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Every published item, in order · 7184 items
Oct 19
IndustryUS vs ChinaSelected23:52
NetEase LobsterAI releases second China office Agent user behaviour report

Cache hit rate alone is a poor proxy for cost: models with nearly identical hit rates showed a more than seven-fold gap in what users actually paid versus list price, making DeepSeek's pricing structure unusually favourable for long agent tasks. Teams deploying office agents in China can use the per-model effective-cost figures to pick cheaper backends, with LobsterAI reachable via desktop, WeChat and Feishu. The report is self-reported product data, so treat the absolute numbers as one product's view rather than market-wide evidence.

Event
宝玉@doteySource
IndustryBuildersMulti-sourceSelected23:46
Latent.space Retrospective of 2026 Technical AI Themes

The month-by-month structure makes it a compact index of which model releases, acquisitions and infrastructure shifts actually dominated 2026, useful for spotting what faded versus what compounded. Builders can mine it for tooling trends like MCP Apps and agent dispatch patterns. Chinese labs appear repeatedly through DeepSeek, Kimi and GLM releases, giving a side-by-side view against US lab moves.

Event
Latent.Space@latentspacepodSource
IndustryBuildersMulti-sourceSelected23:34
Town CEO Jean-Denis Greze Explains Why He Shut Down a Profitable AI Tax Startup

The interview lays out a rare first-person account of killing a business that looked healthy on paper because AI shifts the moat toward incumbents with existing distribution. It also explores the awkward position of application startups that compete with OpenAI and Anthropic while paying them for tokens, and why the forward-deployed engineer model weakens as models improve. Founders weighing vertical AI plays and operators deciding when to pivot will find concrete decision frameworks, including how a US$1/month AI routine can save a restaurant thousands.

Event
a16z@a16zSource
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IndustryBuildersMulti-sourceSelected13:42
Notion AI lead argues agents should run on CPUs, not GPUs

The insight is that agent reliability may come from boring determinism — deterministic code paths for repetitive steps — rather than routing everything through an LLM call, which cuts token costs and latency. Builders designing agent workflows should audit which steps can become plain scripts or CPU-served logic, and consider giving agents a local file system for intermediate state. The claim offers a practical counterpoint to GPU-heavy agent architectures gaining traction across the industry.

Event
Akshay Kothari@akothariSource

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