Serdar Gündoğdu

AI Learning System

Learn AI. Put it to work.

Explore how AI systems work through research, applications, and hands-on experiments. Follow five connected layers from compute and models to agents, deployment, and physical AI. Connect ideas, test what you learn, and use it to build your next project.

Scroll down to explore the five layers →

01

Foundation

Ecosystem, compute, runtime and adaptation foundations; explore programming foundations in POL and experiment with GEX, TFL and ADP.

02

Agent system

Study how agents reason, use context, tools and computer environments; turn research into open-source runtimes and reusable components.

05

Physical AI

World models and swarm research meet WML and ANT / BEE experiments, PDT pump twins, digital triplets, and humanoid exploration.

Indented applications belong to the application above them.

AI Learning System · connected paths

How the system connects.

Each application explores a different part of building AI. The diagram connects them into a learning loop: understand compute and models, build agent systems, and test how they behave. Compare local and cloud deployment, then explore world models, swarm intelligence, and colony experiments. These paths inform digital twins and the longer-term goal of open humanoid engineering. Each experiment brings new questions and lessons back to the starting point.

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Connected applications and their sub-applications Eight stages connect foundations, runtime, agent systems, assurance, deployment, physical AI, industrial twins and embodied AI. Frames group GPU with POL and GEX; USL with ADP; LLM with TFL; HNS with ARL, DPL, CUL and AOS; CTX with MEM; WFM with WML; SWI with ANT and BEE; and ITL with PDT and DTR. SEC and EVL exchange feedback. LCL and CLD connect bidirectionally through their shared laboratory DCL. ENG leads to HEX. Arrows describe learning relationships, not runtime integrations. 01· FOUNDATION Ecosystem, compute, runtimeand adaptation foundations;explore programmingfoundations in POL andexperiment with GEX, TFL andADP. 02· RUNTIME Model runtime and servinglayer; support multipleproviders and hardwarebackends. 03· AGENT SYSTEM Agents reason, use context,tools and computerenvironments; turn researchinto open-source runtimes andreusable components. 04· ASSURANCE Security and evaluationcontracts for bounded,reviewable behavior. 05· DEPLOYMENT Local and cloud deploymentoptions; test workload,memory, privacy and costassumptions. 06· PHYSICAL AI World models and swarmintelligence; connect toindustrial twins and humanoidsystems. 07· INDUSTRIAL TWIN Digital twins and digitaltriplets for industrialsystems and real-worldresearch. 08· EMBODIED AI Humanoid and embodiedintelligence research. Opens in a new tab AIA AI ecosystem Opens in a new tab GPU GPU kernels Opens in a new tab POL Programming basics Opens in a new tab GEX Kernel execution Opens in a new tab USL Model training Opens in a new tab ADP Model adaptation Opens in a new tab LLM Runtime · serving Opens in a new tab TFL Token serving Opens in a new tab HNS Harness engineering Opens in a new tab ARL Agent runtime Opens in a new tab DPL Decision plane System One ↔ System Two Opens in a new tab CUL Computer use Opens in a new tab AOS Agent operating system Open-source agent runtime Opens in a new tab CTX Context · knowledge Opens in a new tab MEM Agent memory Opens in a new tab SEC Trust · security Opens in a new tab EVL Evaluation Opens in a new tab LCL Local compute Opens in a new tab DCL Shared lab Opens in a new tab CLD Cloud deployment Opens in a new tab WFM World models Opens in a new tab WML World model laboratory Opens in a new tab SWI Swarm intelligence Opens in a new tab ANT Ant colony Opens in a new tab BEE Honey bee Opens in a new tab ITL Industrial twin lab Opens in a new tab PDT P-101 interactivedigital twin Opens in a new tab DTR Digital triplet Physical ↔ Twin ↔ Intelligence Opens in a new tab ENG Humanoid engineering Opens in a new tab HEX Humanoid exploration ASERDARGUN.COM AI Learning System
Frames group applications with their sub-applications. Double-headed arrows show reciprocal learning relationships. DCL is the shared laboratory of CLD and LCL; ENG connects to HEX. These paths describe learning relationships, not runtime integrations.

From research to hands-on learning

Explore an idea. Run an experiment.

Follow each research application into its companion learning experience. These links describe learning relationships; each application runs independently.

GPU → POLLive · English only

How do languages express the same program differently?

English-only comparisons of the same tasks across programming languages: types, memory, paradigms and concurrency. Programming foundations that support GPU learning, but are not limited to GPU. Executed examples and hand-reviewed examples remain distinct.

Research · GPU ↗ opens in a new tab Explore · POL ↗ opens in a new tab

Explore the journey