Fine-Tuning Open-Weights Models with QLoRA & Unsloth
A practical hands-on guide to parameter-efficient fine-tuning (PEFT): 4-bit quantization, Low-Rank Adaptation (LoRA), Memory footprint management, and 2x faster training with Unsloth.
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29 deep dives on architectures, research, and the ideas shaping modern AI.
Multi-Agent Orchestration: Designing Reliable Autonomous Workflow Systems
A comprehensive technical architectural guide to building production multi-agent systems: task decomposition, agent delegation, state isolation, handoff contracts, and failure recovery.
Read articleProduction RAG Engineering: Hybrid Search, Reranking & Evaluation
An end-to-end technical deep dive into moving beyond naive vector RAG: dense+sparse hybrid search, cross-encoder reranking, chunking strategies, and automated evaluation metrics.
Read articleTest-Time Compute & Inference Scaling for Reasoning Models
A deep technical analysis of the shift from pre-training compute scaling to inference-time scaling: Monte Carlo Tree Search (MCTS), Process Reward Models (PRMs), and self-correction loops.
Read articleAgent Harnesses: The Engineering System Around Reliable AI Agents
A technical guide to building agent harnesses: the runtime, state, tools, policies, evaluation, and observability that turn an LLM loop into a dependable system.
Read articleDeploying Models in Production: A Technical Guide from Artifact to SLO
A practical deep dive into production model deployment: packaging, serving, scaling, rollout strategies, observability, and the failure modes that matter after training.
Read articleKimi Delta Attention: A Technical Deep Dive into Gated Linear Memory
How Kimi Delta Attention replaces quadratic attention with a gated delta-rule memory, and what that means for long-context language models.
Read articleAgentic AI and the Model Context Protocol (MCP)
A senior engineer's tour of the path from plain RAG to autonomous, multi-agent systems, and how the Model Context Protocol standardizes the way LLM apps connect to tools and data.
Read articleReasoning Models and Test-Time Compute
How the field shifted from scaling training to spending compute at inference — chain-of-thought as computation, RLVR, and the sampling, verification, and search strategies that turn extra thinking into better answers.
Read articleMamba and State Space Models: The Transformer Challenger
A senior-level tour of state space models — from S4 to Mamba's selective SSMs and Mamba-2's state-space duality — as a linear-time alternative to quadratic self-attention.
Read articleMixture-of-Experts: How Frontier LLMs Scale Efficiently
A senior-level walkthrough of Mixture-of-Experts in LLMs — how sparse routing decouples parameter count from per-token compute, plus the losses, failure modes, and real systems that make it work.
Read articleFoundation Models for Causal Inference (CausalFM)
CausalFM is a transformer-based foundation model trained on simulated causal worlds so it can reason about cause and effect, estimate treatment effects, and answer counterfactual what-if questions rather than just detect correlations.
Read articleVL JEPA: A Deep Dive into Vision-Language Predictive Models
A detailed engineering walkthrough of VL JEPA, the Vision-Language Joint Embedding Predictive Architecture, situated within Meta AI's JEPA lineage (I-JEPA, V-JEPA, V-JEPA 2) and the broader shift toward predictive, non-generative representation learning.
Read articleUnderstanding the CLIP Model: Bridging Vision and Language
A deep, practical walkthrough of OpenAI's CLIP: the dual-encoder architecture, the contrastive (InfoNCE) training objective, prompt engineering, worked zero-shot classification, and how CLIP-style encoders became the backbone of modern multimodal systems through 2025-2026.
Read articleTitans Architecture and the MIRAS Framework
A deep dive into Google Research's Titans architecture and the MIRAS framework: how test-time memorization, surprise-driven updates, and attentional bias push models past the quadratic-attention wall.
Read articleTransformers++: The Next Evolution in AI Architecture
A deep, engineering-level tour of Transformers++ — the wave of architectural changes (linear and sparse attention, state-space hybrids, neural memory, mixture-of-experts, and efficient KV-cache serving) that extend the classic Transformer toward long context, multimodality, and continuous learning.
Read articleTransformers: The Architecture That Changed AI Forever
A deep, modern dive into Transformer architecture — attention math, encoder/decoder design, the 2025-2026 stack (RoPE, RMSNorm, GQA, FlashAttention, MoE, long context), efficiency techniques, evaluation, and where the field is heading.
Read article800 Days of Grind: The Full Blueprint
An 800-day blueprint for transformation, backed by the neuroscience of habit formation, identity-based behavior change, deliberate practice, and a green/yellow/red tracking system that survives real life.
Read articleHow the Human Mind Works
A deep dive into how the human mind works — the brain's architecture, conscious and subconscious layers, perception, memory, emotion, creativity, and what modern neuroscience reveals about cognition and consciousness.
Read articleKV Cache Compression & Context Window Extension: YaRN, RoPE & PagedAttention
Managing gigabyte KV caches for 1M+ token contexts: vLLM PagedAttention, Rotary Position Embeddings (RoPE), YaRN frequency scaling, and Grouped-Query Attention.
Read articleDistributed Training at Scale: Megatron-LM 3D Parallelism & DeepSpeed ZeRO-3
Scaling LLM training across 10,000+ GPUs: Tensor Parallelism (TP), Pipeline Parallelism (PP), Sequence Parallelism (SP), and ZeRO-3 Memory Offloading.
Read articleMixture of Depths (MoD): Dynamic Layer Routing & Compute Allocation
Routing compute dynamically per token across transformer layers: top-k routing, capacity bounds, and integrating MoD with Mixture-of-Experts (MoE).
Read articleMechanistic Interpretability & Sparse Autoencoders: Deconstructing LLM Polysemanticity
Extracting monosemantic features from LLM activations using dictionary learning, Sparse Autoencoders (SAEs), and circuit probing.
Read articleDiffusion Models & Flow Matching: Continuous-Time Generative Architectures
The mathematical formulation of Score-based Generative Models, SDE/ODE formulations, Rectified Flow Matching, and Classifier-Free Guidance (CFG).
Read articleRLVR & Reasoning Alignment: Reinforcement Learning with Verifiable Rewards
How RLVR replaces subjective reward models with objective code compilers and math solvers: Group Relative Policy Optimization (GRPO) and self-play.
Read articleSpeculative Decoding & Medusa Heads: Accelerating LLM Token Generation
Scaling LLM inference throughput: draft-target model speculative sampling, tree-based verification, and multi-head parallel token prediction with Medusa.
Read articleVision-Language Multimodal Architecture: SigLIP, LLaVA & Patch Tokenization
Engineering vision-language models: ViT patch embeddings, SigLIP sigmoid loss vs InfoNCE, linear projection layers, and cross-attention spatial grounding.
Read articleDirect Preference Optimization (DPO) & Alignment: Beyond RLHF with PPO
A rigorous mathematical breakdown of LLM alignment techniques: Bradley-Terry preference modeling, PPO loss dynamics, DPO implicit reward derivation, KTO, and ORPO.
Read articleFlashAttention-3 & GPU Kernel Optimization: Accelerating Transformer Attention
An in-depth technical analysis of FlashAttention-1, 2, and 3: IO-awareness, FP8 Tensor Cores, async memory pipelines, and custom Triton kernel design.
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