some parts need to be carried forward so the system can continue later without losing the thread.

That is why we built Ultron as a runtime instead of just another assistant.

The core idea was that one user request should be able to unfold into a set of coordinated operations rather than one big answer. If someone asks for help finding customers, the system should be able to research the market, identify likely companies, pull the right people, qualify them, generate outreach, and keep the work alive after the first pass. That is not a prompt completion problem. That is a systems problem.

So the product is organized around five specialists. Cortex handles research. Specter handles prospecting. Striker handles sales execution. Pulse handles content. Sentinel handles the infrastructure around the whole flow. The system improves when each domain has a clear owner and when the handoff between owners is built in rather than improvised by the user.

We leaned hard into parallel execution because that is a natural fit for growth work. If several external lookups or verification steps can happen at once, the system should do them at once. Serializing everything just because it lives inside a chat interface is a bad design choice.

We also treated skills as core product behavior rather than nice-to-have wrappers. Competitive analysis, qualification, outreach, and content work all have stable shapes. Encoding those shapes makes the product more useful and much less fragile.

That is how we think about vibegrowing.

Not as AI helping with growth in a generic sense.

As a product architecture built for the messy, connected, post-launch work that starts once the software already exists.

https://reddit.com/link/1se5zpe/video/jwzddn31yltg1/player

The first half of company building changed fast.

A founder can now go from idea to working product by describing what they want, iterating with Claude, and tightening the result in a much shorter loop than before. That shift is real. It is why so many more products are getting shipped now.

The second half did not change nearly as much.

Once the product is live, the founder still has to research the category, understand competitors, find the right people, write outreach, create content, follow up, and keep the whole motion connected long enough for revenue to happen. Most tools still treat those as isolated tasks. They generate a useful artifact, then leave the founder to carry the system forward manually.

That was the gap we cared about.

We built Ultron as a runtime for post-launch execution. Not a single assistant trying to do everything through one oversized response, but a system where different specialists own different kinds of work and where the transitions between them are part of the product.

Cortex handles research and intelligence. Specter handles lead generation and enrichment. Striker handles sales execution. Pulse handles content and publishing. Sentinel handles the infrastructure around the whole system. That structure matters because growth work keeps crossing boundaries. A research insight should not stay trapped inside a summary. A qualified lead should not stay trapped inside a list. A sales pattern should not stay trapped inside a thread.

The product gets much better when those outputs become the next actions automatically.

We also built around parallel execution because real growth work rarely deserves to be serialized from top to bottom. If the system needs to search, scrape, enrich, verify, and evaluate at the same time, it should do that whenever the dependencies allow it. That changes the feel of the whole product. It starts acting less like an assistant you wait on and more like an environment where work is actually moving.

The same logic shaped how we think about skills. Repeatable motions should become repeatable system behavior. If a founder needs competitor analysis, outreach generation, qualification, or content scoring, the system should already understand the structure of that work instead of improvising it from zero every time.

That is what vibegrowing means to us.

Vibecoding compressed the path from idea to product.

Vibegrowing should compress the path from product to motion.

https://reddit.com/link/1se5yjj/video/5s4ab3kvxltg1/player

The interesting thing about vibecoding is not just that it made product development faster. It changed founder behavior. People now expect to describe an outcome in natural language, iterate quickly, and get something real on the other side.

What did not change nearly enough is everything that happens after the product works.

Research is still fragmented. Lead gen is still fragmented. Outreach is still fragmented. Content is still fragmented. Most founders still jump between tabs, tools, docs, CRM records, and half-finished threads trying to keep a growth motion alive.

That is the gap we built Ultron for.

