software-engineering.
24 writings found
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When Machines Write Code, Humans Must Learn to Judge
As LLMs generate more code, teams face cognitive surrender and debt proliferation. The future isn't about writing code, it's about verification.
Meta's KernelEvolve: When AI Writes Its Own Performance Code
Meta's KernelEvolve system uses AI agents to automatically optimize low-level hardware kernels, achieving 60% performance gains in hours instead of weeks.
Meta's Adaptive Ranking Model: The Real Cost of Serving Trillion-Parameter Ads
Meta scaled ads recommendations to LLM complexity while keeping latency under a second. Here's why their inference trilemma solution matters beyond advertising.
Treating AI Instructions as Infrastructure, Not Documentation
How encoding team standards as versioned AI instructions solves the consistency problem that plagues AI-assisted development workflows.
Making Team Standards Executable: Infrastructure for AI-Assisted Development
AI coding tools produce wildly different results based on who's prompting. Treating team standards as versioned, executable instructions solves the consistency problem.
Meta's AI is Reshoring American Concrete, One Mix at a Time
How Bayesian optimization is helping U.S. concrete producers ditch imported cement and redesign mixes in days instead of months.
How Facebook Built Friend Bubbles: A Deep Dive into Social ML Architecture
Meta's friend bubbles system combines closeness prediction models, ranking optimization, and performance engineering to surface friend-driven content at scale.
Facebook's Friend Bubbles: When Social Graphs Meet Recommendation Systems
Meta's friend bubbles on Reels reveal how social signals and ML models can coexist in video recommendations without destroying performance.
Facebook's Friend Bubbles: A Masterclass in Social Graph ML
How Meta blends closeness prediction models, multi-task ranking, and prefetch optimization to surface friend-driven content at scale on Reels
Facebook's Friend Bubbles: When Social Graphs Meet Video Recommendations
Meta's approach to blending relationship closeness with content relevance reveals hard truths about building social features at scale.
Patching Security Holes at Scale: When You Have Millions of Lines of Mobile Code
How Meta automates security fixes across massive mobile codebases using AI and custom tooling. A glimpse into enterprise-scale vulnerability management.
Automating Security Fixes at Billions-of-Users Scale
How Meta's security team uses AI to patch vulnerabilities across millions of lines of mobile code without driving engineers insane.
Meta Killed Their FFmpeg Fork and That's Actually Great News
How Meta's collaboration with FFmpeg developers brought multi-lane encoding and real-time quality metrics to everyone, not just billion-user platforms.
Meta Ditched Their FFmpeg Fork and What It Means for Video Infrastructure
How Meta deprecated their internal FFmpeg fork by pushing critical features upstream, and why this matters for the future of video processing at scale.
Meta's RCCLX: Why AMD's GPU Communication Stack Just Got Interesting
Meta open-sources RCCLX with Direct Data Access and FP8 collectives for AMD GPUs. A deep look at what this means for multi-GPU AI workloads.