Engineering that goesdeeper than the brief.

1. 01Vertex fetchInterleaved attribute buffers, index buffer, instance attributes
2. 02Vertex shaderModel→view→clip transform, displacement, skinning, morph targets
3. 03Primitive assemblyTriangle setup, backface cull, frustum + clip-space rejection
4. 04RasterisationFragment generation, early-Z, quad overdraw — where fill rate dies
5. 05Fragment shaderBRDF evaluation, texture sampling, shadow lookups, fog
6. 06Output mergerDepth/stencil test, blending, MRT writes for a deferred pass
7. 07Resolve & postMSAA resolve, tonemap, bloom, TAA history, colour space conversion

// vertex — displacement on the GPU, curl noise sampled once per vertex
uniform float uTime;
uniform float uAmp;
varying vec3 vN;

void main() {
  vec3 p = position;
  float n = curl(p * 1.9 + uTime * 0.15).y;   // 3 taps, no derivative in VS
  p += normal * n * uAmp;

  // recompute the normal from the displaced neighbourhood, else lighting lies
  vec3 t = normalize(cross(normal, vec3(0.0, 1.0, 0.0)));
  vec3 pt = p + t * 0.01 + normal * curl((p + t * 0.01) * 1.9).y * uAmp;
  vN = normalize(cross(pt - p, cross(normal, t)));

  gl_Position = projectionMatrix * modelViewMatrix * vec4(p, 1.0);
}

On the asset side, geometry is authored in Blender, exported as glTF 2.0 and quantised with meshopt. Textures go out as KTX2 with Basis Universal, so one file serves ASTC to a phone and BC7 to a desktop. WebGPU is where this goes next: the same scene graph, WGSL in place of GLSL, and compute passes doing the work the framebuffer ping-pong does today.

Some scenes are never going to fit the budgets above. A configurator with film-grade materials, or an environment built on Nanite geometry, belongs in Unreal Engine 5 with Epic's Pixel Streaming. The frame renders on a GPU host, the browser receives video over WebRTC, and input travels back the other way, so fidelity stops being limited by whatever laptop opened the page.

What you trade is a download for a GPU instance per concurrent viewer, plus a latency floor set by how far the user sits from the nearest region. That is a real cost with a real ceiling on concurrency, which is why we will still argue for WebGL when WebGL is enough. Knowing which of the two a project needs is most of the value here.

1. 01IngestIncremental crawls. Chunking follows the structure of the document rather than a character count, and every chunk keeps a record of where it came from.
2. 02EmbedThe model and the number of dimensions get chosen against your corpus rather than a leaderboard, then pinned to a version so results stop moving.
3. 03RetrieveBM25 and vector search together, then a cross-encoder rerank. We measure recall@k on labelled data before anything goes near production.
4. 04GroundEvery claim carries a citation. With no source to point at, the graph loops back for more context instead of writing something plausible.
5. 05ActTool calls get idempotency keys and bounded retries, plus a compensating action for the case where step three succeeded and step four did not.
6. 06GateAnything with a side effect stops for a person. They see the diff, then approve, edit or reject it. The run suspends to the checkpointer while it waits.

// typed state, one checkpoint per node, every edge explicit
const graph = new StateGraph<RunState>({ channels })
  .addNode("retrieve", retrieve)        // hybrid search + rerank
  .addNode("ground", ground)            // refuses without a citation
  .addNode("act", act)                  // idempotent tool calls
  .addNode("gate", humanApproval)

  // no source? go back for more context, do not guess
  .addConditionalEdges("ground", (s) =>
    s.citations.length > 0 ? "act" : "retrieve")

  // anything irreversible stops for a person
  .addConditionalEdges("act", (s) =>
    s.pending.some(isSideEffect) ? "gate" : END)

  .compile({ checkpointer, interruptBefore: ["gate"] });

Typed channels mean a malformed state fails at the node boundary rather than three steps later, where it is much more annoying to debug. The human gate is interruptBefore: the run suspends to the checkpointer and picks up again on approval, so an agent sitting for a day waiting on finance costs nothing while it waits.

Looking to automate something human? Plenty of work has no API behind it. Someone signs into a portal, reads a PDF, copies six numbers into another system, and does that four hundred times a month. We automate it as a real browser session driven at human pace, with the judgement calls routed to a person and everything it touched written to a trace you can audit afterwards.

We also know the other side of it. Browser fingerprinting and anti-bot systems are close enough to our security work that we can tell you upfront which of three situations you are in: a workflow that automates cleanly, a workflow that should really be an API conversation with the vendor, or one to leave alone. That answer arrives before you spend anything, which is usually worth more than the automation.