- Hungry Minds
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- 🍔🧠 OpenAI's Stack for Streaming to Billions of Customers
🍔🧠 OpenAI's Stack for Streaming to Billions of Customers
PLUS: Designing a Rate Limiter 🚦, Agent Programming Deep Dive 🤯, Developer Salary Report 💵
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📚 Software Engineering Articles
Professional developers switched to this remote agent
Comprehensive guide to cache strategies for high-performance systems
Developer's journey: self-hosting services for tech independence
Remote engineer salary insights reveal surprising trends
Master Go programming coming from JavaScript background
Recent observability shifts reshape monitoring landscape
🗞️ Tech and AI Trends
Meta's $15B Scale AI investment aims for superintelligence breakthrough
Robot chefs revolutionize food industry with 25,000 monthly pizzas
AMD and OpenAI partner for next-gen AI chips
👨🏻💻 Coding Tip
Use inotifywait for automated builds and real-time file monitoring
Time-to-digest: 5 minutes
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OpenAI revealed its real-time data streaming infrastructure that powers ChatGPT and other AI systems at Current 2025. The platform processes massive volumes of data through Kafka and Flink to enable instant responses, continuous learning, and global-scale operations.
The challenge: Building infrastructure that can handle enormous data volumes with sub-second latency while coordinating model training, inference, and feedback loops across a global system.
Implementation highlights:
Multi-region architecture: Deployed Kafka clusters across regions with failover capabilities
PyFlink extensions: Custom adaptations for ML training and experimentation workflows
Kafka Forwarder: Simplified consumption layer converting pull to push-based model
Zero-downtime migrations: Strategies for seamless cluster updates and region failovers
Scale optimization: Handling massive throughput with custom performance tuning
Results and learnings:
Enterprise-ready: Production infrastructure supporting ChatGPT's global user base
Developer friendly: Simplified streaming interfaces reduce complexity for ML teams
Future-proof: Architecture designed to scale with growing AI model and usage demands
The key takeaway is that even cutting-edge AI needs robust data infrastructure. OpenAI shows that stream processing isn't just plumbing - it's the nervous system that makes real-time AI possible.

ARTICLE (ai-talkie-talkie)
How To Communicate The Reality Of AI-Assisted Engineering In Today’s Hype Cycle
ARTICLE (show-me-the-money)
Remote Software Engineer Salary Report
GITHUB REPO (claude-boss-mode)
An AI-powered task-management system you can drop into Cursor, Lovable, Windsurf, Roo, and others
GITHUB REPO (markdown-on-steroids)
🪐 Markdown with superpowers — from ideas to presentations, articles and books
ARTICLE (js-to-go-go)
A JavaScript Developer's Guide to Go
ARTICLE (own-your-stuff)
Self-Host & Tech Independence: The Joy of Building Your Own
ARTICLE (maps-but-better)
Resilient Import Maps - Better Theme Development and Beyond
ARTICLE (ai-cheap-cheap)
LLMs are cheap
ARTICLE (observability-rip)
It's The End Of Observability As We Know It
ARTICLE (agent-smith-mode)
How I Program with Agents
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Brief: Meta nears a $14B investment in Scale AI, hiring its founder Alexandr Wang to lead a new AI lab as Zuckerberg ramps up efforts to catch rivals like OpenAI.
Brief: Researchers are using focused ultrasound stimulation (FUS) to treat inflammation, diabetes, and obesity by hacking nerve signals—potentially replacing pills with wearable, noninvasive devices.
Brief: XRobotics' compact pizza-making robots are producing 25,000 pizzas monthly, saving restaurants 70-80% of staff time, after raising a $2.5M seed round to scale production.
Brief: Frustrated by Meta’s AI lag, Zuckerberg makes his biggest bet yet—a $14B stake in Scale AI—while hiring its founder Alexandr Wang to revamp the company’s AI strategy amid fierce competition.
Brief: AMD debuts its MI400 AI chips designed for hyperscale data centers, with OpenAI’s Sam Altman endorsing them as a cost-efficient rival to Nvidia’s GPUs, promising rack-scale integration and lower power consumption.

This week’s coding challenge:
This week’s tip:
Use Linux's inotifywait
command for real-time file system monitoring and automated development workflows. The command provides granular control over file system events and can trigger custom actions through shell scripting.

Wen?
Local development: Automate build processes without heavyweight watch modes or polling.
Custom deployment pipelines: Trigger immediate deployments when specific files change.
Log monitoring: Create real-time log aggregation systems by watching log directories.
Become the kind of leader that people would follow voluntarily, even if you had no title or position.
Brian Tracy


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