├── .gitignore ├── LICENSE ├── README.md └── episodes ├── Apify.txt ├── ClickHouse.txt ├── Cursor.ai IDE.txt ├── Elasticsearch.txt ├── Embeddings.txt ├── GitHub Actions.txt ├── Lit.txt ├── LocalStack.txt ├── Milvus DB.txt ├── Monorepo.txt ├── Okta.txt ├── PagerDuty.txt ├── Resilience Engineering.txt ├── Snowflake.txt └── Stream Processing.txt /.gitignore: -------------------------------------------------------------------------------- 1 | # Logs 2 | logs 3 | *.log 4 | npm-debug.log* 5 | yarn-debug.log* 6 | yarn-error.log* 7 | lerna-debug.log* 8 | .pnpm-debug.log* 9 | 10 | # Diagnostic reports (https://nodejs.org/api/report.html) 11 | report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json 12 | 13 | # Runtime data 14 | pids 15 | *.pid 16 | *.seed 17 | *.pid.lock 18 | 19 | # Directory for instrumented libs generated by jscoverage/JSCover 20 | lib-cov 21 | 22 | # Coverage directory used by tools like istanbul 23 | coverage 24 | *.lcov 25 | 26 | # nyc test coverage 27 | .nyc_output 28 | 29 | # Grunt intermediate storage (https://gruntjs.com/creating-plugins#storing-task-files) 30 | .grunt 31 | 32 | # Bower dependency directory (https://bower.io/) 33 | bower_components 34 | 35 | # node-waf configuration 36 | .lock-wscript 37 | 38 | # Compiled binary addons (https://nodejs.org/api/addons.html) 39 | build/Release 40 | 41 | # Dependency directories 42 | node_modules/ 43 | jspm_packages/ 44 | 45 | # Snowpack dependency directory (https://snowpack.dev/) 46 | web_modules/ 47 | 48 | # TypeScript cache 49 | *.tsbuildinfo 50 | 51 | # Optional npm cache directory 52 | .npm 53 | 54 | # Optional eslint cache 55 | .eslintcache 56 | 57 | # Optional stylelint cache 58 | .stylelintcache 59 | 60 | # Microbundle cache 61 | .rpt2_cache/ 62 | .rts2_cache_cjs/ 63 | .rts2_cache_es/ 64 | .rts2_cache_umd/ 65 | 66 | # Optional REPL history 67 | .node_repl_history 68 | 69 | # Output of 'npm pack' 70 | *.tgz 71 | 72 | # Yarn Integrity file 73 | .yarn-integrity 74 | 75 | # dotenv environment variable files 76 | .env 77 | .env.development.local 78 | .env.test.local 79 | .env.production.local 80 | .env.local 81 | 82 | # parcel-bundler cache (https://parceljs.org/) 83 | .cache 84 | .parcel-cache 85 | 86 | # Next.js build output 87 | .next 88 | out 89 | 90 | # Nuxt.js build / generate output 91 | .nuxt 92 | dist 93 | 94 | # Gatsby files 95 | .cache/ 96 | # Comment in the public line in if your project uses Gatsby and not Next.js 97 | # https://nextjs.org/blog/next-9-1#public-directory-support 98 | # public 99 | 100 | # vuepress build output 101 | .vuepress/dist 102 | 103 | # vuepress v2.x temp and cache directory 104 | .temp 105 | .cache 106 | 107 | # Docusaurus cache and generated files 108 | .docusaurus 109 | 110 | # Serverless directories 111 | .serverless/ 112 | 113 | # FuseBox cache 114 | .fusebox/ 115 | 116 | # DynamoDB Local files 117 | .dynamodb/ 118 | 119 | # TernJS port file 120 | .tern-port 121 | 122 | # Stores VSCode versions used for testing VSCode extensions 123 | .vscode-test 124 | 125 | # yarn v2 126 | .yarn/cache 127 | .yarn/unplugged 128 | .yarn/build-state.yml 129 | .yarn/install-state.gz 130 | .pnp.* 131 | 132 | .DS_Store 133 | package* 134 | temp/ 135 | src/ -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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This community-driven initiative transforms text contributions into technical podcast episodes. Every midnight, any new or updated text file containing key points for a 10-minute episode is processed and published as a podcast available on [Spotify](https://open.spotify.com/show/4GioQMJoZ9pEyY4SnoI0oA) and [Apple Podcasts](https://podcasts.apple.com/us/podcast/pull-request/id1795035005). 4 | 5 | ## How It Works 6 | 7 | - **Collaborative Contributions:** 8 | Anyone can contribute by uploading or editing text files in the episodes folder. While the file can be empty, it's best if it contains concise key points and highlights for a 10-minute episode. 9 | 10 | - **Dynamic Updates:** 11 | If you've listened to an episode and feel that something is missing, simply update the corresponding text file. Your changes will be integrated into future episodes to keep the content fresh and accurate. 