The handbook's real contribution isn't any single tool—Tripo, gltf-transform, and Three.js all exist independently—it's the glue that binds them into a reproducible headless pipeline. That's the lesso
The architecture is a three-part loop, and I think it's worth being precise about where each piece lives, because the failure modes cluster around the seams.
The first thing to understand about this workflow, and the reason it's genuinely interesting rather than just another prompt-to-3D demo, is that Gemini never touches STL data. When I ask for a vase, t
Let me start with the sharpest way I can frame it: Supervision is not a computer vision framework. It will never train a model, never touch a GPU, never manage inference workers, and never claim to be
The core diagnosis is almost embarrassingly simple: frontier LLMs optimize for the shortest path to a plausible artifact. Give a model "build me a feature" and it will produce code, tests, maybe even
Most of the conversation about enterprise agent platforms fixates on the dashboards. That is the wrong place to look. A trace viewer is a rendering surface; the interesting engineering is in what gets
Open-source TTS in 2026 has a fragmentation problem that shows up on two completely separate axes, and conflating them is why so many teams waste weeks.
Everyone evaluates lifelogging systems on the wrong axis. They compare transcription quality, retrieval accuracy, summarization pipelines — the y-axis of the problem. The real axis, the one that deter
The headline bundles four unrelated things and calls them an upgrade: prompt-to-3D generation, a foundational agent model, MCP integrations into Blender/Photoshop/After Effects, and collaborative mult
Time-to-final-segment (TTFS) is the delay between the moment the last audio in a segment enters the system and the moment that segment's final transcript is emitted. That distinction matters because s
The Agents API isn't an agent. It's the launch mechanism for the execution loop OpenAI already runs internally—the same orchestration layer that powers Codex-powered tasks and ChatGPT agent workflows.
The cleanest way to understand this architecture is to stop thinking of it as one system and start thinking of it as three planes that happen to share a session ID. Each plane has a different job, a d
When people hear "real-time AI avatar tutor," they mentally picture a single wire connecting a user to an animated face. The actual architecture I'm working with is four separate stateful network conn
The naive version of this pipeline takes a weekend. Detection to pose to tracking to homography to event detection is five well-documented stages, each with mature open-source implementations, and wir
The fundamental shift B3D makes is not a better network architecture or a cleverer loss function. It's a decision about where the expensive computation lives. Traditional biomechanics pipelines—PBL, O
Every generative storytelling pipeline eventually hits the same wall. I call it the coherence trilemma: you need character identity to survive across angles and lighting, you need temporal and camera
Corporate tax research is a uniquely hostile evaluation target, and not for the reasons you'd expect. It's not that the domain knowledge is esoteric—it's that the output form and failure modes are fun
Most LLM evaluations in the legal domain are static QA sets. You feed a model a question, it produces an answer, a rubric decides if it's right. The problem is that this bears almost no resemblance to
Basketball is the worst-case scenario for multi-object tracking, and I don't think that's hyperbole. Soccer has open space and long passing lanes. Hockey has white-ice contrast and small rosters. Bask
The reason I'd reach for RF-DETR over a YOLO variant on a basketball feed isn't raw accuracy on some leaderboard — it's the architecture's disposition toward dense, overlapping objects, which is exact
The first thing to get straight about diagram-design is that it is not a layout engine. It does not parse anything, compile anything, or solve for node positions. There is no Dagre, no Graphviz, no TA
The architecture I settled on is really five sub-pipelines running against a single iPhone clip, and it's worth being explicit about them because the naming hides the real engineering problem. You hav
The first thing to understand is what GPU Canvas is not. It is not a 3D engine, and it doesn't pretend to be one. It's a low-level GPU abstraction layer that sits directly on top of the Rive Renderer
For a long time, I treated remote MCP servers as a local-first problem. You spin up your server, you run it on localhost, and then you fight the tunneling problem: ngrok here, Cloudflare Tunnel there,
The entire illusion of a "3D holographic card" rests on a lie: that there is actual depth. There isn't. A trading card is, physically, a folded piece of cardboard with some surface coatings. What make
The core mental model I keep coming back to: TeamAI-CLI is a harness, not a cache. A cache holds copies of things; a harness wires a team's shared knowledge into the exact locations where heterogeneou
The first thing to understand about pure VLM segmentation is that there is no mask head, no convolutional decoder, no upsampling feature pyramid. There is only the transformer, the vocabulary it was t
CS223A is not a survey course. It's a rigorous sequence that builds one mathematical edifice: the kinematics, dynamics, and control of serial-chain manipulators, all expressed through a single consist
Most 3D generative models I've worked with are, at their core, trying to solve a geometry problem that has a fundamental constraint built into it. They represent surfaces as continuous scalar fields —
