Builders - Seek Deep

Limitations define AI development today. Compute is concentrated, access is restricted, and dependencies on proprietary models dictate what can and cannot be built. The landscape is shifting, but one thing remains clear: AI alone is not enough—how we architect AI systems determines their real impact.

Over the last two months, me and my team have been hands-on—building, testing, and optimizing AI LLM models in real-world conditions, testing the limits of what’s possible.We don’t just analyze AI—we build with it. The real challenge isn’t choosing between OpenAI or DeepSeek. It’s about structuring AI systems that break dependencies, optimize cost, and push beyond the limitations of existing infrastructure.

What We Built

The AI infrastructure we built is consist of latest models Deepseek, OpenAI, Llama, Qwen; & we tested these models to build AI-driven system that automates the creation and execution of functional software components.:

High-level intent processing with OpenAI → Structuring tasks, generating specifications, and handling creative workflows.

Autonomous code execution with DeepSeek → Translating structured outputs into deployable backend logic with minimal human intervention.

For example, in one of our tests, we generated full job automation workflows—from writing a job post to implementing a smart contract-based hiring system. OpenAI structured the descriptions, generate prompts for seekers, while DeepSeek handled execution, integrating APIs and automating business logic.

This isn’t just theory. It’s production-level AI orchestration. The future of AI isn’t about isolated models—it’s about agentic, modular systems that operate autonomously.

What we used

Deepseek: https://ollama.com/library/deepseek-r1

LLAMA: https://ollama.com/library/llama3.3

OpenAI: https://platform.openai.com/docs/models#o3-mini

Groq: https://groq.com/

Why DeepSeek Stood Out

Code that ships. Unlike GPT-4, which often returns generalized or theoretical code, DeepSeek-Coder produces more directly executable results—reducing debugging time by an estimated 30% in niche API integrations.

Cost vs. performance. Running DeepSeek is significantly more efficient—about 5x cheaper than GPT-4 for coding tasks, making it viable for large-scale AI-driven development.

Transparency. Unlike OpenAI’s black-box approach, DeepSeek’s open weights gave us direct control over inference, allowing for fine-tuned optimizations that reduced unpredictability in output.

Innovative Learning Behavior: DeepSeek-R1-Zero exhibits a remarkable phenomenon during training—often called an “Aha moment.” In this phase, the model learns to allocate more thinking time to a problem by reevaluating its initial approach. In other words, it’s not just strong performance in terms of cost and output; DeepSeek also evolves its internal thought process, which ultimately contributes to generating more reliable and executable code.

Where OpenAI Still Leads

Creative structuring. OpenAI’s models excel at complex, high-context outputs—whether it’s generating marketing copy, structuring reports, or creating user-facing narratives.

General knowledge. When working with broad, context-heavy tasks, OpenAI’s extensive pretraining gives it a noticeable advantage over DeepSeek’s more specialized training.

Beyond the Model Wars: AI, Web3, and the Next Internet

The GPU Fight: While governments hold AI summits, debate regulations, and fight over chips, it’s good to see the adoption and recognition of AI’s impact at the highest levels. But for builders, the real challenge isn’t policy—it’s execution. Hardware access, rising costs, and model limitations define what can actually be built today.

The AI race isn’t just about performance—it’s about ownership, autonomy, and the infrastructure that supports intelligence at scale. Today’s internet is platform-controlled, data-extractive, and bottlenecked by central gatekeepers. AI amplifies these inefficiencies, reinforcing reliance on proprietary models and closed ecosystems.        

Post-Web infrastructure changes that. AI, blockchain, and decentralized systems are converging to create a trustless, autonomous, and composable digital economy. AI agents won’t just process tasks—they’ll own execution, operate across networks, and participate in economies without intermediaries.        

Beyond AI Wrappers: While some systems rely on AI wrappers like perplexity—which is neither inherently good nor bad but merely wraps around existing LLM capabilities—the intent is to go further by evolving the underlying reasoning process.        

We’re not just building AI tools. We’re building AI-native systems for the next internet.

What’s Next?

We’re now fine-tuning DeepSeek variants for data engineering and agentic AI workflows and building out a LLM case to close the Q1 on strong note — we are actively pushing autonomous execution, cost efficiency, and modular intelligence.

Builders don’t follow trends. They build what’s next.?

If you’re working on something similar or want to know what AI can’t do. Let's connect OR DM me.