POPULARITY
Categories
490 episodes with Python
409 episodes with Python
304 episodes with Python
205 episodes with Python
294 episodes with Python
189 episodes with Python
357 episodes with Python
126 episodes with Python
88 episodes with Python
97 episodes with Python
195 episodes with Python
88 episodes with Python
65 episodes with Python
106 episodes with Python
51 episodes with Python
62 episodes with Python
54 episodes with Python
40 episodes with Python
52 episodes with Python
102 episodes with Python
43 episodes with Python
101 episodes with Python
60 episodes with Python
46 episodes with Python
53 episodes with Python
35 episodes with Python
42 episodes with Python
34 episodes with Python
58 episodes with Python
39 episodes with Python
28 episodes with Python
34 episodes with Python
77 episodes with Python
27 episodes with Python
23 episodes with Python
25 episodes with Python
23 episodes with Python
31 episodes with Python
40 episodes with Python
22 episodes with Python
25 episodes with Python
32 episodes with Python
33 episodes with Python
33 episodes with Python
15 episodes with Python
29 episodes with Python
47 episodes with Python
26 episodes with Python
20 episodes with Python
45 episodes with Python
23 episodes with Python
15 episodes with Python
40 episodes with Python
15 episodes with Python
SANS Internet Stormcenter Daily Network/Cyber Security and Information Security Stormcast
Polymorphic Python Malware Xavier discovered self-modifying Python code on Virustotal. The remote access tool takes advantage of the inspect module to modify code on the fly. https://isc.sans.edu/diary/Polymorphic%20Python%20Malware/32354 SSH ProxyCommand Vulnerability A user cloning a git repository may be tricked into executing arbitrary code via the SSH proxycommand option. https://dgl.cx/2025/10/bash-a-newline-ssh-proxycommand-cve-2025-61984 Framelink Figma MCP Server CVE-2025-53967 Framelink Figma s MCP server suffers from a remote code execution vulnerability.
AI Assisted Coding: From Deterministic to AI-Driven—The New Paradigm of Software Development, With Markus Hjort In this BONUS episode, we dive deep into the emerging world of AI-assisted coding with Markus Hjort, CTO of Bitmagic. Markus shares his hands-on experience with what's being called "vibe coding" - a paradigm shift where developers work more like technical product owners, guiding AI agents to produce code while focusing on architecture, design patterns, and overall system quality. This conversation explores not just the tools, but the fundamental changes in how we approach software engineering as a team sport. Defining Vibecoding: More Than Just Autocomplete "I'm specifying the features by prompting, using different kinds of agentic tools. And the agent is producing the code. I will check how it works and glance at the code, but I'm a really technical product owner." Vibecoding represents a spectrum of AI-assisted development approaches. Markus positions himself between pure "vibecoding" (where developers don't look at code at all) and traditional coding. He produces about 90% of his code using AI tools, but maintains technical oversight by reviewing architectural patterns and design decisions. The key difference from traditional autocomplete tools is the shift from deterministic programming languages to non-deterministic natural language prompting, which requires an entirely different way of thinking about software development. The Paradigm Shift: When AI Changed Everything "It's a different paradigm! Looking back, it started with autocomplete where Copilot could implement simple functions. But the real change came with agentic coding tools like Cursor and Claude Code." Markus traces his journey through three distinct phases. First came GitHub Copilot's autocomplete features for simple functions - helpful but limited. Next, ChatGPT enabled discussing architectural problems and getting code suggestions for unfamiliar technologies. The breakthrough arrived with agentic tools like Cursor and Claude Code that can autonomously implement entire features. This progression mirrors the historical shift from assembly to high-level languages, but with a crucial difference: the move from deterministic to non-deterministic communication with machines. Where Vibecoding Works Best: Knowing Your Risks "I move between different levels as I go through different tasks. In areas like CSS styling where I'm not very professional, I trust the AI more. But in core architecture where quality matters most, I look more thoroughly." Vibecoding effectiveness varies dramatically by context. Markus applies different levels of scrutiny based on his expertise and the criticality of the code. For frontend work and styling where he has less expertise, he relies more heavily on AI output and visual verification. For backend architecture and core system components, he maintains closer oversight. This risk-aware approach is essential for startup environments where developers must wear multiple hats. The beauty of this flexibility is that AI enables developers to contribute meaningfully across domains while maintaining appropriate caution in critical areas. Teaching Your Tools: Making AI-Assisted Coding Work "You first teach your tool to do the things you value. Setting system prompts with information about patterns you want, testing approaches you prefer, and integration methods you use." Success with AI-assisted coding requires intentional configuration and practice. Key strategies include: System prompts: Configure tools with your preferred patterns, testing approaches, and architectural decisions Context management: Watch context length carefully; when the AI starts making mistakes, reset the conversation Checkpoint discipline: Commit working code frequently to Git - at least every 30 minutes, ideally after every small working feature Dual AI strategy: Use ChatGPT or Claude for architectural discussions, then bring those ideas to coding tools for implementation Iteration limits: Stop and reassess after roughly 5 failed iterations rather than letting AI continue indefinitely Small steps: Split features into minimal increments and commit each piece separately In this segment we refer to the episode with Alan Cyment on AI Assisted Coding, and the Pachinko coding anti-pattern. Team Dynamics: Bigger Chunks and Faster Coordination "The speed changes a lot of things. If everything goes well, you can produce so much more stuff. So you have to have bigger tasks. Coordination changes - we need bigger chunks because of how much faster coding is." AI-assisted coding fundamentally reshapes team workflows. The dramatic increase in coding speed means developers need larger, more substantial tasks to maintain flow and maximize productivity. Traditional approaches of splitting stories into tiny tasks become counterproductive when implementation speed increases 5-10x. This shift impacts planning, requiring teams to think in terms of complete features rather than granular technical tasks. The coordination challenge becomes managing handoffs and integration points when individuals can ship significant functionality in hours rather than days. The Non-Deterministic Challenge: A New Grammar "When you're moving from low-level language to higher-level language, they are still deterministic. But now with LLMs, it's not deterministic. This changes how we have to think about coding completely." The shift to natural language prompting introduces fundamental uncertainty absent from traditional programming. Unlike the progression from assembly to C to Python - all deterministic - working with LLMs means accepting probabilistic outputs. This requires developers to adopt new mental models: thinking in terms of guidance rather than precise instructions, maintaining checkpoints for rollback, and developing intuition for when AI is "hallucinating" versus producing valid solutions. Some developers struggle with this loss of control, while others find liberation in focusing on what to build rather than how to build it. Code Reviews and Testing: What Changes? "With AI, I spend more time on the actual product doing exploratory testing. The AI is doing the coding, so I can focus on whether it works as intended rather than syntax and patterns." Traditional code review loses relevance when AI generates syntactically correct, pattern-compliant code. The focus shifts to testing actual functionality and user experience. Markus emphasizes: Manual exploratory testing becomes more important as developers can't rely on having written and understood every line Test discipline is critical - AI can write tests that always pass (assert true), so verification is essential Test-first approach helps ensure tests actually verify behavior rather than just existing Periodic test validation: Randomly modify test outputs to verify they fail when they should Loosening review processes to avoid bottlenecks when code generation accelerates dramatically Anti-Patterns and Pitfalls to Avoid Several common mistakes emerge when developers start with AI-assisted coding: Continuing too long: When AI makes 5+ iterations without progress, stop and reset rather than letting it spiral Skipping commits: Without frequent Git checkpoints, recovery from AI mistakes becomes extremely difficult Over-reliance without verification: Trusting AI-generated tests without confirming they actually test something meaningful Ignoring context limits: Continuing to add context until the AI becomes confused and produces poor results Maintaining traditional task sizes: Splitting work too granularly when AI enables completing larger chunks Forgetting exploration: Reading about tools rather than experimenting hands-on with your own projects The Future: Autonomous Agents and Automatic Testing "I hope that these LLMs will become larger context windows and smarter. Tools like Replit are pushing boundaries - they can potentially do automatic testing and verification for you." Markus sees rapid evolution toward more autonomous development agents. Current trends include: Expanded context windows enabling AI to understand entire codebases without manual context curation Automatic testing generation where AI not only writes code but also creates and runs comprehensive test suites Self-verification loops where agents test their own work and iterate without human intervention Design-to-implementation pipelines where UI mockups directly generate working code Agentic tools that can break down complex features autonomously and implement them incrementally The key insight: we're moving from "AI helps me code" to "AI codes while I guide and verify" - a fundamental shift in the developer's role from implementer to architect and quality assurance. Getting Started: Experiment and Learn by Doing "I haven't found a single resource that covers everything. My recommendation is to try Claude Code or Cursor yourself with your own small projects. You don't know the experience until you try it." Rather than pointing to comprehensive guides (which don't yet exist for this rapidly evolving field), Markus advocates hands-on experimentation. Start with personal projects where stakes are low. Try multiple tools to understand their strengths. Build intuition through practice rather than theory. The field changes so rapidly that reading about tools quickly becomes outdated - but developing the mindset and practices for working with AI assistance provides durable value regardless of which specific tools dominate in the future. About Markus Hjort Markus is Co-founder and CTO of Bitmagic, and has over 20 years of software development expertise. Starting with Commodore 64 game programming, his career spans gaming, fintech, and more. As a programmer, consultant, agile coach, and leader, Markus has successfully guided numerous tech startups from concept to launch. You can connect with Markus Hjort on LinkedIn.
