Apify Team Builds Event Demo Entirely on Its Platform
Key takeaways
- Apify offers a comprehensive platform for building and running applications.
- Internal dogfooding effectively validates a platform's capabilities.
- Event demos can be powerful tools for showcasing product features.
- The platform supports various automation and data extraction tasks.
Who benefits
Summary
The Apify team successfully created a "Wishlist Raffle" event demonstration, showcasing its platform's comprehensive capabilities by building the entire demo using only Apify's own tools and infrastructure.
Why it matters
This demonstration provides a tangible example of a platform's capabilities, offering insights into practical application and potential for developers and businesses seeking automation and data solutions.
How to implement this in your domain
- 1Explore Apify's platform to understand its features for data extraction and automation.
- 2Identify internal business processes that could benefit from automation using a similar platform.
- 3Develop a proof-of-concept project using Apify to test its suitability for specific tasks.
- 4Evaluate the platform's scalability and integration potential with existing systems.
Original post by Kevin Lewis
"How the Apify team built the Wishlist Raffle booth demo on nothing but the platform it's showing off."
View on XOriginally posted by Kevin Lewis on X · view source
Want to go deeper?
Turn these trends into skills with Learnijoy's hands-on AI & tech courses.
Explore coursesMore in AI Engineering & DevTools
Zapier vs. Tray: Enterprise Automation Platform Comparison for 2026
This post compares Zapier and Tray.io, evaluating which platform is better suited for enterprise automation needs by balancing power and ease of use. It argues that the best tools scale for complex requirements while remaining intuitive for all users.
New Optimizer Accelerates LLM Pretraining with Curvature-Conditioned Momentum
This research proposes a curvature-conditioned multiscale momentum method with sphere constraints to accelerate large language model pretraining. It addresses challenges from noise-dominant gradients and ill-conditioned loss landscapes by enhancing progress along flat directions, significantly improving upon existing adaptive optimizers like AdamW and Muon.
Euclidean Fourier Neural Operators Enhance Domain Transferability
This paper introduces Euclidean Fourier Neural Operators (EFNOs) as a domain-independent alternative to traditional FNOs, addressing their limitation in transferring across different periodic domains. EFNOs achieve this by parameterizing the spectral kernel as a continuous function of the physical wavevector, enabling consistent operator learning across varying domain shapes and sizes.