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📚 Software Engineering Articles
The best open source AI coding tool is here
Use Postgres until it breaks: practical insights on scaling PostgreSQL
DoorDash's journey to build a collaborative AI ecosystem
Master 21 frontend system design concepts every engineer should know
How Notion optimizes Redis queues for better performance
Comprehensive GitHub Copilot guide to ship code faster
🗞️ Tech and AI Trends
Meta's AI chief Yann LeCun plans exit for startup venture
FFmpeg demands Google support or stop bug reports
OpenAI launches GPT-5.1 with enhanced developer features
👨🏻💻 Coding Tip
Use PerformanceObserver to track Core Web Vitals and catch early bottlenecks
Time-to-digest: 5 minutes
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Datadog faced the challenge of building a metrics platform that could handle billions of data points per second while maintaining sub-second query response times. Their solution? A custom database engine called Monocle, built from scratch in Rust, that revolutionized their real-time metrics storage.
The challenge: Design a system that can ingest billions of metrics per second while serving queries in milliseconds and keeping operational costs manageable at scale.
Implementation highlights:
Kafka-centric architecture: Used as central write-ahead log, eliminating node-to-node communication
Thread-per-core design: Each CPU core operates in isolation with dedicated workers and no shared state
Custom LSM storage: Optimized for write-heavy workloads with arena allocation and time-based pruning
Two-tier data model: Separate index database for tags and RTDB for time series data
Smart query scheduling: Implemented CoDel algorithm to prevent system overload during query bursts
Results and learnings:
Massive throughput: Successfully processes billions of metrics per second
Sub-second queries: 99% of queries complete in milliseconds
Cost-effective: Achieved 10x better resource utilization compared to previous RocksDB solution
The success of Monocle demonstrates that sometimes building a custom solution, while challenging, can deliver exponentially better results than adapting existing tools. It's like they say in engineering: if you want something done right (and really, really fast), sometimes you have to build it yourself!
ARTICLE (robot sidekick guide)
GitHub Copilot tutorial: How to build, test, review, and ship code faster
ARTICLE (typescript's revenge)
TypeScript's rise in the AI era
GITHUB REPO (transaction-ninja)
DBOS Transact
ARTICLE (profile-all-the-things)
Continuous profiling for native code: Understanding the what, why, and how
ARTICLE (framework-graveyard)
Dead framework theory
ARTICLE (good-vibes-only)
Vibe coding needs a spec, too
ARTICLE (database-time-traveler)
From web developer to database developer in 10 years
ARTICLE (ai-project-whisperer)
How to Use AI to Help With Planning Engineering Projects
ARTICLE (stack-all-the-way)
Vertical Integration is the Only Thing That Matters
ESSENTIAL (list-master-101)
Linked List: Explained in 4 minutes
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Brief: Turing Award winner and Meta's chief AI scientist Yann LeCun plans to depart to launch his own startup focused on world models, amid Meta's broader AI reorganization and shift towards superintelligence development.
Brief: FFmpeg, a critical open-source multimedia framework used by major tech companies, sparks debate after telling Google to either help fund the project or stop using AI to report bugs, highlighting the growing tension between volunteer-maintained projects and corporate demands.
Brief: Former Apple creative director reveals how Tim Cook's decision to introduce ads in Apple Maps contrasts with Steve Jobs' past rejection of OS advertising, highlighting a shift from prioritizing user experience to pursuing new revenue streams.
🤖 Moonshot AI Unveils Kimi K2: A Groundbreaking Open-Source AI Model with Agentic Intelligence (5 min)
Brief: Moonshot AI releases Kimi K2, a 1 trillion parameter AI model with 32 billion active parameters that achieves SOTA performance in coding and math tasks while featuring innovative agentic capabilities that allow it to autonomously use tools and execute complex tasks.
Brief: OpenAI releases GPT-5.1 with warmer, more conversational responses after CEO Sam Altman admitted GPT-5 was made "pretty restrictive" for safety concerns, marking a shift from cautious AI guardrails toward treating adult users with more freedom.
This week’s coding challenge:
This week’s tip:
Use PerformanceObserver with detailed buffering to catch performance bottlenecks that occur before your main script loads. The observer can capture pre-existing entries and provide granular timing data for navigation, resources, and user interactions.
Wen?
SPA performance monitoring: Track Core Web Vitals during client-side route transitions
E-commerce optimization: Monitor LCP shifts when product images load dynamically
Dashboard applications: Detect layout thrashing during data visualization updates
"Don't judge each day by the harvest you reap but by the seeds that you plant."
Robert Louis Stevenson
That’s it for today! ☀️
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See you in a week — Alex.
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