In just a few months, LUNARTECH has sprinted through milestones that many organizations would schedule over years. We launched a high-velocity AI for Executives Bootcamp designed to deliver immediate ROI for leaders. We massively expanded our LUNARTECH Academy curriculum—now a deep, interdisciplinary catalog spanning drones, turbojet engines, autonomous AI navigation (including GPS-denied systems), enterprise-grade AI agents, bioengineering, machine learning, leadership, and more. On the media front, LUNARTECH Media scaled to an industrial-strength content operation powered by 300+ autonomous agents, reaching over 100 million people each month. In partnership with freeCodeCamp—an organization we love and respect—we collaborated on multiple handbooks and courses that have helped bring our total learner community to over one million students. And we released open-source turbojet engine designs for the 40 kilogram-force class (≈392 N)—a high thrust-to-size envelope—built with and for a community of experienced contributors across multiple companies via the Redmoon-40KGF Open-Source Turbojet Repository.
Behind those highlights is a quieter story of engineering the conditions for speed. We are systematically denoising our systems, eliminating waste, and fixating on the signal—the few core actions that compound. Where friction surfaced, we treated it as a design constraint and folded it back into the product. When a goal sat slightly out of reach, we adapted the plan until the distance closed. That posture transformed scattered effort into momentum that feeds back on itself. What follows is a detailed account of what we shipped, why it matters, and how it sets the stage for the next era of LUNARTECH.
Executives don’t struggle for information; they struggle for clarity that fits inside a crowded calendar. We built the AI for Executives Bootcamp around that reality. The program compresses strategy, governance, risk, procurement, and fast-start implementation into a compact experience that still delivers powerful results. It begins at the very basics—terminology, model families, capability boundaries—so nothing essential is assumed. From there it moves without detours into real applications leaders can deploy inside their lines of business.
The difference shows up in the way decisions accelerate. Every concept lands next to a concrete action: pilot selection that resists scope creep, vendor diligence that separates claims from capabilities, policy scaffolds that travel across departments without losing meaning, and measurement plans that make outcomes visible before budgets drift. Legal, security, data, and product leaders leave with a shared map and a shared language. The alignment is subtle on day one and obvious by day thirty, when “What could we do?” becomes “Here’s what we will do next Monday.” The structure is deliberately short, because short is fast and fast is what compounds. A few hours of focused learning replace months of slide decks and hallway recalibrations. The bootcamp doesn’t add noise to an already noisy conversation; it trims the conversation down to what moves the business.
The last few months have been a turning point for LUNARTECH Academy. We didn’t just add courses; we built connective tissue between domains that want to talk to each other. Drones interlock with autonomy. Autonomy interlocks with AI agents. Agents interlock with enterprise workflows. Bioengineering and machine learning share patterns for data hygiene and model evaluation. Leadership sits above all of it, translating intent into execution. The result is a catalog that feels less like a shelf of books and more like a set of tools that fit together.
On the aviation side, our drone and UAV tracks move from airframes and flight dynamics into hands-on piloting and mission planning. The turbojet path digs into architecture, combustion, materials, and test-stand operations, now paired with an open-source companion in the 40 kgf class through the Redmoon-40KGF project. Autonomy goes where GPS cannot—through SLAM, inertial measurement fusion, and robust path planning that keeps systems moving when easy crutches fail; the dedicated track on resilient, GPS-denied navigation is available inside the academy as Master Resilient Navigation: Build GPS-Denied Autonomous Systems. On the AI side, we offer two complementary arcs: one for mastering enterprise-grade agents with observability and guardrails, and one for building your own agent pipelines from scratch, weaving retrieval, tool use, orchestration, and evaluation into a working whole. Bioengineering and machine learning round out the stack with AI-assisted analysis and deployment patterns that care about quality as much as novelty. Leadership is not an afterthought; it equips managers to guide technical teams without losing speed or safety. The full, fast-growing catalog is listed at academy.lunartech.ai/courses.
What ties the academy together is a bias toward artifacts. Learners don’t collect vocabulary; they ship things. A student in the autonomy track leaves with logs and a map from a GPS-denied course. An agent builder leaves with a workflow that solves a real internal task, instrumented enough to fail safely and recover. A propulsion learner leaves with an ECU-aware test plan and the instincts that keep high-energy systems inside safe envelopes. Projects like these travel well. They show up in portfolios and in stand-ups, and they shorten the distance between “I learned it” and “I deployed it.” That, in turn, closes the loop with our other systems: the more the academy produces, the more LUNARTECH Media can teach at scale, and the more our open-source efforts benefit from capable contributors.
Media is not our business model; distribution is the force multiplier for everything else we do. LUNARTECH Media was designed to be the engine that takes useful knowledge and gets it to the people who can use it. In a single month, our content now reaches over 100 million people. That reach is not an accident; it is the product of a carefully orchestrated system where humans set the narrative and standards, and over 300 specialized agents keep the throughput high without letting quality sag.
