Lessons

useState, useEffect, useReducer, and useContext in React 19: the rules, snapshot semantics, dependency arrays, when NOT to useEffect, and the dependency-array footgun that bites everyone.

Master React 19's core primitive: function components, JSX, props, children, composition, conditional rendering, and stable keys — built around a small TODO list you can ship.

Build a small REST API on Node 22 with Express: routes, middleware, JSON body parsing, Zod input validation, status codes, and a clean async error pipeline you won't outgrow.

How Node 22 actually runs your code: V8, the libuv event loop and its phases, microtasks vs macrotasks, blocking pitfalls, and where worker threads, npm, pnpm, and bun fit in 2026.

Every constrained optimization problem has a twin. Learn how to build the Lagrangian, derive the dual problem, and use weak duality, strong duality, and the KKT conditions to certify optima — with worked examples from linear programming and SVMs.

How SEO actually works in the crypto vertical: YMYL, the paid-search ban fallout, jurisdictional content, schema for asset pages, and the unusual link economy you have to navigate.

The shift from 2D-lifting to native 3D generation. Autoregressive mesh transformers (MeshGPT, MeshAnything) and flow-matching latent models (Trellis, Hunyuan3D-2, TripoSG). Architectures, tokenization, conditioning, and which ones are actually usable in 2026.

What it takes to ship a generated mesh into a real game, AR app, or animation: topology cleanup, retopology, LODs, auto-rigging, and the 2026 platform landscape across Meshy, Tripo, Rodin, CSM and others.

The end-to-end production pipeline behind Meshy, Tripo, Rodin, and the open-source SOTA: multi-view diffusion, feed-forward reconstruction, mesh extraction, and the failure modes that still bite. Names the models, not just the concepts.

How AI engines decide which crypto sources to cite, and the on-page patterns that earn those citations. Aimed at SEOs who already do classical optimization and want the cross-over playbook for ChatGPT, Perplexity, and Google AI Overviews.

A working model of crypto for people who already write the content but have never actually used the product. Just enough blockchain, wallets, and exchange mechanics to stop guessing on the brief.

A pattern library for the single highest-leverage page on a centralized exchange: the asset / coin page. Anatomy, schema stack, internal-linking model, and the GEO additions that turn it into a citation magnet.

How modern 3D generation paints a mesh after it's built. UV unwrapping, multi-view diffusion for texture, view-consistency, PBR map decomposition, and the workflow Meshy and Tripo expose to users in 2026.

A working map of the 3D representations that matter in 2026 — meshes, point clouds, voxels, SDFs, NeRFs, and 3D Gaussian Splatting. What each one is, when each wins, and why mesh output still anchors production pipelines.

A field guide to the LLM benchmarks practitioners cite in 2026 — what each one measures, where it's saturated, where contamination risk is high, and why benchmark gains rarely transfer to your task without your own eval.

How frontier LLM costs and latency actually work in 2026 — input vs output token asymmetry, prompt caching, batch API discounts, TTFT and tokens-per-second, reasoning-model amplification, and when self-hosting breaks even.

A working CUDA kernel is the start, not the finish. How to measure occupancy, spot uncoalesced loads and warp divergence, and read the three numbers in Nsight Compute that actually matter.

Why naive matmul on a GPU is bandwidth-starved, and how tiling with __shared__ memory reduces global memory traffic by a factor of the tile size. The classic optimisation, with the kernel that demonstrates it.

The hello-world of CUDA, done properly. Allocate device memory, copy inputs, launch a kernel, copy results back, free, and check every return code. The full driver + kernel in one runnable file.

The five memory spaces a CUDA kernel can see and why they have wildly different speeds. Global vs shared vs constant vs local vs registers, coalesced access, bank conflicts, and a cheat-sheet table you'll actually reference.

How CUDA carves a problem into a grid of blocks of threads. Host vs device code, the __global__ qualifier, the launch syntax, and how every thread figures out which slice of data it owns.

Why GPUs eat CPUs for breakfast on some workloads and choke on others. Serial vs parallel execution, Amdahl's law, the latency-vs-throughput trade-off baked into silicon, and a rule of thumb for when to reach for a GPU.

A practical decision framework for picking an LLM in 2026 — define the task, build an offline eval, measure quality + latency + cost on real candidates, and avoid the classic trap of optimizing for benchmarks instead of your task.

An honest look at the open-weights vs closed-API choice for LLMs in 2026 — covering data privacy, cost at scale, fine-tuning, latency, regulatory concerns, and the gap in raw capability per dollar.