Tesla’s Cybercab starts production with record-breaking efficiency numbers

Tesla's legal challenges in China over FSD could set a precedent, impacting its market strategy and consumer trust globally. The post Tesla faces consumer lawsuit in China over Full Self-Driving feature appeared first on Crypto Briefing.

Tesla's FSD approval in Estonia may accelerate EU-wide adoption, enhancing Tesla's market position and revenue potential in the competitive EV sector. The post Estonia clears Tesla Full Self-Driving for roads, rollout to begin soon appeared first on Crypto Briefing.

Why it matters: Discover how AI powers autonomous vehicles in 2026. Explore Waymo, Tesla, Baidu strategies, NVIDIA Alpamayo models, safety data, and the $5.4T market ahead.

If Tesla and SpaceX merged—as is a reported possibility—then Elon Musk's combined entity would hold billions of dollars' worth of Bitcoin.

Good models don't save bad architecture, and most teams learn that the hard way. The post Most AI Agents Fail in Production Because They’re Built Backwards appeared first on Towards Data Science.

The gap between what agentic AI promises and what it actually delivers in live environments is now one of the most consequential engineering problems in the industry. It is also, frustratingly, one that the field has been slow to name precisely, let alone fix...

AI agents look brilliant in a demo because demos are friendly worlds. The data is curated, the tools behave, and nothing important changes while the agent is in mid-thought. Production is the opposite: data arrives late, facts conflict, permissions bite, APIs time out, and the underlying state changes constantly. That gap is why early “agents in production” often get scoped down to something safer: read-only assistants, human-in-the-loop workflows, or narrow domains with heavily curated data. Several high-profile deployments have also been scaled back after meeting messy real-world constraints. Rather than being a verdict on autonomy, these stumbles are a reminder that autonomy is unforgiving. Small cracks in your data stack become large cracks in agent behavior. The same pattern shows up whenever agents move from toy workflows to systems with real state. As scope increases, weak guarantees create predictable symptoms: overconfident actions on stale data, brittle reasoning when meaning

Google has introduced Agent Executor, an open source runtime aimed at helping enterprises run AI agents more reliably at scale, as attention shifts from building agent prototypes to managing the operational challenges of putting them into production. To address those production-related challenges, the runtime, according to the company, comes with capabilities that are geared towards supporting long-running and distributed agent workflows. Typically, long-running agent workflows are AI-driven tasks that execute over extended periods, from minutes to days, often involving multiple steps, system interactions, pauses for human input, or recovery from interruptions before reaching completion. For such workloads, the runtime includes support for durable execution, allowing workflows to resume after outages or human approvals, along with secure sandboxing for isolating agent components, session consistency controls for distributed workflows, and connection recovery features intended to preser