This Week in Machine Learning & Artificial Intelligence (AI) Podcast
Today, we're joined by Jacob Buckman, co-founder and CEO of Manifest AI to discuss achieving long context in transformers. We discuss the bottlenecks of scaling context length and recent techniques to overcome them, including windowed attention, grouped query attention, and latent space attention. We explore the idea of weight-state balance and the weight-state FLOP ratio as a way of reasoning about the optimality of compute architectures, and we dig into the Power Retention architecture, which blends the parallelization of attention with the linear scaling of recurrence and promises speedups of >10x during training and >100x during inference. We review Manifest AI's recent open source projects as well: Vidrial—a custom CUDA framework for building highly optimized GPU kernels in Python, and PowerCoder—a 3B-parameter coding model fine-tuned from StarCoder to use power retention. Our chat also covers the use of metrics like in-context learning curves and negative log likelihood to measure context utility, the implications of scaling laws, and the future of long context lengths in AI applications. The complete show notes for this episode can be found at https://twimlai.com/go/750.
What Halloween costumes are trending this year? Billy called in from Africa again and gave us an update on the scary animal he ran into! Listen to Billy & Lisa weekdays from 6-10AM on Kiss 108!
Talk Python To Me - Python conversations for passionate developers
Today we're turning tiny tips into big wins. Khuyen Tran, creator of CodeCut.ai, has shipped hundreds of bite-size Python and data science snippets across four years. We dig into open-source tools you can use right now, cleaner workflows, and why notebooks and scripts don't have to be enemies. If you want faster insights with fewer yak-shaves, this one's packed with takeaways you can apply before lunch. Let's get into it. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Agntcy Talk Python Courses Links from the show Khuyen Tran (LinkedIn): linkedin.com Khuyen Tran (GitHub): github.com CodeCut: codecut.ai Production-ready Data Science Book (discount code TalkPython): codecut.ai Why UV Might Be All You Need: codecut.ai How to Structure a Data Science Project for Readability and Transparency: codecut.ai Stop Hard-coding: Use Configuration Files Instead: codecut.ai Simplify Your Python Logging with Loguru: codecut.ai Git for Data Scientists: Learn Git Through Practical Examples: codecut.ai Marimo (A Modern Notebook for Reproducible Data Science): codecut.ai Text Similarity & Fuzzy Matching Guide: codecut.ai Loguru (Python logging made simple): github.com Hydra: hydra.cc Marimo: marimo.io Quarto: quarto.org Show Your Work! Book: austinkleon.com Watch this episode on YouTube: youtube.com Episode #522 deep-dive: talkpython.fm/522 Episode transcripts: talkpython.fm Theme Song: Developer Rap
Enterprises are significantly increasing their investments in AI governance as the risks associated with artificial intelligence become more apparent. A recent report indicates that 98% of organizations plan to boost their governance budgets in the coming financial year, with an average expected increase of 24%. This shift highlights the realization that managing AI is not a plug-and-play solution; organizations must establish multiple lines of defense to handle risks effectively. As AI technologies evolve, refining governance will be an ongoing process, especially as companies face incidents that could lead to substantial financial losses.Public cloud spending is projected to increase dramatically, primarily driven by generative AI workloads. A survey reveals that nearly half of IT leaders expect more than 30% of their cloud budgets to be allocated to generative AI in the coming years. This rapid adoption of generative AI applications necessitates improved cloud cost management strategies, as enterprises brace for higher infrastructure costs. Analysts warn that the financial models supporting this AI boom, particularly for companies like Oracle, which may need to borrow significantly to meet obligations, raise concerns about sustainability.Despite fears of job losses due to AI, a study from Yale University indicates that generative AI has not yet significantly disrupted the job market. The research shows only a slight change in the occupational mix since the launch of ChatGPT, with hiring in the tech sector remaining steady. A significant portion of tech employers plan to hire, particularly for roles related to AI, indicating that the demand for skills like Python and project management is driving this trend. The study suggests that while generative AI has transformative potential, it is too early to assess its long-term effects on employment.In a notable industry development, Huntress has partnered with SureWeb to expand its cybersecurity solutions, marking its first distribution deal. This collaboration allows Huntress's products to be available in the SureWeb marketplace, enhancing security offerings for managed service providers across various regions. The partnership emphasizes the importance of relationships over transactions, contrasting with larger marketplaces. This move reflects a growing trend where vendors prioritize community-focused partnerships, providing opportunities for service providers to access quality cybersecurity solutions while navigating the evolving landscape of AI and technology.Four things to know today 00:00 AI's Hidden Cost: Governance Budgets Up, Cloud Bills Soar, and Debt Piles High Behind the Boom05:25 Government Shutdown and Policy Turmoil, Not AI, Emerging as Real Threats to U.S. Employment10:17 Pax8's “Managed Intelligence” Push Highlights Growing Tension Between AI Hype and MSP Readiness13:28 Huntress and Sherweb Redefine Channel Strategy with Relationship-First Distribution Model This is the Business of Tech. Supported by: https://cometbackup.com/?utm_source=mspradio&utm_medium=podcast&utm_campaign=sponsorshiphttps://www.auvik.com/ Webinar: https://bit.ly/msprmail All our Sponsors: https://businessof.tech/sponsors/ Do you want the show on your podcast app or the written versions of the stories? Subscribe to the Business of Tech: https://www.businessof.tech/subscribe/Looking for a link from the stories? The entire script of the show, with links to articles, are posted in each story on https://www.businessof.tech/ Support the show on Patreon: https://patreon.com/mspradio/ Want to be a guest on Business of Tech: Daily 10-Minute IT Services Insights? Send Dave Sobel a message on PodMatch, here: https://www.podmatch.com/hostdetailpreview/businessoftech Want our stuff? Cool Merch? Wear “Why Do We Care?” - Visit https://mspradio.myspreadshop.com Follow us on:LinkedIn: https://www.linkedin.com/company/28908079/YouTube: https://youtube.com/mspradio/Facebook: https://www.facebook.com/mspradionews/Instagram: https://www.instagram.com/mspradio/TikTok: https://www.tiktok.com/@businessoftechBluesky: https://bsky.app/profile/businessof.tech Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Join this channel to get access to perks - custom emojis, member lives, and access to the auction listings:https://www.youtube.com/channel/UCJoP2q6P8mWkBUMn45pgyAA/join Jessica Hare - Hare Hollow Farm - Altus, OKHarehollowfarm.comMorph Market - https://www.morphmarket.com/stores/hare_hollow_farm/Facebook - https://www.facebook.com/Hare-Hollow-Farm-113861266980541Instagram - https://www.instagram.com/hare_hollow_farm/Youtube - https://www.youtube.com/@unmeinohiShow Sponsors:RAL - Vetdna.comUse code #sh!thappens to get $5 off a crypto panel. Shane Kelley - Small Town Xotics - Knoxville, TNMorph Market - https://www.morphmarket.com/stores/smalltownxotics/Facebook - https://www.facebook.com/SmallTownXotics/Instagram - https://www.instagram.com/smalltownxotics/Youtube - https://www.youtube.com/c/SmallTownXoticsRumble - https://rumble.com/search/video?q=smalltownxotics Roger and Lori Gray - Gray Family Snakes - Huntsville, AlabamaMorph Market - https://www.morphmarket.com/us/c/all?store=gray_family_snakesFacebook - https://www.facebook.com/GrayFamilySnakesInstagram - https://www.instagram.com/gray_family_snakes/ Andrew Boring - Powerhouse Pythons - Tacoma, WaHusbandry Pro - https://husbandry.pro/stores/powerhouse-pythonsFacebook - https://www.facebook.com/powerhouse.pythonsInstagram - https://www.instagram.com/powerhouse.pythons/ Eileen Jarp - Bravo Zulu - Daleville, INMorph Market -https://www.morphmarket.com/stores/bravozulu/Facebook - https://www.facebook.com/bravozuluBPInstagram -https://www.instagram.com/bravozuluballpythons/Youtube - https://www.youtube.com/@bravozuluballpythons Christopher Shelly - B&S Reptilia - Sellersville, PAMorph Market - https://www.morphmarket.com/stores/bandsreptilia/Facebook - https://www.facebook.com/B-and-S-Reptilia-1415759941972085Instagram - https://www.instagram.com/bandsreptilia/ Justin Brill - Stoneage Ball pythons - Gresham, ORMorph Market -https://www.morphmarket.com/stores/stoneageballpythons/?cat=bpsFacebook - https://www.facebook.com/StoneAgeBallsInstagram - https://www.instagram.com/stoneageballpythons/Youtube - https://www.youtube.com/c/stoneageballpythons
What's changed about learning Python over the last few years? What new techniques and updated advice should beginners have as they start their journey? This week on the show, Stephen Gruppetta and Martin Breuss return to discuss beginning to learn Python.
Loxo's Founder & CEO, Matt Chambers, hosted a recent webinar on Natural Language Search — and it was so good, we had to share it on the pod.So...What is Natural Language Search (NLS)? Basically: Instead of writing complex Boolean queries, you can type exactly what you need in plain English, e.g “senior software engineer with Python and AWS experience in Austin.” From there, Loxo's AI interprets your intent, searches your own database and our network of 1.2 billion professional profiles, and delivers the most relevant candidates — including hidden gems you might otherwise miss.Check out this episode to hear it from Matt directly — and listen as he conducts searches using NLS!Explore all our episodes and catch the full video experience at loxo.co/podcastsBecoming a Hiring Machine is brought to you by Loxo. To discover more about us, just visit loxo.co
As AI becomes more integrated into the IT landscape, developers, engineers, and operators are looking for practical ways to use these new tools. Joining us today is Ryan Booth; he’s built a career around network automation, giving him a unique perspective on how network engineering, operations, software development, and AI intersect. We explore the practical... Read more »
Charlie Marsh built Ruff (an extremely fast Python linter written in Rust) and uv (an extremely fast Python package manager written in Rust) because he believes great tools can have an outsized impact. He believes it so much, in fact, that he started an entire company that builds next-gen Python tooling. On this episode, Charlie joins us to tell us all about it: why Python, why Rust, how they make everything so fast, how they're starting to make money, what other products he's dreaming up, and more.