12 | 13 | - **Credits:** 14 | Feel free to add your name to your submission. Contributors who include their name will be credited in the podcast episode. 15 | 16 | - **Content Guidelines:** 17 | - Make sure the filename is properly capitalized (i.e MySQL.txt instead of mysql.txt) 18 | - Focus on technical topics 19 | - Contributions must be in English 20 | 21 | ## Technologies 22 | 23 | - **Thinking Model:** o1 24 | - **Text-to-Speech:** OpenAI 25 | 26 | ## How to Contribute 27 | 28 | 1. **Fork the Repository** 29 | 30 | 2. **Clone Your Fork:** 31 | 32 | ```bash 33 | git clone https://github.com/yourusername/pull-request-podcast.git 34 | ``` 35 | 36 | 3. **Create a New Branch:** 37 | 38 | ```bash 39 | git checkout -b add-episode-content 40 | ``` 41 | 42 | 4. **Add or Update a Text File:** 43 | In the `episodes/` directory, add a new text file (example: `MySQL.txt`) following the guidelines for a 10-minute technical episode. If updating an existing file, include your improvements and optionally add your name in the credits section. 44 | 45 | 5. **Commit Your Changes:** 46 | 47 | ```bash 48 | git add . 49 | git commit -m "Added/Updated episode content for [specific date/topic]" 50 | ``` 51 | 52 | 6. **Push Your Changes:** 53 | 54 | ```bash 55 | git push origin add-episode-content 56 | ``` 57 | 58 | 7. **Create a Pull Request:** 59 | Once your branch is pushed, open a pull request. The maintainers will review your contribution before merging it. Once merged, the updated content will be processed automatically at midnight into a new podcast episode. 60 | 61 | Thanks for listening! ❤️ 62 | 63 | Michael Lugassy 64 | 65 | Follow me on [LinkedIn](https://www.linkedin.com/in/mluggy/) & [X](https://x.com/mluggy). 66 | -------------------------------------------------------------------------------- /episodes/Apify.txt: -------------------------------------------------------------------------------- 1 | Why web scraping still matters (lack of APIs, need for data) 2 | The Apify platform - actors, datasets and runs 3 | Examples of popular actors (Google, Yelp, Zillow, LinkedIn, Wolt) 4 | The "Crawler" actor - a great way to scrape entire websites 5 | Playwright and Puppeteer integration 6 | Using Apify proxies (datacenter, residential) 7 | Using Apify proxies outside of Apify platform (i.e in Axios) 8 | Build your own actor and monetize 9 | 10 | Credits: Michael Lugassy -------------------------------------------------------------------------------- /episodes/ClickHouse.txt: -------------------------------------------------------------------------------- 1 | materialized views must be a chapter 2 | also projections 3 | 4 | compare quickly to aws athena, google bigquery and droid 5 | compare heavily against snowflake 6 | 7 | how data is fed 8 | 9 | importance of fast disk drives 10 | 11 | credit to: Michael Lugassy -------------------------------------------------------------------------------- /episodes/Cursor.ai IDE.txt: -------------------------------------------------------------------------------- 1 | Talking specifically about Cursor.ai IDE 2 | Advantages over Co-Pilot, TabNine, etc. 3 | Basic auto-completion 4 | Chat with your codebase 5 | Composer and the new restore checkpoint feature 6 | Mention files, workspaces, repositories, URLs 7 | Different LLM models (including Private endpoints) 8 | Legal issues to consider 9 | 10 | Credit: Michael Lugassy -------------------------------------------------------------------------------- /episodes/Elasticsearch.txt: -------------------------------------------------------------------------------- 1 | The role of Elasticsearch in the modern data stack 2 | Elasticsearch vs. OpenSearch 3 | Comparison with other NoSQL (MongoDB, etc.), search databases (Solr, Lucene) or services (like Algolia) 4 | Clustering, sharding and replication in brief 5 | Elasticsearch complex DSL, queries, filtering, sorting and complex scoring 6 | Aggregations and Analytics, including Kibana 7 | Advanced usages for Elasticsearch (i.e as an ad-server or general-purpose database) 8 | Embedding in Elasticsearch 9 | 10 | Credit: Michael Lugassy -------------------------------------------------------------------------------- /episodes/Embeddings.txt: -------------------------------------------------------------------------------- 1 | What Are Embeddings? 