Podcasts about distributed systems

In this episode of Elixir Wizards, Charles Suggs and Emma Whamond are joined by Mike Ratliff, co-founder and CTO of GridVar, to talk about the role software plays in the changing energy infrastructure. With over 30 years of experience in technology, Mike shares the path that took him from the early internet and cloud computing into energy and utility software, along with what he has learned about staying adaptable as the industry continues to shift. Mike explains why building software for the power grid comes with a very different set of constraints than building a typical web application and breaks down some of the challenges utilities are facing, including grid interconnection delays, power quality, increasing energy demand, and the growth of distributed energy resources. We also discuss demand response, microgrids, virtual power plants, battery storage, and how software can help utilities better understand and manage a grid that is becoming more complex. Mike also explains why Elixir and the BEAM are a strong fit for always-on energy systems, how an Erlang MQTT server first led him into the ecosystem, and what it takes to introduce Elixir inside an established organization. The episode closes with a broader look at AI-assisted development, the value of domain expertise, and why technical leaders still need communication, judgment, and a compelling story to move important ideas forward. Key topics discussed in this episode: Mike Ratliff's path from software to energy technology Lessons from three decades of technology industry change The value of generalists in modern software engineering Why good technical judgment remains difficult to replace Building software that interacts with physical infrastructure Why utility technology adoption can move slowly Understanding today's grid interconnection backlog Power quality challenges affecting new grid connections Using simulation to accelerate utility engineering studies Centralized and distributed approaches to grid management How solar energy creates the duck curve Using demand response to balance electricity consumption Edge devices supporting real-time grid coordination Microgrids and resilience in distributed energy systems Cybersecurity considerations for increasingly connected power grids Preparing utility infrastructure for extreme weather events Battery storage and the growth of renewable energy How virtual power plants coordinate distributed resources Why Elixir works well for energy software BEAM reliability for always-on utility infrastructure Discovering Elixir through Erlang and MQTT Building an early virtual power plant with Elixir Making the business case for an Elixir migration Why technical leadership also requires effective storytelling Links Mentioned: GridVAR https://www.gridvar.com/ GridPoint https://www.gridpoint.com/ https://en.wikipedia.org/wiki/2025_Iberian_Peninsula_blackout Demand Response: https://en.wikipedia.org/wiki/Demand_response Virtual Power Plant: https://en.wikipedia.org/wiki/Virtual_power_plant Microgrid: https://en.wikipedia.org/wiki/Microgrid Volts podcast: https://www.volts.wtf/

Zipline Roundtable episode: Building Real-Time ML Systems with Zipline + ChrononJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguideBig shout-out to ZiplineAI for the collaboration!// AbstractReal-time ML use cases like personalization and risk decisioning come with a unique set of challenges: serving fresh feature values at low latency for inference, generating temporally consistent backfills for training, and building complex chains of on-demand, batch, and streaming transformations. In this roundtable, practitioners from Intuit, CreditKarma, Depop, and OpenAI share how they use Zipline and the OSS Chronon project to solve these challenges and deploy real-time ML use cases in production.// BioGerman KrikorianGerman is a Software Engineer on the Feature Platform team at Credit Karma. Since joining the company during the early development of its recommendation system, they have played a key role in building and scaling the platform over the years. Their work focuses on feature pipelines and the feature store, which serves as critical infrastructure supporting numerous teams and business verticals across the organization.Ben MagyarBen is an engineer at Depop working on ML and data systems. Before Depop, he worked on Search at Etsy. Most of his work is around the infrastructure and operational problems that come with running ML systems at scale.Raj KatakamRaj architects ML Infrastructure at Credit Karma (Intuit). He holds a Master's in Software Engineering from Carnegie Mellon and a B.Tech in EECE from IIT Kharagpur. His interests include ML Infrastructure, Distributed Systems, Real-Time Data Processing, and Generative AI. His current focus is on providing feature engineering platforms, production GenAI infrastructure, vector databases, ML model serving, and MLOps pipelines for fraud detection, personalized recommendations, financial insights, and model explainability.Mick JermsurawongLed Flyte ML training/experimentation at Stripe, and now led Chronon for ML features at OpenAIHosted by Demetrios// Related LinksWebsite: https://zipline.ai/https://chronon.ai/~~~~~~~~ ✌️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 German on LinkedIn: /e2zdkwh8cxghydg/Connect with Raj on LinkedIn: /rajkiran2190Connect with Mick on LinkedIn:/mick-jermsurawong/

In this episode of Elixir Wizards, Charles Suggs and Emma Whamond are joined by Ellyse Cedeno, founder of Heuristic Salvo and a software engineer and product leader with more than 25 years of experience across early internet platforms, gaming, health tech, and distributed systems. Ellyse shares the winding path that took her from early search engines and Netscape to game development, medical research at Mount Sinai, and eventually to Elixir. Along the way, she talks about staying curious over a long technical career, rediscovering joy through side projects, and why being willing to feel like a beginner again can be one of the most useful skills a developer can build. The conversation explores what it means to grow as an engineer in a world where AI tooling is becoming part of the everyday workflow. Ellyse makes the case that technical skill still matters, but the human parts of software development (like judgment, curiosity, communication, trust, and influence) are becoming increasingly important. We also talk about soft influence and how developers can create change inside organizations without relying on hard authority. Key Topics Discussed in this Episode: Ellyse's career path through early internet platforms, gaming, health tech, and distributed systems Moving from Netscape and search engines to medical research and software consulting Discovering Elixir through an interest in concurrent and distributed systems Why beginner's mindset still matters after decades in tech How neurodivergence, curiosity, and deep focus shape Ellyse's approach to programming Rediscovering joy in programming through side projects and experimentation Building an MMORPG game server in Elixir Exploring hardware, Nerves, and live theremin demos The role of passion projects in professional growth Protecting time for learning in productivity-focused environments Work-life balance differences between the U.S. and Europe How AI tools are changing expectations for modern developers Why AI does not replace judgment, taste, or technical understanding Understanding business needs instead of only focusing on technical preferences Introducing Elixir into a TypeScript-heavy organization Using Elixir microservices to solve specific technical problems What “soft influence” looks like in engineering teams Building trust through one-on-one conversations Knowing when influence is working and when it is not Negotiating technical decisions without turning them into power struggles The relationship between technical competence and interpersonal skill Managing imposter syndrome during pair programming and collaborative work Documentation as a visibility and ownership tool Community involvement, conference speaking, and finding your people Staying curious without burning out Why the human side of software development still matters Links Mentioned: https://en.wikipedia.org/wiki/Netscape Icahn School of Medicine at Mt. Sinai https://icahn.mssm.edu/ Evernote https://evernote.com/ Joplin https://joplinapp.org/ Book: Elixir in Action by Saša Jurić https://www.manning.com/books/elixir-in-action-third-edition Book: The Little LISPer https://www.scribd.com/doc/263131641/The-Little-Lisper Ellyse's Goatmire Talk https://goatmire.com/speaker/ellyse-cedeno Nerves https://nerves-project.org/ xHain Hack & Makespace in Berlin https://x-hain.de/en/ https://cursor.com/ Haskell Programming Language https://www.haskell.org/ Java Programming Language https://www.java.com/en/ Clojure Programming Language https://clojure.org/ Scheme Programming Language https://www.scheme.org/ TypeScript Programming Language https://www.typescriptlang.org/ Nostrum Library https://hexdocs.pm/nostrum/intro.html Gleam Programming Language https://gleam.run/ Book: Getting Past No by William Ury https://www.williamury.com/getting-past-no/ “The Gambler” by Kenny Rogers https://www.youtube.com/watch?v=7hx4gdlfamo Ted Talk: Do schools kill creativity? | Sir Ken Robinson https://youtu.be/iG9CE55wbtY Ellyse's Codeberg https://codeberg.org/ellyxir Ellyse's Game Server Repo https://codeberg.org/ellyxir/gameserver Goatmire Elixir & NervesConf 2026 https://www.goatmire.com/

In this episode of Elixir Wizards, hosts Charles Suggs and Emma Whamond sit down with Marek Šuppa, creator of the Missing GitHub Status page, a project that reconstructs GitHub's historical uptime data and reveals discrepancies between official status reporting and the platform's actual reliability. Marek tells us about his dev journey from open source contributor at DuckDuckGo to machine learning engineer at Cisco-acquired Slido. Then, we discuss GitHub's evolution from a hosted Git service into a critical developer tool. We cover reliability, transparency, AI-driven platform growth, developer workflows, and the challenges of balancing convenience with resilience. Along the way, we cover alternative platforms, self-hosted solutions, and whether recent outages are changing how developers think about ownership, dependency, and the future of software collaboration. Topics Discussed in this Episode: Why did Mr. Shu create the Missing GitHub Status Page? GitHub's reported uptime versus developer experiences How open source contributions shaped Marek's career The evolution of GitHub from tool to critical infrastructure Centralization risks in modern software development Git's distributed roots and today's platform-centric workflows Developer reactions to GitHub outages Transparency and accountability in status reporting AI's impact on developer platforms and infrastructure demands Microsoft's stewardship of GitHub Forgejo, Codeberg, and alternative Git hosting platforms Self-hosted Git solutions and tradeoffs Network effects and platform lock-in The social side of software collaboration Building resilience into developer workflows What GitHub outages teach us about infrastructure dependency Links Mentioned: The Missing GitHub Status Page https://mrshu.github.io/github-statuses/ Slido https://www.slido.com/ https://duckduckgo.com/ The official GitHub Status Page https://www.githubstatus.com/ Statuspage.iohttps://www.atlassian.com/software/statuspage Zig Leaves GitHub https://ziglang.org/news/migrating-from-github-to-codeberg/ Ghostty Leaves GitHub https://mitchellh.com/writing/ghostty-leaving-github GitLab https://about.gitlab.com/ Codeberg https://codeberg.org/ https://git.kernel.org/ Forgejo Lightweight Self-Hosting https://forgejo.org/ Former GitHub CEO Thomas Dohmke launches Entire https://entire.io/news/former-github-ceo-thomas-dohmke-raises-60-million-seed-round Update on Spain and LALIGA blocks of the internet https://vercel.com/blog/update-on-spain-and-laliga-blocks-of-the-internet

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with returning guest Ekue Kpodar for their third conversation together, covering a wide range of topics at the intersection of technology, geopolitics, and the evolving information age. They dig into Ekue's unconventional setup of running local AI models across roughly 15 computers, the growing case for open source models over closed ones from companies like OpenAI and Anthropic, and how Chinese open source models may be positioned to outcompete Western alternatives on a global scale. The conversation also touches on vibe coding and the democratization of software development, the strategic use of small models for IoT and enterprise applications, the role of Israel and China as dominant players in the information age, and how smaller nations and even individuals may wield outsized power as AI continues to collapse the cost of knowledge work. You can find Ekue Kpodar on X @ekpodar and LinkedIn.Timestamps00:00 Stewart welcomes Ekue for their third episode, diving into vibe coding and AI-driven development changes.05:00 Ekue explains using Claude on Chrome to auto-reply on Skool, burning tokens through screenshots, and Playwright as a more efficient alternative.10:00 Stewart describes his Claude-dependent planning and coding agent system breaking after a model update, prompting him to build his own chatbot.15:00 Small models discussed as critical for IoT, defense, and privacy-focused enterprises building internal APIs instead of routing traffic to OpenAI.20:00 Open source versus closed source debated, with Chinese models gaining global traction while US foundational labs remain expensive and restrictive.25:00 SaaS apocalypse explored as AI commoditizes knowledge work, with Linux and Terraform cited as proof open source still generates wealth.30:00 OpenAI's sci-fi terminator fears explained as the reason they stayed closed source, ultimately handing China a strategic open source advantage.35:00 China's economic dumping strategy applied to AI, potentially displacing US model dominance globally the same way manufacturing was disrupted.40:00 Israel's signals intelligence dominance discussed alongside asymmetric warfare, drones defeating tanks, and information control replacing military muscle.45:00 Global information age rankings debated, Israel leading, US and China tied, France and Poland emerging as sovereign tech players.50:00 Qatar, NVIDIA, and Iran cited as proof that rare resources and technology matter more than population size in the 21st century power landscape.Key Insights1. Running local AI models on a network of affordable computers can be more cost-effective than relying entirely on third-party APIs. By using compressed or smaller open source models locally, developers can handle repetitive or lower-stakes tasks without burning through expensive tokens from providers like Anthropic or OpenAI.2. Small AI models are becoming increasingly important for IoT, defense applications, and companies that do not want to send sensitive data to external providers. Organizations can download open source models, run them on internal servers, and build proprietary APIs around them, creating something like an intranet of specialized small models.3. The value created by AI tools is being redistributed away from traditional SaaS companies toward foundational model providers and individual builders. People are canceling subscriptions to software they once paid hundreds per month for, because AI now allows a single person to build comparable tools themselves.4. Open source technology does not eliminate the ability to profit. Linux and Terraform are both open source yet made their creators wealthy. People will still pay for installation, setup, troubleshooting, and customization even when the underlying software is free.5. China is applying its longstanding manufacturing dumping strategy to artificial intelligence by releasing cheap open source models globally, which threatens to erode US dominance in AI the same way Chinese manufacturing undercut other countries for decades.6. In the information age, the size of a country or institution matters far less than its access to rare resources or advanced technology. Qatar, Israel, and NVIDIA each demonstrate that small populations or headcounts can wield enormous global negotiating power through concentrated technological or resource advantages.7. Asymmetric warfare is redefining military power, with inexpensive drones defeating tanks that cost millions to build. This shifts the advantage toward nations that excel at signals intelligence and information management rather than those with the largest conventional military forces.

