JEPA looks simple: two encoders, a predictor and latent loss. But the details matter. EMA target encoders and stop-gradients prevent collapse, while block masking forces semantic prediction. Dr. Sreedath Panat teaches you to build JEPA from scratch: jepa.vizuara.ai

Built an 8-agent workflow with a human-in-the-loop to find course topics, analyse trends, write captions, generate GIFs/videos, and track post performance. Tested with Vizuara AI Pods; next, I’ll add ArcEval to the same platform.

Why should retrieval chunks overlap? Overlap preserves ideas that cross a chunk boundary. The trade-off is duplication, so overlap should protect continuity without flooding retrieval with near-copies. courses.vizuara.ai/

How can one document create many LLM training examples? A context window slides across the token sequence. At every position, earlier tokens become the input and the next token becomes the target. One text produces thousands of prediction tasks. courses.vizuara.ai/

Coding agents usually compact context on a fixed rule. AutoCompact lets the agent learn when to compact and what to keep: SWE-bench Verified 30.4% -> 39.6% on Qwen3-Coder-30B. Context is a budget, not storage. pods.vizuara.ai/ #AIAgents #ContextEngineering

Why do fixed-size chunks fail? Meaning does not respect character counts. Blind splitting can separate a claim from its evidence or a heading from its section, weakening retrieval. courses.vizuara.ai/

Embeddings sound complex, but the first operation is simple. A token ID indexes one row of a learned matrix. That row is its embedding. Training changes the table so those vectors become useful to the model. courses.vizuara.ai/

Two candidates can get the same coding score but show very different engineering judgement. ArcEval measures how candidates decompose problems, use AI, debug, iterate and communicate- not just the final code. Evaluate the process, not just the answer: hire.vizuara.ai

I-JEPA was trained on 1.3M images, but JEPA can work on 20k domain-specific images. Dr. Sreedath Panat recommends a smaller ViT, larger patches, longer EMA warm-up, more epochs and monitoring kNN accuracy plus embedding variance: jepa.vizuara.ai

Same matrix multiply. Same GPU. Same answer. Yet one CUDA kernel runs at 1.3% of cuBLAS speed while another reaches 93.7%. The difference is data movement: coalescing, shared-memory tiling, reuse, vectorised loads and warp-level tiling: kernelworkshop.vizuara.ai

Why can five examples be worse than three? Extra examples help only when they add coverage. Redundant or conflicting demonstrations can dilute the pattern and consume context without improving the answer. courses.vizuara.ai/

7 NeurIPS Workshop 2026 acceptances from Vizuara Research, spanning Scientific ML, Physics-Informed AI, agents, RL evaluation, simulation, PDEs and SLMs. Explore the research and collaboration opportunities: research.vizuara.ai/

What is GPT-2’s embedding matrix? It is a learned table with one row per token and one column per hidden feature. Looking up a token selects the vector that enters the transformer stack. courses.vizuara.ai/

JEPA uses plain Vision Transformers. A 224 × 224 image becomes 196 patch tokens with position embeddings. Attention connects distant patches, while context and target encoders produce patch embeddings. Dr. Sreedath Panat teaches I-JEPA, video and world models: jepa.vizuara.ai

AI FDE: Build an AI Pilot Customers Can Trust - a FREE 30-min workshop with Dr. Rajat Dandeker covering AI pilots, RAG/agents, quality, latency and cost. 📅 Oct 3, 2026 | 🕖 7–7:30 AM IST | Maven: maven.com/p/53b979/ai-fde-bu…

Does a sub-agent get its own context window? Yes. Delegation creates a separate working context, which can isolate a focused task and keep the main agent from carrying every intermediate detail. courses.vizuara.ai/

Same matrix multiply. Same GPU. Same answer. Yet one CUDA kernel runs at 1.3% of cuBLAS speed while another reaches 93.7%. The difference is data movement: coalescing, shared-memory tiling, reuse, vectorisation and warp-level tiling. kernelworkshop.vizuara.ai

Why does “king − man + woman” point toward “queen”? Embedding directions can encode recurring relationships. Subtracting one concept and adding another moves the vector toward a word used in an analogous context. courses.vizuara.ai/

DINOv2 and I-JEPA both use EMA teachers and ViTs, but learn differently. DINO learns augmentation-invariant features; JEPA predicts hidden embeddings. Dr. Sreedath Panat teaches JEPA from Scratch, from I-JEPA to video and world models: jepa.vizuara.ai

A model can score perfectly yet fail in production if its test set leaked information from the future. Data leakage happens when features, preprocessing, or splits expose information the model would not have at prediction time. Split first. Fit preprocessing on training data only. For time-based tasks, use chronological splits; for customer data, keep customers separated. Learn more: complete-pathway.vizuara.ai/