Ultron is not a chatbot sitting on top of a model. It is an AI-native operating system built around five specialists inside one chat interface. Cortex handles research, Specter handles lead gen, Striker handles sales execution, Pulse handles content, and Sentinel handles infrastructure and self-improvement. The point is not just that they exist as separate roles. The point is that they coordinate through tasks instead of forcing one generalist to do everything. ()

The architecture follows that same idea. The platform is structured as interaction, orchestration, core loop, tools, and API. So when a founder types one message, the system can stream activity in real time, manage session state, call the model, execute tools, save state, and keep iterating until the work is complete. That is a very different product shape from a wrapper that produces one polished answer and stops there. ()

Parallel execution ended up being one of the most important design choices. A lot of growth work should not happen serially. If the system needs to run searches, scrape pages, enrich leads, verify emails, and pull external data, those operations can often happen together. The docs describe Ultron as handling up to 75 simultaneous tasks, with agents working across up to 15 workspaces each, and independent tool calls firing concurrently inside a task. That makes the system feel much more like an execution environment than a chat assistant. ()

We also cared a lot about skills. Ultron exposes 35+ on-demand skills because common motions should not be improvised from zero every time. Competitive analysis, cold outreach, content scoring, qualification, and follow-up all have stable patterns. Once those patterns become part of the runtime, the system gets more reliable and the output gets more usable. ()

That is basically what vibegrowing means to us.

Vibecoding says describe the product and let AI figure out the implementation.

Vibegrowing says describe the market motion and let the system execute research, leads, outreach, content, and follow-through without making the founder manually stitch it all together.

That is what we built.

The more we built with Claude, the more obvious one thing became.

The model is only part of the product.

If all you do is wrap a model in a chat UI, add a giant prompt, expose a pile of tools, and hope it can improvise through messy work, you hit the ceiling fast. That pattern is fine for simple tasks. It breaks when the task crosses domains.

Growth crosses domains constantly.

Research becomes lead gen.
Lead gen becomes outreach.
Outreach becomes deal tracking.
Insights from all of that should flow back into content and positioning.

So when we built Ultron, we designed around execution, not just response quality.

The platform has a five-layer structure. The chat layer handles interaction and live streaming. The orchestration layer keeps session state, transcripts, permissions, and cost tracking coherent. The core loop keeps iterating through model calls and tool use until the task is done. Then below that sit the tools and model routing layers.

On top of that, we split the system into five specialists instead of one generalist. Cortex for research, Specter for lead gen, Striker for sales, Pulse for content, Sentinel for infrastructure.

What mattered was not just specialization. It was handoff.

A strong-fit lead found by Specter should become a live task for Striker, not a dead artifact in chat. The research context should already be there. The next action should already be obvious. The system should keep moving the work forward instead of asking the user to manually restitch everything.

Parallel execution was another major design choice.

A lot of growth work is only partially sequential. If the system needs to search the web, scrape pages, enrich contacts, and pull external data, those operations should run together whenever possible. Once we leaned into that, the product started feeling much more like a working environment and much less like a chatbot that happens to know tools exist.

We approached skills the same way.

We wanted reusable execution patterns, not agent theater. Competitive analysis should have a stable shape. Outreach generation should have a stable shape. Qualification and follow-up should have stable shapes. That makes the system more reliable because it is invoking tested work patterns instead of inventing task structure from scratch every time.

That is what vibegrowing ended up meaning for us.

Not AI writing some marketing text.
A Claude-powered system that can carry out the connected work of growing a company after the product already exists.

That was the gap we felt as founders, and that is the gap we built Ultron around.

Would love to hear how others building on Claude think about task handoff, parallel tool use, and skill packaging in real products.

https://reddit.com/link/1se5j57/video/lumn1ds6vltg1/player

Vibecoding solved a very specific problem.

It made the path from idea to product much shorter by letting founders describe what they wanted and iterate directly with the model.

But once the product is live, the next phase looks nothing like coding.

Now you are dealing with fragmented workflows. Market research. Competitor mapping. Lead discovery. Enrichment. Outreach. Follow-ups. Content. Pipeline movement. All of it connected, all of it changing over time.