In Elixir Wizards S15E04, Charles Suggs and Emma Whamond are joined by Somtochi Onyekwere, a software engineer at Fly.io and contributor to the Corrosion distributed database project, to talk about distributed systems, infrastructure resilience, and the growing fragility of centralized cloud platforms.   We discuss what recent outages across major providers reveal about modern infrastructure and why more teams are starting to rethink assumptions around reliability, failover, and system design. Somtochi explains how Fly.io approaches geographic distribution, eventual consistency, and replication across nodes, along with the trade-offs that come with building systems this way.   The conversation explores CRDTs (Conflict-free Replicated Data Types), consensus, split-brain prevention, and what actually happens when distributed systems fail in production. We also talk about testing strategies, rollback planning, property-based testing tools, and how teams can reduce blast radius when things inevitably go wrong.   Along the way, we discuss AI infrastructure, sandboxing AI agents, and how newer workloads may add pressure to already centralized systems. The episode closes with practical advice for developers who want to build more resilient applications without over-complicating their architecture. Topics Discussed in this Episode: Corrosion and distributed database replication Centralized cloud fragility and recent outage patterns Distributed systems versus traditional cloud architectures Multi-region deployment strategies for Phoenix applications CRDTs and conflict resolution in distributed systems Eventual consistency versus strict consistency tradeoffs Consensus, leader election, and split-brain prevention Testing failover and recovery scenarios Property-based testing and Antithesis Rollback planning for database schema migrations Reducing blast radius through system isolation Health checks and blue-green deployment strategies Fly Proxy request routing and replay behavior Cross-region synchronization and replication challenges Single points of failure inside “redundant” systems Backup restoration testing and disaster recovery planning Network partitions and failure handling in production Infrastructure monitoring and operational visibility AI infrastructure workloads and operational strain Sandboxing and securing AI agents Sprites and AI workflows at Fly.io Latency improvements from geographic distribution Distributed systems tradeoffs in real-world environments Transitive dependency failures across cloud providers Practical resilience strategies for modern engineering teams Links Mentioned: https://fly.io https://github.com/superfly/corrosion https://docs.gitops.weaveworks.org/ FluxCD https://fluxcd.io/ Fly.io Stateful Sandbox Environments https://sprites.dev/ Cloudflare Workers AI Inference Platform https://www.cloudflare.com/products/workers-ai/ “An AI Agent Just Destroyed Our Production Data. It Confessed in Writing” Twitter post from PocketOS founder: https://x.com/lifeof_jer/status/2048103471019434248 Oct 2025 AWS Outage https://www.theguardian.com/technology/2025/oct/24/amazon-reveals-cause-of-aws-outage Dec 2025 Cloudflare Outage https://www.theguardian.com/technology/2025/dec/05/another-cloudflare-outage-takes-down-websites-linkedin-zoom July 2025 Crowdstrike Outage https://www.ibm.com/think/news/recent-crowdstrike-outage-what-you-should-know March 2026 Stryker Cyber Attack https://www.stryker.com/us/en/about/news/2026/a-message-to-our-customers-03-2026.html https://aws.amazon.com/ https://cloud.google.com/ https://azure.microsoft.com/en-us https://fly.io/docs/elixir/ CRDTs!! https://smartlogic.io/podcast/elixir-wizards/s13-e03-local-first-liveview-svelte-pwa/ https://antithesis.com/docs/resources/property_based_testing/ https://hex.pm/packages/proper

In Season 15 episode 3, Charles Suggs sits down with Greg Medland, aka “The Elixir Fixer,” to talk about the current state of hiring and the software jobs market in 2026.   Greg shares what he's seeing from both sides of the hiring process as an Elixir-focused recruiter, from shifting company expectations to the growing importance of specialization, communication skills, and real-world product thinking. We discuss how the market has changed since the 2021–2022 hiring boom, why things feel more uncertain today, and how developers are adapting to a slower, more competitive landscape.   The conversation also explores how AI is affecting hiring workflows, résumé quality, technical interviews, and even the rise of fraudulent candidates. Greg explains why human relationships and reputation still matter more than ever, especially in smaller ecosystems like Elixir where community connections carry real weight.   Along the way, we talk about what junior developers are up against, why senior engineers with domain expertise continue to stand out, and what developers can do to position themselves more effectively in today's market. Greg shares practical advice for building a sustainable career, developing a clear professional identity, and navigating a rapidly changing industry.   Topics discussed in this episode: The current state of the Elixir job market Hiring trends and market shifts since 2021–2022 How AI is changing hiring and recruiting workflows Fraudulent candidates and AI-generated résumés Domain expertise vs. generalist engineering skills Product thinking and customer-focused development What companies are looking for in 2026 Junior developer challenges in the current market Why senior specialists remain in demand Networking and relationship-building in tech Open source contributions and visibility in the Elixir community Standing out in a crowded hiring environment Résumé quality and application strategies The role of personal branding for developers Remote work trends and geographic hiring patterns Technical interview expectations and evaluation changes Startup vs. enterprise hiring differences Human connection in an increasingly automated industry Career resilience and long-term positioning Building a sustainable software engineering career   Links mentioned: Socially Responsible Recruitment https://sr2rec.com/en/ Greg's LinkedIn https://www.linkedin.com/in/elixirfixer/ Greg's email address: greg@sr2rec.com

In Season 15 episode 2, Elixir Wizards Sundi Myint and Charles Suggs chat with Micah Cooper to talk about distributed systems, data replication, and what it actually looks like to build these ideas in Elixir.   Micah shares his journey from Ruby to Elixir and walks us through Visor, a library he's building based on the Viewstamps replication algorithm. Inspired by systems like TigerBeetle, Visor explores how you can replicate state across nodes using GenServers, giving you fault tolerance and recovery without relying entirely on traditional database patterns.   We talk about the difference between distributed systems and data replication, where things tend to get misunderstood, and what changes when you start thinking about state this way. The conversation also touches on event sourcing, tradeoffs in system design, and how Elixir's distributed model makes some of these concepts more approachable than you might expect.   Along the way, we talk about building for curiosity, experimenting with new ideas, and how projects like this push the ecosystem forward.   Topics discussed in this episode: Building Visor and working with the Viewstamps replication model Replicating GenServer state across nodes Distributed systems vs. data replication Lessons from TigerBeetle and financial system design Event sourcing challenges and tradeoffs Rethinking database-first architectures Snapshotting, recovery, and fault tolerance The role of Elixir's distributed model Experimentation, learning, and building for curiosity   Links mentioned: Micah's GitHub https://github.com/mrmicahcooper Micah's GitLab https://gitlab.com/mrmicahcooper The Visor repository: https://gitlab.com/mrmicahcooper/visor Visor Hex Package https://hex.pm/packages/visor Ruby on Rails https://rubyonrails.org/ Phoenix LiveView Framework https://www.phoenixframework.org/ Zig Programming Language https://ziglang.org/ TigerBeetle https://tigerbeetle.com/ TigerBeetle internal docs https://github.com/tigerbeetle/tigerbeetle/tree/main/docs/internals The BEAM https://www.erlang-solutions.com/blog/the-beam-erlangs-virtual-machine/ GenServer https://hexdocs.pm/elixir/GenServer.html Apache Kafka https://github.com/apache/kafka RabbitMQ https://www.rabbitmq.com/ Redpanda https://www.redpanda.com/ SQL https://www.ibm.com/think/topics/structured-query-language Kubernetes https://kubernetes.io/ YAML https://yaml.org/ Nomad Workload Orchestrator https://developer.hashicorp.com/nomad Flutter https://flutter.dev/ Commanded https://hexdocs.pm/commanded/Commanded.html Go Programming Language https://go.dev/ Clojure Programming Language https://clojure.org/ Nebulex https://hexdocs.pm/nebulex/Nebulex.html Mnesia https://www.erlang.org/doc/apps/mnesia/mnesia.html Cachex https://hexdocs.pm/cachex/Cachex.html libgraph https://hexdocs.pm/libgraph/Graph.html Horde https://hexdocs.pm/horde/Horde.Registry.html NocFree split keyboard https://www.nocfree.com/ Micah's LinkedIn https://www.linkedin.com/in/micah-cooper-4a737560/ 

Today, we're joined by Harsha Chintalapani, Co-Founder and CTO of Collate, an AI-native semantic intelligence platform. We talk about:Solving complex data challenges to drive success at UberThe dream of getting LLMs to identify context for improved semanticsThe challenges in applying meaning and semantics at the metadata levelHow open source attracts talentThe value of retaining the ability to model in the new world of AI-generated code

Computer scientist Keith Winstein is an expert in how computers communicate. Computer networks create what he calls shared fictions – abstract realities, like a website or a Zoom call, that exist only because the computers on either end agree to act as if they are real. Unfortunately, today's networks lack a shared notion of a “computation,” which hurts market efficiency in cloud computing and frustrates efforts to hold tech companies accountable for the results of their algorithms. As computational power becomes concentrated in a smaller number of companies, Winstein advocates for a shared language of “computational truths,” defining computations precisely so results are reproducible and auditable. His research group hopes this will lead to greater transparency and accountability in the cloud and, ultimately, to greater confidence in the computations that companies do every day on our behalf. The truth matters, Winstein tells host Russ Altman on this episode of Stanford Engineering's The Future of Everything podcast. Have a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your question. You can send questions to thefutureofeverything@stanford.edu. Episode Reference Links: Stanford Profile: Keith Winstein Connect With Us: Episode Transcripts >>> The Future of Everything Website Connect with Russ >>> Threads / Bluesky / Mastodon Connect with School of Engineering >>> Twitter/X / Instagram / LinkedIn / Facebook Chapters: (00:00:00) Introduction Russ Altman introduces guest Keith Winstein, a professor of computer science and electrical engineering at Stanford University (00:02:56) Why Choose Networking The appeal of the shared digital “fictions” created by connected computers. (00:04:22) The Internet's Impact The broader societal implications of networking technologies. (00:05:35) Computational Truth The concept of tracking how data is produced and verified. (00:09:18) Misaligned Cloud Computing How “pay for effort” models create inefficiencies in cloud systems. (00:13:51) Determining Computational Truth The need for verifiable computation that produces consistent results. (00:18:19) Computations & Accountability How identifying computations could improve trust in systems. (00:20:56) Collaborating Online Why latency challenges make online performance collaboration difficult. (00:24:38) Real-Time Performance Systems Creating a custom system for musicians to perform together online. (00:28:00) Latency vs. Bandwidth Why faster internet speeds don't necessarily reduce delay. (00:30:43) Eliminating Latency How buffering layers in software create unnecessary delay. (00:32:41) Balancing Audio Quality & Delay The different trade-offs for musicians, actors, and audiences. (00:34:20) Rethinking Computer Science Education The need to bring playfulness and interactivity back into learning. (00:35:46) The Xylophone-Based Class Teaching computation through real-time sound and music. (00:38:34) Future In a Minute Rapid-fire Q&A: optimism, truth in computing, and innovation. (00:41:01) Conclusion Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Have you ever wondered how Meta makes config rollouts safe at scale? In this episode, Pascal sits down with Ishwari and Joe to discuss Meta's approach for propagating changes across services in seconds and discuss why speed increases the need for strong safeguards. Catch the episode to discover canarying and progressive rollouts, the health checks and monitoring signals used to catch regressions early, and how incident reviews focus on improving systems rather than blaming people. We also hear how data and early AI/ML are slashing alert noise and speeding up bisecting when something goes wrong. Got feedback? Send it to us on Threads (https://threads.net/@metatechpod), Instagram (https://instagram.com/metatechpod) and don't forget to follow our host Pascal (https://mastodon.social/@passy, https://threads.net/@passy_). Fancy working with us? Check out https://www.metacareers.com/.   Links FFmpeg at Meta: Media Processing at Scale - https://engineering.fb.com/2026/03/02/video-engineering/ffmpeg-at-meta-media-processing-at-scale/ Reliably Changing Configuration @ Scale - https://atscaleconference.com/reliably-changing-configuration-scale/  Timestamps Intro 0:06 Introduction and Overview of Configuration Changes 2:31 Understanding Configurations in Distributed Systems 4:02 Meta's Configuration Management Systems 6:43 Safeguards and Incident Prevention 9:22 Deployment Mechanisms: Canary and Progressive Rollouts 12:06 Challenges in Configuration Consumption 14:39 Health Checks and Incident Response 17:13 Mitigation Strategies for Configuration Issues 19:18 Balancing Developer Velocity and Configuration Safety 21:09 Data-Driven Improvements in Incident Management 22:12 Leveraging AI for Change Detection 26:05 Challenges in Deployment and Testing 28:21 Reinventing Change Safety Strategies 30:24 War Stories: Learning from Past Incidents 32:59 Outro 36:10

What if building a distributed SQL engine meant rethinking everything about how query execution works at scale? In this episode, Benjamin sits down with Nikita, Senior Software Engineer at Cloudflare, to explore how R2 SQL leverages object storage and distributed computing to power analytics across 300 global locations, why backward compatibility becomes critical when you can't control infrastructure rollouts, and the key strategies for handling joins and adaptive query execution in a stateless, point-to-point network architecture. Whether you're designing distributed systems or curious about how Cloudflare processes petabytes of data, this conversation reveals the real-world engineering challenges and innovations shaping the future of cloud data platforms.