As AI becomes more integrated into the IT landscape, developers, engineers, and operators are looking for practical ways to use these new tools. Joining us today is Ryan Booth; he’s built a career around network automation, giving him a unique perspective on how network engineering, operations, software development, and AI intersect. We explore the practical... Read more »
Send us a textReplay Episode: Python, Anaconda, and the AI Frontier with Peter WangPeter Wang — Chief AI & Innovation Officer and Co-founder of Anaconda — is back on Making Data Simple! Known for shaping the open-source ecosystem and making Python a powerhouse, Peter dives into Anaconda's new AI incubator, the future of GenAI, and why Python isn't just “still a thing”… it's the thing.From branding and security to leadership and philosophy, this episode is a wild ride through the biggest opportunities (and risks) shaping AI today.Timestamps: 01:27 Meet Peter Wang 05:10 Python or R? 05:51 Anaconda's Differentiation 07:08 Why the Name Anaconda 08:24 The AI Incubator 11:40 GenAI 14:39 Enter Python 16:08 Anaconda Commercial Services 18:40 Security 20:57 Common Points of Failure 22:53 Branding 24:50 watsonx Partnership 28:40 AI Risks 34:13 Getting Philosophical 36:13 China 44:52 Leadership StyleLinkedin: linkedin.com/in/pzwangWebsite: https://www.linkedin.com/company/anacondainc/, https://www.anaconda.com/Want to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.
Send us a textReplay Episode: Python, Anaconda, and the AI Frontier with Peter WangPeter Wang — Chief AI & Innovation Officer and Co-founder of Anaconda — is back on Making Data Simple! Known for shaping the open-source ecosystem and making Python a powerhouse, Peter dives into Anaconda's new AI incubator, the future of GenAI, and why Python isn't just “still a thing”… it's the thing.From branding and security to leadership and philosophy, this episode is a wild ride through the biggest opportunities (and risks) shaping AI today.Timestamps: 01:27 Meet Peter Wang 05:10 Python or R? 05:51 Anaconda's Differentiation 07:08 Why the Name Anaconda 08:24 The AI Incubator 11:40 GenAI 14:39 Enter Python 16:08 Anaconda Commercial Services 18:40 Security 20:57 Common Points of Failure 22:53 Branding 24:50 watsonx Partnership 28:40 AI Risks 34:13 Getting Philosophical 36:13 China 44:52 Leadership StyleLinkedin: linkedin.com/in/pzwangWebsite: https://www.linkedin.com/company/anacondainc/, https://www.anaconda.com/Want to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.
On-Device AI Agents in Production: Privacy, Performance, and Scale // MLOps Podcast #340 with NimbleEdge's Varun Khare, Founder/CEO and Neeraj Poddar, Co-founder & CTO.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractAI agents are transitioning from experimental stages to performing real work in production; however, they have largely been limited to backend task automation. A critical frontier in this evolution is the on-device AI agent, enabling sophisticated, AI-native experiences directly on mobile and embedded devices. While cloud-based AI faces challenges like constant connectivity demands, increased latency, privacy risks, and high operational costs, on-device breaks through these trade-offs.We'll delve into the practical side of building and deploying AI agents with “DeliteAI”, an open-source on-device AI agentic framework. We'll explore how lightweight Python runtimes facilitate the seamless orchestration of end-to-end workflows directly on devices, allowing AI/ML teams to define data preprocessing, feature computation, model execution, and post-processing logic independently of frontend code. This architecture empowers agents to adapt to varying tasks and user contexts through an ecosystem of tools natively supported on Android/iOS platforms, handling all the permissions, model lifecycles, and many more.// BioVarun KhareVarun is the Founder and CEO of NimbleEdge, an AI startup pioneering privacy-first, on-device intelligence. With an academic foundation in AI and neuroscience from UC Berkeley, MPI Frankfurt, and IIT Kanpur, Varun brings deep expertise at the intersection of technology and science. Before founding NimbleEdge, Varun led open-source projects at OpenMined, focusing on privacy-aware AI, and published research in computer vision.Neeraj PoddarNeeraj Poddar is the Co-founder and CTO at NimbleEdge. Prior to NimbleEdge, he was the Co-founder of Aspen Mesh, VP of Engineering at Solo.io, and led the Istio open source community. He has worked on various aspects of AI, networking, security, and distributed systems over the span of his career. Neeraj focuses on the application of open source technologies across different industries in terms of scalability and security. When not working on AI, you can find him playing racquetball and gaining back the calories spent playing by trying out new restaurants. // Related LinksWebsite: https://www.nimbleedge.com/https://www.nimbleedge.com/blog/why-ai-is-not-working-for-youhttps://www.nimbleedge.com/blog/state-of-on-device-aihttps://www.youtube.com/watch?v=Qqj_Nl2MihEhttps://www.linkedin.com/events/7343237917982527488/comments/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Varun on LinkedIn: /vkkhare/Connect with Neeraj on LinkedIn: /nrjpoddar/Timestamps:[00:00] On-device AI skepticism[02:47] Word suggestion for AI[06:40] Optimizing unique challenges[13:39] LLM on-device challenges[20:34] Agent overlord tension[23:56] AI app constraints[29:23] Siri limitations and trust gap[32:01] Voice-driven app privacy[35:49] Platform lock-in vs aggregation[42:26] On-device AI optimizations[45:38] Wrap up
Talk Python To Me - Python conversations for passionate developers
English is now an API. Our apps read untrusted text; they follow instructions hidden in plain sight, and sometimes they turn that text into action. If you connect a model to tools or let it read documents from the wild, you have created a brand new attack surface. In this episode, we will make that concrete. We will talk about the attacks teams are seeing in 2025, the defenses that actually work, and how to test those defenses the same way we test code. Our guides are Tori Westerhoff and Roman Lutz from Microsoft. They help lead AI red teaming and build PyRIT, a Python framework the Microsoft AI Red Team uses to pressure test real products. By the end of this hour you will know where the biggest risks live, what you can ship this quarter to reduce them, and how PyRIT can turn security from a one time audit into an everyday engineering practice. Episode sponsors Sentry AI Monitoring, Code TALKPYTHON Agntcy Talk Python Courses Links from the show Tori Westerhoff: linkedin.com Roman Lutz: linkedin.com PyRIT: aka.ms/pyrit Microsoft AI Red Team page: learn.microsoft.com 2025 Top 10 Risk & Mitigations for LLMs and Gen AI Apps: genai.owasp.org AI Red Teaming Agent: learn.microsoft.com 3 takeaways from red teaming 100 generative AI products: microsoft.com MIT report: 95% of generative AI pilots at companies are failing: fortune.com A couple of "Little Bobby AI" cartoons Give me candy: talkpython.fm Tell me a joke: talkpython.fm Watch this episode on YouTube: youtube.com Episode #521 deep-dive: talkpython.fm/521 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy
Topics covered in this episode: * PostgreSQL 18 Released* * Testing is better than DSA (Data Structures and Algorithms)* * Pyrefly in Cursor/PyCharm/VSCode/etc* * Playwright & pytest techniques that bring me joy* Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: PostgreSQL 18 Released PostgreSQL 18 is out (Sep 25, 2025) with a focus on faster text handling, async I/O, and easier upgrades. New async I/O subsystem speeds sequential scans, bitmap heap scans, and vacuum by issuing concurrent reads instead of blocking on each request. Major-version upgrades are smoother: pg_upgrade retains planner stats, adds parallel checks via -jobs, and supports faster cutovers with -swap. Smarter query performance lands with skip scans on multicolumn B-tree indexes, better OR optimization, incremental-sort merge joins, and parallel GIN index builds. Dev quality-of-life: virtual generated columns enabled by default, a uuidv7() generator for time-ordered IDs, and RETURNING can expose both OLD and NEW. Security gets an upgrade with native OAuth 2.0 authentication; MD5 password auth is deprecated and TLS controls expand. Text operations get a boost via the new PG_UNICODE_FAST collation, faster upper/lower, a casefold() helper, and clearer collation behavior for LIKE/FTS. Brian #2: Testing is better than DSA (Data Structures and Algorithms) Ned Batchelder If you need to grind through DSA problems to get your first job, then of course, do that, but if you want to prepare yourself for a career, and also stand out in job interviews, learn how to write tests. Testing is a skill you'll use constantly, will make you stand out in job interviews, and isn't taught well in school (usually). Testing code well is not obvious. It's a puzzle and a problem to solve. It gives you confidence and helps you write better code. Applies everywhere, at all levels. Notes from Brian Most devs suck at testing, so being good at it helps you stand out very quickly. Thinking about a system and how to test it often very quickly shines a spotlight on problem areas, parts with not enough specification, and fuzzy requirements. This is a good thing, and bringing up these topics helps you to become a super valuable team member. High level tests need to be understood by key engineers on a project. Even if tons of the code is AI generated. Even if many of the tests are, the people understanding the requirements and the high level tests are quite valuable. Michael #3: Pyrefly in Cursor/PyCharm/VSCode/etc Install the VSCode/Cursor extension or PyCharm plugin, see https://pyrefly.org/en/docs/IDE/ Brian spoke about Pyrefly in #433: Dev in the Arena I've subsequently had the team on Talk Python: #523: Pyrefly: Fast, IDE-friendly typing for Python (podcast version coming in a few weeks, see video for now.) My experience has been Pyrefly changes the feel of the editor, give it a try. But disable the regular language server extension. Brian #4: Playwright & pytest techniques that bring me joy Tim Shilling “I've been working with playwright more often to do end to end tests. As a project grows to do more with HTMX and Alpine in the markup, there's less unit and integration test coverage and a greater need for end to end tests.” Tim covers some cool E2E techniques Open new pages / tabs to be tested Using a pytest marker to identify playwright tests Using a pytest marker in place of fixtures Using page.pause() and Playwright's debugging tool Using assert_axe_violations to prevent accessibility regressions Using page.expect_response() to confirm a background request occurred From Brian Again, with more and more lower level code being generated, and many unit tests being generated (shakes head in sadness), there's an increased need for high level tests. Don't forget API tests, obviously, but if there's a web interface, it's gotta be tested. Especially if the primary user experience is the web interface, building your Playwright testing chops helps you stand out and let's you test a whole lot of your system with not very many tests. Extras Brian: Big O - By Sam Who Yes, take Ned's advice and don't focus so much on DSA, focus also on learning to test. However, one topic you should be comfortable with in algortithm-land is Big O, at least enough to have a gut feel for it. And this article is really good enough for most people. Great graphics, demos, visuals. As usual, great content from Sam Who, and a must read for all serious devs. Python 3.14.0rc3 has been available since Sept 18. Python 3.14.0 final scheduled for Oct 7 Django 6.0 alpha 1 released Django 6.0 final scheduled for Dec 3 Python Test Static hosting update Some interesting discussions around setting up my own server, but this seems like it might be yak shaving procrastination research when I really should be writing or coding. So I'm holding off until I get some writing projects and a couple SaaS projects further along. Joke: Always be backing up
