Architecting Intelligence at the Edge
DeepGen is an independent frontier artificial intelligence exploration lab and open prototyping initiative founded by Anupam Das (Senior Software Engineer at Google DeepMind). DeepGen acts as an agile crucible for exploring large language models, agentic workflows, contextual retrieval, and deep learning heuristics driven by technical curiosity, rigorous experimentation, and open-source principles.
Mathematically principled codebases engineered for extreme efficiency, sub-millisecond edge latency, and zero runtime bloat.
Pushing the boundaries of prompt engineering patterns, contextual reasoning, speculative decoding, and multi-agent interaction loops.
Translating frontier research discoveries into practical, publicly auditable developer utilities and reproducible evaluation frameworks.
Interactive Architecture Console
Inspect agent loops live, build typed prompt schemas, profile inference memory, or optimize unstructured prompts into production-grade contracts:
# DeepGen Autonomous Agent Execution Runtime
from deepgen.agents import AgentRuntime, HeuristicPlanner, ToolDispatcher
runtime = AgentRuntime(model="frontier-reasoning-v2", max_iterations=8)
workflow = runtime.compose([
ContextExtractor(strategy="semantic_window", max_tokens=8192),
HeuristicPlanner(self_correction=True, safety_guardrails=True),
ToolDispatcher(tools=[web_search, code_sandbox, vector_retriever])
])
response = workflow.execute("Synthesize topological graph of generative prompt architectures")
print(response.summary, "// Latency:", response.latency_ms, "ms")Core Research & Prototyping Tracks
DeepGen operates as a rapid engineering laboratory focusing on high-impact AI architectures, language tooling, and agentic systems. Filter domains by specialty:
Prototyping next-generation reasoning architectures, synthetic code generation pipelines, speculative decoding, and multimodal developer interfaces.
High-density document categorization, contextual semantic chunking, vector embedding search, and automated retrieval task pipelines.
Lightweight micro-libraries, developer CLI tools, and zero-dependency micro-APIs built to eliminate friction in modern AI workflows.
Designing multi-agent tool execution loops, deterministic guardrails, self-correcting plan trees, and intuitive collaborative interfaces.
Systematic prompt patterns, evaluation benchmarks, structured generation schemas, and responsible generative AI engineering practices.
Profiling token latency curves, quantization precision tradeoffs, and deploying ultra-compact generative intelligence on edge devices.
Founder & Principal Architect
DeepGen was established to bridge frontier academic deep learning breakthroughs with production-grade engineering:
Anupam Das Verified Entity
Based in Zurich, Switzerland. At Google DeepMind, Anupam works on the Gemini app as Tech Lead across surfaces and the backend systems powering them, leading latency optimization for agentic and tool-use features, and launches including Gemini Live camera & screen sharing, Veo 3 in Gemini, and Google AI Ultra for Business. Alumnus of the Indian Institute of Technology Patna (IIT Patna, B.Tech in Computer Science) and recipient of 30 awards at Google (Org Impact, Spot, Peer, Kudos). DeepGen is his independent exploration lab and prototyping initiative for open-source AI utilities and frontier architectures.
Insights & Engineering Reflections
In-depth teardowns, post-mortems, prompt taxonomies, and architectural evaluations authored from the edge of AI research:
A deep dive into why unstructured zero-shot prompts fail in production and how typed JSON-LD schemas stabilize multi-agent execution loops.
Read on Medium → UPCOMING RESEARCH TEARDOWNArchitectural techniques for minimizing token generation overhead and speculative decoding bottlenecks on anycast edge runtimes.
Read on Medium →Connect & Collaborate
Have an idea, research question, or feedback on our open-source prototypes? Connect directly with the laboratory: