- Hungry Minds
- Posts
- ๐๐ง LinkedIn Latency 101: 1 Trick to Serve Over 1B Users
๐๐ง LinkedIn Latency 101: 1 Trick to Serve Over 1B Users
PLUS: Nvidia Beats Tech Giants ๐คฏ, Essential Caching Strategies ๐๏ธ, Redis: Distributed Deep Dive โก
In partnership with
Happy Monday! โ๏ธ
Welcome to the 1385 new hungry minds who have joined us since last Monday!
If you arenโt subscribed yet, join smart, curious, and hungry folks by subscribing here.
๐ THIS WEEKโS MENU ๐ฅ
๐ LinkedIn reduces latency, essential caching strategies, and master Redis: essential developer skills
๐๏ธ Nvidia surpasses tech giants, Anthropic launches new model, and a new company from OpenAIโs co founder
๐จ๐ปโ๐ป Quick byte: TensorBoard visualizes machine learning experiments in PyTorch
Reading time: 5 minutes
A few words from our sponsor this week:
roadmap.sh is a community effort to create roadmaps, guides, and other educational content to help guide developers in picking up a path and guiding their learnings.
Itโs currently the 6th most starred project on GitHub! ๐คฏ
Join for free today! โก
Food for Thought
A mindset, an example, and an action item to start the week
"The best way to predict the future is to invent it."
Mindset: Proactive innovation shapes destiny; don't wait for change, create it.
Example: Apple's iPhone revolutionized mobile technology, embodying Kay's philosophy of inventing the future.
Action item: Identify one process in your work that needs improvement and brainstorm three innovative solutions.
The Rabbit Hole
Deep dives, trends, and resources curated to stay ahead
๐พ SIDE DISHES ๐พ
COURSE (reinforce it)
5 Free Courses on Reinforcement Learning
GITHUB REPO (woo-haa)
A next-generation crawling and spidering framework
ESSENTIAL (leaky)
How to track a memory leak end to end
ARTICLE (lang-drop)
Why we no longer use LangChain for building our AI agents
ESSENTIAL (senior?)
Senior Engineer Fatigue
ARTICLE (CSS inline?)
Inline conditionals in CSS
ARTICLE (1.2B daily)
Scaling smoothly: RevenueCat's data-caching techniques for 1.2 billion daily API requests
ARTICLE (js wizardry)
How to compose JavaScript functions that take multiple parameters
GITHUB (cognitive optimization)
Cognitive load is what matters in the end
The Weekly Digest
Software, AI, and startup news worth your time
Brief: Nvidia's market cap surpasses tech giants, reaching $3.335 trillion through a 160% surge in share price in 2024 post 10-for-1 share split.
Brief: Ilya Sutskever, co-founder of OpenAI, launches Safe Superintelligence Inc. (SSI), a startup solely dedicated to creating a secure and robust AI system prioritizing safety standards over commercial pressures.
Brief: SpaceX introduces a compact and affordable satellite internet antenna, expanding its Starlink service to provide high-speed internet access in remote areas.
Brief: Claude 3.5 Sonnet sets new industry benchmarks in AI with exceptional reasoning, knowledge, and coding proficiency, outperforming predecessor models and delivering cost-effective pricing.
Brief: Meta introduces the Threads API at Cannes Lions Festival, offering developers tools to create bespoke integrations, manage content presence, and facilitate engagement through post publishing and reply management.
Brief: Netflix introduces Netflix House in Philadelphia and Dallas, transforming former department stores into immersive venues merging art, dining, and shopping experiences showcasing beloved Netflix content.
The Quick Byte
One coding tip because youโre technical after all
This weekโs coding challenge:
This weekโs tip:
TensorBoard provides visualization and tooling needed for machine learning experimentation in PyTorch. It tracks metrics like loss and accuracy, visualizes the model graph, and shows parameter distributions, helping in debugging and optimizing models.
Wen?
Model Training Monitoring: Track progress of training over epochs to see if the model is improving and converging.
Parameter Tuning: Monitor changes and impacts of hyperparameter adjustments on model performance.
Visualization of Model Graphs: Helps in understanding and debugging the model architecture.
Why?
Insight into Model Behavior: Provides insights into the training process, helping identify issues like overfitting or underfitting.
Optimization: Facilitates the optimization of models by fine-tuning based on visual feedback from performance metrics.
Debugging: Enhances debugging capabilities by visualizing computational graphs and tracking variable changes throughout the training process.
The Job Feast
A fresh cheese job board if youโre looking for a change
Burp-A-Laugh
The most important meal of your day
Thatโs it for today! โ๏ธ
Enjoyed this issue? Send it to your friends here to sign up, or share it on Twitter!
If you want to submit a section to the newsletter or tell us what you think about todayโs issue, reply to this email or DM me on Twitter! ๐ฆ
Thanks for spending part of your Monday morning with Hungry Minds.
See you in a week โ Alex.
Icons by Icons8.
*I may earn a commission if you get a subscription through the links marked with โaff.โ (at no extra cost to you).