Shot Composer is a browser-based 3D previsualization engine that runs entirely client-side. No install, no account, no backend. You open it, you get a WebGL viewport (Three.js under the hood), and you
ripwire is a single C++23 binary that delivers something most of us assumed required a small infrastructure project: repository-level context for coding agents, computed deterministically, on demand,
The first thing to understand is that when I say "Astra," I mean the coding agent—the multimodal model that takes inputs and writes code—not Google's Project Astra, which is a different thing entirely
The claim that "data labeling is dead" deserves more than a shrug. What's actually dying is the assumption that fine-grained visual perception—the kind required to tell a Celtics player from a Knicks
Let me be precise about what VISTA is, because the name gets thrown around loosely. VISTA is not a model. It's a harness—a structured wrapper that takes a general-purpose VLM and gives it the tools, m
The interesting part isn't that threejs-game-director routes work — it's how it decides what work exists at all. When a task arrives, the director first inspects two artifacts: whether artifacts/game-
The central insight behind Deep Agents' context management is that a conversation summary is not a replacement for the work itself—it's a navigation aid. When I think about what compaction should do,
Before you evaluate the Delta X as a product, you have to understand the kinematic family it belongs to. A parallel-link delta robot is not a serial arm with a different cosmetic design — it's a funda
The most damaging thing an LLM can do with FFmpeg isn't make an error — it's make a confident error. Ask a vanilla agent to "make this video vertical," and it will happily emit a filtergraph that assu
The uncomfortable truth about autonomous software engineering is that the thing we celebrate most—an agent's ability to decompose a vague goal into concrete steps—is exactly what creates the chaos tha
The shift that the skills map forced on me was less about learning new tools and more about abandoning a deeply ingrained habit. For twenty years, "coding" meant opening a file and starting to type. T
The instinct is to think CPU-feasibility is about parameter count, and that's half the story. The other half — the half that actually determines whether you get real-time output on a 4-core VPS — is t
The core constraint of robot learning has never been algorithmic capacity—it's been data. Teleoperating a robot to collect demonstrations is slow, expensive, and requires physical access to hardware.
The end-to-end flow starts with you wearing a pair of Snap Spectacles (2024) running a Lens Studio client. That client is doing two jobs simultaneously: it's your display, and it's your input sensor.
I've spent enough time profiling Three.js sites to know that "it's just one file" sounds like a feature until you actually watch the network panel. The base64 overhead is real and mechanical: every GL
The thing that separates Pixal3D from the earlier wave of image-to-3D models isn't raw speed—it's where the conditioning happens. Most feed-forward reconstruction models take a single image, pass it t
I've spent enough time staring at photogrammetry outputs in DCC tools to have a visceral reaction to them. The raw mesh that comes out of RealityCapture, Metashape, or a Gaussian-splat extraction is,
The simplest way to see what Tardigrade is doing differently is to look at the state object you're holding in memory. In a conventional harness, you've got messages: Message[] and you mutate it every
The first thing that struck me about GenOffice's architecture is that it's not one application pretending to be a suite—it's six Electron-based desktop applications sharing a common core engine layer.
The headline numbers matter here, but less for what they say about raw intelligence and more for what they reveal about design philosophy. A 90.8% on Terminal-Bench 2.1 isn't just a benchmark achievem
The thing that makes Reef interesting isn't any single component—it's the architectural claim that agent improvement should be a live property of the system, not an offline chore. Reef is not a prompt
I've spent enough time staring at raw Whisper outputs to appreciate what Microsoft built here. MAI-Transcribe 2 isn't just another ASR model—it's a complete transcription pipeline compressed into a si
The first thing to internalize about this stack is that it is not a pipeline you can assemble from four interchangeable parts. Each model is solving a fundamentally different problem, and the reason t
TRELLIS isn't a diffusion model in the classic sense. It's a structured latent flow pipeline built around a representation Microsoft Research calls SLAT (Structured LATent). The "structured" part is t
The problem OpenArtifacts solves is one I've bumped into repeatedly: local-first knowledge management and multiplayer agentic collaboration live in fundamentally different worlds, and the bridge betwe
When a vendor claims "100ms latency," they're usually talking about Time to First Audio Byte (TTFB)—the interval between sending text to the TTS engine and receiving the first chunk of audio back on t
The entire Photon thesis rests on a single architectural conviction: that the GPU kernel launch overhead dominating real-time inference is not an unavoidable cost, but a compiler artifact that can be
The first thing that struck me about Lily is not what it does, but what it removed. Most local inference stacks I've worked with are accretions — you start with a tensor framework, add a model loading
When I look at Lily, I don't see a local inference framework. I see an appliance — the local-inference equivalent of a dedicated hardware coprocessor that happens to be expressed as software. Perplexi