2 | Core Use Cases 3 | Common Techniques & traditional models (Word2Vec, GloVe, FastText) 4 | LLMs (BERT, GPT, RoBERTa, OpenAI, etc.) 5 | Capturing Context and Semantics 6 | Dimensionality & Efficiency (including trade-offs) 7 | Similarity searches using cosine similarity or Euclidean distance 8 | Custom and Domain-Specific Embeddings 9 | Fine-tuning or training embeddings from scratch on domain-specific data 10 | Advanced usages for Embeddings (beyond text) 11 | 12 | Credit: Michael Lugassy -------------------------------------------------------------------------------- /episodes/GitHub Actions.txt: -------------------------------------------------------------------------------- 1 | How it stacks against Jenkins, GitLab CI, Argo and other CI/CD tools 2 | Fully integrated with GitHub repositories for seamless CI/CD based on events like push, pull request, and issue comments 3 | Define workflows using YAML, enabling version-controlled CI/CD pipelines directly within your repository 4 | Automatically trigger workflows on GitHub events, providing real-time automation for builds, tests, and deployments 5 | Run jobs in parallel and define matrix strategies to test multiple configurations (OS, language versions, etc.) 6 | Leverage and share thousands of pre-built actions from the GitHub Marketplace to accelerate development 7 | Execute jobs in GitHub-hosted runners across Linux, Windows, and macOS, or use custom Docker containers for consistent environments 8 | Choose between GitHub-hosted runners or set up your own self-hosted runners for greater control and custom environments 9 | Manage sensitive data securely with encrypted secrets and environment variables accessible in the workflow 10 | Use caching to speed up workflows by storing dependencies, and manage artifacts for later job steps or debugging 11 | Implement conditional execution, utilize job dependencies, and orchestrate complex workflows for granular control 12 | Access detailed logs and diagnostics directly from the GitHub UI, simplifying troubleshooting of workflows 13 | Seamlessly integrate with external APIs, services, and third-party platforms using built-in actions and community contributions 14 | Benefit from GitHub’s robust security infrastructure, including fine-grained token permissions and automated security checks for workflow integrity 15 | 16 | Credit: Michael Lugassy -------------------------------------------------------------------------------- /episodes/Lit.txt: -------------------------------------------------------------------------------- 1 | Why Lit instead of React, Angular or Vue? 2 | Speed of shadow DOM 3 | Security of shadow DOM 4 | 5 | Credit: Michael Lugassy -------------------------------------------------------------------------------- /episodes/LocalStack.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mluggy/pull-request-podcast/89cd2dee5aab7d9e3e23127109e8dc1bb5cfaf98/episodes/LocalStack.txt -------------------------------------------------------------------------------- /episodes/Milvus DB.txt: -------------------------------------------------------------------------------- 1 | What is a vector database? 2 | Why traditional databased are not enough? 3 | How to use the concept of vector similiarity? 4 | Which indexes and when to use them? - IVF_FLAT, HNSW, IVF_SQ8 5 | Usage examples: 6 | - RAG 7 | - Recommendation systems. 8 | - Audio search. 