That is why we built Ultron differently.

We did not want a single assistant trying to do everything through one oversized prompt. We wanted a system that could break work apart, run the independent parts in parallel, and move tasks between specialists when the job crossed into a new domain.

So the product is built around five agents.

Cortex handles research and intelligence.
Specter handles prospecting and enrichment.
Striker handles outreach and deal movement.
Pulse handles content and publishing.
Sentinel handles infrastructure and system health.

The key product decision was letting these agents coordinate through tasks instead of just sitting there as branded personas.

If a prospect is found and qualified, the system should not stop at showing it to the user. It should save it, attach the context, create the next action, and let the right specialist pick it up. That is how the product starts acting less like a conversation and more like an operating layer.

The platform architecture supports that. We structured it as interaction, orchestration, execution loop, tools, and model access. The execution loop is where most of the interesting behavior lives. The system can call the model, execute tools, inspect results, and continue iterating until the work is actually complete.

We also leaned hard into parallelism because so many growth tasks should happen concurrently by default. Searches, scrapes, enrichments, and lookups should not block each other unless there is a real dependency. Once we built around that idea, the whole product got faster and more useful.

The same thinking shaped skills. We wanted reusable execution patterns that the system could invoke repeatedly, instead of relying on fresh improvisation every time a founder asks for something common like competitor analysis, qualification, or outreach generation.

That is the full idea behind vibegrowing.

Vibecoding says describe the product and let AI build it.
Vibegrowing says describe the market motion and let the system execute it.

That is what Ultron is for.

I am curious whether other builders working on Claude-based products are seeing the same thing, where the real leverage comes less from the model itself and more from runtime design, task flow, and parallel execution.

Cursor, Claude Code, Replit, Bolt. Describe the product, iterate in natural language, ship something real in a weekend.

That works because coding has a clean loop. The context is the codebase. The output is deterministic. The feedback cycle is immediate.

Growth is not like that at all.

The context is your market, your ICP, your competitors, your content history, your outreach history, your deal stages, your constraints, your timing. The outputs are probabilistic. The feedback loop takes days or weeks. And the work is cross-functional by default.

That is why we stopped thinking about this as “AI for marketing” and started thinking about it as an architecture problem.

What we ended up building in Ultron looks more like this:

A five-layer stack.

Layer 1 is interaction: chat, streaming activity, inline rendering.
Layer 2 is orchestration: session state, permissions, transcript persistence, cost tracking.
Layer 3 is the core loop: compress context, call the model, execute tools, loop until complete.
Layer 4 is the tool surface: web search, browser actions, CRM, email, enrichment, docs, publishing.
Layer 5 is model access and routing.

That matters because a growth task is rarely one thing.

“Help me find customers for this product” is not one prompt. It is competitor research, lead discovery, segmentation, message drafting, and follow-up structure. If the system is designed like a single chat response, it breaks immediately.

So we went with specialists instead of one generalist.

Five agents, each with one domain:

• Cortex for research
• Specter for lead gen
• Striker for sales execution
• Pulse for content
• Sentinel for monitoring and system reliability

And the more important part: they hand work off through tasks.

Specter finds a lead that matches the ICP, saves it, creates a task for Striker, Striker picks it up, reads the research context from memory, writes the email, logs the outcome, updates the deal state. That chain is the product.

The thing I underestimated most was memory.

Not “chat history.” Actual structured memory.

Ultron stores plain markdown memory entries by type: user, feedback, project, reference. Then on every turn, a separate retrieval step pulls the 5 most relevant entries into context before the model sees the task.

That is what makes vibegrowing feel different from just asking ChatGPT for ideas.

You are not starting over every time. You are building on a system that already knows what happened on Monday when you come back on Thursday.

That was the real design insight for us:

vibecoding works because code gives you persistent structure
vibegrowing only works if you build persistent structure for the business side too

Curious whether other people building in this space landed on the same conclusion.