『Agents and Engineers | Agentic AI, Software & Agentic Engineering』のカバーアート

Agents and Engineers | Agentic AI, Software & Agentic Engineering

Agents and Engineers | Agentic AI, Software & Agentic Engineering

著者： Dan Gerlanc
無料で聴く

The podcast about agentic AI, agentic software engineering, and entrepreneurship. Each episode is a conversation with people building with agentic AI. Join me as I follow the stories, the behind-the-scenes, and the people behind the code. About your host, Dan Gerlanc: Dan brings his experience as a 4x founder with 20 years of experience in ML and software to find unique insights on the impact of AI in tech, software engineering, and entrepreneurship.© 2026 Dan Gerlanc LLC 経済学
エピソード
  • AI Agents for Bayesian Data Science

    AI Agents for Bayesian Data Science
    2026/09/01
    Thomas Wiecki, Founder, PyMC Labs, is the author of PyMC, one of the most popular frameworks for Bayesian modeling, and the founder of PyMC Labs, a Bayesian AI consultancy. He holds a PhD in Computational Cognitive Neuroscience from Brown University and previously served as VP of Data Science and Head of Research at Quantopian Inc., where he built and led a team of data scientists to develop a hedge fund leveraging insights from a crowd-sourced pool of 300,000 researchers.In this episode, we explore how AI agents have made Bayesian methods more accessible to non-experts. Bayesian models are useful for both forecasting and decision-making under uncertainty. These models can act as scenario simulators, making it possible to ask what-if questions about marketing, seasonality, or health interventions while exposing the mechanisms behind an outcome.We also discuss how agentic engineering has finally helped deliver on some of the original promises of data science, how to effectively embedding agents in tools like Slack or Discord to democratize access to company information, and what parts of data science still require talking with humans to understand the problem.Full episode notesTranscriptChapters(00:00) - Agentic engineering meets Bayesian decision science(05:27) - Bayesian models versus black-box prediction(10:22) - Confounders and causal mechanisms(12:41) - From notebooks to model-aware agents(15:41) - The rise of agentic interfaces(19:18) - Alchemize and verified code translation(29:49) - Agentic data science beyond the silo(34:25) - Daimon and collaborative multiplayer AI(37:43) - Verification and unsettled methods(42:53) - Parallel agents and PyMC Forecast(51:08) - The Bayesian method of software engineering(53:59) - Agents as software's primary users(57:08) - Institutional knowledge and bus-factor risk(01:03:56) - Faster research and open source(01:12:05) - Causal models for AI's future⠀Links from the show--------------------Master Agentic Data ScienceShow Us Your Agent SkillsPyMCPyMC LabsBayesian modelingcausal questionsmedia mix modelingDaimonAlchemizePyMC Forecastprior data fitted networksNumPyro ForecastStanJAXPyTorch⠀Guests-------Thomas Wiecki, Founder, PyMC LabsWebsiteLinkedInGitHubTwitter⠀Follow the podcast-------------------LinkedInThreadsInstagramTikTok⠀Follow Dan Gerlanc-------------------XLinkedInThreadsBluesky
    続きを読む 一部表示
    1 時間 14 分
  • Search is Eating AI