Johann Schleier-Smith is the Technical Lead for AI at Temporal Technologies, working on reliable infrastructure for production AI systems and long-running agent workflows. Durable Execution and Modern Distributed Systems, Johann Schleier-Smith // MLOps Podcast #364Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps Merch: https://shop.mlops.community/Big shoutout to ⁨ @Temporalio  for the support, and to  @trychroma  for hosting us in their recording studio// AbstractA new paradigm is emerging for building applications that process large volumes of data, run for long periods of time, and interact with their environment. It's called Durable Execution and is replacing traditional data pipelines with a more flexible approach. Durable Execution makes regular code reliable and scalable.In the past, reliability and scalability have come from restricted programming models, like SQL or MapReduce, but with Durable Execution, this is no longer the case. We can now see data pipelines that include document processing workflows, deep research with LLMs, and other complex and LLM-driven agentic patterns expressed at scale with regular Python programs.In this session, we describe Durable Execution and explain how it fits in with agents and LLMs to enable a new class of machine learning applications.// Related Linkshttps://t.mp/hello?utm_source=podcast&utm_medium=sponsorship&utm_campaign=podcast-2026-03-13-mlops&utm_content=mlops-johannhttps://t.mp/vibe?utm_source=podcast&utm_medium=sponsorship&utm_campaign=podcast-2026-03-13-mlops&utm_content=mlops-johannhttps://t.mp/career?utm_source=podcast&utm_medium=sponsorship&utm_campaign=podcast-2026-03-13-mlops&utm_content=mlops-johann ~~~~~~~~ ✌️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 Johann on LinkedIn: /jssmith/

This interview was recorded for GOTO Unscripted.https://gotopia.techCheck out more here:https://gotopia.tech/articles/421Félix GV - Current Interests: Multi-Planetary Databases, Data Sovereignty & LifeloggingOlimpiu Pop - Technologist & Tech JournalistRESOURCESFélixhttps://bsky.app/profile/felixgv.ninjahttps://github.com/FelixGVhttps://www.linkedin.com/in/felixgvOlimpiuhttps://x.com/olimpiupophttps://github.com/zrollhttps://www.linkedin.com/in/olimpiupopLinkshttps://venicedb.orghttps://github.com/linkedin/venicehttps://rocksdb.orghttps://duckdb.orgDESCRIPTIONFélix GV, a former engineer at LinkedIn and architect of the Venice database system, discusses the complexity of building planetary-scale data systems. He explains Venice's unbundled architecture where each component—from Kafka-based pub/sub to RocksDB-powered servers—operates as an independent distributed system. Félix details their rigorous chaos engineering practices, including regular load tests that push data centers beyond normal capacity to ensure reliability.The discussion covers fundamental distributed systems concepts like the CAP theorem and the trade-offs between consistency and availability in multi-region deployments. He also explains why Venice, as a derived data system, deliberately sacrifices strong consistency for high throughput and availability, and concludes by discussing their experimental integration of DuckDB for SQL-based analytics and data exploration capabilities.RECOMMENDED BOOKSKasun Indrasiri & Danesh Kuruppu • gRPC: Up and Running • https://amzn.to/3sBGBJJTomer Shiran, Jason Hughes & Alex Merced • Apache Iceberg: The Definitive Guide • https://amzn.to/488Z30kWilliam Smith • Arrow Flight Protocols and Practices • https://amzn.to/4o2Q2fdAdi Polak • Scaling Machine Learning with Spark • https://amzn.to/3N9vx1HMark Needham, Michael Hunger & Michael Simons • DuckDB in Action • https://amzn.to/45QwSliSimon Aubury & Ned Letcher • Getting Started with DuckDB • https://amzn.to/3VPk4qBlueskyInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

At ITEXPO / MSP EXPO, Doug Green, Publisher of Technology Reseller News, spoke with Tejas Patel, Software Engineer at Amazon, for a technical deep dive into how one of the world's largest platforms manages scale, reliability, and the growing role of AI in operations. Amazon operates in an environment defined by extreme traffic variability—from daily fluctuations to massive surges during Prime events. Patel explained that the company relies on distributed systems and microservices architecture to scale every layer of the stack, including databases, caching layers, and application servers. “We scale everything at a massive scale,” he noted, adding that AI-driven traffic prediction models help prepare systems for anticipated spikes, ensuring elasticity and resilience under pressure. Even with rigorous lower-environment testing and simulated traffic, real-world production environments introduce unpredictable behaviors. When outages or functional errors occur, the first priority is customer impact mitigation. “The short-term goal is to make our functionalities available for customers as soon as possible,” Patel said. After stabilizing services, engineering teams conduct root cause analysis and implement long-term fixes to prevent recurrence. On-call teams remain a core part of this model, though that may evolve. AI is increasingly part of that evolution. Patel described how AI systems can detect latency drops, identify anomalies, trigger workflows, and begin root cause investigations—sometimes before engineers are alerted. While still in a supervised phase, AI is gradually moving from passive support to more autonomous operational roles. “AI has a lot of protocols built where it can talk to all the systems,” he explained, envisioning a future where AI mitigates issues proactively while engineers oversee the broader architecture. For MSPs and channel professionals looking to understand large-scale technology environments, Patel emphasized the foundational importance of distributed systems. “Distributed system is everywhere,” he said. “It's the backbone of a large-scale product.” As AI models and inference platforms continue to expand globally, scalable distributed infrastructure will remain essential to delivering reliable, uninterrupted user experiences. Visit https://www.amazon.com/

In this episode of the Crazy Wisdom podcast, host Stewart Alsop sits down with Daniel Bar, co-founder of Space Computer, a satellite-based secure compute protocol that creates a "root of trust in space" using tamper-resistant hardware for cryptographic applications. The conversation explores the fascinating intersection of space technology, blockchain infrastructure, and trusted execution environments (TEEs), touching on everything from cosmic radiation-powered random number generators to the future of space-based data centers and Daniel's journey from quantum computing research to building what they envision as the next evolution beyond Ethereum's "world computer" concept. For more information about Space Computer, visit spacecomputer.io, and check out their new podcast "Frontier Pod" on the Space Computer YouTube channel.Timestamps00:00 Introduction to Space Computer02:45 Understanding Layer 1 and Layer 2 in Space Computing06:04 Trusted Execution Environments in Space08:45 The Evolution of Trusted Execution Environments11:59 The Role of Blockchain in Space Computing14:54 Incentivizing Satellite Deployment17:48 The Future of Space Computing and Its Applications20:58 Radiation Hardening and Space Environment Challenges23:45 Kardashev Civilizations and the Future of Energy26:34 Quantum Computing and Its Implications29:49 The Intersection of Quantum and Crypto32:26 The Future of Space Computer and Its VisionKey Insights1. Space-based data centers solve the physical security problem for Trusted Execution Environments (TEEs). While TEEs provide secure compute through physical isolation, they remain vulnerable to attacks requiring physical access - like electron microscope forensics to extract secrets from chips. By placing TEEs in space, these attack vectors become practically impossible, creating the highest possible security guarantees for cryptographic applications.2. The space computer architecture uses a hybrid layer approach with space-based settlement and earth-based compute. The layer 1 blockchain operates in space as a settlement layer and smart contract platform, while layer 2 solutions on earth provide high-performance compute. This design leverages space's security advantages while compensating for the bandwidth and compute constraints of orbital infrastructure through terrestrial augmentation.3. True randomness generation becomes possible through cosmic radiation harvesting. Unlike pseudo-random number generators used in most blockchain applications today, space-based systems can harvest cosmic radiation as a genuinely stochastic process. This provides pure randomness critical for cryptographic applications like block producer selection, eliminating the predictability issues that compromise security in earth-based random number generation.4. Space compute migration is inevitable as humanity advances toward Kardashev Type 1 civilization. The progression toward planetary-scale energy control requires space-based infrastructure including solar collection, orbital cities, and distributed compute networks. This technological evolution makes space-based data centers not just viable but necessary for supporting the scale of computation required for advanced civilization development.5. The optimal use case for space compute is high-security applications rather than general data processing. While space-based data centers face significant constraints including 40kg of peripheral infrastructure per kg of compute, maintenance impossibility, and 5-year operational lifespans, these limitations become acceptable when the application requires maximum security guarantees that only space-based isolation can provide.6. Space computer will evolve from centralized early-stage operation to a decentralized satellite constellation. Similar to early Ethereum's foundation-operated nodes, space computer currently runs trusted operations but aims to enable public participation through satellite ownership stakes. Future participants could fractionally own satellites providing secure compute services, creating economic incentives similar to Bitcoin mining pools or Ethereum staking.7. Blockchain represents a unique compute platform that meshes hardware, software, and free market activity. Unlike traditional computers with discrete inputs and outputs, blockchain creates an organism where market participants provide inputs through trading, lending, and other economic activities, while the distributed network processes and returns value through the same market mechanisms, creating a cyborg-like integration of technology and economics.

In this episode of the Crazy Wisdom podcast, host Stewart Alsop sits down with Peter Schmidt Nielsen, who is building FPGA-accelerated servers at Saturn Data. The conversation explores why servers need FPGAs, how these field-programmable gate arrays work as "IO expanders" for massive memory bandwidth, and why they're particularly well-suited for vector database and search applications. Peter breaks down the technical realities of FPGAs - including why they "really suck" in many ways compared to GPUs and CPUs - while explaining how his company is leveraging them to provide terabyte-per-second bandwidth to 1.3 petabytes of flash storage. The discussion ranges from distributed systems challenges and the CAP theorem to the hardware-software relationship in modern computing, offering insights into both the philosophical aspects of search technology and the nuts-and-bolts engineering of memory controllers and routing fabrics.For more information about Peter's work, you can reach him on Twitter at @PTRSCHMDTNLSN or find his website at saturndata.com.Timestamps00:00 Introduction to FPGAs and Their Role in Servers02:47 Understanding FPGA Limitations and Use Cases05:55 Exploring Different Types of Servers08:47 The Importance of Memory and Bandwidth11:52 Philosophical Insights on Search and Access Patterns14:50 The Relationship Between Hardware and Search Queries17:45 Challenges of Distributed Systems20:47 The CAP Theorem and Its Implications23:52 The Evolution of Technology and Knowledge Management26:59 FPGAs as IO Expanders29:35 The Trade-offs of FPGAs vs. ASICs and GPUs32:55 The Future of AI Applications with FPGAs35:51 Exciting Developments in Hardware and BusinessKey Insights1. FPGAs are fundamentally "crappy ASICs" with serious limitations - Despite being programmable hardware, FPGAs perform far worse than general-purpose alternatives in most cases. A $100,000 high-end FPGA might only match the memory bandwidth of a $600 gaming GPU. They're only valuable for specific niches like ultra-low latency applications or scenarios requiring massive parallel I/O operations, making them unsuitable for most computational workloads where CPUs and GPUs excel.2. The real value of FPGAs lies in I/O expansion, not computation - Rather than using FPGAs for their processing power, Saturn Data leverages them primarily as cost-effective ways to access massive amounts of DRAM controllers and NVMe interfaces. Their server design puts 200 FPGAs in a 2U enclosure with 1.3 petabytes of flash storage and terabyte-per-second read bandwidth, essentially using FPGAs as sophisticated I/O expanders.3. Access patterns determine hardware performance more than raw specs - The way applications access data fundamentally determines whether specialized hardware will provide benefits. Applications that do sparse reads across massive datasets (like vector databases) benefit from Saturn Data's architecture, while those requiring dense computation or frequent inter-node communication are better served by traditional hardware. Understanding these patterns is crucial for matching workloads to appropriate hardware.4. Distributed systems complexity stems from failure tolerance requirements - The difficulty of distributed systems isn't inherent but depends on what failures you need to tolerate. Simple approaches that restart on any failure are easy but unreliable, while Byzantine fault tolerance (like Bitcoin) is extremely complex. Most practical systems, including banks, find middle ground by accepting occasional unavailability rather than trying to achieve perfect consistency, availability, and partition tolerance simultaneously.5. Hardware specialization follows predictable cycles of generalization and re-specialization - Computing hardware consistently follows "Makimoto's Wave" - specialized hardware becomes more general over time, then gets leapfrogged by new specialized solutions. CPUs became general-purpose, GPUs evolved from fixed graphics pipelines to programmable compute, and now companies like Etched are creating transformer-specific ASICs. This cycle repeats as each generation adds programmability until someone strips it away for performance gains.6. Memory bottlenecks are reshaping the hardware landscape - The AI boom has created severe memory shortages, doubling costs for DRAM components overnight. This affects not just GPU availability but creates opportunities for alternative architectures. When everyone faces higher memory costs, the relative premium for specialized solutions like FPGA-based systems becomes more attractive, potentially shifting the competitive landscape for memory-intensive applications.7. Search applications represent ideal FPGA use cases due to their sparse access patterns - Vector databases and search workloads are particularly well-suited to FPGA acceleration because they involve searching through massive datasets with sparse access patterns rather than dense computation. These applications can effectively utilize the high bandwidth to flash storage and parallel I/O capabilities that FPGAs provide, making them natural early adopters for this type of specialized hardware architecture.

