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DevFlow Conf 2027

Wednesday, 12 May 2027 – Friday, 14 May 2027·Moscone West, San Francisco

  1. AI Engineering Lightning Talk

    Lightning: Agents in Production Q&A

    Wed 12 May, 09:00 – 09:10·Room 2A

    A rapid-fire Q&A session on running AI agents in production: reliability, guardrails, and what breaks first when agents meet real users.

  2. EINREICHER-390 1786925885475 From the street

    Wed 12 May, 09:00 – 09:30·Main Stage

    A first-time speaker walk of the live call. What a person sees from the street to the receipt, then back to their own proposal after the organizers decide.

  3. Platform & Infra Talk

    Four hundred engineers, one repository

    Wed 12 May, 09:30 – 10:00·Room 2B

    What actually breaks at that size, in the order it breaks: code ownership, CI queueing, and the review culture nobody wrote down. Two things we would do again and one we would not.

  4. AI Engineering Keynote

    Serving 70B models on a budget

    Wed 12 May, 09:30 – 10:15·Main Stage

    A working account of cutting inference cost by an order of magnitude without giving up latency: continuous batching, speculative decoding, and the three quantisation choices that actually mattered. Includes the two approaches that lost us a month.

  5. Platform & Infra Talk

    UPDATED: Taming 40-Minute CI: Incremental Builds at Monorepo Scale

    Wed 12 May, 10:00 – 10:30·Room 2A

    Our monorepo CI took 40 minutes on a good day. This talk walks through how we cut it to 6 minutes with content-addressed caching, remote execution, and a test-selection model — including the two migrations that failed first. You'll leave with a decision framework for which incremental-build investments pay off at which repo sizes, and the graphs to convince your platform team. This session now includes a live demo of remote build caching. Attendees should bring a laptop.

  6. AI Engineering Talk

    Your AI Pair Programmer Is Lying to You: Verification Patterns That Scale

    Wed 12 May, 10:00 – 10:30·Room 2B

    Code generation is easy; trusting it is hard. This session covers verification patterns for AI-generated code — property tests, mutation coverage, snapshot judges, and CI gates — with data from 18 months of running them on a 200-engineer codebase. Includes what we stopped doing because it didn't catch anything.

  7. Developer Experience Lightning Talk

    Docs That Answer Back: Retrieval-Grounded Documentation Sites

    Wed 12 May, 10:00 – 10:10·Room 2A

    A 10-minute tour of turning a static docs site into one that answers questions with citations, stays honest when it doesn't know, and costs under $50/month to run. Live demo, real failure cases, and a checklist you can apply to your own docs this week.

  8. Platform & Infra Talk

    Your build is slow because of four things

    Wed 12 May, 11:00 – 11:30·Room 2A

    Build times decay for boringly consistent reasons. We instrumented ours for a year; this is what we found, in order of how much time each cost, and what fixing them actually took.

  9. Platform & Infra Talk

    Feature flags are a database problem

    Wed 12 May, 11:30 – 12:00·Main Stage

    Every flag system starts as a boolean and ends as a query planner. How we cut evaluation latency to microseconds, and why the interesting part turned out to be deletion, not rollout.

  10. AI Engineering Talk

    Prompt injection in production: a field report

    Wed 12 May, 14:00 – 14:30·Room 2A

    Six months of attempts against a customer-facing assistant, what got through, and the four mitigations that survived contact. No threat-model diagrams — just the payloads and what they cost us.

  11. Developer Experience Talk

    Onboarding an engineer in a day

    Wed 12 May, 14:00 – 14:30·Room 2B

    We put a stopwatch on the first merged pull request and treated every hour as a bug. Eleven days down to one, and the three fixes that did most of it.

  12. Developer Experience Talk

    Golden paths, not golden handcuffs

    Wed 12 May, 15:00 – 15:30·Workshop Lab

    A paved road only works if leaving it is allowed. How we measured which teams stepped off ours, why they did, and what we changed instead of enforcing compliance.

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