Tim Eades grew up poor, but forced himself into college. He is a 4 time CEO, an investor, and on the boards of several different companies - but more interestingly, he is an old punk. He saw the Sex Pistols live back in the day, which he mentioned had great sound quality. He's been married for 25 years, and is on the board of a charity that his wife runs. That charity delivery 20,000 birthday cakes to underprivileged children a year.
Being a multi-time CEO, Tim has some experience around starting companies. He interviewed many cybersecurity leaders, asking about identity and why vulnerabilities around it was still a problem. During a Liverpool game, he downloaded a powerpoint template and put together a pitch to build a company and solve this problem.
Dane shares his excitement about the Model Context Protocol (MCP), exploring its potential impact on the future of technology. The discussion turns to the growing need for sustainable content monetization and fair compensation for creators in an AI-driven world, and how this connects to Cloudflare’s mission to build a better internet.
The conversation also:
Explores how Cloudflare leverages AI internally to enhance developer productivity and improve code quality while keeping developers as owners of their work.
Covers Cloudflare’s innovative organizational structure and their journey toward becoming an AI-first company.
Dwarkesh Patel is the host of the Dwarkesh Podcast. He joins Big Technology Podcast to discuss the frontiers of AI research, sharing why his timeline for AGI is a bit longer than the most enthusiastic researchers. Tune in for a candid discussion of the limitations of current methods, why continuous AI improvement might help the technology reach AGI, and what an intelligence explosion looks like. We also cover the race between AI labs, the dangers of AI deception, and AI sycophancy. Tune in for a deep discussion about the state of artificial intelligence, and where it’s going.
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Are you using Polars for your data science work? Maybe you've been sticking with the tried-and-true Pandas? There are many benefits to Polars directly of course. But you might not be aware of all the excellent tools and libraries that make Polars even better. Examples include Patito which combines Pydantic and Polars for data validation and polars_encryption which adds AES encryption to selected columns. We have Christopher Trudeau back on Talk Python To Me to tell us about his list of excellent libraries to power up your Polars game and we also talk a bit about his new Polars course.
Episode overview:
Guidione Machava has a confession: he's tired of being called an "African designer." The Mozambican product designer, now based in France and fresh from stints at Shopify and Paris-based 23point5, reckons that geographic qualifiers automatically strip away a third of your professional value before you've even started.
It's a provocative stance from someone who's built his career bridging African markets and global tech giants. Since launching- MozDevz - Mozambique's largest developer community - over a decade ago, Machava has been methodically executing what he calls his "Maria Sharapova strategy": a systematic approach to becoming world-class that he lifted from a Tim Ferriss podcast.
The strategy worked. From building communities across six African countries to creating a business directory that attracted 300,000 SMEs, to founding Kabum Digital (Mozambique's leading tech publication), Machava has consistently punched above his weight class. His secret? "Piggybacking" on successful people and refusing to let his environment dictate his ambitions.
Andile Masuku probes Machava on the realities of designing for African versus Western markets, why physical product development taught him to appreciate software's forgiving nature, and his mission to prove that world-class design talent can emerge from anywhere, provided you're strategic about how you position it.
Key insights:
- On strategic positioning: Despite building African communities and solving African problems, Machava deliberately brands himself as a "world-class designer" rather than a "world-class African designer." His reasoning? International clients and collaborators unconsciously devalue geography-qualified talent, even when they won't admit it.
- On market realities: Designing for Western markets versus African markets isn't just about different user needs, it's about fundamentally different quality bars. "In Africa, designing a product that works well is a plus. In France, it's the bare minimum," he observes.
- On the intersection economy: His time at 23.5—building design tools for made-to-order, sustainable fashion—taught him that the intersection of digital and physical economies is where the hardest, most rewarding innovation happens. Unlike software, physical products offer no "rollback to previous version" option.
- On manufactured serendipity: Rather than waiting for opportunities, Machava systematically identified people in positions he wanted to occupy, then found ways to provide value to them. The approach landed him interviews with executives from IDEO, Google, and Facebook for his World Class Designer podcast.
Notable moments:
1. How a Tim Ferriss interview with tennis champion Maria Sharapova became Machava's career template for achieving world-class performance in design
2. Why Shopify's hierarchy of priorities—solve merchants' problems first, make money second, never reverse that order—fundamentally changed how he approaches product design
3. The brutal economics lesson he learned at 23point5: physical product margins are tiny, error tolerance is minimal, and mistakes literally end up in landfills
4. His unconventional path from economics degree to postgraduate design studies, convincing Open Window Institute for Creative Arts & Technologies to let him skip three years of undergraduate work
The contrarian take:
Machava's most provocative insight centres on geographic positioning. Whilst celebrating African innovation has become fashionable, he argues that leading with continental identity in global markets is a strategic error. "If you say just 'world-class designer,' it's a completely different perspective," he notes, drawing from conversations with international colleagues who've confirmed his suspicions about unconscious bias.
