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最近我也在思考 Workflow 和 Agent 到底什么关系，我的一个初步想法： Workflow 本质上是工具，只是工具中用到了 AI 能力，所有能被定义成 Work Flow 的就应该能被做成工具。 Agent 更像是 AI，它能主动规划、去调用工具，Workflow 应该是 Agent 的一个可以被调用的工具。 你怎么看？
@frxiaobei 的观点：
I Le discours tenu par les « biens pensants » du gouvernement et des éditorialistes des médias sur le travail et les français commençant à me chauffer le foie, la rate et tutti quanti, petit #Fil ou #Thread pour remettre les choses au point (@pascalperri)
II Quelques faits : Le temps de travail par actif des français est supérieur (1489h) à celui des allemands (1335h). Le contrat de travail français prévoit en moyenne 38,2 heures par semaine, contre 36,6 heures pour les Allemands,
He visto y escuchado últimamente varias justificaciones de la intervención del Estado para proveer ciertos bienes públicos, sosteniendo que sin el Estado no existirían dichos bienes por la presencia de free-riders o gorrones. Hilo corto. 🧵
El fenómeno del free-rider ha sido sobredimensionado. Lejos de ser un problema grave, su conducta es económica, legal y ética: no viola derechos, no impone costos ni consume bienes económicos en sentido estricto.
Claude Code is All You Need When I first joined Anthropic I was surprised to learn that lots of the team used Claude Code as a general agent, not just for code. I’ve since become a convert! I use Claude Code to help me with almost all the work I do now, here’s how:
Why? In Claude Code Everything is a File, and it knows how to use your computer like you do. Name your files well, and CC will be able to search them like you would. This lets you make custom setups for memory, todos, journals, screenshots and more.
Congrats to @NVIDIA, the first public $4T company! Today, compute is 100000x cheaper, and $NVDA 4000x more valuable than in the 1990s when we worked on unleashing the true potential of neural networks. Thanks to Jensen Huang (see image) for generously funding our research 🚀
Blog posts on relevant milestones, with links to the original references: 2010: Breakthrough of end-to-end deep learning on NVIDIA GPUs. Our simple but deep neural network (NN) on GPUs broke the MNIST benchmark. No incremental layer-by-layer training. No unsupervised pre-training https://t.co/MfcBRTf2qm 2011: DanNet on NVIDIA GPUs triggers deep CNN revolution https://t.co/g0A05dlETs 2011: DannNet, the deep convolutional NN, wins Chinese handwriting competition https://t.co/cfc4rhtPon 2011: DanNet achieves first superhuman visual pattern recognition https://t.co/MHpWsQmaAd March 2012: DanNet becomes first NN to win an image segmentation competition https://t.co/tUcK9v0Z3n Sept 2012: DanNet becomes first NN to win a medical imaging contest https://t.co/sclXwEyT0Y May 2015: Highway Networks - over 10x deeper than previous neural nets, based on LSTM's 1991 principle of residual connections. Open-gated variant: ResNet (published 7 months later). Deep learning is all about depth. LSTM: unlimited depth for recurrent nets. Highway Nets: for feedforward nets https://t.co/Mr46rQnqPC 2017: history of computer vision contests won by deep CNNs on NVIDIA GPUs https://t.co/VxZOIF4ALo 2022: ChatGPT uses principles of 1991 (when compute was 10 million times more expensive than today) - the 1991 system is now called an unnormalised linear Transformer. Tweet: https://t.co/loW60fKCyU Overview: https://t.co/jYOUdmqZUM 2022: annotated history of modern AI and deep learning https://t.co/Ys0dw5hkF4 Today's training sets are much bigger: in 2010, it was just MNIST, now it's the entire Internet!
Harry and Meghan's right-hand man appears to extend an olive branch to two senior royal household staff including William's aide Jason Knauf who exposed Meghan Markle 'bullying' allegations
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