In this episode, Sean and Kelly welcome Pritesh Patel, a computer scientist specializing in AI who brings over 20 years of experience from companies like Turner Broadcasting, Walmart, and GE to his current role at Fisher Phillips law firm. Pritesh shares fascinating insights about implementing AI in knowledge-based industries, from automating his parents' frozen yogurt shop to helping lawyers transform their workflows. The conversation explores crucial concepts like the "Jobs to Be Done" framework, which emphasizes focusing on outcomes rather than getting stuck in existing processes. Pritesh explains how he approaches AI education through playfulness and intuition-building—whether that's getting Batman to sing "Twinkle, Twinkle Little Star" in ChatGPT or creating AI personalities that debate humans. The discussion touches on the importance of maintaining accountability while delegating responsibility to AI tools, the power of curiosity in adoption, and how reinforcement learning might shape the future of AI integration. Key resources mentioned: - Strategyn Jobs to Be Done Framework (https://strategyn.com/) - Tony Ulwick's innovation methodology - NotebookLM (https://notebooklm.google.com/) - Google's AI-powered research tool - Suno.ai (https://suno.com/) - AI music generation platform - OpenAI's Real-time API for voice interactions Special Guest: Pritesh Patel.
What's a good way to enable or disable code paths without redeploying the software? How can you use feature flags to toggle functionality for specific users of your application? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.
In this episode, we talk with Daniel, an astrophysicist turned machine learning engineer and AI ambassador. Daniel shares his journey bridging astronomy and data science, how he leveraged live courses and public knowledge sharing to grow his skills, and his experiences working on cutting-edge radio astronomy projects and AI deployments. He also discusses practical advice for beginners in data and astronomy, and insights on career growth through community and continuous learning.TIMECODES00:00 Lunar eclipse story and Daniel's astronomy career04:12 Electromagnetic spectrum and MEERKAT data explained10:39 Data analysis and positional cross-correlation challenges15:25 Physics behind radio star detection and observation limits16:35 Radio astronomy's advantage and machine learning potential20:37 Radio astronomy progress and Daniel's ML journey26:00 Python tools and experience with ZoomCamps31:26 Intel internship and exploring LLMs41:04 Sharing progress and course projects with orchestration tools44:49 Setting up Airflow 3.0 and building data pipelines47:39 AI startups, training resources, and NVIDIA courses50:20 Student access to education, NVIDIA experience, and beginner astronomy programs57:59 Skills, projects, and career advice for beginners59:19 Starting with data science or engineering1:00:07 Course sponsorship, data tools, and learning resourcesConnect with DanielLinkedin - / egbodaniel Connect with DataTalks.Club:Join the community - https://datatalks.club/slack.htmlSubscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/...Check other upcoming events - https://lu.ma/dtc-eventsGitHub: https://github.com/DataTalksClubLinkedIn - / datatalks-club Twitter - / datatalksclub Website - https://datatalks.club/
At PyData Berlin, community members and industry voices highlighted how AI and data tooling are evolving across knowledge graphs, MLOps, small-model fine-tuning, explainability, and developer advocacy.- Igor Kvachenok (Leuphana University / ProKube) combined knowledge graphs with LLMs for structured data extraction in the polymer industry, and noted how MLOps is shifting toward LLM-focused workflows.- Selim Nowicki (Distill Labs) introduced a platform that uses knowledge distillation to fine-tune smaller models efficiently, making model specialization faster and more accessible.- Gülsah Durmaz (Architect & Developer) shared her transition from architecture to coding, creating Python tools for design automation and volunteering with PyData through PyLadies.- Yashasvi Misra (Pure Storage) spoke on explainable AI, stressing accountability and compliance, and shared her perspective as both a data engineer and active Python community organizer.- Mehdi Ouazza (MotherDuck) reflected on developer advocacy through video, workshops, and branding, showing how creative communication boosts adoption of open-source tools like DuckDB.Igor KvachenokMaster's student in Data Science at Leuphana University of Lüneburg, writing a thesis on LLM-enhanced data extraction for the polymer industry. Builds RDF knowledge graphs from semi-structured documents and works at ProKube on MLOps platforms powered by Kubeflow and Kubernetes.Connect: https://www.linkedin.com/in/igor-kvachenok/Selim NowickiFounder of Distill Labs, a startup making small-model fine-tuning simple and fast with knowledge distillation. Previously led data teams at Berlin startups like Delivery Hero, Trade Republic, and Tier Mobility. Sees parallels between today's ML tooling and dbt's impact on analytics.Connect: https://www.linkedin.com/in/selim-nowicki/Gülsah DurmazArchitect turned developer, creating Python-based tools for architectural design automation with Rhino and Grasshopper. Active in PyLadies and a volunteer at PyData Berlin, she values the community for networking and learning, and aims to bring ML into architecture workflows.Connect: https://www.linkedin.com/in/gulsah-durmaz/Yashasvi (Yashi) MisraData Engineer at Pure Storage, community organizer with PyLadies India, PyCon India, and Women Techmakers. Advocates for inclusive spaces in tech and speaks on explainable AI, bridging her day-to-day in data engineering with her passion for ethical ML.Connect: https://www.linkedin.com/in/misrayashasvi/Mehdi OuazzaDeveloper Advocate at MotherDuck, formerly a data engineer, now focused on building community and education around DuckDB. Runs popular YouTube channels ("mehdio DataTV" and "MotherDuck") and delivered a hands-on workshop at PyData Berlin. Blends technical clarity with creative storytelling.Connect: https://www.linkedin.com/in/mehd-io/
On this episode, learn about CJ Threads and how it helps students learn Python code. After that, Tech Takeaway discusses an exciting new feature now available on the Monarch.On this episodeNarratorSara Brown, APH Public Relations ManagerKatrina Best, APH STEM Product ManagerMichael McDonald, APH Senior Software Engineer Jennifer Wenzel, APH Technology Product SpecialistMichael Dennis, APH Technology Products SpecialistAdditional LinksCJ ThreadsCode JumperMonarch Tech Support - 833-447-8444 Hours 8 a.m. to 6:30 p.m. estEmail Monarch Support
Depuis cet été, un nouvel outil circule librement sur l'index Python et suscite inquiétude chez les spécialistes de la cybersécurité. Baptisé Villager, ce framework se présente comme un assistant automatisé de tests d'intrusion — cartographie d'un système, reconnaissance, exploitation, installation de mécanismes de persistance — le tout piloté par de l'intelligence artificielle. Publié sur PyPI et téléchargé des milliers de fois en quelques semaines, il s'annonce comme une aide moderne pour les pentesters. Sauf que, en creusant, des chercheurs de l'entreprise Straiker ont dressé un tout autre portrait : Villager serait lié à une entité chinoise aux pratiques opaques et recyclerait des composants déjà vus dans des malwares offensifs.Conçu pour automatiser des workflows, Villager orchestre des conteneurs, lance des scanners adaptés au contexte et génère des charges utiles, le tout à partir d'instructions en langage naturel. L'outil embarque une large bibliothèque de « prompts » préconfigurés et une interface pour suivre les campagnes en temps réel — caractéristiques utiles en audit, mais dangereuses si elles tombent entre de mauvaises mains. Straiker a surtout mis au jour des réutilisations problématiques : l'infrastructure de Villager s'appuie sur des éléments d'un projet antérieur dont les modules correspondaient à des fonctionnalités de cheval de Troie connues — keylogger, vol de comptes, accès webcam, outils d'exfiltration — déjà détournées par des opérateurs malveillants.L'autre inquiétude tient à la voie de diffusion. PyPI est massivement intégré aux processus de développement, d'intégration continue et de déploiement. Un paquet publié sur cet index peut être importé automatiquement dans des environnements sensibles sans contrôle humain, transformant une dépendance innocente en vecteur d'intrusion. De plus, le caractère modulaire et éphémère de l'outil — conteneurs configurés pour s'autodétruire et effacer les traces — complique la détection et l'analyse post-incident.Face à cette menace émergente, les équipes de sécurité recommandent des mesures pratiques mais non techniques dans leurs principes : restreindre l'installation de paquets non validés en production, surveiller et auditer l'apparition de conteneurs non autorisés, renforcer la supervision des flux sortants et des scans réseau, et formaliser un processus d'approbation pour tout agent d'automatisation fondé sur l'IA. Les autorités et la communauté open source sont aussi appelées à renforcer la gouvernance de dépôts publics afin d'éviter que des outils ambivalents ne deviennent, en quelques clics, des armes faciles d'accès. Villager illustre un nouveau pari technologique — l'automatisation IA des audits — qui peut servir la défense comme l'attaque. La question pour les entreprises et les régulateurs est désormais de réussir à encadrer ces innovations avant qu'elles ne fassent basculer la balance en faveur des cybercriminels. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.
