About this episode
Feb 2026
MLA 029 OpenClaw
OpenClaw is a self-hosted AI agent daemon that executes autonomous tasks through messaging apps like WhatsApp and Telegram using persistent memory. It integrates with Claude Code to enable software development and administrative automation directly from mobile devices. Links Note ... Show More
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MLA 030 AI Job Displacement & ML Careers
ML engineering demand remains high with a 3.2 to 1 job-to-candidate ratio, but entry-level hiring is collapsing as AI automates routine programming and data tasks. Career longevity requires shifting from model training to production operations, deep domain expertise, and masterin ... Show More
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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
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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
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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
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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
AI agents differ from chatbots by pursuing autonomous goals through the ReACT loop rather than responding to turn-based prompts. While coding agents are currently the most reliable due to verifiable feedback loops, the market is expanding into desktop and browser automation via tools like Claude co-work and open claw.
Links
Fundamental Definitions
- Agent vs. Chatbot: Chatbots are turn-based and human-driven. Agents receive objectives and dynamically direct their own processes.
- The ReACT Loop: Every modern agent uses the cycle:
Thought -> Action -> Observation. This interleaved reasoning and tool usage allows agents to update plans and handle exceptions. - Performance: Models using agentic loops with self-correction outperform stronger zero-shot models. GPT-3.5 with an agent loop scored 95.1% on HumanEval, while zero-shot GPT-4 scored 67.0%.
The Agentic Spectrum
- Chat: No tools or autonomy.
- Chat + Tools: Human-driven web search or code execution.
- Workflows: LLMs used in predefined code paths. The human designs the flow, the AI adds intelligence at specific nodes.
- Agents: LLMs dynamically choose their own path and tools based on observations.
Tool Categories and Market Players
- Developer Frameworks: Use LangGraph for complex, stateful graphs or CrewAI for role-based multi-agent delegation. OpenAI Agents SDK provides minimalist primitives (Handoffs, Sessions), while the Claude Agent SDK focuses on local computer interaction.
- Workflow Automation: n8n and Zapier provide low-code interfaces. These are stable for repeatable business tasks but limited by fixed paths and a lack of persistent memory between runs.
- Coding Agents: Claude Code, Cursor, and GitHub Copilot are the most advanced agents. They succeed because code provides an unambiguous feedback loop (pass/fail) for the ReACT cycle.
- Desktop and Browser Agents: Claude Cowork( (released Jan 2026) operates in isolated VMs to produce documents. ChatGPT Atlas is a Chromium-based browser with integrated agent capabilities for web tasks.
- Autonomous Agents: open claw is an open-source, local system with broad permissions across messaging, file systems, and hardware. While powerful, it carries high security risks, including 512 identified vulnerabilities and potential data exfiltration.
Infrastructure and Standards
- MCP (Model Context Protocol): A universal standard for connecting agents to tools. It has 10,000+ servers and is used by Anthropic, OpenAI, and Google.
- Future Outlook: By 2028, multi-agent coordination will be the default architecture. Gartner predicts 38% of organizations will utilize AI agents as formal team members, and the developer role will transition primarily to objective specification and output evaluation.
