System Design on the Road

System Design on the Road

Learn system design wherever you are. Each episode builds from first principles to the decisions engineers face in interviews and real systems, using concrete examples, trade-offs, recall checks, and one-minute explanation exercises. Season 1 covers networking, APIs, data modeling, indexes, caching, sharding, consistent hashing, consistency, and architectural estimates in 16 episodes. AI narration using Liam at a relaxed pace.

  1. Oct 22

    S2 E26 — Design an AI Evaluation Platform: Reproducibility, Grading, and Release Gates

    An evaluation score improves even though the model has not changed. Daniel and Maya design a platform that can trace the difference to data, graders, configuration, infrastructure, or actual model behavior. Frozen inputs, task attempts, grading versions, failure classification, and aggregation form a reproducible run. Infrastructure errors and retries must not distort model comparisons. The interview ends with uncertainty and release gates, asking what evidence a higher score really provides for a deployment decision. Chapters 00:00:00 Welcome and an unexplained score gain 00:02:26 Frozen datasets, models, and run inputs 00:05:29 Task execution, grading, and aggregation 00:08:46 Grader changes and comparison validity 00:12:32 Worker failures, retries, and fairness 00:16:33 Uncertainty and reproducible comparisons 00:20:15 Release gates and evaluation operations 00:31:14 Final score interpretation and recap Daniel is the interviewer, voiced by Liam. Maya is the engineer, voiced by Bella. AI narration at a calm pace, with three thinking pauses. Opening music: Stealth Groover by Kevin MacLeod (incompetech.com). Licensed under Creative Commons Attribution 4.0: https://creativecommons.org/licenses/by/4.0/ . Edited excerpt with EQ, volume automation, a smooth fade, and narration.

  2. Oct 22

    S2 E25 — Design an AI Research Assistant: Sources, Budgets, and Checkpoints

    Three sources tell two different stories and the research deadline is approaching. Daniel and Maya design an assistant that can choose what to investigate, preserve its evidence, and stop within time and spending limits. A working lookup expands into parallel research, conflicting sources, checkpoints, and recovery. Tool results and pages remain evidence rather than authorities. Evaluation asks whether the answer is supported and useful, including when uncertainty or insufficient evidence is the conclusion the assistant should report. Chapters 00:00:00 Welcome and conflicting research sources 00:02:30 Answer requirements and evidence records 00:05:47 A complete research lookup 00:09:29 Conflicting sources and uncertainty 00:13:24 Parallel research and bounded budgets 00:17:15 Checkpoints, tools, and untrusted content 00:21:29 Evaluating useful, supported research 00:30:22 Final answer and evidence trail Daniel is the interviewer, voiced by Liam. Maya is the engineer, voiced by Bella. AI narration at a calm pace, with three thinking pauses. Opening music: Stealth Groover by Kevin MacLeod (incompetech.com). Licensed under Creative Commons Attribution 4.0: https://creativecommons.org/licenses/by/4.0/ . Edited excerpt with EQ, volume automation, a smooth fade, and narration.

  3. Oct 22

    S2 E24 — Design Enterprise RAG: Retrieval, Permissions, and Citations

    An employee may read a document when it is retrieved, but loses access before the generated answer is released. Daniel and Maya design enterprise RAG around that permission race rather than relevance alone. Document versions, ingestion, retrieval, evidence selection, and answer generation form the baseline. Deletion and indexing delays test the copies between source and response. Citations and evaluation ask whether the final answer has adequate evidence and whether the user is still authorized to receive it. Chapters 00:00:00 Welcome and a wrongly disclosed answer 00:02:24 Source records, versions, and permissions 00:05:41 Ingestion, retrieval, and generation 00:09:12 Revocation races and release-time checks 00:13:14 Relevance, evidence, and citations 00:17:08 Index failures, deletion, and freshness 00:20:58 Evaluating retrieval and answer evidence 00:30:50 Final authorized answer flow and recap Daniel is the interviewer, voiced by Liam. Maya is the engineer, voiced by Bella. AI narration at a calm pace, with three thinking pauses. Opening music: Stealth Groover by Kevin MacLeod (incompetech.com). Licensed under Creative Commons Attribution 4.0: https://creativecommons.org/licenses/by/4.0/ . Edited excerpt with EQ, volume automation, a smooth fade, and narration.

  4. Oct 22

    S2 E23 — Design a Cloud Coding Environment: Sandboxing, Secrets, and Workspace Recovery

    A repository's test script runs hostile code inside a cloud development workspace. Daniel and Maya design isolation while preserving useful files and developer progress. Sandboxing, network access, secrets, and permission to cause external effects require distinct boundaries. A machine vanishing before a patch is saved tests durable workspace state. Prepared images, capacity, cleanup, and recovery connect the security model to the everyday experience of opening a project, running code, and returning to saved work. Chapters 00:00:00 Welcome and an untrusted test script 00:02:32 Workspace state and execution boundaries 00:05:48 A complete cloud development session 00:09:07 Sandboxing hostile repository code 00:13:11 Secrets, network access, and side effects 00:17:03 Workspace persistence and machine loss 00:20:56 Images, capacity, and cleanup 00:30:42 Final recovery and isolation guarantees Daniel is the interviewer, voiced by Liam. Maya is the engineer, voiced by Bella. AI narration at a calm pace, with three thinking pauses. Opening music: Stealth Groover by Kevin MacLeod (incompetech.com). Licensed under Creative Commons Attribution 4.0: https://creativecommons.org/licenses/by/4.0/ . Edited excerpt with EQ, volume automation, a smooth fade, and narration.

