Ever seen a cool event on a website but couldn’t find the calendar invites?
This chrome extension automatically scrapes events on any website and adds them to your calendar. Way more convenient than adding them manually https://t.co/WLfAJCZgsPtwitter.com/i/web/status/1…
3/ MyMap
Summarize any website and turn it into a beautiful, easy to digest slide show of the key points
@victorzhrn https://t.co/kvICFGaH6jtwitter.com/i/web/status/1…
4/ Playtest
Turn any website into an interactive mini-game
Powered by @AnthropicAI’s Claude, make your web content more engaging with 1 line of Js https://t.co/cjcoZwTLEVtwitter.com/i/web/status/1…
5/ Anything Protocol
AI Zapier to automate your life and business easily. Open source, hackable workflow protocol
@carllippert
6/ Silic
Generative AI for clothing. Design, model, and print brand new garment designs on demand
@AiSilic https://t.co/Iy09V6MnuAtwitter.com/i/web/status/1…
@AiSilic That’s it for this week
Also, I didn’t present this time, but check out .
Follow @AlexReibman for on-the-ground hack reports and to see what’s up next timeagentops.ai
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The #NobelPrize in economics was just awarded to 3 top economists. #EconTwitter seems to be over it, but the data science/ML community is totally missing out!
Here's why Data Scientists should start paying attention and what they can take away 🧵
The prize was awarded to David Card, @metrics52, and Guido Imbens for their monumental contributions to statistical methodology and causal inference.
They used and developed strategies that were a true paradigm shift bridging the gap between data and causation in economics
One part of the prize went to David Card from UC Berkeley.
Card is most well-known for his famous minimum wage study that paradoxically revealed that an increase in the minimum wage did *not* reduce employment. How?
The study applied a strategy called Difference in Differences
Big tech teams win because they have the best ML Ops. These teams
- Deploy models at 10x speed
- Spend more time on data science, less on engineering
- Reuse rather than rebuild features
How do they do it? An architecture called a Feature Store. Here's how it works
🧵 1/n
In almost every ML/data science project, your team will spend 90-95% of the time building data cleaning scripts and pipelines
Data scientists rarely get to put their skills to work because they spend most of their time outside of modeling
Enter: The Feature Store
This specialized architecture has:
- Registry to lookup/reuse previously built features
- Feature lineages
- Batch+stream transformation pipelines
- Offline store for historical lookups (training)
- Online store for low-latency lookups (live inferences)