Extract full product DNA from any live URL. Brand voice, UX patterns, content strategy, visual design, accessibility, SEO, performance, tech stack detection, CRO audit, design system maturity, PLG detection, enterprise readiness. 64 extraction passes. 20 intelligence engines. 138 tests.

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Design 'EfficientDenseNet' — a DenseNet variant under 10M params that incorporates VoVNet-style one-shot aggregation (OSA), depthwise separable bottlenecks, squeeze-and-excitation attention, LayerNorm pre-activation, and aggressive compound scaling. The goal is to train 2-3× faster than DenseNet-121 while maintaining or improving accuracy on ImageN

Design a multi-class classification and interpretability pipeline that distinguishes rheumatoid arthritis (RA), psoriatic arthritis (PsA), and osteoarthritis (OA) from hand/feet X-ray images. The architecture combines a CNN backbone (e.g., DenseNet or EfficientNet) for disease classification with an attention-based or Grad-CAM explanation module to

Design a neuro-symbolic memory architecture for autonomous agricultural agents, inspired by the brain's Complementary Learning Systems (CLS). The architecture decouples memory into a fast-learning episodic Knowledge Graph for temporal/spatial sequences (crop cycles, disease spread) and a slow-learning semantic ML layer that compresses mature data i

Design an ML-enhanced receiver chain for IoT-to-LEO-satellite communication, where conventional signal-processing algorithms (e.g., channel estimation, synchronization, detection) are augmented or replaced by learned models under low-SNR, high-Doppler, and resource-constrained IoT conditions.

StackMyAI.com is a blog that breaks down complex AI tools and SaaS platforms into simple, actionable insights. This repo includes scripts and data used to power our guides and comparisons — ensuring accuracy and transparency.

Design ModernBERT-Pro, an enhanced encoder-only transformer incorporating Multi-head Latent Attention (MLA) for global layers, fine-grained Mixture-of-Experts FFN layers, Mamba-2 hybrid blocks for linear-complexity local mixing, and a multi-stage curriculum pretraining pipeline. The target is a ~350M-param model (with ~2.5x effective FLOPs via spar

Design a deep reinforcement learning algorithm capable of incrementally expanding its action space as new actions become available (e.g., new abilities unlocked in video game levels), without retraining from scratch or catastrophically forgetting previously learned action-value mappings.

Design a deep multimodal emotion recognition (MER) architecture fusing speech (acoustic/prosodic features), facial expression video frames, and transcribed text for real-time mental health monitoring. The architecture will use modality-specific encoders (CNN/transformer for audio and video, pretrained LM for text) with a cross-modal attention fusio

Design a reinforcement learning architecture for autonomous vulnerability discovery in security capture-the-flag (CTF) challenges, building on the Agent Web Model's hierarchical abstraction layers. The system will model CTF environments as Markov decision processes and develop RL algorithms (e.g., deep Q-learning or PPO with hierarchical extensions

Design a generative model (diffusion/flow matching) for molecular geometry generation, leveraging graph neural networks (e.g., GCN, MPNN) as the backbone for representing molecular structures, with a focus on guided generation for pharmaceutical drug discovery.

Design a vision-based reinforcement learning model that enables a robotic system to detect, track, and physically intercept a fly in 3D space. The model combines a lightweight object-detection backbone for real-time fly localization with a learned control policy (e.g., PPO or SAC) that outputs motor commands to maneuver a catching mechanism (e.g.,