Alex Chepurnoy is a cryptographer & researcher who famously wrote a Bitcoin client in Haskell in only 3600 lines of code. He is currently working on Ergo, a proof of work blockchain which improves upon Bitcoin's design in order to achieve smart contracts and DeFi. How does it work? Let's find out! Time stamps: 00:01:11 Introducing Alex Chepurnoy 00:01:51 Alex's Bitcoin Discovery & Early Development 00:02:37 Namecoin, SmartContract.com, and Cardano Involvement 00:05:15 Satoshi Theories & Code Analysis 00:07:00 Rewriting Bitcoin & Distributed Systems Perspective 00:08:39 Consensus Protocols & Altcoin Proliferation 00:10:20 Bitcoin's Early Appeal & Peer-to-Peer Motivation 00:14:08 Bitcoin's Revolutionary Monetary Model 00:15:45 Staying in Crypto: Problems to Solve 00:17:19 Bitcoin as Digital Gold & Smart Contracts 00:21:29 Ethereum vs. Bitcoin: Contractual Capabilities 00:23:02 Ergo's Approach: Contracts & Protocol Upgrades 00:26:56 Namecoin's History & Technical Innovations 00:31:10 Merged Mining & Sidechain Politics 00:34:35 Early Bitcoin Contributions & BTC Scala Client 00:38:49 Conference Presentations & ZeroJoin 00:41:49 Demurrage, Storage Rent, and Bitcoin Upgrades 00:45:01 NFTs, Inscriptions, and Bitcoin Community Divisions 00:50:10 Hard Forks, Immutability, and Ethereum Classic 00:55:17 Markets, Transaction Fees, and Bitcoin's Security Budget 00:57:59 Lightning Network Limitations & Off-Chain Cash 01:01:58 Challenging Bitcoin's Scaling & Off-Chain Solutions 01:06:38 Ergo's Protocol Design & Civil War Lessons 01:08:25 Ergo's Innovations for Bitcoin 01:15:38 Quantum Resistance & Hard Fork Challenges 01:19:51 Consensus Cleanup & Upgrade Difficulties 01:23:10 Community Proposals & Development Gridlock 01:25:07 Alex's Tech Stack & Personal Devices 01:31:07 Satoshi's Identity & Coding Style 01:38:34 NXT, Bitcoin 2.0, and Ethereum's Success 01:45:35 Proof of Work vs. Proof of Stake 01:50:44 Philosophy of Proof of Work & Fair Distribution 01:53:09 VCs, Token Dumps, and Proof of Work Revival 01:54:16 Proof of Stake Attacks & Network Resilience 01:59:20 Ergo's Network Parameters & Smart Contracts 02:21:17 Privacy Features: Mixers & Stealth Addresses 02:28:40 Monetary Policy, Emission, and Pre-mine 02:34:09 Monero vs. Zcash: Community & Funding 02:48:03 Bridging Blockchains & Rosen Bridge 02:51:04 Peer-to-Peer Finance & Smart Contract Design 02:53:57 Future Vision: Interconnected PoW Blockchains 02:56:41 Double Merged Mining Sidechains 03:17:45 Community Resources & Getting Involved 03:20:11 Conclusion & Final Thoughts

Jenish Shah, a back-end engineer focused on distributed systems at Netflix, provides more insights on how to handle failures in a distributed systems setup. He shares details on how he built a library that handles exceptions uniformly, regardless of the underlying communication protocol. Read a transcript of this interview: http://bit.ly/3JpmIBn Subscribe to the Software Architects' Newsletter for your monthly guide to the essential news and experience from industry peers on emerging patterns and technologies: https://www.infoq.com/software-architects-newsletter Upcoming Events: QCon San Francisco 2025 (November 17-21, 2025) Get practical inspiration and best practices on emerging software trends directly from senior software developers at early adopter companies. https://qconsf.com/ QCon AI New York 2025 (December 16-17, 2025) https://ai.qconferences.com/ QCon London 2026 (March 16-19, 2026) https://qconlondon.com/ The InfoQ Podcasts: Weekly inspiration to drive innovation and build great teams from senior software leaders. Listen to all our podcasts and read interview transcripts: - The InfoQ Podcast https://www.infoq.com/podcasts/ - Engineering Culture Podcast by InfoQ https://www.infoq.com/podcasts/#engineering_culture - Generally AI: https://www.infoq.com/generally-ai-podcast/ Follow InfoQ: - Mastodon: https://techhub.social/@infoq - X: https://x.com/InfoQ?from=@ - LinkedIn: https://www.linkedin.com/company/infoq/ - Facebook: https://www.facebook.com/InfoQdotcom# - Instagram: https://www.instagram.com/infoqdotcom/?hl=en - Youtube: https://www.youtube.com/infoq - Bluesky: https://bsky.app/profile/infoq.com Write for InfoQ: Learn and share the changes and innovations in professional software development. - Join a community of experts. - Increase your visibility. - Grow your career. https://www.infoq.com/write-for-infoq

In this episode of The Crazy Wisdom Podcast, Stewart Alsop talks with Jacob Hall and Kyriakos Skiouris, co-founders of Agingo, about the evolution of blockchain from linear ledgers to volumetric, multi-agent architectures. Together they explore how concepts like sovereignty, auditability, and immutability can redefine trust, governance, and digital agency in both human and artificial systems. The conversation touches on blockchain's philosophical and technical frontiers—what an “AGI for blockchain” might mean, why immutability will matter in the age of AI, and how decentralization could restore autonomy without chaos. You can learn more about Agingo and their upcoming talks at agingo.com and reach them via support@agingo.com.Check out this GPT we trained on the conversationTimestamps00:00 Stewart Alsop welcomes Jacob Hall and Kyriakos Skiouris of Agingo, setting the stage for a conversation on blockchain as a paradigm shift beyond crypto.05:00 They explore trust, contracts, and the difference between real-world agreements and smart contracts, questioning how sovereignty depends on auditability.10:00 The guests reflect on Bitcoin's origins, Satoshi's intent, and the ideological fractures that shaped crypto's culture and early altruism.15:00 They discuss manipulation, value, and how blockchain technology parallels alchemy—transforming belief into perceived value.20:00 The idea of social imaginaries emerges, using everyday systems like traffic lanes as metaphors for collective trust and order.25:00 The talk moves toward digital etiquette, communication decay, and the cultural lag behind technological acceleration.30:00 Agingo introduces the concept of volumetric blockchain, multi-agent validation, and four-dimensional nanochains replacing linear ledgers.35:00 They unpack volumetric security, the tesseract metaphor, and blockchain as a living system mirroring consciousness.40:00 Discussion turns to blockchain as language and history, linking immutability, perception, and meaning.45:00 Business use cases arise—tokenized films, compliance, and real-world asset representation on decentralized infrastructure.50:00 They imagine blockchain as infrastructure for AGI, distributed systems modeled after nature's intelligence.55:00 Closing reflections on centralization, sovereignty, and the need for open, non-binary conversations about trust and autonomy in the digital age.Key InsightsBlockchain's next evolution is volumetric, not linear. Jacob Hall and Kyriakos Skiouris argue that traditional blockchains like Bitcoin and Ethereum are “choo-choo trains”—linear systems limited by their own history. Agingo's model introduces volumetric blockchain, where multiple agents and dimensions of time operate simultaneously, allowing for more secure, adaptive, and physics-like computation.Sovereignty depends on auditability at speed. True digital sovereignty, they suggest, isn't just owning your data but being able to verify it instantly. If you can't audit a transaction or vote in real time, you've lost control of it. Fast, transparent auditability becomes the foundation of autonomy and trust in digital systems.Language, contracts, and blockchains are all ledgers of meaning. The conversation reframes contracts as linguistic and symbolic structures—records of shared trust. Blockchain, in this light, is not just code but a living language that keeps history intact and immutable, anchoring truth in a world of mutable data.Bitcoin's promise was idealistic, but its structure is fragile. Hall recalls the early altruism of the Bitcoin community, contrasting it with the dogmatic, profit-driven culture that followed. The failure to evolve past linear design and ideological rigidity mirrors historical schisms in religion and governance.Immutability will become essential in the AI era. As AI systems learn to rewrite their own data, humans will crave immutable records. Blockchain's permanence provides a safeguard against subtle, undetectable shifts in digital reality—an anchor for truth as models become more autonomous.Volumetric systems mirror consciousness. Their design mimics the distributed, multi-agent nature of the human brain. Just as neurons work in parallel, a volumetric blockchain processes data through overlapping agents that validate one another, creating a kind of digital nervous system with emergent intelligence.Decentralization must include cultural and ethical intelligence. True progress, they conclude, isn't just technical—it's cultural. Without new forms of etiquette, communication, and mutual respect, decentralization risks reproducing the same hierarchies it seeks to replace. Blockchain's next leap must integrate human values with technological sovereignty.

In this episode of The Engineering Room, Dave Farley speaks with Sam Newman, renowned author of "Building Microservices" and "Monolith to Microservices," about distributed systems, architectural decisions, and the future of software development.-------------------------Sam Newman on "X" (formerly "Twitter"): https://x.com/samnewman?lang=en

Cisco's Vijoy Pandey - SVP & GM of Outshift by Cisco - explains how AI agents and quantum networks could completely redefine how software, infrastructure, and security function in the next decade.You'll learn:→ What “Agentic AI” and the “Internet of Agents” actually are→ How Cisco open-sourced the Internet of Agents framework and why decentralization matters→ The security threat of “store-now, decrypt-later” attacks—and how post-quantum cryptography will defend against them→ How Outshift's “freedom to fail” model fuels real innovation inside a Fortune-500 company→ Why the next generation of software will blur the line between humans, AI agents, and machines→ The vision behind Cisco's Quantum Internet—and two real-world use cases you can see today: Quantum Sync and Quantum AlertAbout Today's Guest:​​Meet Vijoy Pandey, the mind behind Cisco's Outshift—a team pushing the boundaries of what's next in AI, quantum computing, and the future internet. With 80+ patents to his name and a career spent redefining how systems connect and think, he's one of the few leaders truly building the next era of computing before the rest of us even see it coming.Key Moments:00:00 Meet Vijoy Pandey & Outshift's mission04:30 The two hardest problems in computer science: Superintelligence & Quantum Computing06:30 Why “freedom to fail” is Cisco's innovation superpower10:20 Inside the Outshift model: incubating like a startup inside Cisco21:00 What is Agentic AI? The rise of the Internet of Agents27:00 AGNTCY.org and open-sourcing the Internet of Agents32:00 What would an Internet of Agents actually look like?38:19 Responsible AI & governance: putting guardrails in early49:40 What is quantum computing? What is quantum networking?55:27 The vision for a global Quantum InternetWatch Next: https://youtu.be/-Jb2tWsAVwI?si=l79rdEGxB-i-Wrrn  -- This episode of IT Visionaries is brought to you by Meter - the company building better networks. Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools. Meter changes that with a single integrated solution that covers everything wired, wireless, and even cellular networking. They design the hardware, write the firmware, build the software, and manage it all so your team doesn't have to.That means you get fast, secure, and scalable connectivity without the complexity of juggling multiple providers. Thanks to meter for sponsoring. Go to meter.com/itv to book a demo.---IT Visionaries is made by the team at Mission.org. Learn more about our media studio and network of podcasts at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, Will Wilson, CEO and co-founder of Antithesis, explores Deterministic Simulation Testing (DST) with host Sriram Panyam. Wilson was part of the pioneering team at FoundationDB that developed this revolutionary testing approach, which was later acquired by Apple in 2015. After seeing that even sophisticated organizations lacked robust testing for distributed systems, Wilson co-founded Antithesis in 2018 to make DST commercially available. Deterministic simulation testing runs software in a fully controlled, simulated environment in which all sources of non-determinism are eliminated or controlled. Unlike traditional testing or chaos engineering, DST operates in a separate environment from production, allowing for aggressive fault injection without risk to live systems. The key breakthrough is perfect reproducibility -- any bug found can be recreated exactly using the same random seed. Antithesis built "The Determinator," a custom deterministic hypervisor that simulates entire software stacks including virtual hardware, networking, and time. The system can compress years of stress testing into shorter timeframes by running simulations faster than wall-clock time. All external interfaces that could introduce non-determinism (network calls, disk I/O, system time) are mocked or controlled by the simulator. The approach has proven effective with major organizations including MongoDB, Palantir, and Ethereum. For Ethereum's critical "Merge" upgrade in 2022, Antithesis found and helped fix several serious bugs that could have been catastrophic for the live network. The platform typically finds bugs that traditional testing methods miss entirely -- such as those arising from rare race conditions, complex timing issues, and unexpected system interactions. This episode is sponsored by Monday Dev