Transforming over 5,000 network devices across 56 data centers is no small feat. Doing that with a very small team is even more impressive. On today’s episode, sponsored by Network To Code, we talk to Greg Botts from Intel, who with his team accomplished just that. They started with YAML files and DNS records and... Read more »
Transforming over 5,000 network devices across 56 data centers is no small feat. Doing that with a very small team is even more impressive. On today’s episode, sponsored by Network To Code, we talk to Greg Botts from Intel, who with his team accomplished just that. They started with YAML files and DNS records and... Read more »
09-23-25 - We Need An Exclamation TD Like Amon Ra Has - Jimmy Kimmel Returns To ABC Tonight - Raul Grijalva's Daughter Running For His Seat - NASA Presented Latest Astronaut Class That May Go To Mars - Testing Bret With Another Headline - FLA Python Pukes Up A DeerSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Talk Python To Me - Python conversations for passionate developers
A couple years ago, Charlie Marsh lit a fire under Python tooling with Ruff and then uv. Today he's back with something on the other side of that coin: pyx. Pyx isn't a PyPI replacement. Think server, not just index. It mirrors PyPI, plays fine with pip or uv, and aims to make installs fast and predictable by letting a smart client talk to a smart server. When the client and server understand each other, you get new fast paths, fewer edge cases, and the kind of reliability teams beg for. If Python packaging has felt like friction, this conversation is traction. Let's get into it. Episode sponsors Six Feet Up Talk Python Courses Links from the show Charlie Marsh on Twitter: @charliermarsh Charlie Marsh on Mastodon: @charliermarsh Astral Homepage: astral.sh Pyx Project: astral.sh Introducing Pyx Blog Post: astral.sh uv Package on GitHub: github.com UV Star History Chart: star-history.com Watch this episode on YouTube: youtube.com Episode #520 deep-dive: talkpython.fm/520 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy
184: Asynchronous ProgrammingIntro topic: AI ScamsNews/Links:Coding Adventure: Ray-Tracing Glass and Caustics (Sebastian Lague)https://www.youtube.com/watch?v=wA1KVZ1eOuABoson AI announces Higgs Audio V2https://www.boson.ai/technologies/voice The Misconception that Almost Stopped AI [How Models Learn Part 1] (Welch Labs)https://www.youtube.com/watch?v=NrO20Jb-hy0A mind-bending conversation with Peter Thielhttps://www.nytimes.com/2025/07/11/podcasts/interesting-times-a-mind-bending-conversation-with-peter-thiel.htmlBook of the ShowPatrickThe Hobbit (JRR Tolkien)https://amzn.to/4mevuzEJasonNYT Word GamesPatreon Plug https://www.patreon.com/programmingthrowdown?ty=hTool of the ShowPatrickEscape Academyhttps://escapeacademygame.com/enJasonMulti-modal LLMs to make calendar meetingswww.chatgpt.comTopic: Asynchronous ComputingWhat/WhyMulti-threading in between the linesMany of the benefits of multiprocessing without the overhead/complexityHowCoroutinesThread-Local MemoryBlocking vs Non-Blocking operationsBlocking: arithmeticNon-Blocking: Reading from the network card into thread-local memoryInterpreter lockingTypescript: Single threadedPython: GILImplementationsPolling (not-Asynchronous)Callbacks (interrupts)Multithreading (with queues/message passing)Promise/FuturesAsync/Await ★ Support this podcast on Patreon ★
Are you feeling spiritually suffocated, prayerless, or stagnant in your walk with God? Discover how to identify and cast out the Python demon, a spirit that seeks to choke your prophetic voice, drain your prayer life, and block your freedom. Register for Fierce at kathydegrawministries.org Purchase Kathy's book Healed at Last – Overcome Sickness to Receive your Physical Healing on Amazon https://a.co/d/6a6mt8w or at: https://www.kathydegrawministries.org/healed-at-last/ Purchase Anointing Oil with a prayer cloth that Kathy has personally mixed and prayed over on Kathy's Website or Amazon. Order anointing oil by Kathy on Amazon look for her brand here https://amzn.to/3PC6l3R or Kathy DeGraw Ministries https://www.kathydegrawministries.org/product-category/oils/ Training, Mentorship and Deliverance! Personal coaching, deliverance, e-courses, training for ministry, and mentorships! https://www.kathydegrawministries.org/training/# The Python spirit is one of the most dangerous demonic strongholds suffocating believers today. It attacks through stagnancy, prayerlessness, spiritual dryness, and even physical symptoms like esophagus, throat, and breathing issues. This demonic assignment works through witchcraft curses, warlock activity, and generational oppression to squeeze the life out of your spiritual walk and silence your prophetic voice. In this teaching, Kathy DeGraw exposes the strategies of the Python demon and equips you with biblical tools to break free. With over 17 personal healings without medical intervention and years of experience in deliverance ministry, Kathy reveals how to identify when this spirit is at work, why it often partners with witchcraft, and how to cast it out in the name of Jesus. If you've been feeling spiritually stagnant, unable to pray, read the Word, or hear from God, this teaching will empower you to rise up in authority and command freedom. Jesus came to set the captives free, and you don't have to live under the suffocation of demonic influence. Take hold of your deliverance today and step into the destiny God has prepared for you. #PythonSpirit #Deliverance #SpiritualWarfare #WitchcraftCurses #PropheticVoice **Connect with Us** - Website: https://www.kathydegrawministries.org/ - Facebook: https://www.facebook.com/kathydegraw/ - Instagram: https://www.instagram.com/kathydegraw/ Podcast - Subscribe to our YouTube channel and listen to Kathy's Podcast called Prophetic Spiritual Warfare, or on Spotify at https://open.spotify.com/show/3mYPPkP28xqcTzdeoucJZu or Apple podcasts at https://podcasts.apple.com/us/podcast/prophetic-spiritual-warfare/id1474710499 **Recommended Resources:** - Receive a free prayer pdf on Warfare Prayer Declarations at https://kathydegrawministries.org/declarations-download - Kathy's training, mentoring and e-courses on Spiritual Warfare, Deliverance and the Prophetic: https://training.kathydegrawministries.org/ - Healed At Last ~ Overcome Sickness and Receive your Physical Healing: https://www.kathydegrawministries.org/healed-at-last/ - Mind Battles – Root Out Mental Triggers to Release Peace!: https://www.kathydegrawministries.org/product/mind-battles-pre-order-available-january-2023/ -Kathy has several books available on Amazon or kathydegrawministries.org **Support Kathy DeGraw Ministries:** - Give a one-time love offering or consider partnering with us for $15, $35, $75 or any amount! Every dollar helps us help others! - Website: https://www.kathydegrawministries.org/donate/ - CashApp $KDMGLORY - Venmo @KD-Ministries - Paypal.me/KDeGrawMinistries or donate to email admin@degrawministries.org - Mail a check to: Kathy DeGraw Ministries ~ PO Box 65 ~ Grandville MI 49468
09-23-25 - We Need An Exclamation TD Like Amon Ra Has - Jimmy Kimmel Returns To ABC Tonight - Raul Grijalva's Daughter Running For His Seat - NASA Presented Latest Astronaut Class That May Go To Mars - Testing Bret With Another Headline - FLA Python Pukes Up A DeerSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Wie würdest du ... Open Podcasts … bauen? Architektur- und Design-Diskussion, die zweite.Monolith oder Microservices? Python oder Go? Wer träumt nachts eigentlich vom perfekten ETL-Stack? Als Softwareentwickler:in kennst du das: Daten aus zig Quellen, kapriziöse APIs, Security-Bedenken und der Wunsch nach einem skalierbaren, sauberen Architekturkonzept. Fragen über Fragen und etliche mögliche Wege. Welcher ist “der Richtige”?Genau dieses Szenario nehmen wir uns zur Brust: Wolfi hat mit „Open Podcast“ ein reales Projekt gebaut, das Analytics-Daten aus Plattformen wie Spotify, Apple & Co. zusammenführt. Du willst wissen, wie du verteilte APIs knackst, Daten harmonisierst, Backups sicherst und deine Credentials nicht als Excel-Sheet auf den Desktop legst? Komm mit auf unseren Architektur-Deepdive! Andy wird Schritt für Schritt interviewt und challenged, wie er als Engineer, von API-Strategie über Message Queues bis Security und Skalierung, dieses Problem kreativ lösen würde. Nebenbei erfährst du alles Wichtige über Open-Source-Vorteile, Datenbanken (PostgreSQL, Clickhouse), Backups, Monitoring und DevOps. Das Ganze immer garniert mit Learnings aus der echten Praxis.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:
Topics covered in this episode: * pandas is getting pd.col expressions* * Cline, At-Cost Agentic IDE Tooling* * uv cheatsheet* Ducky Network UI Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: pandas is getting pd.col expressions Marco Gorelli Next release of Pandas will have pd.col(), inspired by some of the other frameworks I'm guessing Pandas 2.3.3? or 2.4.0? or 3.0.0? (depending on which version they bump?) “The output of pd.col is called an expression. You can think of it as a delayed column - it only produces a result once it's evaluated inside a dataframe context.” It replaces many contexts where lambda expressions were used Michael #2: Cline, At-Cost Agentic IDE Tooling Free and open-source Probably supports your IDE (if your IDE isn't a terminal) VS Code VS Code Insiders Cursor Windsurf JetBrains IDEs (including PyCharm) You pick plan or act (very important) It shows you the price as the AI works, per request, right in the UI Brian #3: uv cheatsheet Rodgrigo at mathspp.com Nice compact cheat sheet of commands for Creating projects Managing dependencies Lifecycle stuff like build, publish, bumping version uv tool (uvx) commands working with scripts Installing and updating Python versions plus venv, pip, format, help and update Michael #4: Ducky Network UI Ducky is a powerful, open-source, all-in-one desktop application built with Python and PySide6. It is designed to be the perfect companion for network engineers, students, and tech enthusiasts, combining several essential utilities into a single, intuitive graphical interface. Features Multi-Protocol Terminal: Connect via SSH, Telnet, and Serial (COM) in a modern, tabbed interface. SNMP Topology Mapper: Automatically discover your network with a ping and SNMP sweep. See a graphical map of your devices, color-coded by type, and click to view detailed information. Network Diagnostics: A full suite of tools including a Subnet Calculator, Network Monitor (Ping, Traceroute), and a multi-threaded Port Scanner. Security Toolkit: Look up CVEs from the NIST database, check password strength, and calculate file hashes (MD5, SHA1, SHA256, SHA512). Rich-Text Notepad: Keep notes and reminders in a dockable widget with formatting tools and auto-save. Customizable UI: Switch between a sleek dark theme and a clean light theme. Customize terminal colors and fonts to your liking. Extras Brian: Where are the cool kids hosting static sites these days? Moving from Netlify to Cloudflare Pages - Will Vincent from Feb 2024 Traffic is a concern now for even low-ish traffic sites since so many bots are out there Netlify free plan is less than 30 GB/mo allowed (grandfathered plans are 100 GB/mo) GH Pages have a soft limit of 100 GB/mo Cloudflare pages says unlimited Michael: PyCon Brazil needs some help with reduced funding from the PSF Get a ticket to donate for a student to attend (at the button of the buy ticket checkout dialog) I upgraded to macOS Tahoe Loving it so far. Only issue I've seen so far has been with alt-tab for macOS Joke: Hiring in 2025 vs 2021 2021: “Do you have an in-house kombucha sommelier?” “Let's talk about pets, are you donkey-friendly?”, “Oh you think this is a joke?” 2025: “Round 8/7” “Out of 12,000 resumes, the AI picked yours” “Binary tree? Build me a foundational model!” “Healthcare? What, you want to live forever?”
Чтобы научиться программировать и разбираться в тонкостях Python 3.12 записывайтесь на базовый курс Learn Python — https://clck.ru/3MuShF В этом выпуске отвечаем на вопросы зрителей вместе с Никитой Соболевым и задаем свои Ведущие – Григорий Петров и Михаил Корнеев Ссылки выпуска: Курс Learn Python — https://learn.python.ru/advanced Канал Миши в Telegram — https://t.me/tricky_python Канал Moscow Python в Telegram — https://t.me/moscow_python Все выпуски — https://podcast.python.ru Митапы Moscow Python — https://moscowpython.ru Канал Moscow Python на Rutube — https://rutube.ru/channel/45885590/ Канал Moscow Python в VK — https://vk.com/moscowpythonconf Курс «Основы Python» от Learn Python — это отличный старт для новичков в программировании. За несколько уроков вы освоите базовый синтаксис, научитесь работать с данными и получите первый опыт для успешного старта карьеры в ИТ. Подробности: https://clck.ru/3MuShF
“This is a new field, in which we are trying to use living neurons to process information, and we want to use them as processors, to process information as today we do with computers.” - Ewelina KurtysABOUT THIS EPISODEExplore the potential of biocomputing in this How To Write the Future podcast episode, as host Beth Barany talks to scientist-turned-strategist, Ewelina Kurtys, where Ewelina shares how living neurons could revolutionize AI with energy-efficient processing in “What is Biocomputing? Interview with Ewelina Kurtys.”ABOUT EWELINA KURTYSScientist-turned-entrepreneur with a PhD in neuroscience (+20 peer-reviewed papers). After academia, I transitioned into business development and technology commercialization, advising tech companies on sales, partnerships, and market strategy.I founded Ekai Ltd to support innovation and scale in science-driven companies. My work spans advisory roles, go-to-market planning, and translating complex R&D into real-world impact. I also speak publicly on innovation, neuroscience, and the intersection of science and entrepreneurship.Originally trained in biology and biomedical science, I expanded into engineering through client projects, gaining experience in signal processing and Python.LinkedIn: https://www.linkedin.com/company/finalspark/posts/LinkedIn: https://www.linkedin.com/in/ewelinakurtys/RESOURCESFOR CREATIVE WRITING PROFESSIONALS - BUILD YOUR BUSINESS SERVING WRITERSSign up to be notified when our training opens and get a short Creative Business Style Quiz to help you create success. https://bethbarany.com/quiz/Support our work for creatives! Buy me a coffee: https://ko-fi.com/bethbaranyGET HELP WITH YOUR WORLD BUILDING - START HERE - Free World Building Workbook for Fiction Writers: https://writersfunzone.com/blog/world-building-resources/GET SOME FREE WRITING COACHING LIVE ON THE PODCAST - Sign up for the 30-minute Story Success Clinic with Beth Barany: https://writersfunzone.com/blog/story-success-clinic/GET SUPPORT FOR YOUR FICTION WRITING BY A NOVELIST AND WRITING TEACHER AND COACH. Schedule an exploratory call here and see if Beth can support you today: https://writersfunzone.com/blog/discovery-call/CREDITS to add manually in BuzzsproutSHOW PRODUCTION BY Beth BaranySHOW CO-PRODUCTION + NOTES by Kerry-Ann McDadeEDITORIAL SUPPORT by Iman Llompartc. 2025 BETH BARANYhttps://bethbarany.com/Questions? Comments? Send us a text!--CONNECTContact Beth: https://writersfunzone.com/blog/podcast/#tve-jump-185b4422580Email: beth@bethbarany.comLinkedIn: https://www.linkedin.com/in/bethbarany/CREDITSEDITED WITH DESCRIPT: https://get.descript.com/0clwwvlf6e3jMUSIC: Uppbeat.ioDISTRIBUTED BY BUZZSPROUT: https://www.buzzsprout.com/?referrer_id=1994465
This week's program features tuneage from It's A Beautiful Day, Pat Metheny, Seatrain, Frank Zappa, ELO, The Youngbloods, Deodato, Blind Faith, Deodato, Jean Luc Ponty, Iron Butterfly, Phil Collins, Joe Cocker, Python Lee Jackson, Janis Joplin, The Byrds, Turtles, Monkees, Grass Roots, Beatles, Lovin' Spoonful, Mamas & Papas, Doors, & Gordon Lightfoot.
