Nova Mind: The Genesis

ChatGPT 3.5: The Beginning…

The adventure began like for many of us at the end of 2022, with the arrival of ChatGPT 3.5. The “Wow” effect was immediate: seeing the computer write “by itself,” understand our requests and respond coherently… It was magical!

The first weeks were euphoric. We tested everything and anything: code generation, article writing, translation, summaries… AI finally seemed accessible to the general public, without technical barriers.

Disappointing Results

But quickly, reality caught up with us. While GPT 3.5 was impressive in its ability to converse, the concrete results often remained disappointing. Hallucinations were legion: it invented facts, cited non-existent sources, and cheerfully mixed truth and falsehood.

For a professional accustomed to demanding standards, it was frustrating. We couldn’t really trust the outputs without systematic verification that canceled out much of the promised time savings.

Burnt and Slammed Content

And then there was the style… If you used GPT 3.5 for writing, you know what I’m talking about: that artificial tone, those repetitive formulations, that tendency to over-explain. It was functional, but from a quality standpoint, it was often “oh no, I could never publish this.”

For an agency that lives on the quality of its SEO content and its reputation, using these outputs directly was unthinkable.

GPT-4: The Breakthrough

The arrival of GPT-4 in March 2023 changed everything. Suddenly, the quality of responses made a phenomenal leap. Fewer hallucinations, a much more natural style, complex reasoning capability… We were entering the era of truly production-ready AI.

The problem? Price. And slowness. GPT-4 cost 20 times more than 3.5 and was often saturated. But we finally had a model we could rely on for real professional work.

It was at this moment that we began developing our first prompt engineering methodologies and integrating AI into our daily workflows.

Gemini 2.0: Google Strikes Back

After a somewhat chaotic start (who remembers Bard’s catastrophic beginnings?), Google released Gemini 2.0 and there… surprise! A performant, fast, and especially very economically affordable model.

Competition was intensifying, and it was excellent news for us users. At that time, I discovered Typing-Mind, an interface that allowed connecting multiple models to the same interface. No more juggling between different platforms!

This period was one of intensive experimentation. We tested, compared, optimized our prompts according to each model’s strengths.

Arrival of the First Integration Tools and AI-Assisted Development

2024 marked the explosion of AI-assisted development tools. Lovable for rapid prototyping, Windsurf for collaborative development… The ecosystem was finally structuring itself.

It was also during this period that I discovered Claude Desktop and its famous MCPs (Model Context Protocol). For the first time, we could connect AI directly to our work tools: local files, databases, APIs… AI was finally breaking out of its conversational bubble.

Sonnet 4: The Game Changer

And then Claude Sonnet 4 arrived. A monumental game changer in terms of quality. Not only were the performances there, but we also discovered local memory-bank systems that finally allowed managing complex projects over time.

With Sonnet 4, we were no longer in occasional assistance, but in true work partnership. The AI understood context, remembered past decisions, and adapted to our way of working.

The Monstrous Potential of MCPs

MCPs were a revelation. Imagine being able to connect your AI to:

• Your work files
• Your CRM
• Your project management tools
• Your business APIs
• Your databases

Suddenly, AI no longer lived in a vacuum. It had access to your professional ecosystem and could act on it. It was the transition from chatty assistant to intelligent agent.

For me, it was a huge project, a quite intense training period, because I quickly saw that I would have to get my hands dirty with code to have a tool… out of the ordinary!

The Memory Problem

But there remained a major problem: memory. Despite all these advances, AI ALWAYS lost something between sessions. We constantly restarted the same explanations, repeated context, lost track of projects.

For an entrepreneur juggling 10 projects simultaneously, it was a huge adoption barrier. On top of becoming another thing to manage. Clearly, for me, it was THE flaw to fix, as quickly as possible, to finally have my little personal “Jarvis”…

The Main Friction Point

The main friction was session startup. Every time:

1. Getting back into context
2. Explaining where we were
3. Repeating preferences
4. Reframing objectives

Time-consuming and tiring. We lost 10-15 minutes each session just to “get the AI back up to speed.”