The agent stack watches signals across AI, aerospace, autonomy, and bio, then distills them into drafts that editors can trust and shape. It turns scripts into videos, cuts footage, adds captions, and packages pieces for the shifting norms of different platforms. It notices what audiences watch through, what they skip, and where they want more depth, then brings that information back to curriculum designers and product leads. The effect is a closed loop. Articles get written and actually read. Videos get produced and actually watched. Feedback returns to the people who can turn it into better courses, tighter playbooks, and sharper documentation. Even though we “don’t do media,” by reach we now function as one of the largest education-driven media presences in our space. That visibility is not the finish line; it is the runway. It brings accomplished guests to the podcast, experienced contributors to the open-source projects, and students who are ready to do the work.
Education only matters if it touches people. Over the past months, our courses have been taken by over one million students in total, and a meaningful share of that reach has been enabled by our partnership with freeCodeCamp. We view freeCodeCamp as long-term collaborators—partners we love and respect—whose mission aligns with ours. Together we have produced multiple handbooks and courses that open the door for learners everywhere and then guide them toward deeper practice inside LUNARTECH Academy. The partnership lowers barriers without lowering standards: learners start with approachable, high-quality materials, then progress into advanced tracks where expectations rise in step with capability. The ladder is clear: learn the fundamentals, build an artifact, apply it in context, share the result, repeat. That rhythm builds confidence in a way that slides never can. It also grows the community’s competence, which shows up everywhere else we operate: code reviews are sharper, safety discussions are more grounded, and collaboration moves without constant translation. Scale, in other words, has been put to work.
High-thrust, small-form-factor turbojets are rare, difficult to manufacture, and easy to misunderstand. We chose to treat those facts as the starting brief rather than a deterrent. In the past few months we published open-source designs for a 40 kilogram-force turbojet engine—about 392 newtons of thrust in a compact package—with the clear intent to build in public, document the process, and involve experienced contributors from around the world. This is a living engineering program, not a static drop. It includes parts catalogs and sketches, safety protocols and ethics guidelines, ECU and ground-support procedures, and a roadmap that tells contributors what to try next and how to report what they find, all centralized in the Redmoon-40KGF repository.
The collaboration is deliberately curated. We welcome mechanical engineers with turbomachinery, thermal, and materials experience; electrical engineers who can reason about sensing, power electronics, and embedded control; and rocket or aerospace engineers used to high-temperature systems and test-stand operations. The doors are open, but not unguarded. High-energy hardware demands discipline. Review gates and test matrices protect the project from the two classic failure modes of open efforts: the casual suggestion that introduces risk, and the impressive tweak that cannot be reproduced. Transparency is our safety harness. When experiments succeed, we know why. When they fail, we know why as well, and we document it so no one repeats the same mistake at a higher speed.
The path forward is incremental by design. We will stabilize a reliable 40 kgf baseline across multiple sites, using the same build steps and the same telemetry schema. From that foundation, we will run small, well-bounded experiments along several promising branches—compressor and turbine refinements that squeeze more efficiency from the same geometry; thermal strategies, materials, and coatings that preserve margins without cutting lifetime; fuel and ignition control that makes starts more reliable and flameouts less likely; rotordynamics and bearings that buy stability at higher RPM windows; and nozzle work that adds thrust without loading the hot section beyond its limits. Each change is measured, reversible, and staged behind checklists that keep people safe. The goal is a fully capable adapted configuration that earns its way through test data, not adjectives.
What will it enable once hardened? Heavy-lift UAVs whose payloads make rescue and research practical. Experimental jet platforms that move the workbench outdoors. University and lab programs that treat propulsion as a first-class learning object. Most importantly, it will enable a generation of builders to approach serious hardware with the right mix of curiosity and respect. That combination is contagious. It is how communities raise their standards without raising their voices.
Modern autonomy cannot depend on blue skies and perfect satellites. Warehouses, tunnels, urban canyons, and contested airspace make GPS intermittent or impossible. Our course tracks lean into that truth. We teach simultaneous localization and mapping that withstands messy light and shifting textures, inertial measurement integration that buys stability when sensors wobble, and path planning that favors conservative safety margins over cleverness. We pair those capabilities with agentic AI systems that plan, act, and adapt within the constraints an enterprise cares about. The agents understand tools and data. They can explain their choices and accept guardrails. The autonomy respects physics and people. For those ready to go deep on this specialty, the academy’s GPS-denied program is available here: Master Resilient Navigation: Build GPS-Denied Autonomous Systems.
Teaching those pieces together matters because the handoff matters. An agent that can plan but cannot execute is a presentation. An autonomous stack that can move but cannot explain itself is a liability. When both halves mature together, we get systems that function in the world and fit inside the organizations that adopt them. The immediate effect is quiet, almost boring: fewer brittle demos, more reliable pilots, less heroics from engineers. The long-term effect is compounding: teams stop reinventing the basics and start layering value on top of dependable foundations.