Trust at Scale: Security and Governance for Open Source Models // MLOps Podcast #338 with Hudson Buzby, Solutions Architect at JFrog.Appreciate JFrog for their support in bringing this blog to life.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractFor better or for worse, machine learning has traditionally escaped the gaze of security and infrastructure teams, operating outside traditional DevOps practices and not always adhering to organizations development or security standards. With the introduction of open source catalogs like HuggingFace and Ollama, a new standard has been established for locating, identifying, and deploying machine learning and AI models. But with this new standard comes a plethora of security, governance, and legal challenges that organizations need to address before they can comfortably allow developers to freely build and deploy ML/AI applications. In this conversation will discuss ways that enterprise scale organizations are addressing these challenges to safely and securely build these development environments. // BioHudson Buzby is a solution engineer with an emphasis on MLOps, LLMOps, Big Data, and Distributed Systems, leveraging his expertise to help organizations optimize their machine learning operations and large language model deployments. His role involves providing technical solutions and guidance to enhance the efficiency and effectiveness of AI-driven projects.// Related Linkshttps://www.youtube.com/channel/UCh2hNg76zo3d1qQqTWIQxDg~~~~~~~~ ✌️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 Hudson on LinkedIn: /hudson-buzby/

This interview was recorded for GOTO Unscripted.https://gotopia.techRead the full transcription of this interview hereBrooklyn Zelenka - Author of Numerous Libraries Including Witchcraft & Founded the Vancouver Functional Programming MeetupJulian Wood - Serverless Developer Advocate at AWSRESOURCESBrooklynhttps://bsky.app/profile/expede.wtfhttps://octodon.social/@expede@types.plhttps://github.com/expedehttps://www.linkedin.com/in/brooklynzelenkahttps://notes.brooklynzelenka.comJulianhttps://bsky.app/profile/julianwood.comhttps://twitter.com/julian_woodhttp://www.wooditwork.comhttps://www.linkedin.com/in/julianrwoodLinkshttps://automerge.orghttps://discord.com/invite/zKGe4DCfgRhttps://www.robinsloan.com/notes/home-cooked-apphttps://github.com/ipvm-wghttps://www.localfirst.fmhttps://localfirstweb.devDESCRIPTIONDistributed systems researcher Brooklyn Zelenka unpacks the paradigm shift of local-first computing, where applications primarily run on users' devices and synchronize seamlessly without central servers.In a conversation with Julian Wood, she explains how this approach reduces latency, enables offline functionality, improves privacy through encryption, and democratizes app development—all while using sophisticated data structures. Perfect for collaborative tools and "cozy web" applications serving smaller communities, local-first software represents a fundamental rethinking of how we've built software for the past 30 years.RECOMMENDED BOOKSFord, Parsons, Kua & Sadalage • Building Evolutionary Architectures 2nd EditionFord, Richards, Sadalage & Dehghani • Software Architecture: The Hard PartsMark Richards & Neal Ford • Fundamentals of Software ArchitectureFord, Parsons & Kua • Building Evolutionary ArchitecturesNeal Ford • Functional ThinkingMichael Feathers • Working Effectively with Legacy CodeBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

Today, the Elixir Wizards wrap up Season 14 “Enter the Elixirverse.” Dan, Charles, and Sundi look back at some common themes: Elixir plays well with others, bridges easily to access languages and tools, and remains a powerful technology for data flow, concurrency, and developer experience. We revisit the popular topics of the year, from types and tooling to AI orchestration and reproducible dev environments, and share what we're excited to explore next.   We also invite your questions and takeaways to help shape future seasons and conference conversations. Season 14 doubles as a handy primer for anyone curious about how Elixir integrates across the stack.   Key topics discussed in this episode:   * Lessons from a season of interoperability * Set-theoretic types and what new compiler warnings unlock * AI in practice: LLM orchestration, fallbacks, and real-world use * SDUI and GraphQL patterns for shipping UI across web/iOS/Android * Dataframes in Elixir with Explorer for analytics workflows * Python interoperability (ErlPort, PythonX) and when to reach for it * Reproducible dev environments with Nix and friends * Performance paths: Rustler and Zig for native extensions * Bluetooth & Nerves: Blue Heron and hardware integrations * DevEx upgrades: LiveView, build pipelines, and standard project setup * Observability and ops: Prometheus/Grafana and sensible deployments * Community feedback, conferences, and what's on deck for next season   Links mentioned in this episode: Cars.com S14E06 SDUI at Scale with Elixir https://youtu.be/nloRcgngTk?si=g4Zd4N1s56Ronrtw https://hexdocs.pm/phoenixliveview/Phoenix.LiveView.html https://wordpress.com/ https://elixir-lang.org/ S14E01 Zigler: Zig NIFs for Elixir https://youtu.be/hSAvWxh26TU?si=d55tVuZbNw0KCfT https://ziglang.org/ https://hexdocs.pm/zigler/Zig.html https://github.com/blue-heron/blueheron https://github.com/elixir-explorer/explorer S14E08 Nix for Elixir Apps https://youtu.be/yymUcgy4OAk?si=BRgTlc2VK5bsIhIf https://nixos.org/ https://nix.dev/ S14E07 Set Theoretic Types in Elixir https://youtu.be/qMmEnXcHxL4?si=Ux2lebiwEp3mc0e S14E10 Python in Elixir Apps https://youtu.be/SpVLrrWkRqE?si=ld3oQVXVlWHpo7eV https://www.python.org/ https://hexdocs.pm/pythonx/ https://github.com/Pyrlang/Pyrlang https://github.com/erlport/erlport S14E03 LangChain: LLM Integration for Elixir https://youtu.be/OwFaljL3Ptc?si=A0sDs2dzJ0UoE2PY https://github.com/brainlid/langchain S14E04 Nx & Machine Learning in Elixir https://youtu.be/Ju64kAMLlkw?si=zdVnkBTTLHvIZNBm S14E05 Rustler: Bridging Elixir and Rust https://youtu.be/2RBw7B9OfwE?si=aRVYOyxxW8fTmoRA https://github.com/rusterlium/rustler Season 3: Working with Elixir https://youtube.com/playlist?list=PLTDLmInI9YaDbhMRpGuYpboVNbp1Fl9PD&si=hbe7qt4gRUfrMtpj S14E11 Vibe Coding the LoopedIn Crochet App https://youtu.be/DX0SjmPE92g?si=zCBPjS1huRDIeVeP Season 5: Adopting Elixir  YouTubeLaunchisode and Outlaws Takeover with Chris Keathley, Amos King, and Anna Neyzberg S13E01 Igniter: Elixir Code Generation https://youtu.be/WM9iQlQSF_g?si=e0CAiML2qC2SxmdL Season 8: Elixir in a Polyglot Environment https://youtube.com/playlist?list=PLTDLmInI9YaAPlvMd-RDp6LWFjI67wOGN&si=YCI7WLA8qozD57iw !! We Want to Hear Your Thoughts *!!* Have questions, comments, or topics you'd like us to discuss on the podcast? Share your thoughts with us here: https://forms.gle/Vm7mcYRFDgsqqpDC9

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubRead the full transcription of the interview hereSheen Brisals - AWS Serverless Hero, Engineering Leader & Co-Author of "Serverless Development on AWS"Vlad Khononov - Author of "Balancing Coupling in Software Design" & "Learning Domain Driven Design" & Creator of the Balanced Coupling ModelRESOURCESVladhttps://bsky.app/profile/vladikk.bsky.socialhttps://vladikk.comhttps://github.com/vladikkhttps://www.linkedin.com/in/vladikkSheenhttps://bsky.app/profile/sheenbrisals.bsky.socialhttps://twitter.com/sheenbrisalshttps://www.linkedin.com/in/sheen-brisalshttps://sbrisals.medium.comLinkshttps://www.informit.comhttps://youtu.be/6hTZXR2brWEDESCRIPTIONSheen Brisals sits down with software engineer and author Vlad Khononov to explore his latest book, "Balancing Coupling in Software Design". Vlad shares his journey from a failed microservices project to his deep dive into the principles of coupling, drawing insights from a 1970s structured design book.The duo discusses the timeless nature of coupling in software, how modern systems like microservices and cloud architectures still rely on age-old design principles, and the importance of understanding complexity for better problem decomposition and estimation. Vlad also reveals his unique approach to the book—integrating AI-generated poetry into each chapter to help readers grasp complex concepts. With a focus on modularity as the antidote to complexity, Vlad emphasizes that by managing coupling, engineers can create more maintainable, scalable systems.RECOMMENDED BOOKSVlad Khononov • Balancing Coupling in Software DesignVlad Khononov • Learning Domain-Driven DesignSheen Brisals & Luke Hedger • Serverless Development on AWSGlenford Myers • Composite/Structured DesignVaughn Vernon • Implementing Domain-Driven DesignEric Evans • Domain-Driven Designvan Kelle, Verschatse & Baas-Schwegler • Collaborative Software DesignNick Tune & Jean-Georges Perrin • Architecture ModernizaBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

Qian Li of DBOS, a durable execution platform born from research by the creators of Postgres and Spark, speaks with host Kanchan Shringi about building durable, observable, and scalable software systems, and why that matters for modern applications. They discuss database-backed program state, workflow orchestration, real-world AI use cases, and comparisons with other workflow technologies. Li explains how DBOS persists not just application data but also program execution state in Postgres to enable automatic recovery and exactly-once execution. She outlines how DBOS uses workflow and step annotations to build deterministic, fault-tolerant flows for everything from e-commerce checkouts to LLM-powered agents. Observability features, including SQL-accessible state tables and a time-travel debugger, allow developers and business users to understand and troubleshoot system behavior. Finally, she compares DBOS with tools like Temporal and AWS Step Functions. Brought to you by IEEE Computer Society and IEEE Software magazine.

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubRead the full transcription of the interview hereAnne Currie - Co-Author of "The Cloud Native Attitude" & "Building Green Software"Sarah Wells - Independent Consultant & Author & Author of "Enabling Microservice Success"RESOURCESAnnehttps://bsky.app/profile/annecurrie.bsky.socialhttps://www.strategically.greenSarahhttps://bsky.app/profile/sarahjwells.bsky.socialhttps://www.sarahwells.devhttps://linkedin.com/in/sarahjwells1DESCRIPTIONSarah Wells and Anne Currie dive into “The Cloud Native Attitude” and uncover why it's more than just using cloud infrastructure. It's about breaking bottlenecks, embracing rapid change, and aligning the entire organization.Anne reflects on how Kubernetes has risen since the book's first edition, but the core principles remain. They discuss why CI/CD is key, how cloud native supports sustainability, and why true transformation demands more than just a lift-and-shift. The conversation wraps up with practical advice on identifying real bottlenecks and securing buy-in for a successful cloud native journey.RECOMMENDED BOOKSAnne Currie & Jamie Dobson • The Cloud Native AttitudeAnne Currie, Sarah Hsu, & Sara Bergman • Building Green SoftwareSarah Wells • Enabling Microservice SuccessBill Gates • How to Avoid a Climate DisasterLiz Rice • Container SecurityBurns, Beda & Hightower • Kubernetes: Up & RunningMatthew Skelton & Manuel Pais • Team TopologiesBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinSummary:In this episode of The Geek Narrator podcast, host Kaivalya Apte interviews Kyle Kingsbury, a renowned expert in database and distributed systems safety analysis. They discuss the world of testing distributed systems, the challenges faced, common bugs and patterns. Kyle shares insights on the importance of understanding system documentation, the role of formal verification, and the balance between performance and safety in testing. He also provides valuable advice for aspiring engineers in the field of distributed systems.Chapters:00:00 Introduction to Kyle Kingsbury and His Work06:59 Common Bugs in Distributed Systems12:37 Functional Bugs vs Safety Bugs17:54 Changes in Testing Over the Years26:03 False Positives and Negatives in Testing32:33 The Importance of Experimentation in Testing39:28 Tools and Technologies for Testing48:58 The Role of Formal Verification57:04 Reusability of TestsImportant links:Distributed systems class: https://github.com/aphyr/distsys-classWrite your own distributed system: https://github.com/jepsen-io/maelstromJepsen Analyses: https://jepsen.io/analysesKey takeaways:- Reading documentation is a crucial first step in testing systems.- Testing distributed systems involves understanding their semantics and guarantees.- Common bugs often arise from mismanagement of definite versus indefinite failures.- Testing strategies for cloud-based systems require cooperation with providers.- Performance testing can reveal unexpected behaviours in systems under stress.- Formal verification remains a challenging but valuable tool in ensuring system safety.- The testing process is iterative and requires collaboration with engineering teams.- Aspiring engineers should immerse themselves in practical experiences to build intuition.For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!#databasearchitecture #distributedsystems #cloudcomputing #testing #jepsen

Chris Love, co-author of the book Core Kubernetes, joins host Robert Blumen for a conversation about kubernetes security. Chris identifies the node layer, secrets management, the network layer, contains, and pods as the most critical areas to be addressed. The conversation explores a range of topics, including when to accept defaults and when to override; differences between self-managed clusters and cloud-service provider-managed clusters; and what can go wrong at each layer -- and how to address these issues. They further discuss managing the node layer; network security best practices; kubernetes secrets and integration with cloud-service provider secrets; container security; pod security, and Chris offers his views on policy-as-code frameworks and scanners. Brought to you by IEEE Computer Society and IEEE Software magazine.

