Two days, built for engineers.
Sessions, keynotes, and breaks across every stage. Released day by day as the lineup locks in.
Where to be, and when.
- 10:30 – 11:15Los GatosI packaged my application in a container image, and now what?Cloud, DevOps & Platform EngineeringAurélie Vache
- 10:30 – 11:15CupertinoSecuring AI Agents on Kubernetes: Identity, Sandboxes, and Policy EnforcementAI Engineering & DataRoland Huß
- 10:30 – 11:15Palo AltoForensic DDD: Reverse-Engineering the Domain Your Legacy System Never DocumentedSoftware Architecture & Engineering ExcellenceRaj Navakoti
- 10:30 – 11:15SeattleSenior Architecture 2.0: Unlearning & Job Crafting – Stop being the ultimate bottleneck in your own codebaseEngineering careersKinga Bazior
- 11:30 – 12:15Los GatosSame Bug Twice: What Happens When AI Writes Your Code And Your TestsSoftware Architecture & Engineering ExcellenceMourjo Sen
- 11:30 – 12:15SeattleNo Framework, No Server: Making AI Agents Collaborate in One TerminalAI Engineering & DataLech Kalinowski
- 11:30 – 12:15CupertinoAI Tokenomics: Principles for Cost-Efficient GenAIAI Engineering & DataGrzegorz Wasilewski
- 12:30 – 13:15CupertinoPlatforms That Don't Suck - Creating tools that teams might actually loveCloud, DevOps & Platform EngineeringKarolina Ochlik
- 12:30 – 13:15Los GatosPinpointing misconceptions with tracingSoftware Architecture & Engineering ExcellenceTomas Dambrauskas
- 12:30 – 14:00Silicon Valley(Cancelled due to speaker illness)Private AI with Docker and UpCloudCloud, DevOps & Platform EngineeringPaweł Piwosz
- 12:30 – 13:15Palo AltoAgents are easy, enterprises are where they breakAI Engineering & DataKonrad Bujak
- 13:30 – 14:15Cupertino(MCP Security) - How Your Friendly MCP Tool Might Betray YouCybersecurityDaniel Ostrovsky
- 13:30 – 14:15Los GatosHow will we prompt AGI? A History of Harness Hacks.AI Engineering & DataIvan Charapanau
- 13:30 – 14:15RedmondBeyond Coding Assistants: Orchestrating the Entire SDLCAI Engineering & DataIllia Slepau
- 13:30 – 14:15Palo AltoBeyond Hello World: building production ready Go ServicesSoftware Architecture & Engineering ExcellenceIrina Branovic
- 14:30 – 15:15CupertinoAgents Propose, Git DisposesCloud, DevOps & Platform EngineeringJaroslaw Gajewski
- 14:30 – 15:15Los GatosAgile isn't dead, you're just doing it wrongEngineering careersKarolina Ochlik
- 14:30 – 15:15Palo AltoSourcecode translation as a step in Legacy ModernizationSoftware Architecture & Engineering ExcellenceLeszek Włodarski
- 14:30 – 15:15RedmondFrom GenAI Training to Production. Lessons learned from Building AI Agents for Financial Services ClientsAI Engineering & DataAnna Żółtańska
- 15:30 – 16:15RedmondFrom 3GPP Spec to Live Network: Safely Deploying 6G Security in a Multi-RAT, Legacy OAM Environment.Software Architecture & Engineering ExcellencePawel Owczarski
- 15:30 – 16:15SeattleTerraform, day 1001Cloud, DevOps & Platform EngineeringPiotr Trębacz
- 15:30 – 17:00Silicon ValleySecuring AI Agents with Fine Grained AuthorizationCybersecuritySohan Maheshwar
- 15:30 – 16:15Palo AltoBuilding an AI-Native Engineering Team: Lessons LearnedAI Engineering & DataHimash Tehan
- 16:30 – 17:15CupertinoDesign Systems That Explain ThemselvesSoftware Architecture & Engineering ExcellenceSzymon Chudy
- 16:30 – 17:15Los GatosBuilding the next generation of AI developer toolsAI Engineering & DataKrzysztof Cieślak
- 16:30 – 17:15Palo AltoGitHub Actions moves to CosmosDB: data migration at internet scaleSoftware Architecture & Engineering ExcellenceBassem Dghaidi
- 16:30 – 17:15SeattleWhy Agentic AI Projects Fail and How to Make Them WorkAI Engineering & DataIlona Pietras
- 16:30 – 17:15RedmondMFA? Game over! Watch your protection collapse – liveCybersecurityChristoph Menzel
Same Bug Twice: What Happens When AI Writes Your Code And Your Tests
AI can generate code faster than humans can write tests. More than speed though, when AI writes both the source and the test, it introduces a dangerous blind spot. Bugs that occur in the source code could also affect the test suite. The risk of bias and hallucinations in AI-generated source and tests mean that there is no longer a last line of defense that prevents the end user from a sub-par experience Hand-written example-based tests cannot keep up with AI generated code. Property-based testing (PBT) offers a different solution that relies on deterministic verification of a problem space. Instead of enumerating individual test cases, PBT explores the invariants in a system. It tests the code by systematically trying to find inputs that could break the invariant. When it finds a violation, it shrinks the failing input to the smallest possible case, surfacing bug reports that AI-generated code could likely have missed. The key idea here is that the exploration is deterministic, backed by mathematical probing of the problem space. We will walk through how PBT fights the logical holes of probabilistic source code and how it fits directly with modern trends in software engineering like spec-driven development.
Save your seat before it fills.
Early pricing runs while the programme is still being finalised.