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52 Weeks of Cloud

Noah Gift

A weekly podcast on technical topics related to cloud computing including: MLOPs, LLMs, AWS, Azure, GCP, Multi-Cloud and Kubernetes.

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20 episodes
Avg 9 min
English

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#229
Wednesday · 1 min
Clip: MCP lesson 1.8 six metrics that grade agent code
Audio from the Deterministic MCP Agents course lesson `mcp-1.8-six-metrics-that-grade-agent-code`, remixed for audio with its own intro and outro. Transcript: clip.srt.
#228
Wednesday · 2 min
Daily: MCP lesson 1.7 languages that survive
Audio from the Deterministic MCP Agents course lesson `mcp-1.7-languages-that-survive`, remixed for audio with its own intro and outro. Transcript: daily.srt.
#227
Wednesday · 1 min
Clip: MCP lesson 1.6 inside the MCP handshake to tool call
Audio from the Deterministic MCP Agents course lesson `mcp-1.6-inside-mcp-handshake-to-tool-call`, remixed for audio with its own intro and outro. Transcript: clip.srt.
#226
Wednesday · 4 min
Daily: MCP lesson 1.5 agents as state machines
Audio from the Deterministic MCP Agents course lesson `mcp-1.5-agents-as-state-machines`, remixed for audio with its own intro and outro. Transcript: daily.srt.
#225
Tuesday · 1 min
Clip: MCP lesson 1.4 certainty or scope
Audio from the Deterministic MCP Agents course lesson `mcp-1.4-certainty-or-scope`, remixed for audio with its own intro and outro. Transcript: clip.srt.
#224
Tuesday · 4 min
Daily: MCP lesson 1.2 PMAT context for your agent
Audio from the Deterministic MCP Agents course lesson `mcp-1.2-pmat-context-for-your-agent`, remixed for audio with its own intro and outro. Transcript: daily.srt.
#223
Sep 18, 2025 · 3 min
ELO Ratings Questions
ELO ratings work for chess (κ=0.92) but fail catastrophically for AI agents (κ=0.31). Random users aren't chess arbiters. Code quality isn't win/loss. We explore psychometric failures, cognitive biases destroying data validity, and why quantitative metrics (McCabe complexity, test coverage) achieve 2.18x better reliability than human preferences.
#222
Sep 17, 2025 · 4 min
The 2X Ceiling: Why 100 AI Agents Can't Outcode Amdahl's Law"
AI coding agents face the same fundamental limitation as parallel computing: Amdahl's Law. Just as 10 cooks can't make soup 10x faster, 10 AI agents can't code 10x faster due to inherent sequential bottlenecks.
#221
May 21, 2025 · 10 min
Plastic Shamans of AGI
The plastic shamans of OpenAI
#220
May 21, 2025 · 14 min
The Toyota Way: Engineering Discipline in the Era of Dangerous Dilettantes
I examined Toyota's production methodology as a direct counter to naive AI automation claims. Rigorous engineering practices remain essential when integrating narrow AI agents into software development.
#219
May 16, 2025 · 11 min
DevOps Narrow AI Debunking Flowchart
I debunk claims of AI replacing developers. Narrow AI remains a buggy, but useful tool while DevOps proves its worth.
#218
May 14, 2025 · 14 min
No Dummy, AI Isn't Replacing Developer Jobs
Critical examination of false claims about AI replacing software developers. Six key factors explain job losses: non-productive employees, low-skilled developers, basic automation, outsourcing, routine corporate layoffs, and deceptive marketing.
#217
May 14, 2025 · 10 min
The Narrow Truth: Dismantling IntelligenceTheater in Agent Architecture
AI agents are narrow systems wrapped in deceptive interfaces. Each component has non-ML equivalents that expose the magical thinking behind AGI promises.
#216
May 14, 2025 · 8 min
The Pirate Bay Hypothesis: Reframing AI's True Nature
In this thought-provoking episode, we tackle the fundamental question of AI intelligence by comparing large language models to a hypothetical full-text search engine containing all code, books, and intellectual property ever created. This reframing reveals how AI might be less magical than we think—simply retrieving and rearranging existing information rather than demonstrating true intelligence.
#215
May 5, 2025 · 10 min
Claude Code Review: Pattern Matching, Not Intelligence
I share my hands-on experience with Anthropic's Claude Code tool, praising its utility while challenging the misleading "AI" framing. I argue these are powerful pattern matching tools, not intelligent systems, and explain how experienced developers can leverage them effectively while avoiding common pitfalls.
#214
May 5, 2025 · 7 min
Deno: The Modern TypeScript Runtime Alternative to Python
Deno stands tall. TypeScript runs fast in this Rust-based runtime. It builds standalone executables and offers type safety without the headaches of Python's packaging and performance problems.
#213
May 4, 2025 · 16 min
Reframing GenAI as Not AI - Generative Search, Auto-Complete and Pattern Matching
I expose the reality behind today's "AI" hype. What we call AI is actually generative search and pattern matching - useful but not intelligent. Like the Wizard of Oz, tech companies use smoke and mirrors to market what are essentially statistical models as sentient beings.
#212
May 4, 2025 · 45 min
Academic Style Lecture on Concepts Surrounding RAG in Generative AI
I demystify RAG technology and challenge the AI hype cycle. I argue current AI is merely advanced search, not true intelligence, and explain how RAG grounds models in verified data to reduce hallucinations while highlighting its practical implementation challenges.
#211
Mar 21, 2025 · 2 min
Pragmatic AI Labs Interactive Labs Next Generation
Pragmatic Labs has launched updated interactive labs with enhanced Rust learning capabilities, featuring a browser-based development environment with Cargo project creation, code compilation, and Visual Studio integration. The platform hosts numerous applied Rust courses covering GUI development, serverless, data engineering, AI, MLOps, and Python integration, positioning itself as a premier Rust learning destination. Additionally, the platform plans to expand with hundreds of new labs showcasin
#210
Mar 21, 2025 · 9 min
Meta and OpenAI LibGen Book Piracy Controversy
Meta and OpenAI used Library Genesis (LibGen), a pirated book repository containing 7.5 million books and 81 million research papers, to train their AI models. Mark Zuckerberg reportedly approved this usage. Meta employees understood the "medium-high legal risk" involved and implemented measures to hide their actions, including removing copyright notices and ISBN numbers.

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