This interview was recorded for GOTO Unscripted.https://gotopia.techRead the full transcription of this interview hereThomas Johnson - Co-Founder & CTO at MultiplayerJulian Wood - Serverless Developer Advocate at AWSRESOURCESTomhttps://x.com/tomjohnson3https://www.linkedin.com/in/tomjohnson3https://github.com/tomjohnson3Julianhttps://bsky.app/profile/julianwood.comhttps://twitter.com/julian_woodhttp://www.wooditwork.comhttps://www.linkedin.com/in/julianrwoodLinkshttps://www.multiplayer.appDESCRIPTIONJulian Wood and Tom Johnson explore the complexities of modern software development, with Tom sharing his journey from machine learning and distributed systems to founding Multiplayer, a company focused on simplifying development by automating documentation, debugging, and system design.They discuss the challenges of building and managing complex software architectures, especially with microservices and cloud platforms, and the need for better tooling to address these issues. Tom emphasizes the importance of simplicity, collaboration, and transparency in development, especially when it comes to API design and managing system dependencies. They also explore best practices for team communication, the evolving role of platform engineering, and the shift toward a future where infrastructure is abstracted away, allowing developers to focus on software creation.Together, they envision a world where better tools and AI lower the barrier to entry for developers, driving innovation and enabling more people to bring their ideas to life.RECOMMENDED BOOKSSimon Brown • Software Architecture for Developers Vol. 2David Farley • Modern Software EngineeringKim, Humble, Debois, Willis & Forsgren • The DevOps HandbookSimon Wardley • Wardley MapsSimon Wardley • Wardley Mapping, The KnowledgeDavid Anderson, Marck McCann & Michael O'Reilly • The Value Flywheel EffectMike Amundsen • Restful Web API Patterns & Practices CookbookBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

In this episode of Book Overflow, Carter and Nathan discuss the second half of Grokking Concurrency by Kirill Bobrov! Join them as they discuss the mutexes, semaphores, the reactor pattern, and more!-- Books Mentioned in this Episode --Note: As an Amazon Associate, we earn from qualifying purchases.----------------------------------------------------------Grokking Concurrency by Kirill Bobrovhttps://amzn.to/3GRbnby (paid link)----------------Spotify: https://open.spotify.com/show/5kj6DLCEWR5nHShlSYJI5LApple Podcasts: https://podcasts.apple.com/us/podcast/book-overflow/id1745257325X: https://x.com/bookoverflowpodCarter on X: https://x.com/cartermorganNathan's Functionally Imperative: www.functionallyimperative.com----------------Book Overflow is a podcast for software engineers, by software engineers dedicated to improving our craft by reading the best technical books in the world. Join Carter Morgan and Nathan Toups as they read and discuss a new technical book each week!The full book schedule and links to every major podcast player can be found at https://www.bookoverflow.io

In this episode of Flying High with Flutter, we're joined by Dominik Tornow, principal engineer and author of Thinking in Distributed Systems. Dominik shares his journey into distributed systems, breaks down complex concepts like the CAP theorem, liveness vs. safety, and item potency, and offers practical tips for building reliable and scalable systems.On the show:

Eiso Kant, CTO of poolside AI, discusses the company's approach to building frontier AI foundation models, particularly focused on software development. Their unique strategy is reinforcement learning from code execution feedback which is an important axis for scaling AI capabilities beyond just increasing model size or data volume. Kant predicts human-level AI in knowledge work could be achieved within 18-36 months, outlining poolside's vision to dramatically increase software development productivity and accessibility. SPONSOR MESSAGES:***Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/***Eiso Kant:https://x.com/eisokanthttps://poolside.ai/TRANSCRIPT:https://www.dropbox.com/scl/fi/szepl6taqziyqie9wgmk9/poolside.pdf?rlkey=iqar7dcwshyrpeoz0xa76k422&dl=0TOC:1. Foundation Models and AI Strategy [00:00:00] 1.1 Foundation Models and Timeline Predictions for AI Development [00:02:55] 1.2 Poolside AI's Corporate History and Strategic Vision [00:06:48] 1.3 Foundation Models vs Enterprise Customization Trade-offs2. Reinforcement Learning and Model Economics [00:15:42] 2.1 Reinforcement Learning and Code Execution Feedback Approaches [00:22:06] 2.2 Model Economics and Experimental Optimization3. Enterprise AI Implementation [00:25:20] 3.1 Poolside's Enterprise Deployment Strategy and Infrastructure [00:26:00] 3.2 Enterprise-First Business Model and Market Focus [00:27:05] 3.3 Foundation Models and AGI Development Approach [00:29:24] 3.4 DeepSeek Case Study and Infrastructure Requirements4. LLM Architecture and Performance [00:30:15] 4.1 Distributed Training and Hardware Architecture Optimization [00:33:01] 4.2 Model Scaling Strategies and Chinchilla Optimality Trade-offs [00:36:04] 4.3 Emergent Reasoning and Model Architecture Comparisons [00:43:26] 4.4 Balancing Creativity and Determinism in AI Models [00:50:01] 4.5 AI-Assisted Software Development Evolution5. AI Systems Engineering and Scalability [00:58:31] 5.1 Enterprise AI Productivity and Implementation Challenges [00:58:40] 5.2 Low-Code Solutions and Enterprise Hiring Trends [01:01:25] 5.3 Distributed Systems and Engineering Complexity [01:01:50] 5.4 GenAI Architecture and Scalability Patterns [01:01:55] 5.5 Scaling Limitations and Architectural Patterns in AI Code Generation6. AI Safety and Future Capabilities [01:06:23] 6.1 Semantic Understanding and Language Model Reasoning Approaches [01:12:42] 6.2 Model Interpretability and Safety Considerations in AI Systems [01:16:27] 6.3 AI vs Human Capabilities in Software Development [01:33:45] 6.4 Enterprise Deployment and Security ArchitectureCORE REFS (see shownotes for URLs/more refs):[00:15:45] Research demonstrating how training on model-generated content leads to distribution collapse in AI models, Ilia Shumailov et al. (Key finding on synthetic data risk)[00:20:05] Foundational paper introducing Word2Vec for computing word vector representations, Tomas Mikolov et al. (Seminal NLP technique)[00:22:15] OpenAI O3 model's breakthrough performance on ARC Prize Challenge, OpenAI (Significant AI reasoning benchmark achievement)[00:22:40] Seminal paper proposing a formal definition of intelligence as skill-acquisition efficiency, François Chollet (Influential AI definition/philosophy)[00:30:30] Technical documentation of DeepSeek's V3 model architecture and capabilities, DeepSeek AI (Details on a major new model)[00:34:30] Foundational paper establishing optimal scaling laws for LLM training, Jordan Hoffmann et al. (Key paper on LLM scaling)[00:45:45] Seminal essay arguing that scaling computation consistently trumps human-engineered solutions in AI, Richard S. Sutton (Influential "Bitter Lesson" perspective)

(05:29) Brought to you by Swimm.io⁠⁠⁠⁠Start modernizing your mainframe faster with Swimm.Understand the what, why, and how of your mainframe code.Use AI to uncover critical code insights for seamless migration, refactoring, or system replacement.Tired of API dependencies slowing down your development and testing?Dive into my conversation with Tom Akehurst, creator of WireMock, and discover the art of using API mocking to build successful software in complex distributed environments.Key topics discussed:The origin story of WireMock, born from integration challenges at DisneyHow WireMock became a leading API mocking tool with millions of monthly downloadsInsights on building and maintaining successful open-source projectsThe key benefits of API mocking for developer productivity and experienceThe shift from the traditional testing pyramid to a “testing trophy” approachLeveraging API mocking for API-first design and rapid prototypingThe distinction between API mocking and contract testingThe future of API testing and development in the age of microservices and AIWhether you're a seasoned developer or just starting out your journey in API development, this episode provides valuable insights into the power of API mocking and the journey of building a successful open-source project.  Timestamps:(02:11) Career Turning Points(08:08) WireMock OSS Success Story(15:15) Welcoming & Aligning with Contributors(18:05) Benefits of WireMock & API Mocking Tools(19:59) API Mocking & Testing Pyramid(22:05) API Mocking vs Contract Testing(25:25) The Economics of API Mocking(27:27) API First Design(32:32) Impact to the Developer Experience & Productivity(35:32) Working More Effectively with Distributed Systems(38:15) API Virtualization/Simulation(41:13) AI Advancement in API Development(44:25) Building API for AI Agents(47:25) 3 Tech Lead Wisdom_____Tom Akehurst's BioTom Akehurst is the creator of WireMock, the open source API mocking tool, which he's now been working on for well over a decade. Lately he's also the CTO and co-founder of WireMock, Inc., where he's helping complex engineering organisations effectively adopt API simulation techniques in order to build better software faster.Tom has been developing software for over 20 years. He's built large-scale web systems for media, travel, hospitality, retail and government, applying lean, eXtreme Programming, Continuous Delivery and DevOps principles along the way.Follow Tom:LinkedIn – linkedin.com/in/tomakehurstEmail – tom@wiremock.orgWireMock – wiremock.org_____Our SponsorsEnjoy an exceptional developer experience with JetBrains. Whatever programming language and technology you use, JetBrains IDEs provide the tools you need to go beyond simple code editing and excel as a developer.Check out FREE coding software options and special offers on jetbrains.com/store/#discounts.Make it happen. With code.Manning Publications is a premier publisher of technical books on computer and software development topics for both experienced developers and new learners alike. Manning prides itself on being independently owned and operated, and for paving the way for innovative initiatives, such as early access book content and protection-free PDF formats that are now industry standard.Get a 40% discount for Tech Lead Journal listeners by using the code techlead24 for all products in all formats.Like this episode?Show notes & transcript:techleadjournal.dev/episodes/210.Follow @techleadjournal onLinkedIn,Twitter, andInstagram.Buy me acoffee or become apatron.

Lex interviews Sam Williams - founder of Arweave. This episode delves into the innovative aspects of Arweave, a protocol designed for permanent data storage and computation within the Web3 ecosystem. The discussion covers a range of topics, from the economic models underpinning Arweave to its potential applications in decentralized finance (DeFi) and beyond. Notable discussion points: The Founding of Arweave and its Mission – Sam Williams' interest in distributed computing and concerns about authoritarianism led him to create Arweave in 2017. Inspired by the Snowden leaks, he saw the need for a blockchain-based permanent storage solution to protect journalism, historical records, and digital assets from censorship. Decentralized vs. Distributed Storage – Williams explained how Arweave differs from alternatives like IPFS and Filecoin. Unlike traditional storage, which requires ongoing payments, Arweave uses a one-time payment model. This storage endowment leverages declining storage costs to ensure long-term data persistence without relying on centralized infrastructure. Arweave's Expansion into Decentralized Compute – Arweave has evolved beyond storage to develop decentralized computing through "Arweave IO." This enables parallelized smart contract execution, making it possible to run AI models, financial automation, and decentralized apps on-chain—aligning with Web3's shift toward autonomous, intelligent systems.MENTIONED IN THE CONVERSATION Topics: Arweave, permanent data storage, Web3, decentralized systems, distributed systems, blockchain, economic models, IPFS, Filecoin, decentralized computing, decentralized finance, compute ABOUT THE FINTECH BLUEPRINT 

Asanka Abeysinghe, CTO at WSO2, joins host Giovanni Asproni to discuss cell-based architecture -- a style that's intended to combine application, deployment, and team architecture to help organizations respond quickly to changes in the business environment, customer requirements, or enterprise strategy. Cell-based architecture is aimed at creating scalable, modular, composable systems with effective governance mechanisms. The conversation starts by introducing the context and some vocabulary before exploring details about the main elements of the architecture and how they fit together. Finally, Asanka offers some advice on how to implement a cell-based architecture in practice. Brought to you by IEEE Computer Society and IEEE Software magazine. Related Episodes SE Radio 396: Barry O'Reilly on Antifragile Architecture SE Radio 331: Kevin Goldsmith on Architecture and Organizational Design SE Radio 263: Camille Fournier on Real-World Distributed Systems SE Radio 236: Rebecca Parsons on Evolutionary Architecture SE Radio 213: James Lewis on Microservices SE Radio 210: Stefan Tilkov on Architecture and Micro Services SE Radio 203: Leslie Lamport on Distributed Systems

