标签：#trans

Musk kept inflating the already-big promises being made by SpaceX CFO Bret Johnsen and Gwynne Shotwell on the company's first call.

Lucid's new CEO Silvio Napoli listed four must-win priorities, including the successful launch of its midsize EV, finishing a factory in Saudi Arabia, cutting expenses, and robotaxis.

The USB port in your car is likely not powerful enough to charge your laptop, but there are other options.

BMW is forcing a weird Spider-Man ad onto its dashboard displays

Waymo has dropped the waitlist for its robotaxi service in Dallas, the latest step in the company's bid to scale its self-driving technology across the United States, U.K., and Europe.

When a premium car brand like BMW says it has a "special surprise" in store for drivers, I'd expect something more luxurious than having a movie commercial beamed onto the dashboard. That's exactly what's happening to many BMW owners, however, who are being shown banner ads for Spider-man: Brand New Day on their Control Display […]

An analysis of the last seven years of Tesla earnings calls shows just little attention Musk pays to Tesla's car business.

Chris Richardson discusses leveraging Team Topologies and internal platforms to accelerate microservices delivery. He explains six key platform patterns - from security and observability to build and deployment - and shares strategies for minimizing cognitive load on stream-aligned teams while avoiding common platform engineering pitfalls. By Chris Richardson

There are several steps you can take to prevent your phone from getting dangerously hot.

Arun Joseph shares real-world insights on scaling enterprise agentic platforms like Deutsche Telekom’s LMOS. He discusses bridging organizational fault lines, replacing tool sprawl with core platform abstractions, and moving beyond basic chatbots to operational intelligence systems through ephemeral agents and an Agent Definition Language (ADL). By Arun Joseph

Welcome back to TechCrunch Mobility, your hub for the future of transportation and now, more than ever, the role AI is playing in it.

The automaker is gearing up to make some big changes to its EV line in the near future. Here's what to expect.

Uber has partnered with — and in some cases made direct investments in — about 30 autonomous vehicle companies over the past two years. Here's the list and the latest on the partnerships.

Also has big plans beyond the TM-B. The startup mostly refers to itself as a "vehicle" company, and has plans to make four-wheel pedal-assist cargo vehicles for Amazon.

The National Highway Traffic Safety Administration (NHTSA) is probing nearly 1.2 million Tesla vehicles after receiving complaints about a suspension failure that could cause "a loss of vehicle directional control," as reported earlier by Reuters. The preliminary investigation includes the 2018-2020 Model 3 and 2021-2023 Model Y, according to a filing from the NHTSA's Office […]

The leading U.S. automakers are mentioning EVs on their investor calls at pre-pandemic rates, according to new data from TechCrunch and Hudson Labs.

Tesla had already reportedly prepped for the idea in the event that Beijing invades Taiwan.

The NHTSA has given Amazon's Zoox permission to charge for rides. It's the first AV with no manual controls to get it.

Last week, a Baseten inference engineer who goes by @waterloo_intern published a technical blog post titled "22,580: From GPT-2 to Kimi K3, Explained." It hit 2.4 million views in days. He didn't write a press release. He wrote runnable PyTorch code — starting from GPT-2's attention block, stepping through every architectural change, explaining one problem and one cost per iteration. It's the best transformer lineage explanation I've seen. I devoured his post, then cross-checked the key claims against 5 original papers. Here's the full picture. The 22,580x Number In February 2019, OpenAI released GPT-2 — 124M parameters. Seven years later, Moonshot AI open-sourced Kimi K3 — 2.8T parameters. You could fit 22,580 GPT-2s inside one Kimi K3 . But this isn't a "throw more compute at it" story. It's a story about how we store, update, and retrieve memory . Starting Point: GPT-2 class Block ( nn . Module ): def forward ( self , x ): x = x + self . attn ( self . ln_1 ( x )) x = x + self . mlp ( self . ln_2 ( x )) return x Every time the model generates a new token, it recomputes Q, K, V projections for all historical tokens, then runs an O(N²) softmax attention. K and V from tokens 1 through N-1? Thrown away. Token N+1 arrives? Recompute everything. That's why KV Cache was invented. KV Cache: Store It, Don't Recompute Simple idea: cache the already-computed keys and values. For the next token, new Q only needs one dot product against the cached K. Problem solved — but a new one created. KV cache grows linearly with sequence length. At 1M tokens × d_model × layers, that's dozens of GB of VRAM. Every decoding step reads all of it from HBM. The bottleneck isn't compute. It's memory bandwidth. This is the key to understanding every improvement that follows. Linear Attention: Fixed-Size Memory Can we compress O(N²D) into O(ND²)? The idea: replace softmax with a feature map. # Standard softmax (must materialize N×N first) attention = softmax(QKᵀ / √d) × V # Linear attention (fold

Florida wants to use federal EV charger funds to build an air taxi network connecting golf courses, luxury apartment buildings, and airports.