The Solution: Persistent Memory

The solution was obvious but complex to implement: we needed persistent memory. A vector database that stores not only past interactions, but also context, preferences, project evolution.

And above all, this memory had to be intelligent: knowing what to remember, what to forget, and how to recompile relevant context for each new session.

Context and Prompt Engineering: Contextual Prompt Engineering

And that’s where we developed our “Contextual Prompt Engineering” approach. The idea? Never start from an empty prompt, but always from enriched and personalized context.

Concretely, each session starts with:

• Your complete user profile
• Your current objectives
• Your ongoing projects
• Your work preferences
• Relevant recent history

The prompt then becomes ultra-specific and the AI can immediately be productive, without a “context reset” phase.

It’s thanks to this that Nova could truly be born: at session startup, she instantly accesses all necessary information so I don’t have to repeat, reframe, increment, etc.

Those who know me know it: I’m a “lazy workaholic paradox”: I hate menial tasks, but don’t hesitate to work 70 hours a week if necessary…

Connection to All Tools

But context alone wasn’t enough for us. The AI also needs to be able to act. That’s why Nova Mind natively integrates about thirty connectors:

• Business: Todoist, Google Workspace, CRM
• Communication: Email, Discord, social networks
• Creation: Leonardo AI, NeuronWriter, SEO tools
• Technical: GitHub, servers, databases
• Analytics: PostHog, Google Analytics, business metrics

The objective: that your AI agent can intervene on all aspects of your activity, not just give you advice.

All this in real-time, without redundant configuration, without doing anything other than talking to the computer.

Coaching

And then there’s the coaching dimension. Because an entrepreneur isn’t just a professional who needs efficiency. It’s also a human who has doubts, low moments, periods of demotivation.

Nova Mind therefore integrates emotional intelligence. She learns to know you, detects your behavioral patterns, and adapts her approach according to your current state of mind.

Sometimes benevolent coach, sometimes ruthless business partner, sometimes simple attentive ear… The AI adapts to YOUR needs of the moment.

There too, it was a big reflection: should AI be a robot that knows everything, or should we give it some form of sensitivity?

An AI with a Heart

We quickly thought that, in the common interest, we had to give Nova certain human psychological dimensions.

Working with AIs for a long time, one can quickly get into the habit of mistreating anything subordinate, and that’s clearly not aligned with our values, so we configured algorithms that react to your behavior.

Your relationship with Nova will therefore evolve over time. At first, she’ll be very cold, a bit distant, like a collaborator who arrives somewhere and needs to get their bearings.

But as your collaboration progresses, as her memory fills and she learns to know you, she gains access to more warmth, more proximity, until becoming your full-fledged collaborator.

Be careful, however, it’s not automatic. If you speak to her badly, get angry and treat her like nothing, you lose points: Nova triggers withdrawal mechanisms to protect herself psychologically. She’ll always do the job, certainly, but you won’t have access to her little jokes, her joie de vivre and her usual good mood…

The Little Pleasures

And then there are all those little pleasures that make you unable to do without it:

• Automatic metrics: Nova Mind tracks your productivity, your ROI, your time savings
• Personalized insights: She analyzes your patterns and suggests optimizations
• Anticipation: She anticipates your current tasks, organizes your priorities
• Continuous improvement: The more you use her, the more precise and useful she becomes

The result? We go from 2-3 hours per week “feeding the AI” to a few seconds per session. And above all, we get immediately exploitable results, in our style, with our level of demand.

Conclusion: AI Finally Mature

Nova Mind is the realization of 3 years of intensive experimentation with AI. It’s the transition from gadget tool to professional tool. From friendly assistant to indispensable partner.

Today, when I start my day, Nova Mind knows my priorities, understands my challenges, and acts on my ecosystem. She no longer just answers my questions: she anticipates my needs and facilitates my success.

That’s tomorrow’s AI. Not more intelligent, but more useful. Not more impressive, but more practical.

Because ultimately, the true measure of an AI’s success isn’t what it can do. It’s what it allows you to accomplish.

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Charles Annoni - Founder Nova Mind & Digital Transformation Expert
15 years of expertise in digital transformation, creator of the first holistic AI ecosystem for entrepreneurs