CTO Series: Navigating Growth, A Playbook for Scaling Engineering Teams With Toni Ala-Piirto   In this BONUS episode, we dive into the journey of Toni Ala-Piirto, a seasoned software leader with 15 years of experience designing and implementing distributed systems. Toni opens up about pivotal lessons from his leadership career, balancing tech strategy with business goals, and the nuances of scaling engineering teams during rapid growth. Whether you're a CTO, a team lead, or a tech enthusiast, this conversation is packed with practical insights.   The Evolution of a Leader: A Journey, Not a Single Moment   “Leadership isn't built in a single defining moment but shaped by many experiences.”   Toni recounts a key challenge early in his career involving a major performance issue for a customer. This experience taught him the importance of viewing systems holistically rather than focusing solely on individual contributions. His “boring” leadership style—marked by forward-thinking and crisis prevention—emphasizes preparation and identifying risks without over-engineering solutions.   Aligning Tech and Business: The Power of Collaboration   “Technology vision and business strategy should speak the same language.”   Toni highlights the importance of close collaboration with product managers, sales, and finance to ensure tech strategy aligns with broader business goals. Regular cross-department discussions foster trust and ensure that the product roadmap is both innovative and achievable.   Key Practice: Build relationships with key stakeholders through daily touchpoints to create alignment.   The Roadmap to Success: Vision vs. Execution   “Short-term details drive long-term visions.”   Toni explains their approach to roadmapping, with detailed 6-month plans that address “how” to achieve goals and a broader vision for the longer term. This allows the team to stay agile while keeping future innovations in view.   Pro Tip: Avoid spending excessive time on estimations; use past experience to guide epic-level planning.   “The first six months are about execution—the rest is about imagining what's possible.”   Scaling Teams During Rapid Growth   “The true challenge of scaling is transferring knowledge while preserving team culture.”   Toni reflects on the growth journey from a small team to a larger organization. As the team grew, onboarding and knowledge transfer became crucial. His solution? Pair testing and collaborative learning to help developers understand the product deeply, not just the code.   Tactical Tips: Implement a “test buddy” system for collaborative testing and learning. Encourage developers to test the product to build domain knowledge and foster cross-functional understanding.   “Your people need to understand the product—not just the code—to scale effectively.”   Maintaining Culture Amid Growth   “Growth changes culture—how you hire and lead defines the next chapter.”   Toni shares how adding new team members can shift team dynamics. The key to sustaining a positive culture is hiring individuals who take ownership and serve as role models. Leaders should seek out those who aim to improve the team, not just perform their tasks.   “The best hires don't just do their job—they make the whole team better.”   Cross-Functional Insights and Learning the CTO Role   “CTOs operate at the intersection of tech and business—a shift from pure development.”   Toni admits that stepping into the CTO role required him to expand his understanding of business operations, strategic planning, and cross-functional collaboration. He emphasizes that this broadened perspective is essential for impactful decision-making.   “The biggest shift for me was seeing the business as a whole—not just the tech stack.”   Key Influences: The Five Dysfunctions of a Team   “Understanding team dynamics is as crucial as technical expertise.”   Toni cites Patrick Lencioni's The Five Dysfunctions of a Team as a pivotal read. The book shaped his approach to fostering accountability and ensuring team commitment. Toni underscores that accountability isn't about blame—it's about ownership and follow-through.   Scaling with a Talent Strategy in Mind   “Growth requires not just more people but the right investments.”   Toni discusses integrating talent strategy into roadmaps by aligning with business goals, including company size and revenue targets. Strategic hiring and investment in growth ensure that the team remains equipped to deliver on future plans.   About Toni Ala-Piirto   Toni Ala-Piirto is a seasoned software professional with 15 years of experience leading architecture and design for projects of all sizes. He excels in creating practical, fit-for-purpose distributed systems and is known for his hands-on approach and commitment to continuous improvement. Toni consistently delivers solutions that meet specific project needs while aligning with broader business objectives. You can link with Toni Ala-Piirto on LinkedIn.a

Lukas Gentele, CEO of Loft Labs, joins host Robert Blumen for a discussion of kubernetes vclusters (virtual clusters). A vcluster is a kubernetes cluster that runs kubernetes application on a host kubernetes cluster. The conversation covers: vcluster basics; sharing models; what is owned by the vcluster and what is shared with the host; attached nodes versus shared nodes; the primary use case: multi-tenancy vcluster per tenant; alternatives - namespace per tenant, full cluster per tenant; trade-offs - isolation; less resource use; spin up time; scalability; how many clusters and how many vclusters should an org have? Deployment models for vclusters - helm chart with standard resources; vcluster operator; persistent storage models for vclusters; vcluster snapshotting, recovery, and migration. how many vclusters can run on a cluster? ingress, TLS and DNS. Brought to you by IEEE Computer Society and IEEE Software magazine.

For the Season 13 finale, Elixir Wizards Dan and Charles are joined by Spin42 Engineers Marc Lainez, Thibault Poncelet, and Loïc Vigneron to discuss their work retrofitting a 2007 VW Polo and creating an Open Vehicle Control System (OVCS). Using Elixir, Nerves, and Raspberry Pis, the team is reimagining vehicle technology to extend the lifespan of older cars and reduce waste—all while making the process approachable and open source. The Spin42 team shares the technical details behind OVCS and how they use Elixir and Nerves to interact with the CAN bus and build a Vehicle Management System (VMS) to coordinate various vehicle components. They dive into the challenges of reverse engineering CAN messages, designing a distributed architecture with Elixir processes, and ensuring safety with fail-safe modes and emergency shutoffs. Beyond the technical, the team discusses their motivation for the project—upgrading older vehicles with modern features to keep them on the road, building an open-source platform to share their findings with others, and above all-- to just have fun. They explore potential applications for OVCS in boats, construction equipment, and other vehicles, while reflecting on the hurdles of certifying the system for road use. If you've ever wondered how Elixir and Nerves can drive innovation beyond software, this episode is packed with insights into automotive computing, hardware development, and the collaborative potential of open-source projects. Topics Discussed in this Episode: Retrofitting a 2007 VW Polo with electric engines and modern tech Building an open-source Vehicle Control System (OVCS) using Elixir and Nerves Leveraging Elixir to interact with the CAN bus and parse proprietary messages Designing a Vehicle Management System (VMS) to coordinate vehicle components Developing custom hardware for CAN communication Creating a YAML-based DSL for CAN message and frame descriptions Building a distributed architecture using Elixir processes Ensuring safety with fail-safe modes and emergency shutoffs Using Flutter and Nerves to build a custom infotainment system Exploring autonomous driving features with a ROS2 bridge Developing remote control functionality with a Mavlink transmitter Testing OVCS features at scale with a Traxxas RC car (OVCS Mini) Challenges of certifying OVCS for road use and meeting regulatory requirements Encouraging community contributions to expand OVCS functionality Balancing open-source projects with contract work to sustain development The fun and fulfillment of experimenting with Elixir beyond traditional applications Links mentioned: https://www.spin42.com/ https://nerves-project.org/ Quadcopter https://github.com/Spin42/elicopter https://github.com/linux-can/can-utils https://docs.kernel.org/networking/can.html https://github.com/open-vehicle-control-system/cantastic https://github.com/commaai/opendbc https://en.wikipedia.org/wiki/CANbus#CANFD https://comma.ai/ https://en.wikipedia.org/wiki/CANFD https://webkit.org/wpe/ https://docs.nvidia.com/jetson/archives/r35.4.1/DeveloperGuide/text/SD/WindowingSystems/WestonWayland.html https://buildroot.org/ https://vuejs.org/ https://flutter.dev/ https://github.com/smartrent/elixirflutterembedder https://www.raspberrypi.com/products/raspberry-pi-5/ The Rabbit Pickup https://www.hemmings.com/stories/value-guide-1980-83-volkswagen-pickup https://www.expresslrs.org/software/mavlink https://industrial-training-master.readthedocs.io/en/melodic/source/session7/ROS1-ROS2-bridge.html https://github.com/ros2/rcl https://github.com/open-vehicle-control-system/traxxas Contact Marc, Thibault, and Loïc: info@spin42.com Special Guests: Loïc Vigneron, Marc Lainez, and Thibault Poncelet.

AJ (Alykhan Jetha), CEO and CTO of Marketcircle, joins the Elixir Wizards to share his experience building and evolving Daylite, their award-winning CRM and business productivity app for Apple users. He details his experiences as a self-taught programmer and how Marketcircle has navigated pivots, challenges, and opportunities since its founding in 1999. AJ explains why they migrated Daylite's backend to Elixir, focusing on their sync engine, which demands high concurrency and fault tolerance. He highlights how Elixir has improved performance, reduced cloud costs, and simplified development with its approachable syntax and productive workflows. The conversation also touches on the technical hurdles of deploying native apps for Apple devices and the potential for integrating new technologies like LiveView Native to streamline cross-platform development. For technical founders, AJ emphasizes the importance of leveraging your strengths (“superpowers”), staying deeply connected to the development process, and finding stability in tools like Elixir amidst a rapidly evolving tech ecosystem. He also shares Marketcircle's roadmap for migrating more customers to Elixir-powered systems and explores the potential for new features in their native apps. Tune in for insights on building resilient systems, navigating technical and business challenges, and how Elixir is shaping Marketcircle's future. Topics discussed in this episode: AJ's journey as a self-taught programmer and entrepreneur Marketcircle's evolution since 1999 and lessons from their pivots Daylite's growth as a flagship product for Apple users Migrating to Elixir for high concurrency and fault tolerance How Elixir improved performance and reduced cloud costs The simplicity of Elixir and its impact on developer onboarding Challenges in managing a growing microservices architecture Insights into deploying native apps for the Apple ecosystem Exploring LiveView Native for future cross-platform development Advice for technical founders: leveraging your superpowers Staying connected to development to maintain system understanding The role of Elixir in improving development efficiency and stability Planning gradual customer migrations to an Elixir-powered backend Potential new features for Daylite's native apps Benefits of collaboration with the Elixir community #ElixirMullet -- native app in the front, Elixir in the back Navigating a rapidly evolving tech ecosystem as a founder Leveraging Elixir to future-proof Marketcircle's systems Balancing technical and business priorities in a startup environment AJ's thoughts on the future of Elixir in powering business tools Links mentioned: https://www.marketcircle.com/ Daylite.app https://www.nextcomputers.org/ https://www.digitalocean.com/ Python Async https://docs.python.org/3/library/asyncio.html https://github.com/sinatra/sinatra https://github.com/dependabot https://kafka.apache.org/ https://www.djangoproject.com/ https://github.com/socketry/falcon https://github.com/puma/puma https://www.swift.org/blog/announcing-swift-6/ https://en.wikipedia.org/wiki/Async/await https://www.ffmpeg.org/ https://www.sqlite.org/ https://github.com/commanded/commanded https://pragprog.com/titles/khpes/real-world-event-sourcing/ https://en.wikipedia.org/wiki/ShipofTheseus https://reactnative.dev/ https://www.electronjs.org/ https://en.wikipedia.org/wiki/WebOS https://www.linkedin.com/in/alykhanjetha/ https://bsky.app/profile/ajetha.bsky.social Special Guest: Alykhan Jetha.