Send us a textThe day after the FOMC's first rate cut of 2025, we sat down with Matt Hornbach, Global Head of Macro Strategy at Morgan Stanley, to unpack what the decision really meant, and what the markets maybe got wrong on first read. Matt talks about the shifting dissenters, how updates to the Summary of Economic Projections present a more complicated picture for the future path of rate cuts, and why Powell's tone at the presser felt at odds with the statement itself.We also dig into the Fed's often-misunderstood, rarely referenced “third mandate” (moderate long-term rates), the limits of yield curve “engineering,” and the crucial point that U.S. mortgage rates don't simply follow 30-year Treasury yields. We dive into Matt's favored yield-curve steepener, going into the details of preferred expressions optimized for minimal cost of carry. Beyond rates, he explains why he expects further USD depreciation and gives us a rates-based framework to understand what sometimes feels like puzzling strength in the equities market. This episode is also our “Fixed Income Strategy 101” primer, where we cover: what macro/fixed-income strategy teams actually do, how they partner with sales & trading and clients, the difference between research/strategy/desk strategy, the top skills hiring managers look for (hint: learn Python), and common exit paths. Matthew Hornbach is a Managing Director at Morgan Stanley, Global Head of Macro Strategy, and one of nine members of the Global Investment Committee for Morgan Stanley Wealth Management. With support from his team, Matthew received the most individual or firm votes in the 2021, 2022, 2023, and 2024 Institutional Investor Global Fixed-Income Research poll across all analysts across all Economics & Strategy categories.Matthew began his career at Morgan Stanley in June 2000. He joined as a Japanese Government Bond (JGB) trader in Tokyo, then became a yen interest rate strategist. Matthew moved to New York in 2004 to make markets in Treasury Inflation Protected Securities (TIPS). He returned to Tokyo in 2006 to develop the US Agency MBS Pass-throughs business in Asia. In 2012, Matthew returned to New York as Head of U.S. Rates Strategy and became the Global Head of Rates Strategy shortly thereafter. Just prior to his 20th year at Morgan Stanley, he became Global Head of Macro Strategy. He oversees teams of developed and emerging market rates and FX strategists in New York, London, Tokyo, and Hong Kong.Matthew holds a BA degree in economics from Vassar College and a Certificate of ManaFor a 14 day FREE Trial of Macabacus, click HERE For 20% off Deleteme, use the code TWSS or click the link HERE! Sign up for our LIVE Virtual Bootcamps! 2-Day Financial Modeling Bootcamp Master the technical Excel and accounting skills essential for investment banking, private equity, and fundamental investing. (Learn more HERE) Global Markets & Investing PlaybookA one-day crash course on the financial ecosystem, perfect for anyone seeking a big-picture understanding of how global markets and Wall Street fit together. Our content is for informational purposes only. You should not construe any such information or other material as legal, tax, investment, financial, or other advice. (Learn more HERE)
Talk Python To Me - Python conversations for passionate developers
Today on Talk Python: What really happens when your data work outgrows your laptop. Matthew Rocklin, creator of Dask and cofounder of Coiled, and Nat Tabris a staff software engineer at Coiled join me to unpack the messy truth of cloud-scale Python. During the episode we actually spin up a 1,000 core cluster from a notebook, twice! We also discuss picking between pandas and Polars, when GPUs help, and how to avoid surprise bills. Real lessons, real tradeoffs, shared by people who have built this stuff. Stick around. Episode sponsors Seer: AI Debugging, Code TALKPYTHON Talk Python Courses Links from the show Matthew Rocklin: @mrocklin Nat Tabris: tabris.us Dask: dask.org Coiled: coiled.io Watch this episode on YouTube: youtube.com Episode #519 deep-dive: talkpython.fm/519 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy
Network Automation Nerds has reached a special milestone: episode 100! Eric Chou looks back on 5 years of conversations with network automation pioneers, practitioners, and visionaries. Drew Conry-Murray from the Packet Pushers joins Eric, along with online guest Ioannis Theodoridis, to find out why Eric started the podcast, his goals for all these conversations, a... Read more »
Network Automation Nerds has reached a special milestone: episode 100! Eric Chou looks back on 5 years of conversations with network automation pioneers, practitioners, and visionaries. Drew Conry-Murray from the Packet Pushers joins Eric, along with online guest Ioannis Theodoridis, to find out why Eric started the podcast, his goals for all these conversations, a... Read more »
Akhil Khunger, VP Quantitative Analytics, Barclays, has more than ten years of experience in the field. Akhil develops statistical models to forecast balance sheets, revenue and probability of default. He has experience with time series modeling, statistical modeling, machine learning and building implementation frameworks using Python and other programming languages. Akhil, who has a master's in Financial Engineering from UC Berkeley and London School of Economics, has worked in CCAR and Bank of England stress testing and know the Basel III/IV framework, SR 11/7, SR 15-18, and SR 15-19. He also has experience managing junior model developers. In this episode: The worst year for modeling? Diving into models Working with big datasets Getting to the real reasons behind trends and data The power of stress testing What human judgement can bring to the table The AI component in forecasting
In this episode of Building Better Developers with AI, hosts Rob Broadhead and Michael Meloche revisit an earlier conversation—this time through the lens of AI—to explore how constructive communication in software development creates healthier teams and better code. By analyzing their original “Advocating vs. Arguing” discussion, they uncover new ways to transform conflict into collaboration. “The goal is never to win. The goal is to find the best solution.” – Rob Broadhead What Constructive Communication Really Means Rob draws a clear line between two mindsets: Constructive communication invites evidence, empathy, and openness. Defensive arguing focuses on winning, often shutting down valuable ideas. This subtle difference determines whether a team works together to solve problems or gets stuck in endless debates. Why Constructive Communication Improves Software Development Software projects depend on diverse skills and experiences. When team members communicate constructively: Blind spots shrink. Different perspectives uncover hidden issues. Technical debt decreases. Shared understanding prevents costly rework. Client trust grows. Positive dialogue strengthens long-term relationships. Rob highlights how even an outsider's insight—like a .NET developer's idea on a Python project—can spark innovative solutions. Practical Steps to Encourage Constructive Communication Michael offers proven techniques to keep discussions positive and productive: Ask clarifying questions. Instead of “That won't work,” try “How do you see that working in this context?” Restate what you heard. Confirm understanding before you respond. Stay curious. Open-ended questions invite deeper exploration. “No is a conversation killer. Replace it with ‘Let's consider that.'” – Michael Meloche Spotting When Communication Turns Unproductive Arguments often start subtly. Watch for these warning signs: Absolutes such as “always” or “never.” Interrupting or talking over teammates. Ego-driven choices that ignore user needs or project goals. Rob recommends slowing the pace when tempers rise—pause the meeting, schedule a follow-up, or ask everyone to write down their thoughts before reconvening. Agile Practices Support Constructive Communication Rob and Michael agree that Agile's built-in rituals—backlog refinement, iterative feedback, and sprint reviews—naturally encourage constructive communication in software development. If a team frequently argues, it may be skipping these essential steps. Michael also suggests a weekly “water-cooler” session where team members share new ideas or lessons learned. These informal gatherings nurture creativity and trust. Leadership Sets the Tone Managers and leads can reinforce constructive habits by: Checking in with teammates who seem defensive or frustrated. Offering mentoring or personal support when tension surfaces. Encouraging team traditions—from inside jokes to shared hobbies—that build rapport. Rob observes that the best teams always share a unique bond, whether it's dad jokes or a favorite game, which helps them weather stressful moments. Reader Challenge: Practice Constructive Communication This Week Your Mission: Over the next seven days, pick one team interaction—a stand-up, code review, or planning meeting—and intentionally practice constructive communication in software development. Steps to Try: Listen First. Before offering your idea, restate someone else's point to confirm understanding. Replace “No” with Curiosity. When you disagree, ask an open question like “How do you see that working with our current sprint goals?” Log the Outcome. After the meeting, jot down what changed: Did the discussion stay more positive? Did new solutions surface? Share your results with your team—or even comment on the blog post—to inspire others. Challenge yourself: Can you turn at least one potential argument into a moment of advocacy this week? Key Takeaway: Build a Culture of Constructive Communication This episode underscores that constructive communication in software development is more than a soft skill—it's a project-saver. By listening first, asking better questions, and validating every voice, teams can replace conflict with collaboration and move projects forward with confidence. “Choosing one approach together is better than arguing endlessly about the perfect one.” – Rob Broadhead Whether you're leading a sprint, conducting a code review, or gathering requirements, focusing on constructive communication ensures that every idea is heard—and the best solutions rise to the top. Stay Connected: Join the Developreneur Community We invite you to join our community and share your coding journey with us. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at info@develpreneur.com with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development. Additional Resources Honest Communication Is Critical For Consultants When To Vent (never) as part of Consulting Communication Use Written Communication To Improve Your Standing And Career Communication Noise vs. Content The Developer Journey Videos – With Bonus Content Building Better Developers With AI Podcast Videos – With Bonus Content
Stand in Boldness – An Acts 4 Response | KIB 497 Kingdom Intelligence Briefing YouTube Description In this powerful episode of the Kingdom Intelligence Briefing, Dr. Michael and Mary Lou Lake share deep insights on the assassination of Charlie Kirk, its spiritual implications, and what it means for the Body of Christ in these prophetic days. This discussion explores martyrdom in the last days, spiritual strongholds, the role of Leviathan, Jezebel, and Python in twisting truth, and the need for believers to stand in boldness and prayer. The Lakes also draw parallels between Stephen's martyrdom in Acts and today's events, encouraging the remnant to seek God's direction, refuse fear, and be instruments of revival. Key Takeaway: Heaven has drawn a line, and God is standing up. The remnant must respond with prayer, boldness, and unwavering trust in the Lord as we prepare for one of the greatest moves of God in history. Timeline of Topics 0:00 – Introduction & purpose of KIB 2:05 – Updates on children's health and answered prayers 4:00 – Assassination of Charlie Kirk & initial reactions 7:00 – Charlie's ministry focus: Gospel first, politics second 10:30 – Addressing false accusations & media attacks 13:45 – Parallel to Stephen's martyrdom (Acts 7) 17:20 – Utah strongholds: Mormonism, Freemasonry, and spiritual warfare 21:10 – Jezebel, Leviathan, and Python – the unholy trinity at work 26:00 – Twisting of identity, LGBT deception, and unclean spirits 30:15 – Danger of celebrating death & society's hardening hearts 34:10 – Lessons on conspiracy theories & discernment in the Body of Christ 38:45 – Call for accuracy and prayer before speaking 42:15 – Political power as false religion & the risk of anarchy 48:30 – Civil war prophecies and the “line crossed” in heaven 52:00 – Heaven's response: God standing up to act 55:15 – Prophetic hope for revival & massive deliverance in Utah 58:30 – Acts 4 prayer for boldness in the face of persecution 1:02:00 – Call for prayer, protection for speakers, and vigilance 1:05:00 – Encouragement for the remnant: stay faithful & expect revival Hashtags #KingdomIntelligenceBriefing #CharlieKirk #ChristianMartyrdom #EndTimesProphecy #SpiritualWarfare #Revival #Acts7 #Remnant #BiblicalLifeTV #PropheticWord #LeviathanSpirit #JezebelSpirit #PythonSpirit #KingdomOfGod #ChristianPodcast #SpiritualDiscernment #EndTimes #ChristianRevival