Brooke Hartley May lives in Oakland, California, on the East Bay, claiming to get more sunshine than her neighbors in San Francisco. She was a history major in college, which makes her path to technology a bit different. She still enjoys writing and reading in long form to this day. Outside of tech, she is married with a 4 year old son - and a pug. She enjoys life as a parent, startup founder, but was sad that the Oakland A's left Oakland, amongst other teams.
A few years ago, Brooke and her now co-founder observed that people were viewing AI as this end-all-be-all solution. But what quickly happened was that the data needed to make AI effective was not in quite the same state.
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Brian #1: Free-threaded Python no longer “experimental” as of Python 3.14
“PEP 779 ("Criteria for supported status for free-threaded Python") has been accepted, which means free-threaded Python is now a supported build!” - Hugo van Kemenade
As noted in the discussion of PEP 779, “The Steering Council (SC) approves PEP 779, with the effect of removing the “experimental” tag from the free-threaded build of Python 3.14.”
We are in Phase II then.
“We are confident that the project is on the right path, and we appreciate the continued dedication from everyone working to make free-threading ready for broader adoption across the Python community.”
“Keep in mind that any decision to transition to Phase III, with free-threading as the default or sole build of Python is still undecided, and dependent on many factors both within CPython itself and the community. We leave that decision for the future.”
How long will all this take? According to Thomas Wouters, a few years, at least: “In other words: it'll be a few years at least. It can't happen before 3.16 (because we won't have Stable ABI support until 15) and may well take longer.”
typed-ffmpeg offers a modern, Pythonic interface to FFmpeg, providing extensive support for complex filters with detailed typing and documentation.
Inspired by ffmpeg-python, this package enhances functionality by addressing common limitations, such as lack of IDE integration and comprehensive typing, while also introducing new features like JSON serialization of filter graphs and automatic FFmpeg validation.
Features :
Zero Dependencies: Built purely with the Python standard library, ensuring maximum compatibility and security.
User-Friendly: Simplifies the construction of filter graphs with an intuitive Pythonic interface.
Comprehensive FFmpeg Filter Support: Out-of-the-box support for most FFmpeg filters, with IDE auto-completion.
Integrated Documentation: In-line docstrings provide immediate reference for filter usage, reducing the need to consult external documentation.
Robust Typing: Offers static and dynamic type checking, enhancing code reliability and development experience.
Filter Graph Serialization: Enables saving and reloading of filter graphs in JSON format for ease of use and repeatability.
Graph Visualization: Leverages graphviz for visual representation, aiding in understanding and debugging.
Validation and Auto-correction: Assists in identifying and fixing errors within filter graphs.
Input and Output Options Support: Provide a more comprehensive interface for input and output options, including support for additional codecs and formats.
Partial Evaluation: Enhance the flexibility of filter graphs by enabling partial evaluation, allowing for modular construction and reuse.
Media File Analysis: Built-in support for analyzing media files using FFmpeg's ffprobe utility, providing detailed metadata extraction with both dictionary and dataclass interfaces.
“When working with Django applications, it's common to have a mix of fast unit tests and slower end-to-end (E2E) tests that use pytest's live_server fixture and browser automation tools like Playwright or Selenium. ”
Tim is running E2E tests last for
Faster feedback from quick tests
To not tie up resources early in the test suite.
He did this with
custom “e2e” marker
Implementing a
pytest_collection_modifyitems
hook function to look for tests using the
live_server
fixture, and for them
automatically add the e2e marker to those tests
move those tests to the end
The reason for the marker is to be able to
Just run e2e tests with -m e2e
Avoid running them sometimes with -m "not e2e"
Cool small writeup.
The technique works for any system that has some tests that are slower or resource bound based on a particular fixture or set of fixtures.
Dr. David Bray, Former Chief Information Officer of the Federal Communications Commission and CEO at Lead Do Adapt Ventures, and the Honorable Ellen McCarthy, former Assistant Secretary of State for the Bureau of Intelligence and Research join the show for a candid, wide-ranging conversation about the evolving landscape of data, digital trust, and national security. We unpack how the explosion of connected devices, AI-generated content, and synthetic data is reshaping decision-making, security, and public trust at every level of government and society and explore challenges from authenticating digital content to decentralizing emergency response, and the urgent need to empower individuals and local communities in the face of complex, rapidly changing information ecosystems.