Jonas Hübotter, PhD student at ETH Zurich's Institute for Machine Learning, discusses his groundbreaking research on test-time computation and local learning. He demonstrates how smaller models can outperform larger ones by 30x through strategic test-time computation and introduces a novel paradigm combining inductive and transductive learning approaches. Using Bayesian linear regression as a surrogate model for uncertainty estimation, Jonas explains how models can efficiently adapt to specific tasks without massive pre-training. He draws an analogy to Google Earth's variable resolution system to illustrate dynamic resource allocation based on task complexity. The conversation explores the future of AI architecture, envisioning systems that continuously learn and adapt beyond current monolithic models. Jonas concludes by proposing hybrid deployment strategies combining local and cloud computation, suggesting a future where compute resources are allocated based on task complexity rather than fixed model size. This research represents a significant shift in machine learning, prioritizing intelligent resource allocation and adaptive learning over traditional scaling approaches. SPONSOR MESSAGES: CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. https://centml.ai/pricing/ Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on ARC and AGI, they just acquired MindsAI - the current winners of the ARC challenge. Are you interested in working on ARC, or getting involved in their events? Goto https://tufalabs.ai/ Transcription, references and show notes PDF download: https://www.dropbox.com/scl/fi/cxg80p388snwt6qbp4m52/JonasFinal.pdf?rlkey=glk9mhpzjvesanlc14rtpvk4r&st=6qwi8n3x&dl=0 Jonas Hübotter https://jonhue.github.io/ https://scholar.google.com/citations?user=pxi_RkwAAAAJ Transductive Active Learning: Theory and Applications (NeurIPS 2024) https://arxiv.org/pdf/2402.15898 EFFICIENTLY LEARNING AT TEST-TIME: ACTIVE FINE-TUNING OF LLMS (SIFT) https://arxiv.org/pdf/2410.08020 TOC: 1. Test-Time Computation Fundamentals [00:00:00] Intro [00:03:10] 1.1 Test-Time Computation and Model Performance Comparison [00:05:52] 1.2 Retrieval Augmentation and Machine Teaching Strategies [00:09:40] 1.3 In-Context Learning vs Fine-Tuning Trade-offs 2. System Architecture and Intelligence [00:15:58] 2.1 System Architecture and Intelligence Emergence [00:23:22] 2.2 Active Inference and Constrained Agency in AI [00:29:52] 2.3 Evolution of Local Learning Methods [00:32:05] 2.4 Vapnik's Contributions to Transductive Learning 3. Resource Optimization and Local Learning [00:34:35] 3.1 Computational Resource Allocation in ML Models [00:35:30] 3.2 Historical Context and Traditional ML Optimization [00:37:55] 3.3 Variable Resolution Processing and Active Inference in ML [00:43:01] 3.4 Local Learning and Base Model Capacity Trade-offs [00:48:04] 3.5 Active Learning vs Local Learning Approaches 4. Information Retrieval and Model Interpretability [00:51:08] 4.1 Information Retrieval and Nearest Neighbor Limitations [01:03:07] 4.2 Model Interpretability and Surrogate Models [01:15:03] 4.3 Bayesian Uncertainty Estimation and Surrogate Models 5. Distributed Systems and Deployment [01:23:56] 5.1 Memory Architecture and Controller Systems [01:28:14] 5.2 Evolution from Static to Distributed Learning Systems [01:38:03] 5.3 Transductive Learning and Model Specialization [01:41:58] 5.4 Hybrid Local-Cloud Deployment Strategies

To kick off Elixir Wizards Season 13, The Creator's Lab, we're joined by Zach Daniel, the creator of Igniter and the Ash framework. Zach joins hosts Owen Bickford and Charles Suggs to discuss the mechanics and aspirations of his latest brainchild, Igniter—a code generation and project patching framework designed to revolutionize the Elixir development experience. Igniter isn't just about generating code; it's about generating smarter code. By leveraging tools like Sourcerer and Rewrite, Igniter allows developers to modify source code and batch updates by directly interacting with Elixir's AST instead of regex patching. This approach streamlines new project setup and package installations and enhances overall workflow. They also discuss the strategic implications of Igniter for the broader Elixir community. Zach hopes Igniter will foster a more interconnected and efficient ecosystem that attracts new developers to Elixir and caters to the evolving needs of seasoned Elixir engineers. Topics discussed in this episode: Advanced package installation and code generation improve the developer experience Scripting and staging techniques streamline project updates Innovative methods for smoother installation processes in Elixir packages High-level tools apply direct patches to source code Progressive feature additions simplify the mix phx.new experience Chaining installers and composing tasks for more efficient project setup Continuous improvement in developer experiences to boost Elixir adoption Encourage listeners to collaborate by sharing code generation patterns Introduction of a new mix task aimed at removing the "unless" keyword in preparation for Elixir 1.18 You can learn more in the upcoming book "Building Web Applications with Ash Framework" by Zach and Rebecca Links mentioned: https://smartlogic.io/ https://alembic.com.au/blog/igniter-rethinking-code-generation-with-project-patching https://hexdocs.pm/igniter/readme.html https://github.com/ash-project/igniter https://www.zachdaniel.dev/p/serialization-is-the-secret https://www.zachdaniel.dev/p/welcome-to-my-substack https://ash-hq.org/ https://hexdocs.pm/sourceror/readme.html https://smartlogic.io/podcast/elixir-wizards/s10-e09-hugo-lucas-future-of-elixir-community/ https://github.com/hrzndhrn/rewrite https://github.com/zachdaniel https://github.com/liveshowy/webauthn_components https://hexdocs.pm/elixir/Regex.html https://github.com/msaraiva/vscode-surface https://github.com/swoosh/swoosh https://github.com/erlef/oidcc https://alembic.com.au/ https://www.zachdaniel.dev/ Special Guest: Zach Daniel.

Sriram Panyam, CTO at DagKnows, discusses SaaS Control Planes with SE Radio host Brijesh Ammanath. The discussion starts off with the basics, examining what control planes are and why they're important. Sriram then discusses reasons for building a control plane and the challenges in designing one. They explore design and architectural considerations when building a SaaS control plane, as well as the key differences between a control plane and a data plane. This episode is sponsored by QA Wolf.

In this episode of the Crazy Wisdom podcast, Stewart Alsop speaks with Anand Dwivedi, a Senior Data Scientist at ICE, returning for his second appearance. The conversation covers a range of topics including the evolution of machine learning models, the integration of AI into operating systems, and how innovations like Neuralink may reshape our understanding of human-machine interaction. Anand also touches on the role of cultural feedback in shaping human learning, the implications of distributed systems in cybersecurity, and his current project—training a language model on the teachings of his spiritual guru. For more information, listeners can connect with Anand on LinkedIn.Check out this GPT we trained on the conversation!Timestamps00:00 Introduction and Guest Welcome00:25 Exploring GPT-4 and Machine Learning Innovations03:34 Apple's Integration of AI and Privacy Concerns06:07 Digital Footprints and the Evolution of Memory09:42 Neuralink and the Future of Human Augmentation14:20 Cybersecurity and Financial Crimes in the Digital Age20:53 The Role of LLMs and Human Feedback in AI Training29:50 Freezing Upper Layers and Formative Feedback30:32 Neuroplasticity in Sports and Growth32:00 Challenges of Learning New Skills as Adults32:44 Cultural Immersion and Cooking School34:21 Exploring Genetic Engineering and Neuroplasticity38:53 Neuralink and the Future of AI39:58 Physical vs. Digital World41:20 Existential Threats and Climate Risk45:15 Attention Mechanisms in LLMs48:22 Optimizing Positive Social Impact54:54 Training LLMs on Spiritual LecturesKey InsightsEvolution of Machine Learning Models: Anand Dwivedi highlights the advancement in machine learning, especially with GPT-4's ability to process multimodal inputs like text, images, and voice simultaneously. This contrasts with earlier models that handled each modality separately, signifying a shift towards more holistic AI systems that mirror human sensory processing.AI Integration in Operating Systems: The conversation delves into how AI, like Apple Intelligence, is being integrated directly into operating systems, enabling more intuitive interactions such as device management and on-device tasks. This advancement brings AI closer to daily use, ensuring privacy by processing data locally rather than relying on cloud-based systems.Neuralink and Transhumanism: Anand and Stewart discuss Neuralink's potential to bridge the gap between human and artificial intelligence. Neuralink's brain-computer interface could allow humans to enhance cognitive abilities and better compete in a future dominated by intelligent machines, raising questions about the ethics and risks of such direct brain-AI integration.Cultural Feedback and Learning: Anand emphasizes the role of cultural feedback in shaping human learning, likening it to how AI models are fine-tuned through feedback loops. He explains that different cultural environments provide varied feedback to individuals, influencing the way they process and adapt to information throughout their lives.Cybersecurity and Distributed Systems: The discussion highlights the dual-edged nature of distributed systems in cybersecurity. While these systems offer increased freedom and decentralization, they can also serve as breeding grounds for financial crimes and other malicious activities, pointing to the need for balanced approaches to internet freedom and security.Generative Biology and AI: A key insight from the episode is the potential of AI models, like those used for language processing, to revolutionize fields such as biology and chemistry. Anand mentions the idea of generative biology, where AI could eventually design new proteins or chemical compounds, leading to breakthroughs in drug discovery and personalized medicine.Positive Social Impact Through Technology: Anand introduces a thought-provoking idea about using AI and data analytics for social good. He suggests that technology can help bridge disparities in education and resources globally, with models being designed to measure and optimize for positive social impacts, rather than just profits or efficiency.

In this episode of PodRocket, Joel Hooks, creator of egghead.io, talks about the power of durable, event-driven workflows, the practicalities and benefits of serverless as a billing model, the intricacies distributed systems, and more. Links https://joelhooks.com https://x.com/jhooks https://www.linkedin.com/in/joelhooks https://egghead.io https://www.coursebuilder.dev/tips/using-inngest-to-add-email-automation-feature-to-pro-next-js-adt43 We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Let us know by sending an email to our producer, Emily, at emily.kochanekketner@logrocket.com (mailto:emily.kochanekketner@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understand where your users are struggling by trying it for free at [LogRocket.com]. Try LogRocket for free today.(https://logrocket.com/signup/?pdr) Special Guest: Joel Hooks.

In this episode, Brian LeRoux, co-founder of Begin.com, discusses the evolution and rise of serverless full stack development. Brian shares insights on the history and future of JavaScript, the benefits of serverless architecture, and how front-end developers can leverage these technologies to build scalable and maintainable applications. Links https://brian.io https://webdev.rip https://github.com/brianleroux https://www.npmjs.com/~brianleroux https://twitter.com/brianleroux https://indieweb.social/@brianleroux https://www.linkedin.com/in/brianleroux https://begin.com https://arc.codes https://enhance.dev We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Let us know by sending an email to our producer, Emily, at emily.kochanekketner@logrocket.com (mailto:emily.kochanekketner@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understand where your users are struggling by trying it for free at [LogRocket.com]. Try LogRocket for free today.(https://logrocket.com/signup/?pdr) Special Guest: Brian LeRoux.

Summary In this week's episode, Anna (https://x.com/AnnaRRose) and Guille (https://x.com/GuilleAngeris) chat with Ying Tong Lai (https://x.com/therealyingtong) from Geometry Research (https://geometry.dev/) and Bryan Gillespie (https://x.com/bryan_gillespie) from Inversed Tech (https://inversed.tech/) about their latest research and works to date. They dive into the pair's recent work ‘SoK: Programmable Privacy in Distributed Systems (https://eprint.iacr.org/2024/982)', exploring the classifications and frameworks being introduced. Here's some additional links for this episode: SoK: Programmable Privacy in Distributed Systems by Benarroch, Gillespie, Lai and Miller (https://eprint.iacr.org/2024/982) Private Programmability in Zcash - Research Results and Community Discussion (https://forum.zcashcommunity.com/t/48016) Zcash Halo2 GitHub (https://github.com/zcash/halo2) Zk0x02 - An intro to Zcash and zkSNARKs - Ariel Gabizon (Zcash) (https://www.youtube.com/watch?v=Kx4cIkCY2EA) Moving SNARKs from the generic to algebraic group model by Ariel Gabizon (https://medium.com/@arielgabizon/moving-snarks-from-the-generic-to-algebraic-group-model-56549d60b90d) Explaining SNARKs Part I: Homomorphic Hidings by Ariel Gabizon (https://electriccoin.co/blog/snark-explain/) Differential Privacy in Constant Function Market Makers by Chitra, Angeris and Evans (https://fc22.ifca.ai/preproceedings/30.pdf) A Note on Privacy in Constant Function Market Makers by Angeris, Evans and Chitra (https://angeris.github.io/papers/cfmm-privacy.pdf) On Privacy Notions in Anonymous Communication by Kuhn, Beck, Schiffner, Jorswieck, and Strufe (https://arxiv.org/pdf/1812.05638) ZK Hack Montreal has been announced for Aug 9 - 11! Apply to join the hackathon here (https://zk-hack-montreal.devfolio.co/). Episode Sponsors Aleo (http://aleo.org/) is a new Layer-1 blockchain that achieves the programmability of Ethereum, the privacy of Zcash, and the scalability of a rollup. As Aleo is gearing up for their mainnet launch in Q1, this is an invitation to be part of a transformational ZK journey. Dive deeper and discover more about Aleo at http://aleo.org/ (http://aleo.org/). If you like what we do: * Find all our links here! @ZeroKnowledge | Linktree (https://linktr.ee/zeroknowledge) * Subscribe to our podcast newsletter (https://zeroknowledge.substack.com) * Follow us on Twitter @zeroknowledgefm (https://twitter.com/zeroknowledgefm) * Join us on Telegram (https://zeroknowledge.fm/telegram) * Catch us on YouTube (www.youtube.com/channel/UCYWsYz5cKw4wZ9Mpe4kuM_g)

Infrastructure engineer and Kubernetes ingress-Nginx maintainer James Strong joins host Robert Blumen to discuss the Kubernetes networking layer. The discussion draws on content from Strong's book on the topic and covers a lot of ground, including: the Kubernetes network's use of different IP ranges than the host network; overlay network with its own IP ranges compared to using expanded portions of the host network ranges; adding routes with kernel extension points; programming kernel extension points with IP tables compared to eBPF; how routes are updated as the host network gains or loses nodes, the use of the Linux network namespace to isolate each pod; routing between pods on the same host; routing between pods across the host network; the container-network interface (CNI); the CNI ecosystem; differences between CNIs; choosing a CNI when running on a public cloud service; the Kubernetes service abstraction with a cluster-wide IP address; monitoring and telemetry of the Kubernetes network; and troubleshooting the Kubernetes network. Brought to you by IEEE Software magazine and IEEE Computer Society.