Intro topic: Buying a Car

News/Links:

• Cognitive Load is what Matters
  • https://github.com/zakirullin/cognitive-load
• Diffusion models are Real-Time Game Engines
  • https://gamengen.github.io/
• Your Company Needs Junior Devs
  • https://softwaredoug.com/blog/2024/09/07/your-team-needs-juniors
• Seamless Streaming / Fish Speech / LLaMA Omni
  • Seamless: https://huggingface.co/facebook/seamless-streaming
  • Fish: https://github.com/fishaudio/fish-speech
  • LLaMA Omni: https://github.com/ictnlp/LLaMA-Omni

Book of the Show

• Patrick:
  • Thought Emporium Youtube
    • https://youtu.be/8X1_HEJk2Hw?si=T8EaHul-QMahyUvQ
• Jason:
  • Novel Minds
    • https://www.novelminds.ai/
      
      

Patreon Plug https://www.patreon.com/programmingthrowdown?ty=h

Tool of the Show

• Patrick:
  • Escape Simulator
    • https://pinestudio.com/games/escape-simulator/
• Jason:
  • Cursor IDE
    • https://www.cursor.com/
      
      

Topic: Vector Databases (~54 min)

• How computers represent data traditionally
  • ASCII values
  • RGB values
• How traditional compression works
  • Huffman encoding (tree structure)
  • Lossy example: Fourier Transform & store coefficients
• How embeddings are computed
  • Pairwise (contrastive) methods
  • Forward models (self-supervised)
• Similarity metrics
• Approximate Nearest Neighbors (ANN)
• Sub-Linear ANN
  • Clustering
  • Space Partitioning (e.g. K-D Trees)
• What a vector database does
  • Perform nearest-neighbors with many different similarity metrics
  • Store the vectors and the data structures to support sub-linear ANN
  • Handle updates, deletes, rebalancing/reclustering, backups/restores
• Examples
  • pgvector: a vector-database plugin for postgres
  • Weaviate, Pinecone
  • Milvus

• James Morse: Software Engineer at Cisco
  • System Administrator to DevOps
  • Difference between DevOps and MLOps
  • Getting Started with DevOps
• Luke Marsden: CEO of Helix ML
  • How to start a business at 15 years old
  • BTRFS vs ZFS
  • MLOps: the intersection of software, DevOps and AI
  • Fine-tuning AI on the Cloud
  • Some advice for folks interested in ML Ops
• Yuval Fernbach: CTO MLOps & JFrog
  • Starting Qwak
  • Going from a jupyter notebook to production
  • ML Supply Chain
  • Getting started in Machine Learning
• Stephen Chin: VP of DevRel at Neo4J
  • Developer Relations: The Job
  • What is a Large Language Model?
  • Knowledge graphs and the Linkage Model
  • How to Use Graph databases in Enterprise
  • How to get into ML Ops

175: Resume Writing

Intro topic: DSLR Photography vs Camera Phone

News/Links:

• Free Internet while flying by abusing edits to your profile name
  • https://robertheaton.com/pyskywifi/
• Making Animated Characters with AI Art
  • https://www.youtube.com/watch?v=zSN76gb_Z28
• On 10x Engineers
  • https://stackoverflow.blog/2024/06/19/the-real-10x-developer-makes-their-whole-team-better/
• The Beauty and Challenges of AI-Generated Artistic Gymnastics
  • https://www.youtube.com/watch?v=YwJIYj3hPAU
    
    

Book of the Show

• Patrick:
  • The Three Body Problem by Cixin Liu
    • https://amzn.to/3xNEoRB
• Jason:
  • The Checklist Manifesto
    • https://amzn.to/3W2JjpM

Patreon Plug https://www.patreon.com/programmingthrowdown?ty=h

Tool of the Show

• Patrick:
  • Super Mario Bros. Wonder (Nintendo Switch)
  • https://amzn.to/3S9VJLf
• Jason:
  • Amazon Q
  • https://marketplace.visualstudio.com/items?itemName=AmazonWebServices.amazon-q-vscode
    
    

Topic: Resume Writing (Courtesy of Matthew C.)

• Why have a resume?
  • Many jobs require it to get into the consideration
  • Today many are screened for keywords automatically
  • Log for future you
• What is a resume?
  • One-page description
  • Key accomplishments & experiences
  • Comparison to CV
  • References
• How to write a good resume?
  • Do’s
    • Include your github if it has good contributions
    • Be specific (dates, locations, skills)
    • Isolate your specific contributions
    • Be accurate/honest
    • Be concise
    • Be ready to discuss any point you have on the resume
    • List hobbies/activities/extracurriculars
  • Don’ts
    • Have mistakes (especially dates)
    • Use images (most companies use text extraction)
    • Use it as a design portfolio
    • Put social qualities (e.gs. hard-working, motivated, friendly)
    • Use fancy templates/tools
  • Resources
    • Manager Tools: How to scan resumes https://www.manager-tools.com/2016/05/how-scan-resume-part-1
    • Google docs
    • Latex/Lyx for CVs
• How to think about your career and how it impacts your future resume writing (career planning)
  • Technologies and architectures more than specifics of project details
  • How various choices may age over time