    Search is Eating AI
    2026/08/25
    Dan is joined by Hugo Bowne-Anderson and Doug Turnbull. Hugo is an independent data and AI scientist who has advised and taught teams at Netflix, Meta, and Amazon. Doug is an independent consultant and search expert with experience at Shopify, Reddit, and Wikipedia, and the author of "Relevant Search" and "AI Powered Search." In this episode, they discuss what agentic search actually means and the 3 primary ways to implement agentic search. Hugo argues that search is becoming a core skill because agents can handle questions that require repeated retrieval and synthesis. To improve agentic search, most teams should start with evals. Establish a retrieval baseline, find areas for improvement, and iterate. Defining a “good” retrieval baseline is product-specific as a healthcare assistant may need its first result to be correct, while an e-commerce system may succeed by offering several useful options. Doug and Hugo argue that the lack of good training examples means its often useful to read and write your own search code. As to other code, Hugo describes the "dark factory" pattern, where code is written by AI agents but cannot be directly read by humans. How do we design systems for a world where the primary consumers of code are AI agents, not humans? Full episode notes Transcript Chapters (00:00) - Agents, engineers, and reverse centaurs(01:24) - Three paths for agentic search(05:32) - Teaching production-ready enterprise agents(07:06) - Why search is eating AI and data(10:24) - Start with evals and retrieval baselines(16:08) - Writing code to build judgment(18:45) - Learning and building beyond code(24:39) - Verification in a world of abundant code(29:52) - Resisting agent-driven feature sprawl(33:38) - Specialized models and better agent interfaces ⠀ Links from the show -------------------- Build Production-Ready AI Agents for the EnterpriseVanishing Gradientsretrieval-augmented generationBM25NDCGTerence Taodark software factorieslights-out manufacturingUnix philosophyOpenClawSimon Willison ⠀ Guests ------- Hugo Bowne-Anderson, Independent Data and AI Scientist WebsiteLinkedInXGitHubBluesky ⠀ Doug Turnbull, Principal, SoftwareDoug WebsiteLinkedInXGitHubBluesky ⠀ Follow the podcast ------------------- LinkedInThreadsInstagramTikTok ⠀ Follow Dan Gerlanc ------------------- XLinkedInThreadsBluesky
    続きを読む 一部表示
    41 分
  • The Handoff Packet for Local Agentic AI

    The Handoff Packet for Local Agentic AI
    2026/08/18

    Paul Everitt is a Python and Web Developer Advocate at JetBrains. He previously co-founded Zope Corporation, helped bootstrap the Python Software Foundation and Plone Foundation, and served as a US Navy officer who started www.navy.mil in 1993.

    Paul and Dan discuss how local agentic AI could reshape software engineering, from smaller models supported by static analysis and better tool calling to workflows built around specs, tests, and focused handoff packets. They compare local, cloud, and hybrid model setups and examine what happens when agents work with less supervision.

    The episode's titular handoff packet sits at the center of a three-level workflow. A large model understands the project, a medium model creates an execution plan, and a small local model carries out a tightly scoped task. The packet gives that smaller model the context, constraints, tools, and success criteria it needs.

    They also discuss spec drift, Markdown debt, community-maintained Python training data, and the responsibility developers retain to read, test, and own the code they ship.

    Full episode notes

    Transcript

    Chapters

    • (00:00) - A profession changing by the hour
    • (02:54) - Why Python still matters with humans in the loop
    • (04:37) - Static analysis for small local models
    • (11:51) - DwarfStar and the local inference stack
    • (17:28) - Apple's on-device AI push
    • (23:29) - Why unsupervised agent runs fall apart
    • (28:46) - The big brain, medium brain, little brain
    • (41:19) - Spec drift and Markdown debt
    • (47:45) - Pyrefly and duck typing for agents
    • (51:22) - Better tool calling for local coding models
    • (01:00:10) - Community-maintained Python training data
    • (01:03:42) - Bring back the engineering

    ⠀

    Links from the show

    --------------------

    • arXiv
    • Pydantic AI
    • LM Studio
    • OpenRouter
    • DeepLearning.AI spec-driven development course
    • Spec Kit
    • Superpowers
    • Pyrefly
    • Mellum
    • Flower
    • Liquid AI

    ⠀

    Guests

    -------

    Paul Everitt, Python and Web Developer Advocate at JetBrains

    • Website
    • LinkedIn
    • DeepLearning.AI: Spec-Driven Development with Coding Agents

    ⠀

    Follow the podcast

    -------------------

    • LinkedIn
    • Threads
    • Instagram
    • TikTok

    ⠀

    Follow Dan Gerlanc

    -------------------

    • X
    • LinkedIn
    • Threads
    • Bluesky
    続きを読む 一部表示
    1 時間 6 分
adbl_web_anon_alc_button_suppression_t1
まだレビューはありません