Uncover emerging trends and practices from the world’s most innovative software professionals to help you validate your software roadmap. Celebrating 17 years.
San Francisco
Joined February 2010
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350 Duolingo employees have used BuildingHub to create internal tools, with 800+ agent-built apps and no new backend for each one: buff.ly/fuSprXc
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The pattern is deliberate: managed code at the API layer, user code on the client, and no app touching secrets.
Connectors share one consistent /api/... contract designed for agents to read and call.
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Sarah Deitke is speaking at QCon San Francisco, Nov 16-18.
#QConSF #APIDesign
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Prefill is compute-intensive. Decode is memory-bound.
At Cerebras, Nancy Zheng's team built a production disaggregated inference platform to run them separately: buff.ly/ZXFGhJt
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Separating the phases can deliver up to 5x higher throughput, but each request now spans heterogeneous hardware.
Routing, scheduling, interconnects, and failure handling become distributed systems problems.
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Nancy is speaking at QCon San Francisco, Nov 16-18.
#QConSF #DistributedSystems
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By the time you've investigated an AI incident, the system may already have changed.
Vanessa Huerta Granda, Resilience Engineering Manager @Enova, on Learning From Incidents when systems do not stand still: buff.ly/Ham8G0W
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Models change. Agents get new tools and permissions. Prompts change.
The question is what incident-learning practices still hold up, and what needs to change with them.
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Vanessa is speaking at QCon San Francisco, Nov 16-18.
#SiteReliability #IncidentManagement
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Writing code is getting easier. Operating what gets shipped is not.
Sanjay Singh, Staff Software Engineer @LinkedIn, on who owns production when AI increases software change volume: buff.ly/uCcKatw
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Deploy → Observe → Operate → Incident → Diagnose → Mitigate → Learn → Improve.
The session follows the full lifecycle and asks which responsibilities belong in the platform versus the application.
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Sanjay is speaking at QCon San Francisco, Nov 16-18.
#SoftwareReliability #QConSF
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How do you standardize an AI platform enough to scale safely without centralizing every workflow?
Dan Wolf of BlackRock on the architecture behind AIROS: buff.ly/1AjOkgE
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One use case processes 150,000 research documents a week across 10,000 companies. Another has enabled domain experts to build 1,000+ dashboards and applications.
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Dan is speaking at QCon San Francisco, Nov 16-18.
#AIEngineering #SoftwareArchitecture
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“The codebase is now the agent’s runtime.”
Mark Khuzam of Netflix on what changes when coding agents have to work inside a mature production codebase: buff.ly/sG8IkJ7
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Netflix standardized canonical patterns, then added a context layer that carries architecture decisions through the SDLC. A pre-review agent catches drift. Another keeps documentation aligned after merge.
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Platform engineering now has another user: the agent.
Mark is speaking at QCon SF, Nov 16-18.
#PlatformEngineering #AIEngineering
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Netflix moved 100+ database workloads to Aurora PostgreSQL in 12 months by turning migration into an internal platform capability.
Ammar Khaku & Kshitij Gupta on how:
buff.ly/8HMP9O8
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Automated data movement. Byte-for-byte validation. Resumable workflows. Escape hatches for hard cases.
The result included cutovers with single-digit minutes of write downtime.
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Ammar and Kshitij are speaking at QCon San Francisco, Nov 16-18.
#DatabaseEngineering #PlatformEngineering
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