About this episode
Apr 2025
MLA 024 Agentic Software Engineering
Agentic engineering shifts the developer role from manual coding to orchestrating AI agents that automate the full software lifecycle from ticket to deployment. Using Claude Code with MCP servers and git worktrees allows a single person to manage the output and quality of an enti ... Show More
45m 34s
May 2025
MLG 034 Large Language Models 1
Explains language models (LLMs) advancements. Scaling laws - the relationships among model size, data size, and compute - and how emergent abilities such as in-context learning, multi-step reasoning, and instruction following arise once certain scaling thresholds are crossed. The ... Show More
50m 48s
May 2025
MLG 035 Large Language Models 2
At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup ... Show More
45m 25s
Jul 2022
Santiago Valderrama on Getting Smarter on Machine Learning, One Problem at a Time - Ep. 173
Want to learn about AI and machine learning? There are plenty of resources out there to help — blogs, podcasts, YouTube tutorials — perhaps too many. Machine learning engineer Santiago Valdarrama has taken a far more focused approach to helping us all get smarter about the field. ... Show More
27m 15s
Dec 2019
Serverless NLP Model Training
<p class="p1"><span class="s1">Alex Reeves joins us to discuss some of the challenges around building a serverless, scalable, generic machine learning pipeline.<span class= "Apple-converted-space"> </span> The is a technical deep dive on architecting solutions and a discussion of ... Show More
29m 2s
May 2021
474: The Machine Learning House
In this episode, I discuss the architecture of a “machine learning house”, representing the skills and learnings you can use as foundations to build your data science career.
Additional materials: www.superdatascience.com/474
5m 44s
May 2023
Creating instruction tuned models (Practical AI #223)
At the recent ODSC East conference, Daniel got a chance to sit down with Erin Mikail Staples to discuss the process of gathering human feedback and creating an instruction tuned Large Language Models (LLM). They also chatted about the importance of open data and practical tooling ... Show More
26m 33s
Claude Code distinguishes itself through a deterministic hook system and model-invoked skills that maintain project consistency better than visual-first tools like Cursor. Its multi-surface architecture allows developers to move sessions between CLI, web sandboxes, and mobile while maintaining persistent context.
Links
Agent Comparison
- Cursor: VS Code fork. Uses visual interactions (Cmd+K, Composer mode), multi-line tab completion, and background cloud agents. Credit-based billing ($20 to $200).
- Codex CLI: Terminal-first Rust agent. Uses GPT-5.3-Codex. Features three autonomy modes (Suggest, Auto-approve, Full Auto). Included in $20 ChatGPT Plus.
- Antigravity: Agent-first interface using Gemini 3 Pro. Manager View orchestrates parallel agents that produce verifiable task lists and recordings.
- Claude Code: Terminal, IDE, and mobile sessions. Uses Sonnet/Opus 4.5/4.6. Differentiates via deep composability and cross-surface persistence.
Persistent Memory and Skills
- CLAUDE.md: 4-tier hierarchy (Enterprise, Project, User, Local). Loads recursively, enabling monorepo support where child directories load lazily. Imports use
@ syntax. - Skills: Model-invoked capability folders. Three-stage loading (metadata, instructions, supporting resources) minimizes context use. Claude triggers them based on description fields.
- Commands: User-triggered slash commands.
/compact preserves topics while trimming history, /init generates memory files, and /checkpoint manages rollbacks.
Enforcement and Integration
- Hooks: Deterministic shell commands or LLM prompts. Fired at 10 events, including
PreToolUse (blocking), PostToolUse (formatting), and Stop (self-correction). Exit code 2 blocks actions, code 0 allows. - MCP: Standard for connecting external tools (PostgreSQL, GitHub, Sentry). Tool Search activates when metadata exceeds 10% context window. Claude Code can serve its own tools via MCP.
- Subagents: Isolated context workers.
Explore uses Haiku for discovery, Plan uses Sonnet for research. isolation: worktree provides filesystem-level separation. - Agent Teams: Persistent multi-pane coordination via tmux. Modes: Hub-and-Spoke, Task Queue, Pipeline, Competitive, and Watchdog.
Operations and Security
- Checkpoints: Granular undo allows independent rollback of code changes or conversation history.
- Thinking Triggers: Keywords
Think to Ultrathink adjust reasoning compute allocation. - Headless:
--print or --headless flags enable CI/CD. GitHub Action uses four parallel agents to score review findings above 80% confidence. - Sandboxing: Uses Apple Seatbelt (macOS) or Bubblewrap (Linux). Restricts filesystem and network access, reducing permission prompts by 84%.
- Output Styles: Modifies system prompts for
Default, Explanatory, or Learning personas.
