Nia is an API layer that gives agents continuously monitored, always up-to-date context from libraries, research papers, and technical documentation, without hours of manual ingestion. It also works as an MCP server or agent skill for products like Cursor and Claude Code, so coding agents can pull reliable external context on demand and avoid version drift and hallucinations.
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Hey congrats on the launch! I feel like this tool can really go hand in hand with my product would love to try this out and have a chat sometime!
@alan_rivera Thank you so much! I am available 24/7, just DM me on Linkedin: www.linkedin.com/in/arlan-rakhmetzhanov
@divyansh_tiwari7 Thank you for your support!
@arlanrakh congratulations on the launch bro!
Share Code Snippets 2.0
Congrats on your launch @arlanrakh
@rahulit TYSM!!
Very good problem you are trying to address. I face the limitations you mentioned everyday during my interactive programming with AI.
@sadhar Thanks you so much!! Hope you will try it out! Means a lot
Oasi
Congrats on launching Nia! The multi-file references and automated reviews sound like a massive time-saver.
Can’t wait to see it in action!
thank you so much! lmk if you are gonna test it
Great product, i would love to use it with the team. One suggestion: include questions asked in Slack responses, it's now obvious to others
which question was asked
@vladzima Just shipped a new update where Nia recaps the question asked earlier in the thread. It should be live in 5 minutes.
Example:
@Nia AI how does their recommendation algorithm works?
Response from Nia:
Question:
@Nia AI how does their recommendation algorithm works?
Sources:
• twitter/the-algorithm/README.md
• twitter/the-algorithm/follow-recommendations-service/README.md
• twitter/the-algorithm/follow-recommendations-service/README.md
• twitter/the-algorithm/product-mixer/core/src/main/scala/com/twitter/product_mixer/core/pipeline/recommendation/BUILD
• twitter/the-algorithm/follow-recommendations-service/server/src/main/scala/com/twitter/follow_recommendations/flows/post_nux_ml/BUILD
• twitter/the-algorithm/follow-recommendations-service/common/src/main/scala/com/twitter/follow_recommendations/common/base/RecommendationFlow.scala
• twitter/the-algorithm/follow-recommendations-service/README.md
• twitter/the-algorithm/src/scala/com/twitter/recos/user_tweet_entity_graph/RecommendationHandler.scala
• twitter/the-algorithm/src/scala/com/twitter/recos/user_user_graph/RecommendUsersHandler.scala
• twitter/the-algorithm/follow-recommendations-service/thrift/src/main/thrift/recommendations.thriftBased on the repository code, I'll explain how Twitter's recommendation algorithm works, primarily focusing on the Follow Recommendations Service (FRS) as it's one of the main components detailed in the codebase.From the main README.md and follow-recommendations-service documentation, here's how the recommendation system works:1. Overall Architecture
The recommendation algorithm is responsible for serving feeds of Tweets and other content across all Twitter product surfaces (e.g., For You Timeline, Search, Explore, Notifications).2. Follow Recommendations Process
The Follow Recommendations Service (FRS) works through several key steps:a) Candidate Generation
- Located in follow-recommendations-service/common/src/main/scala/com/twitter/follow_recommendations/common/candidate_sources/
- Uses various user signals and algorithms to identify potential accounts to recommend
- Includes methods like:
- Triangular loops
- Two-hop random walk
- User-user graph analysisb) Filtering
- Located in follow-recommendations-service/common/src/main/scala/com/twitter/follow_recommendations/common/predicates
- Applies filtering logic to improve quality and health
- Happens both before and after ranking
- Heavier filtering (higher latency operations) typically occurs post-rankingc) Ranking
- Uses both Machine Learning (ML) and heuristic rule-based ranking
- Process:
1. ML features are fetched (feature hydration)
2. Creates DataRecord for each <user, candidate> pair
3. Sends pairs to ML prediction service
4. Returns prediction score based on:
- Probability of follow given recommendation
- Probability of positive engagement after follow
5. Uses these scores to rank candidatesd) Transform
- Performs necessary transformations on candidates:
- Deduplication
- Attaches social proof (e.g., "followed by XX user")
- Adds tracking tokense) Truncation
- Final step that trims the candidate pool to a specified size
- Ensures only the most relevant candidates are presented3. Social Proof and Recommendations
From the RecommendationHandler.scala, we can see that the system also handles:
- Tweet recommendations
- Social proof hydration
- Different types of recommendations (tweets, hashtags, URLs)The system is highly customizable per use case, with different display locations having their own specific flows and configurations. This allows Twitter to optimize recommendations based on where and how they're being displayed in the product.This is a high-level overview based on the provided repository code. The actual implementation involves many more intricate details and specialized algorithms for different recommendation scenarios.
@arlanrakh Awesome!