Introducing Honey Nudger, and Why We're Launching with a Founder's Circle

For the past year, the AI community has been on a dead sprint to build bigger, faster, and smarter models. We’ve meticulously engineered our prompts, fine-tuned our foundation models, and architected complex RAG pipelines. We've become masters at building powerful engines, capable of generating incredibly precise outputs.

The AI industry has mostly solved for accuracy.

But a new, more important question is surfacing in every engineering stand-up and every boardroom: Is our perfectly accurate LLM application actually effective? Is it moving the one metric that matters to our business?

This is the great alignment gap. And today, we're introducing the solution.

From Accuracy to Effectiveness

Meet Honey Nudger. It’s not just another component for your engine—it’s the guidance system.

Honey Nudger is an open-source, autonomous agent that optimizes your AI for effectiveness. It’s a self-learning layer that attaches to your existing production application and, through a recursive learning loop, gets measurably better at achieving your specific business goals.

Think of it like this: your current AI is a high-performance car. You've spent countless hours building the engine and placing it in a sleek exterior. Honey Nudger upgrades it with self-driving software. You don’t have to manually steer it with endless prompt engineering anymore—you just set the destination, and it learns the best possible route to get there, turn by turn, interaction by interaction.

The Honeycomb Cycle: How It Learns on Its Own

So how does an AI teach itself to be effective? It runs on a perpetual, closed-loop learning cycle we call the Honeycomb Cycle.

It all starts with a Nudger. The Nudger is our autonomous SLM-based expert. Its job is to take the standard output from your foundation model and intelligently rewrite it, creating an optimized output called a nudge.

This nudge then interacts with the world through your application (taking the place of your standard output). As your users engage, your system sends back signals of what happened— asynchronously—via the Honey Nudger API. We call this signal Honey.

Honey isn't a predefined metric you configure upfront. It's any signal of value you choose to send back via a simple API call. It can be a click ("user_clicked_link": 1), a conversion ("sale_completed": 99.99), a session length ("user_session_duration": 3045), or even a negative signal ("user_abandoned_cart": -1). If you can track it, Honey Nudger can learn from it.

This stream of Honey is the fuel for the entire system. It feeds into our RLHFp (Reinforcement Learning from Human Feedback, passive) engine. The "passive" is key—you don't need to do any active labeling or provide manual feedback. The system learns directly from the downstream outcomes you're already tracking.

The RLHFp engine analyzes the Honey and does two things instantly:

• It distills what worked into new RAG Hints. The system identifies the patterns in successful nudges and turns them into emergent knowledge, making that wisdom immediately available to the Nudger for the very next user interaction.
• It curates high-value examples for retraining. The best-performing nudge-to-Honey pairs are automatically flagged and prepared for fine-tuning.

From this curated set of high-value examples, the cycle proactively seeks to harden what it has learned. It automatically triggers a PEFT training run, distilling the most effective, emergent RAG Hints into the Nudger's long-term memory—its model weights. This critical step keeps the agent lightweight and adaptive by preventing the hint library from growing infinitely larger, allowing it to constantly evolve with your application and users.

Learning Is Meaningless Without Proof

This is where Honey Nudger truly sets itself apart. Every newly trained "challenger" Nudger is automatically placed into a live, multi-armed bandit A/B test against the reigning champion. This isn't just an internal process; it's a transparent accountability layer that mathematically proves the impact of each change. It answers the ultimate question—Is my AI actually getting better?—and ensures that only statistically superior models are automatically scaled to 100%, guaranteeing continuous, measurable improvement.

And it’s efficient. When the system detects that training runs are leading to diminishing returns based on successive A/B test losses, it automatically enters Torpor—a hibernation state that scales down compute and testing cycles while it continues to serve nudges and harvest Honey, waiting for a new pattern of opportunity to emerge.

In Summary

The Nudger creates a nudge. The nudge generates Honey. The Honey trains a better Nudger. This is the Honeycomb Cycle. It repeats indefinitely, turning your application into a truly self-improving system. This makes it an indispensable tool for any team building AI-powered product features, from dynamic e-commerce recommendations to personalized user onboarding flows.

Built in the Open, For the Open

Honey Nudger stands on the shoulders of giants. The core technology is made possible by decades of work from nearly 250 open-source libraries. It is only fitting that we carry their work forward in the open, for the entire AI community to benefit from.

We believe that self-learning AI is a fundamental capability that should be accessible to every developer, not a secret weapon hoarded by Big Tech. This is our way of paying it forward—ensuring that the power to create truly adaptive and effective AI doesn't get left behind, but is placed directly into the hands of individual builders.

Why a Private Launch?

We believe Honey Nudger represents a paradigm shift—a powerful, autonomous system that shouldn't be built in a vacuum. Its evolution needs to be guided by a core group of builders who are as obsessed with this problem as we are and can help ensure the long-term success of the project.

So, we've chosen not to do a wide-open public launch (yet). Instead, we're opening up a private Founder's Circle for the open-source codebase.

We're looking for our first partners—the builders, the tinkerers, the leaders who will help us steer this ship. You will get early access to the code, a direct line to the founding team, and a foundational role in shaping the future of AI alignment on the open web.

If you believe, like we do, that the future of AI is not just accuracy, but effectiveness, then we want you in the inner circle.

Come build the future with us.

Apply for the Founder's Circle Here