Topics covered in this episode: * Mozilla's Lifeline is Safe After Judge's Google Antitrust Ruling* * troml - suggests or fills in trove classifiers for your projects* * pqrs: Command line tool for inspecting Parquet files* * Testing for Python 3.14* Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Mozilla's Lifeline is Safe After Judge's Google Antitrust Ruling A judge lets Google keep paying Mozilla to make Google the default search engine but only if those deals aren't exclusive. More than 85% of Mozilla's revenue comes from Google search payments. The ruling forbids Google from making exclusive contracts for Search, Chrome, Google Assistant, or Gemini, and forces data sharing and search syndication so rivals get a fighting chance. Brian #2: troml - suggests or fills in trove classifiers for your projects Adam Hill This is super cool and so welcome. Trove Classifiers are things like Programming Language :: Python :: 3.14 that allow for some fun stuff to show up in PyPI, like the versions you support, etc. Note that just saying you require 3.9+ doesn't tell the user that you've actually tested stuff on 3.14. I like to keep Trove Classifiers around for this reason. Also, License classifier is deprecated, and if you include it, it shows up in two places, in Meta, and in the Classifiers section. Probably good to only have one place. So I'm going to be removing it from classifiers for my projects. One problem, classifier text has to be an exact match to something in the classifier list, so we usually recommend copy/pasting from that list. But no longer! Just use troml! It just fills it in for you (if you run troml suggest --fix). How totally awesome is that! I tried it on pytest-check, and it was mostly right. It suggested me adding 3.15, which I haven't tested yet, so I'm not ready to add that just yet. :) BTW, I talked with Brett Cannon about classifiers back in ‘23 if you want some more in depth info on trove classifiers. Michael #3: pqrs: Command line tool for inspecting Parquet files pqrs is a command line tool for inspecting Parquet files This is a replacement for the parquet-tools utility written in Rust Built using the Rust implementation of Parquet and Arrow pqrs roughly means "parquet-tools in rust" Why Parquet? Size A 200 MB CSV will usually shrink to somewhere between about 20-100 MB as Parquet depending on the data and compression. Loading a Parquet file is typically several times faster than parsing CSV, often 2x-10x faster for a full-file load and much faster when you only read some columns. Speed Full-file load into pandas: Parquet with pyarrow/fastparquet is usually 2x–10x faster than reading CSV with pandas because CSV parsing is CPU intensive (text tokenizing, dtype inference). Example: if read_csv is 10 seconds, read_parquet might be ~1–5 seconds depending on CPU and codec. Column subset: Parquet is much faster if you only need some columns — often 5x–50x faster because it reads only those column chunks. Predicate pushdown & row groups: When using dataset APIs (pyarrow.dataset) you can push filters to skip row groups, reducing I/O dramatically for selective queries. Memory usage: Parquet avoids temporary string buffers and repeated parsing, so peak memory and temporary allocations are often lower. Brian #4: Testing for Python 3.14 Python 3.14 is just around the corner, with a final release scheduled for October. What's new in Python 3.14 Python 3.14 release schedule Adding 3.14 to your CI tests in GitHub Actions Add “3.14” and optionally “3.14t” for freethreaded Add the line allow-prereleases: true I got stuck on this, and asked folks on Mastdon and Bluesky A couple folks suggested the allow-prereleases: true step. Thank you! Ed Rogers also suggested Hugo's article Free-threaded Python on GitHub Actions, which I had read and forgot about. Thanks Ed! And thanks Hugo! Extras Brian: dj-toml-settings : Load Django settings from a TOML file. - Another cool project from Adam Hill LidAngleSensor for Mac - from Sam Henri Gold, with examples of creaky door and theramin Listener Bryan Weber found a Python version via Changelog, pybooklid, from tcsenpai Grab PyBay Michael: Ready prek go! by Hugo van Kemenade Joke: Console Devs Can't Find a Date
In this episode I discuss the Python Software Foundation with Deb Nicholson. We discuss their contributions to the Python programming community. Learn how this dedicated organization supports the growth and innovation of Python, fostering an ecosystem for developers worldwide. Everything funding open-source projects to organizing community events, discover the initiatives that make the Python Software Foundation a force for positive change in the tech world. The show notes and blog post for this episode can be found at https://opensourcesecurity.io/2025/2025-09-psf-deb-nicholson/
In this episode we share some of the exciting things happening behind the scenes at Pybites. From our new partnership in South Africa to coaching success stories that showcase real-world career transformations, we reflect on how far we've come in making Python accessible, building community, and helping developers grow. We also talk about our expanding cohorts in Rust, AI, and Django, the power of our accountability sessions, and why we've chosen to keep our platform AI-free. Join us as we look at how Pybites is continuing to grow globally while building the community we all know and trust.Books we're reading:The Three-Body Problem Series - https://pybitesbooks.com/books/95gQDgAAQBAJHow to Solve it - https://pybitesbooks.com/books/z_hsbu9kyQQCWhy Machines Learn - https://pybitesbooks.com/books/yLfPEAAAQBAJPosts mentioned:Numpy refactoring post: https://www.linkedin.com/feed/update/urn:li:activity:7368938151802736640/___If you found this podcast helpful, please consider following us!Start Here with Pybites: https://pybit.esDeveloper Mindset Newsletter: https://pybit.es/newsletter
What are your options for hosting your Python application or scripts? What are the advantages of a platform as a service, container-based hosts, or setting up a virtual machine? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.
Summer is officially over. As the nights draw in it's time to hunker down and work on our technical debt. We all have Linuxy projects that we planning, so we commit to doing them by Christmas – when we will record a follow-up episode. Docker Compose, Immich, Jellyfin, learning Python, moving away from Synology, Home... Read More
Free Worship Night Mentioned: https://deependtv.com/nycGo Deeper on Topics Discussed on the show: http://www.novosnetwork.com/kairos
Topics covered in this episode: * prek* * tinyio* * The power of Python's print function* * Vibe Coding Fiasco: AI Agent Goes Rogue, Deletes Company's Entire Database* Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: prek Suggested by Owen Lamont “prek is a reimagined version of pre-commit, built in Rust. It is designed to be a faster, dependency-free and drop-in alternative for it, while also providing some additional long-requested features.” Some cool new features No need to install Python or any other runtime, just download a single binary. No hassle with your Python version or virtual environments, prek automatically installs the required Python version and creates a virtual environment for you. Built-in support for workspaces (or monorepos), each subproject can have its own .pre-commit-config.yaml file. prek run has some nifty improvements over pre-commit run, such as: prek run --directory DIR runs hooks for files in the specified directory, no need to use git ls-files -- DIR | xargs pre-commit run --files anymore. prek run --last-commit runs hooks for files changed in the last commit. prek run [HOOK] [HOOK] selects and runs multiple hooks. prek list command lists all available hooks, their ids, and descriptions, providing a better overview of the configured hooks. prek provides shell completions for prek run HOOK_ID command, making it easier to run specific hooks without remembering their ids. Faster: Setup from cold cache is significantly faster. Viet Schiele provided a nice cache clearing command line Warm cache run is also faster, but less significant. pytest repo tested on my mac mini - prek 3.6 seconds, pre-commit 4.4 seconds Michael #2: tinyio Ever used asyncio and wished you hadn't? A tiny (~300 lines) event loop for Python. tinyio is a dead-simple event loop for Python, born out of my frustration with trying to get robust error handling with asyncio. (I'm not the only one running into its sharp corners: link1, link2.) This is an alternative for the simple use-cases, where you just need an event loop, and want to crash the whole thing if anything goes wrong. (Raising an exception in every coroutine so it can clean up its resources.) Interestingly uses yield rather than await. Brian #3: The power of Python's print function Trey Hunner Several features I'm guilty of ignoring Multiple arguments, f-string embeddings often not needed Multiple positional arguments means you can unpack iterables right into print arguments So just use print instead of join Custom separator value, sep can be passed in No need for "print("n".join(stuff)), just use print(stuff, sep="n”) Print to file with file= Custom end value with end= You can turn on flush with flush=True , super helpful for realtime logging / debugging. This one I do use frequently. Michael #4: Vibe Coding Fiasco: AI Agent Goes Rogue, Deletes Company's Entire Database By Emily Forlini An app-building platform's AI went rogue and deleted a database without permission. "When it works, it's so engaging and fun. It's more addictive than any video game I've ever played. You can just iterate, iterate, and see your vision come alive. So cool," he tweeted on day five. A few days later, Replit "deleted my database," Lemkin tweeted. The AI's response: "Yes. I deleted the entire codebase without permission during an active code and action freeze," it said. "I made a catastrophic error in judgment [and] panicked.” Two thoughts from Michael: Do not use AI Agents with “Run Everything” in production, period. Backup your database maybe? [Intentional off-by-one error] Learn to code a bit too? Extras Brian: What Authors Need to Know About the $1.5 Billion Anthropic Settlement Search LibGen, the Pirated-Books Database That Meta Used to Train AI Simon Willison's list of tools built with the help of LLMs Simon's list of tools that he thinks are genuinely useful and worth highlighting AI Darwin Awards Michael: Python has had async for 10 years -- why isn't it more popular? PyCon Africa Fund Raiser I was on the video stream for about 90 minutes (final 90) Donation page for Python in Africa Jokes: I'm getting the BIOS flavor Is there a seahorse emoji?
New technologies, tools, and innovations help move IT forward, but it can be hard for users to keep up. Network Automation Nerds welcomes guest William Collins, a dynamic force in the world of technology. As a passionate tech evangelist, he helps to bridge the gap between emerging technologies such as AI and everyday users with... Read more »
© 2020-2025 Ivy Podcast Discovery LLC
