software-engineering.
106 writings found
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Why Tokenmaxxing Misses the Mark for AI Coding
Token optimization in AI agents creates perverse incentives. Real value comes from measurable outcomes: PR merges, release velocity, and actual developer productivity gains.
Making Teams AI Native: Where Testing Becomes the Bottleneck
How agentic engineering is shifting computational bottlenecks from coding to validation, and why robust testing is now the competitive advantage for AI-native teams.
Meta's Multi-Stage Sequence Learning: What It Means for Recommendation Systems
Meta achieves LLM-style scaling laws in ads recommendations through a two-stage sequence model. Here's what this architecture teaches us about scaling complex ML systems.
Enterprise AI Needs a Memory Problem Solved
Stack Internal's new platform tackles enterprise knowledge fragmentation, turning distributed team context into decision-grade information for AI agents and humans alike.
Why AI Adoption is Stalling: The Context Engineering Problem
The real bottleneck in AI adoption isn't model capability. It's context engineering. Here's why developers need to care about this emerging discipline.
Why AI Adoption Is Stalling: The Context Engineering Problem
The real bottleneck in AI adoption isn't capability, it's context. Michael Foree explains why connecting AI to your actual work is harder than it should be.
Meta's Hierarchical Interest Representation: A Shift in Graph-Based Ranking
How Meta's new representation layer tackles sparse signals in deep funnel ads using hierarchical clustering, multimodal features, and efficient transformers.
Meta's Hierarchical Interest Representation: What It Means for Ad Tech
Inside Meta's new representation learning system that maps user intent across billions of entities. A deep dive into graph learning at scale.
How AI Agents Are Reshaping Developer Work and Career Paths
Agentic coding is shifting development toward strategy while increasing decision fatigue. Why human judgment and community matter more than ever.
Building AI responsibly: lessons from Microsoft's NIST approach
Sarah Bird on why irresponsible AI stems from experimentation without impact consideration, and how developers can adopt NIST principles for thoughtful AI workflows.
Building Responsible AI: The NIST Framework and Developer Accountability
Microsoft's Chief Product Officer for Responsible AI discusses the NIST approach, why experimentation without impact consideration breeds irresponsible systems, and human-AI workflow design.
Meta's Hierarchical Interest Representation: A New Approach to Graph-Scale ML
Exploring Meta's breakthrough in recommendation systems that combines sparse engagement signals with world knowledge to power ads across billions of users.
Building Responsible AI with NIST Approach
Exploring Microsoft's approach to responsible AI with insights from Sarah Bird.
How Meta Rebuilt Its Storage Stack for AI Workloads
Meta rearchitected its BLOB storage layer to eliminate GPU stalls and cut data ingestion times. Here is what it means for AI infrastructure at scale.
How Meta Cracked the Zero-Notice Disaster Problem: Instantaneous PowerLoss Storms
Inside Meta's extreme disaster testing and how they solved the ultimate chicken-and-egg problem