As our distribution expanded, the conversations became more technical and immediately applicable. The podcast convenes practitioners and recognized thought leaders—frequently from organizations such as AWS, Microsoft, Databricks, and other engineering-driven companies—who share firsthand experience building and operating real systems. Discussions center on architecting and governing AI agents, production machine learning workflows, data science practices that hold up under scale, and the practical realities of deploying large language models. The tone is professional and direct: what shipped, why it worked, where it broke, and how the teams adjusted. We cover topics like tool-use and orchestration for agents, evaluation and observability for LLM applications, model lifecycle and MLOps, data quality and lineage, and responsible AI controls that fit enterprise constraints.
Behind the products sits a discipline we practice every day. Denoising starts with ownership and ends with fewer moving parts. We collapse duplicate tools, write documents that do real work, and replace manual handoffs with agentic assistance wherever it makes quality higher and response times shorter. Focus means choosing the initiatives that actually move learning, safety, and deployment metrics. We favor programs that create artifacts others can build upon. Adaptation is the solvent we use on stubborn problems. If a path is blocked, we change the plan rather than lowering the bar. Smaller steps, tighter loops, and steady progress replace the drama of all-or-nothing gambits. Over a few months, these habits compound into something that feels like time compression. The same hours produce more outcomes, and those outcomes are more likely to survive outside the lab.
Curriculum creates artifacts that media can translate into accessible stories and tutorials. Media grows the community, which returns with code, data, designs, and critiques. Open-source projects like the 40 kgf turbojet absorb that energy and convert it into safer, stronger, more reproducible engineering. The executive bootcamp raises the decision quality of the people who set priorities and budgets, which means the right projects start sooner and the wrong ones end earlier. Across all of it, agents reduce the cost of iteration without replacing human judgment. Those links sound abstract until you watch them move: a lecture about GPS-denied mapping turns into a video that reaches a million people, one of whom brings the method into a factory, where it unlocks a better safety workflow that the team later teaches back into the academy. Flywheels look like magic from the outside and like calendars from the inside.
The shape of the past few months is easy to see. The AI for Executives Bootcamp condensed ambiguity into decisions and roadmaps, and it is accessible for leaders here: AI for Executives Bootcamp. LUNARTECH Academy became a host of a bunch—bunch—bunch more courses, gathered at academy.lunartech.ai/courses, and the connective tissue between them grew stronger. LUNARTECH Media scaled to 300+ agents and crossed the 100-million-impressions mark in a single month. Our courses collectively passed one million learners, with freeCodeCamp—our respected partner on multiple handbooks and courses—serving as a key gateway for many who then deepened their practice inside the academy. The open-source turbojet effort moved from an idea to a documented, governed program at 40 kilogram-force, with the work in motion at the Redmoon-40KGF repository. None of these numbers exist for their own sake; they exist so that competence becomes contagious.
We are candid that our main project lineup remains in the clouds until each item is ready to teach, ship, or open-source. The infrastructure is in place; the next phase is translation. For autonomy, that means labs that step cleanly from simulation into the field with the same code and the same instrumentation—beginning with the GPS-denied program now live here: Master Resilient Navigation. For enterprise agents, that means sector-specific playbooks that bake in observability, escalation, and policy from day one. For the turbojet, it means community sprints that harden build procedures, replicate baselines across sites, and merge the most promising branch improvements into a single adapted configuration, all tracked openly in the Redmoon-40KGF repo. For leadership, it means training that teaches managers how to steward systems where code and people share responsibility, supported by the foundational executive bootcamp.
Across all of it, the principles remain steady. We will keep the systems quiet so the signal stays loud. We will move in small steps that arrive often rather than large steps that arrive late. We will prefer proof over promise, and we will put our proof where others can see it, critique it, and improve it.
The past few months were about putting weight on the bar: launching the AI for Executives Bootcamp, expanding LUNARTECH Academy with a host of powerful courses (including GPS-denied autonomy), scaling LUNARTECH Media to 300+ agents and 100M+ monthly reach, partnering closely with freeCodeCamp on multiple handbooks and courses to reach learners at scale, and open-sourcing a 40 kilogram-force turbojet engine with experienced global collaborators at the Redmoon-40KGF repository. The next few months will be about lifting heavier—turning infrastructure into deployments, turning deployments into standards, and turning standards into shared capabilities.
If there is a single thread that explains the pace, it is this: we are not trying to do everything. We are trying to do the few things that compound, then remove the obstacles that keep them from compounding faster. Short, concise, and powerful learning. Builder-grade curricula. Agent-amplified distribution. Open-source engineering with safety at the center. A culture that values speed with standards. We’re not just getting started—we’re getting faster. And we’re building it with you.