9 | 10 | Credit: Omri Fima -------------------------------------------------------------------------------- /episodes/Monorepo.txt: -------------------------------------------------------------------------------- 1 | Benefits vs. single-repo or multi-repo 2 | Use concrete examples for Node.js (NPM/pnpm/yarn) and Python (pip) 3 | Store all projects, libraries, and services in a single repository for a single source of truth 4 | Make consistent, atomic commits that span multiple applications or modules, reducing integration issues 5 | Share and version dependencies and common libraries effortlessly across teams 6 | Apply uniform build, test, linting, and deployment setups with centralized configuration management 7 | Improve developer awareness of changes across projects, fostering collaboration and consistent coding practices 8 | Efficiently update and refactor shared code and APIs across multiple projects with confidence 9 | Utilize tools like Bazel, Nx, Lerna, or Pants to execute incremental builds and tests, optimizing performance 10 | Streamline branch management and merges to reduce integration conflicts and maintain code quality 11 | Centralize test suites and CI pipelines, enabling comprehensive and faster regression testing 12 | Promote reuse of code patterns and libraries, reducing duplication and maintaining consistency across applications 13 | Leverage automated tools to map interdependencies, ensuring efficient build and deployment pipelines 14 | Employ strategies like selective checkouts, caching, and distributed builds to address performance challenges in very large repositories 15 | -------------------------------------------------------------------------------- /episodes/Okta.txt: -------------------------------------------------------------------------------- 1 | Why organizations use Okta for workforce identity and access management 2 | How it stacks against Google SSO, Microsoft Entra, and other identity providers 3 | SAML, OpenID Connect, and other basic concepts 4 | Multi-Factor Authentication (MFA), OTP, SMS, Passkeys & Adaptive Security 5 | Contextual data (location, device, behavior) to enforce real-time, risk-based access decisions 6 | Built-in integrations (OIN) 7 | Best practices for session and password policies 8 | How do you connect your own apps and services to Okta? 9 | Monitoring of authentication events, risk assessments, and security analytics 10 | Tailored UI experience and branding 11 | Zero Trust Security & Contextual Access 12 | -------------------------------------------------------------------------------- /episodes/PagerDuty.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mluggy/pull-request-podcast/89cd2dee5aab7d9e3e23127109e8dc1bb5cfaf98/episodes/PagerDuty.txt -------------------------------------------------------------------------------- /episodes/Resilience Engineering.txt: -------------------------------------------------------------------------------- 1 | How complex systems fail 2 | what is failure cascades 3 | from preventing failures to designing for resilience 4 | designing systems that fail partially, not catastrophically 5 | resilience patterns - timeouts, Circuit Breakers, bulkheads 6 | 7 | Credit: Omri Fima -------------------------------------------------------------------------------- /episodes/Snowflake.txt: -------------------------------------------------------------------------------- 1 | Works on every major cloud provider but still closed source 2 | Infinite scale, including price ranges for both compute and storage 3 | Support for JSON, Parquet, ORC, AVRO, etc. and which should you use 4 | How Snowflake compares to other cloud data warehouses (AWS Redshift, Google BigQuery, Azure Synapse) 5 | Time Travel & Fail-safe 6 | Data Sharing, Query Editor (including AI) & Collaboration 7 | Query Optimization & Caching 8 | Maybe some UDFs and advanced querying beyond standard SQL 9 | 10 | Credit: Michael Lugassy -------------------------------------------------------------------------------- /episodes/Stream Processing.txt: -------------------------------------------------------------------------------- 1 | stream vs batch processing 2 | streams vs queues 3 | Windows - Tumbling windows, Sliding windows, Session windows 4 | Stream processing patterns - State management, joins, branching, feedabck loops. 5 | Apache Kafka Streams and AWS Firehose 6 | Common challenges - out of order data, recovery, exactly-once. 7 | 8 | Credit: Omri Fima, Michael Lugassy 9 | --------------------------------------------------------------------------------