I built deepseek-mcp (github.com/arizen-dev/deepseek-mcp) and run ActiveWizards (activewizards.com) - an AI engineering studio shipping production systems since 2014.
Essays on agent architecture, evaluation, and intelligent organizations: arizenai.com
Production-Ready AI Agents Book (2025): amazon.com/dp/B0G43PPDSP
Yes, true creativity usually or mostly comes from real constraints, in my experience.
As, if there are no constraints in some specific area, there is no kinda "survival need" to improve there, hence brain is not working as hard/smart/deep as it could.
"GPT‑5.6 delivers a step change in design judgment. With only high-level direction, GPT‑5.6 creates tasteful, ergonomic, and functional interfaces. Its stronger computer-use capabilities let it inspect and refine the rendered result—not just generate the underlying code or content—so it can catch visual and functional issues and apply finishing touches before handing the work back."
This one is really promising, as it may allow to close major gap with Claude in design/UI skills
Some anecdata on Fusion: I run same query I used for Fable on OR Fusion and results were worse.
It felt, like Fable was able to kinda grasp very deep knowledge/intelligence layers and outline solution not only in agreeable way, but rather it proposed to prioritize solution items, with discarding some of the items, which made a lot of sense to me.
While Fusion felt more like a bit diversified answer of the same class of pre-Fable SOTA models, without touching the depth of knowledge/intelligence layers, which Fable was able to get, in my very limited tests I did, while Fable was accessible.
I ended up using DeepSeek V4 Flash as main workload model, while keeping DeepSeek V4 Pro and Qwen 3.7 Plus as advisors on system architecture and other advanced matters to guide DS Flash.
opensourcing software may enable leverage of wider network of contributors to given piece of software,hence software can evolve much more quickly and efficiently.
Happiness is a derivative of purpose. If someone optimizes their life strictly for happiness while deprioritizing purpose, they likely won't achieve either.
Pursuing a meaningful goal almost always requires enduring unpleasant phases and friction along the way.
I wanted to share a reference implementation I architected for moving AI Agents from local prototypes to production services.
The Context:
It is relatively easy to get an agent working on a local machine where you can watch the terminal output and restart it if it gets stuck. However, the architecture often breaks down when moving to a headless, hosted environment where the agent needs to handle loops, persistent state, and structured output failures autonomously.
The Solution:
This repo is a 10-lesson lab where you build an "AI Codebase Analyst" designed to handle those operational constraints.
Key Architectural Decisions:
1) State Management (LangGraph): We use LangGraph to implement the State Machine pattern rather than a linear Chain. This provides a standardized way to handle cyclic logic (loops) and persistence without writing "spaghetti code" while loops.
2) Reliability (Pydantic): Treating the LLM as a probabilistic component. We wrap tool calls in strict Pydantic schemas to catch and retry malformed JSON before it hits the application logic.
3) Deployment (Docker): A production-ready Dockerfile setup for serverless environments.
The Repo Structure:
starter branch: A clean boilerplate to build from scratch.
main branch: The full solution code.
curriculum/ folder: The step-by-step guide.
Happy to answer questions about the stack or the trade-offs involved.
Essays on agent architecture, evaluation, and intelligent organizations: arizenai.com
Production-Ready AI Agents Book (2025): amazon.com/dp/B0G43PPDSP
Contacts: github.com/arizen-dev bobriakov.igor [at] gmail