Issue: Strike price is "TBD within 10 business days." Contract formation fails; 409A violation — strike must equal FMV on grant date.
Fix: Get 409A appraisal NOW, set specific dollar figure in Warrant Agreement before signing.
Issue: DGCL Section 157 requires board authorization. CEO cannot unilaterally issue warrant. Warrant is voidable without board resolution.
A comprehensive guide to understanding how neural prosthetics work, the market landscape, and how to build your own.
Deep-research note on whether to add a ComfyUI-style image-to-mesh backend to the PartField repo, and if so, which one(s). Written April 2026.
We already have it. The PartField repo has Microsoft TRELLIS-image-large fully wired up via trellis_manager.py and the POST /trellis/image_to_3d FastAPI endpoint (api.py:430), with GLB + Gaussian-PLY output, TTL-based GPU offload, and an async job queue. This is the same model most "serious" ComfyUI image-to-3D workflows use today.
Goal: $1M–$10M in orders by ~July 2026. Pre-silicon chip startup. FPGA dev kits + NRE + silicon LOIs with deposits.
Product: Transformer-optimized edge AI inference chip. <5W, <$10/chip, 20x Jetson Orin Nano, PyTorch-native. FPGA prototype shipping now via GitHub. First silicon Q1 2026 (GlobalFoundries 12nm).
Target segments: humanoid/industrial robotics, defense drones/autonomy, AVs, edge vision, wearables.
Two-gate review: Nur screens first, then a 3-person panel (Devrim/Harrison/Yan) must unanimously approve before the candidate advances.
flowchart TD
zapier_source([Zapier Integration<br/>roster.so, LinkedIn, manual])
sourcing_task[/Source Candidates<br/>nur, recruiter — 10/day/]
new[New Candidate<br/>+ai_score]
initial_review{Initial Review<br/>nur}
The /compose-2 system generates complete residential floor plans procedurally — including layout, furniture, decorations, and dynamic objects — and renders them as an interactive 3D scene with primitive geometry. Every spatial decision is captured in a deterministic pipeline driven by a seeded PRNG, so the same seed produces the same house. The output is fully described in YAML so the data is portable to other consumers (game engines, simulators, asset pipelines).
This document describes the architecture in depth, including the data flow, the constraint system, the rendering strategy, the validation pipeline, and a roadmap of future additions.
Building an AI agent that can interpret natural language like "create a scene where I'm going to assemble legos" and produce a fully realized 3D environment with physics-ready objects, lighting, and scripted behaviors is one of the most demanding applications of agentic AI. It combines the hardest problems in the field: multi-step planning over ordered physical constraints, retrieval over structured asset catalogs with physics metadata, code generation in a domain-specific context (C# game scripts), and tight tool integration with a real-time engine.
This article examines how leading AI-powered creation tools—Cursor, Devin, Replit Agent, GitHub Copilot, Bolt.new, and Vercel v0—architect their backends, and distills the patterns that matter for a 3D scene generation agent embedded in a C++ game engine. We compare seven major agent frameworks (LangChain/LangGraph, CrewAI, AutoGen/Semantic Kernel, DSPy, H
Date: 2026-04-15 Scope: Evaluate approaches for an AI agent that creates interactive training scenes for robots in LuckyEngine
LuckyEngine is a C++ physics simulator (Vulkan rendering, MuJoCo physics, entt ECS) with a Python SDK (LuckyRobots) and RL/IL framework (LuckyLab). Users need to create diverse, physically-plausible training environments for robots — kitchens, warehouses, obstacle courses, manipulation tasks, etc.
LuckyLab is an IsaacLab-inspired framework that decomposes the MDP into 5 manager classes, wraps two RL backends (skrl, SB3) and one IL backend (LeRobot), and provides config-as-code for a single working task (Go2 velocity tracking with SAC). It is architecturally coherent — the patterns are internally consistent — but the framework-to-value ratio is terrible. 9,934 lines exist to do what the proposed sim API redesign achieves in ~4 lines of user code.
These are actual bugs, not style complaints: