QCon San Francisco Software Development Conference
@QConSF
Uncover emerging trends and practices from the world’s most innovative software professionals to help you validate your software roadmap. Celebrating 17 years.
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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: https://t.co/I4KIWVOtGm
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.
Prefill is compute-intensive. Decode is memory-bound.
At Cerebras, Nancy Zheng's team built a production disaggregated inference platform to run them separately: https://t.co/7GL9veZeRJ
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.
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: https://t.co/sMqlp6xh9w
@enova 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.
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: https://t.co/OlBinhw5Z7
@LinkedIn 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.
How do you standardize an AI platform enough to scale safely without centralizing every workflow?
Dan Wolf of BlackRock on the architecture behind AIROS: https://t.co/b74VkD60pV
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.
“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: https://t.co/sX5AagS1Lp
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.
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:
https://t.co/KjHlinjtPT
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.