Tesla

Enterprise Agents Have a Structure Problem - Ishita Daga, Tesla

Jul 20, 2026 · 12:08

Ishita Daga, a senior machine learning engineer at Tesla, argues that enterprise agents fail because of three structural problems — ambiguity, staleness, and preference — rather than needing bigger models or more RAG. For ambiguity, she proposes a hierarchy of sources of truth: a curated semantic layer (best for known KPIs), canonical tables (parametric queries for flexibility), and a database graph (full schema but hard to maintain). To solve staleness, she recommends a context lifecycle embedding live data sources (GitHub, CRM, semantic layers) and a feedback loop that logs events, evaluates agent performance, and updates context automatically. On preference, she notes that different teams calculate the same metric differently (e.g., average milestone time by start vs. completion) and that current solutions like semantic layers or agent memory still fail to capture user-level routing, calling this an open problem requiring further research.

Your Personal Open-Source Humanoid Robot for $8,999 — JX Mo, K-Scale Labs

Jul 2, 2025 · 19:26

Jingxiang Mo, founding engineer at K-Scale Labs, introduces their open-source humanoid robots: the 5-foot K-Bot (pre-order for $8,999, delivered by October) and the 1.5-foot Z-Bot, both fully open-source from hardware to ML models. The K-Bot uses MIT CHiTA actuators, provides up to 250 TOPS compute, runs an RL-based whole-body controller trained with MJX in 1-2 hours, and offers a Python/Rust SDK (pip install kos) with a digital twin simulator for rapid development. Mo emphasizes modularity—swappable end-effectors and upgradable heads—and positions K-Scale as the first US consumer humanoid robotics company, targeting developers and households. The robots are significantly cheaper than competitors (Tesla Optimus at ~60K, Unitree at 40K) and include VR teleoperation, OTA software updates, and a bimonthly hackathon community.