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📚 Software Engineering Articles
Cybersecurity for MCPs
Memory maps boost Go file access speed by 25x
The hidden costs of mandatory code reviews
Team ditches Next.js App Router after one year
Essential traits of great senior data engineers
Measuring engineering productivity through practical metrics
🗞️ Tech and AI Trends
Meta cuts 600 AI roles amid research reorganization
Google's Quantum Echoes breakthrough accelerates real-world quantum computing
Anthropic launches Claude Code for web development
👨🏻💻 Coding Tip
What is pin protection in Rust?
Time-to-digest: 5 minutes
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Deep learning powers everything from fraud detection to dynamic pricing at Uber, but these models are black boxes. Engineers and stakeholders constantly ask: "Why did the model make this decision?" Without answers, trust erodes and debugging becomes guesswork. That's why Uber's Michelangelo team built Integrated Gradients (IG) explainability directly into their ML platform—making it possible to understand model decisions at massive scale.
The challenge:
Build a production-ready explainability system that works across multiple frameworks (TensorFlow and PyTorch), handles complex multi-layer architectures with embeddings, and computes attributions fast enough to fit into existing ML pipelines without becoming a bottleneck.
Implementation highlights:
Wrapped battle-tested libraries: Leveraged Alibi for TensorFlow and Captum for PyTorch instead of building from scratch, then added a unified wrapper layer for consistent attribution across frameworks
Config-driven integration: Made IG declarative through YAML configs—teams enable explainability by editing the same files they already use for training and deployment
Parallelized with Ray: Cut attribution runtime by 80%+ using Ray's task-parallel execution to distribute workloads across CPU and GPU workers
Multi-layer attribution support: Tracked gradient flows through embedding layers and aggregated high-dimensional attributions into interpretable category-level scores
Data drift protection: Built validation logic that gracefully handles category mismatches and evolving data sources during evaluation without breaking explanations
Results and learnings:
Cross-framework coverage: Now supporting explainability for both TensorFlow and PyTorch models across Uber's entire ML platform
Production-ready speed: 80%+ reduction in attribution time makes IG practical for large-scale batch jobs and tight iteration cycles
Broad adoption: Enabled use cases from regulatory compliance and fraud investigation to feature validation and operational monitoring
Uber proved that deep learning explainability doesn't have to be a research project—it can be a production feature. By prioritizing developer experience, framework flexibility, and computational efficiency, they made "why did the model do that?" a question every team can answer.
ESSENTIAL (measure-all-the-things)
How Engineering Teams Set Goals and Measure Performance
ESSENTIAL (fast-and-furious)
Speed vs. Velocity
ARTICLE (next-js-breakup-letter)
One Year with Next.js App Router
ARTICLE (pytorch-gets-royal)
Introducing PyTorch Monarch
ARTICLE (node-js-oopsie-handler)
Functional Error Handling in Node.js With The Result Pattern
ESSENTIAL (tree-hugger-cpp)
Looking at binary trees in C++
ESSENTIAL (productivity-detective)
Measuring Engineering Productivity
ARTICLE (javascript-detective-mode)
Find where a specific object was allocated in JavaScript with DevTools
ARTICLE (go-fast-or-go-home)
How Memory Maps Deliver 25x Faster File Access in Go
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Brief: Google's new Quantum Echoes algorithm demonstrates the first-ever verifiable quantum advantage, running 13,000x faster than classical supercomputers and enabling precise molecular structure analysis through enhanced NMR capabilities.
Brief: New Ramp AI Index reveals paid AI adoption declined in September, with tech sector leading at 73% penetration while other industries lag, though retention rates improve from 60% to 80% as OpenAI and Anthropic project billions in revenue.
Brief: OpenAI researchers claimed GPT-5 solved unsolved Erdős math problems, but quickly retracted after experts revealed it only found existing solutions, leading to criticism from DeepMind's CEO and highlighting risks of rushed AI announcements.
Brief: Anthropic introduces Claude Code on the web, allowing developers to run parallel coding tasks in isolated cloud environments with GitHub integration, mobile support, and automated PR creation for faster development workflows.
Brief: OpenAI introduces ChatGPT Atlas, a new browser extension that brings AI assistance across the web with features like smart sidebar, memory retention, agent mode, and privacy controls, available for Plus, Pro, and Business users on macOS.
This week’s coding challenge:
This week’s tip:
Rust's Pin<Box<T>> enables self-referential structs by preventing memory moves, crucial for async state machines and zero-copy parsers. Understanding pin projection and structural pinning is essential for building safe async abstractions.
Wen?
Custom async runtimes: Building futures that reference their own stack-allocated data requires careful pin management
Zero-copy parsers: Self-referential structs that point into their own buffers for efficient parsing without allocations
Unsafe FFI wrappers: Interfacing with C libraries that expect stable memory addresses across multiple function calls
"When everything seems to be going against you, remember that the airplane takes off against the wind, not with it."
Henry Ford
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See you in a week — Alex.
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