Ankit Gupta
Showing 1–6 of 6 transcripts.
- Y Combinator30 min
How Meesho Became India’s Biggest Shopping App
Founders Vidit Aatja and Sanjeev Bikhchandani established Meesho in 2016 to connect rural Indian sellers with consumers, eventually pivoting from a WhatsApp-based social commerce model to a direct-to-consumer app after Jio's data cost reduction enabled mobile app adoption. This bold 2021 strategic shift, which terminated the platform's core business to launch a native application, secured Meesho's dominance in the Indian market by capturing over 50% of the country's online commerce volume by 2026. Today, the company serves 250 million unique consumers and is deploying its proprietary "Vani" voice AI agent to further democratize e-commerce for rural users with low digital literacy.
- Y Combinator24 min
Why Two IIT Engineers Turned Down $550K Jobs To Build A Startup
GigaML, a Y Combinator-backed startup founded by IIT Kharagpur alumnus Varun and his Stanford-educated co-founder, has pivoted from EdTech to developing AI agents that achieve 60% to 70% support deflection rates for major clients like DoDash and DoorDash. The company addresses the enterprise bottleneck of manual configuration by launching "AI forward deployed engineers" that automatically iterate policies to improve business metrics, a strategy enabled by their "0.1% talent" recruitment philosophy and heavy reliance on human-coded expertise rather than sales teams. By leveraging the YC trust network to secure early contracts with eight-person operations, GigaML aims to unlock broader enterprise AI adoption through a generic automation layer that eliminates the need for extensive human engineering resources.
- Y Combinator38 min
Recursion Is The Next Scaling Law In AI
Ankit Gupta, Francois Chaubard
Two 2025 research initiatives, Hierarchical Reasoning Models (HRM) and Tiny Recursive Models (TRM), challenge standard scaling laws by utilizing inference-time recursion to achieve state-of-the-art reasoning with drastically fewer parameters. HRM reaches 27 million parameters using a three-level weighted hierarchy, while the simplified TRM distills the architecture to just 7 million parameters yet achieves 87% accuracy on ARC-Prize benchmarks by treating recurrence as a dynamic latent memory tape. These systems overcome historical RNN limitations through Deep Equilibrium Models and latent recursion, offering a pathway to efficient, deep reasoning that diverges from traditional Chain-of-Thought constraints.
- Y Combinator6 min
How To Get Your First Users
Startup founders are urged to launch a Minimum Evolvable Product and secure paying customers through direct outreach to ensure rapid, pressure-driven evolution rather than aiming for immediate perfection. This strategy is particularly critical in the AI sector, where high computational costs necessitate targeting prosumers or businesses with deeper pockets over price-sensitive consumers. By treating early ventures as simple organisms capable of significant adaptation, founders can navigate path dependency where initial user choices fundamentally steer the product's final form and market relevance.
- Y Combinator9 min
Transformers Explained: The Discovery That Changed AI Forever
This event traces the evolution of AI from early neural networks plagued by vanishing gradients to the 2017 introduction of the transformer architecture, which replaced sequential processing with parallel self-attention. Key milestones include the LSTM's ability to model long-range dependencies, Google Translate's adoption of attention-based sequence-to-sequence models, and the subsequent bifurcation of transformers into encoder-focused BERT and decoder-focused GPT series. These developments enabled the shift from single-task specialists to general-purpose large language models, establishing the foundation for current state-of-the-art systems like ChatGPT and Claude.
- Y Combinator13 min
OpenAI vs. Deepseek vs. Qwen: Comparing Open Source LLM Architectures
OpenAI, Alibaba Cloud, and DeepSeek have each launched significant open-weight language models featuring distinct Mixture of Experts architectures and advanced long-context capabilities. While OpenAI's GPT-OSS prioritizes inference efficiency on consumer hardware, Alibaba's Qwen 3 introduces flexible dense and sparse variants with dual reasoning modes, and DeepSeek's V3.1 achieves superior memory efficiency through Multi-Head Latent Attention. Despite differing engineering strategies for scaling and alignment, all three families demonstrate comparable performance benchmarks derived from trillions of tokens and complex post-training pipelines.