  5. Oct 22

    S2 E22 — Design a Browser Agent: Sessions, Permissions, and Recovery

    A browser agent clicks Submit and disconnects before seeing the result. Daniel and Maya design recovery that preserves the uncertainty instead of automatically repeating a potentially consequential action. Goals, sessions, attempts, observations, and permissions have separate records and authorities. Untrusted page instructions test that separation. Checkpoints, human takeover, cleanup, and monitoring complete the lifecycle, with a clear distinction between an attempted click and a verified result. Chapters 00:00:00 Welcome and an uncertain submit action 00:02:32 Goals, sessions, and permission boundaries 00:05:40 An action-observation session baseline 00:08:56 Uncertain effects and safe retries 00:12:57 Untrusted pages and prompt injection 00:17:07 Checkpoints, human takeover, and cleanup 00:21:01 Monitoring attempts and verified outcomes 00:30:17 Final authority and recovery boundaries Daniel is the interviewer, voiced by Liam. Maya is the engineer, voiced by Bella. AI narration at a calm pace, with three thinking pauses. Opening music: Stealth Groover by Kevin MacLeod (incompetech.com). Licensed under Creative Commons Attribution 4.0: https://creativecommons.org/licenses/by/4.0/ . Edited excerpt with EQ, volume automation, a smooth fade, and narration.

  6. Oct 22

    S2 E21 — Design a Collaborative Prompt Playground: Versioning and Reproducible Runs

    A teammate changes a prompt while an experiment is running, and later nobody knows which instructions produced the answer. Daniel and Maya design a collaborative playground that allows editable drafts without losing immutable run inputs. Concurrent edits, run snapshots, model settings, and result history establish traceability. Reconnection and revoked access test the shared room. Comparing outputs becomes a recorded experiment with explicit conditions, rather than a comparison between whatever happens to be in the editor now. Chapters 00:00:00 Welcome and an untraceable experiment 00:02:22 Drafts, versions, and run identities 00:05:44 Editing and running a prompt 00:09:02 Concurrent edits and save conflicts 00:13:10 Immutable snapshots for reproducible runs 00:17:20 Reconnection and revoked workspace access 00:21:15 Comparing runs under recorded conditions 00:30:01 Final draft-to-result traceability Daniel is the interviewer, voiced by Liam. Maya is the engineer, voiced by Bella. AI narration at a calm pace, with three thinking pauses. Opening music: Stealth Groover by Kevin MacLeod (incompetech.com). Licensed under Creative Commons Attribution 4.0: https://creativecommons.org/licenses/by/4.0/ . Edited excerpt with EQ, volume automation, a smooth fade, and narration.

  7. Oct 22

    S2 E20 — Design a Model Distribution System: Downloads, Rollouts, and Rollbacks

    The model files have downloaded, but the worker is not ready to serve. Daniel and Maya design distribution as a sequence of transfer, verification, staging, loading, and traffic activation. Immutable manifests and content identities define the release. Fleet-wide downloads, interrupted transfers, partially staged files, and mixed versions test the plan. A rollout needs readiness evidence and a usable rollback target; moving all the bytes successfully is only one step toward safely changing the model behind a request. Chapters 00:00:00 Welcome and a model that is not ready 00:02:41 Immutable releases and artifact identity 00:06:08 Download, verify, load, and activate 00:09:19 Fleet-wide downloads and bandwidth 00:12:42 Resuming downloads and staging safely 00:16:37 Mixed versions, rollout, and rollback 00:20:41 Security, readiness, and operational cost 00:30:06 Final controlled release and recap Daniel is the interviewer, voiced by Liam. Maya is the engineer, voiced by Bella. AI narration at a calm pace, with three thinking pauses. Opening music: Stealth Groover by Kevin MacLeod (incompetech.com). Licensed under Creative Commons Attribution 4.0: https://creativecommons.org/licenses/by/4.0/ . Edited excerpt with EQ, volume automation, a smooth fade, and narration.

  8. Oct 22

    S2 E19 — Design an LLM Inference Service: Batching, Admission Control, and Fairness

    A short question waits behind a prompt containing an entire book. Daniel and Maya design a shared inference service that uses GPUs efficiently while protecting latency and tenant fairness. Batch formation, request length, resource budgets, admission, and queueing shape the working path. Slow consumers and worker failures complicate an answer already streaming to a client. The final design explains which throughput gains are worth their waiting time and what can still be promised after partial output. Chapters 00:00:00 Welcome and a blocked short request 00:02:45 Latency contracts and request ownership 00:06:17 A working inference request path 00:10:01 Batching, long prompts, and latency 00:13:54 Admission budgets and tenant fairness 00:18:22 Worker failure during streamed output 00:22:25 Monitoring and safe service changes 00:29:30 Final inference flow and tradeoffs Daniel is the interviewer, voiced by Liam. Maya is the engineer, voiced by Bella. AI narration at a calm pace, with three thinking pauses. Opening music: Stealth Groover by Kevin MacLeod (incompetech.com). Licensed under Creative Commons Attribution 4.0: https://creativecommons.org/licenses/by/4.0/ . Edited excerpt with EQ, volume automation, a smooth fade, and narration.

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About

Learn system design wherever you are. Each episode builds from first principles to the decisions engineers face in interviews and real systems, using concrete examples, trade-offs, recall checks, and one-minute explanation exercises. Season 1 covers networking, APIs, data modeling, indexes, caching, sharding, consistent hashing, consistency, and architectural estimates in 16 episodes. AI narration using Liam at a relaxed pace.

Information

  • Creator
    System Design on the Road
  • Years Active
    2026
  • Episodes
    42
  • Rating
    Clean
  • Copyright
    © System Design on the Road
  • Show Website
    System Design on the Road