What It’s Like to Co-Build With AI

When I started the Huhb project, it wasn’t a company — it was a question:

Could I co-build a real product with AI as a development partner — not just a code assistant?

What followed was not just an experiment in AI tooling — but in patience, design thinking, and product management.

Here’s what that experience actually looks like.

✅ The Good
Working with models like GPT-4 has made parts of the build process faster and more structured:

It’s fast at boilerplate — CRUD logic, test generation, helper functions

It’s helpful for writing internal docs, logging, and architectural comments

It’s a great thought partner when exploring design tradeoffs or edge cases

❌ The Bad
But it’s far from perfect:

It forgets context quickly. I’ve had to keep running action logs and notes to feed back in

It hallucinates under pressure, especially in longer sessions

Sometimes I’ve had to “cheat” — starting over or jumping to Claude or Gemini when things go sideways

🧩 The Workflow That Evolved
Instead of pair programming, it’s felt more like a series of product strategy meetings:

I define a goal or outcome

We debate scope, explore ideas, and refine the concept

I document the deliverable, and only then do we build it — together

That’s how Huhb was born.

🚀 Huhb: From Prompt Chaos to Predictable Workflows
As I started applying this AI-collaboration model to real-world use cases, patterns began to emerge. Everywhere I turned, the developer experience around LLMs was painful:

The models were fine. The surrounding tooling wasn’t.

That led to the creation of Huhb — an AI orchestration platform for developers who are tired of building the same glue logic over and over.

🔧 Why We’re Building Huhb
Here are eight real problems I ran into — and how we solved each one:

1. Vendor Lock-In & Provider Switching "Switching models meant rewriting code, reformatting payloads, and adapting to behavior quirks."

✅ Huhb decouples tasks from providers. Define your task once — we handle routing, payload transformation, and output formatting.

1. Prompt Fragility & Engineering Fatigue "Small prompt edits broke workflows. Model updates made behavior unpredictable."

✅ Huhb introduces versioned, reusable prompt templates. You get consistency, strategy controls, and portable task logic.

1. Orchestration Complexity "Most workflows are multi-step. But AI tools treat them like single-turn chats."

✅ Huhb supports true multi-step, async, and bulk workflows — including built-in callbacks and plugin chaining.

1. File-Based Processing Is Painful "Getting usable output from PDFs, CSVs, or DOCXs is harder than it should be."

✅ Huhb includes chunking, extraction, and output stitching. No more reinventing parsing for every doc format.

1. Cost and Latency Are a Black Box "You don’t know how much it’ll cost until the bill comes. And retries kill your margins."

✅ Huhb tracks tokens, latency, fallback retries, and cost estimates. You can even set quality or speed preferences in advance.

1. Fallbacks Are Still DIY "If one model fails or times out, it's up to you to route elsewhere."

✅ Huhb has native fallback support per task — no custom retry logic required.

1. Modularity and Reuse Are Missing "Tools are either too raw, or too rigid."

✅ Huhb lets you override tasks, inject your own templates, add preprocessors, and make it your own.

1. Developer Tooling Is Underbuilt "What I really want is a CLI and SDK — not another drag-and-drop canvas."

✅ Huhb gives you both. Run workflows, upload files, and track jobs — all from your terminal.

🧭 Why Huhb Is Different
Now that orchestration tools are emerging everywhere, the big question is: What makes Huhb stand out?

1. Developer-First — Not Enterprise-First Others target procurement teams. We’re building for builders — just like Twilio and Stripe did.

You don’t need permission to start using Huhb.

1. The API Aggregation Playbook We’ve done this before at Leaf and Hapi. One consistent interface across fragmented providers.

The more providers we support, the more valuable Huhb becomes.

1. MCP: Context-Aware AI
   Our Modular Context Plugin layer gives you smarter, more relevant output — not just “better routing.”

2. We’re Creating a New Category
   We’re not an AI tool. We’re an AI integration platform.

Add AI to your app in 5 minutes — not 5 months.

1. Self-Service + Scale Usage-based pricing

Plugin-first flexibility

CLI + SDK + GraphQL API

Postprocessors, callbacks, analytics — all built in

This is the Twilio of AI — not the SAP of AI.

🔗 Where This Started
If you missed the original spark behind this build, you can read Part 1 here.

🛠 What’s Next
We’re adding:

Live model benchmarking

Output plugins: JSON, Markdown, PDF, CSV

Public onboarding + plan enforcement

Admin analytics + cost controls

Still in alpha. Still co-building. Still learning.

If you’re wrestling with model chaos, brittle prompts, or cross-provider messiness — you’re not alone.
Let’s fix it together.