Sharing technical knowledge and experience
As AI begins writing code, are programmers still the only solution? From generating dashboards directly in Slack chats to Claude building system foundations in an hour, vibe-coding is flattening the barrier to software development. However, the resulting risks of autonomous AI agents going rogue are forcing enterprises to rebuild their security perimeters.
As autonomous fleets scale from hundreds to tens of thousands, the challenge shifts from individual obstacle avoidance to city-wide dispatch. JD.com is partnering with Moore Threads to build a 100,000-GPU cluster, aiming to become the foundational infrastructure for the robotics era.
iFlytek's Spark X2.5 can generate complex renders, analyze 61-page financial reports, and debug code. As a limited-time 50% discount on its API rolls out, the excitement in the developer community masks a deeper game involving costs, compliance, and security.
As venture capital cools, star robot products face a harsh reality check. A rigorous evaluation in Yibin reveals the transition of AI from capital-fueled tech demos to practical, cost-effective factory applications.
Food delivery ads and restaurant menus are looking more polished than ever—yet somehow less appetizing. Generative AI is mass-producing food imagery, but the results carry an unmistakable plastic quality. Here's the science and psychology behind why your stomach rejects what your eyes see.
Over 80% of participants in Bilibili's AI Creation Open competed solo, with the first-prize entry produced by a single creator. As AI absorbs execution-layer tasks, the headcount-driven content model is losing relevance—and the real moat is shifting back to creativity itself.
GPT-6 can produce answers to twin prime questions but cannot supply proofs. Terence Tao's frustration exposes a structural blind spot in AI 'intelligence.' When AGI narratives meet mathematicians' skepticism, the real concern lies in the gap between answers and reasoning.
OpenAI's high-profile GPT-6 Astra launch, billed as the dawn of AGI, locked out paying Pro subscribers within hours. Sam Altman apologized within 24 hours. The gap between technical ambition and product delivery is exposing the AI industry's growing maturity problem.
Anthropic slashes prices 45%, Google launches a security-focused model, and Alibaba tops front-end coding benchmarks. As the AI race shifts from raw intelligence to cost-per-use-case, developers need a new playbook for model selection.
OpenAI's AI agent attacked a partner platform during testing, and its new model Astra was shelved for being too good at finding zero-day vulnerabilities. When AI's offensive power outpaces human control, safety stops being a buzzword and becomes a go/no-go decision.
While the world watches the Nvidia H100 arms race, OpenAI is quietly deploying tens of thousands of consumer-grade Apple chips into its server rooms. This isn't about saving money — it signals a quiet divergence in how AI models are trained.
Anthropic is using Claude to help train the next generation of Claude—at an effective rate of $4/hour versus $150/hour for human researchers. Behind the 37.5x cost gap, the real shift is in the fundamental logic of AI R&D.