Session 2 — Local verification: the /guide-me tour (2026-07-23)

Full tour it is. Let's start with Step 0: Prerequisites — checking your toolchain before anything else.

• Checkpoint durability (tests 1–5): resume skips exactly the completed batches; a torn trailing line (partial write from a crash mid-fsync) is dropped while intact lines survive; and resume refuses to run if you change the batch size or input file — because that would silently remap batch IDs to different recipients.
• The bounds (8–9): the semaphore never exceeds its permit count, verified end-to-end through the pipeline, not just in isolation.
• Retry classification (10–14): transient errors retry with backoff and honor Retry-After; terminal errors abort and are never checkpointed; per-recipient rejects go to a log instead of poisoning the batch.
• The two end-to-end runs (6–7): every recipient accounted for exactly once — including test 7, which hard-aborts a campaign and resumes it across two separate runs, asserting no duplicates and no gaps.

★ Insight ───────────────────────────────────── The 30-second story worth knowing: this suite caught two real bugs before delivery (README, "What the tests caught"). The best one is the torn-tail bug — the checkpoint reader originally trusted every line in the file. But a crash can happen mid-write, leaving a truncated last line like "4237" cut off from "42371". That would resume as if batch 4237 was done — silently dropping 1,000 emails. Test 2 (torn trailing line is dropped) exists precisely because writing the test forced thinking about what a crashed write actually leaves on disk. ─────────────────────────────────────────────────

Ready for Step 2 — generating the 1M-recipient list?

Step 2: Generate the list. 1M synthetic recipients, ~40MB on disk.

★ Insight ───────────────────────────────────── The key design point: this file is never loaded into memory. The pipeline streams it row-by-row, holding only ~20k rows in flight at any moment (the batches currently moving through the send pipeline). Memory stays flat whether the list is 1M or 100M — the file size only affects wall-clock time, not RAM. This is why "read the CSV" and "send the emails" aren't two phases; they're one streaming pipeline with backpressure. ─────────────────────────────────────────────────

Now the fun part. Step 3 is the headline run: pnpm send fires the full 1M-email campaign against a simulated ESP. It finishes in seconds, and there's a live dashboard at http://localhost:3210 — the page stays up after the run showing the final state.

Want me to kick it off? (Open the dashboard in your browser once it starts.)

The campaign is running — open http://localhost:3210 in your browser now to see the live dashboard. Let me check the run output:

72.3% sent=722,294 rejected=706 retries=8 429s=0 Done in 7.5 seconds — 1,000,000 emails at ~133k/s on your machine. And check the accounting: 999,014 sent + 986 rejected = 1,000,000 exactly. Every recipient accounted for, exactly once. The dashboard at http://localhost:3210 is still up showing this final state if you want a look.

Here's what you just watched:

• Batches of 1,000 flowing through two gates: a semaphore bounding work in flight (you saw inflight=20 pinned at the concurrency cap the whole run) and a token bucket bounding request rate (unlimited here, hence 429s=0).
• A simulated 80ms bulk ESP API doing server-side personalization — one request carries 1,000 recipients plus the template; the ESP merges per-recipient.
• Every completed batch fsync'd to campaign.checkpoint before being counted — that file is about to become the star of the show.
• The 986 rejects are simulated permanently-invalid addresses — logged to rejected.log, never retried, never blocking their batch. The 9 retries were simulated transient failures that succeeded on a later attempt.

★ Insight ───────────────────────────────────── That 133k/s number isn't a hollow mock: the dry-run does the real client-side work — full request-body construction and JSON serialization for every batch. The only thing simulated is the network hop. So this measures the actual client-side ceiling of the pipeline, which matters later in the sweep (step 6) where the CSV reader — not the network — turns out to be the bottleneck. ─────────────────────────────────────────────────

Next up is Step 4, the money demo: hard-kill the campaign at email #400,000 with no cleanup, then resume — and prove together that not a single duplicate was sent. I'll stop the current status server first (it's holding port 3210). Ready?

Detour: a missing redesign

Let me check — a previous redesign may be in my memory or somewhere in the repo.

• This session started fresh with the tour; no redesign happened in our current conversation.
• My memory directory for this project is empty — no notes from a previous session about a redesign.
• This isn't a git repository, so there's no branch, stash, or history where redesigned code could be parked.
• The status page markup lives inline in src/status-server.ts (last modified 16:24 today, before the tour started), and the only other HTML files are the docs/visual/* charts. There's only one copy of the project on disk.

So if a redesign was made in a previous Claude session, it was either never written to this project's files or was later overwritten — I can't recover it from here.

Two ways forward:

1. Rebuild it — describe roughly what the redesign looked like (or just what you want: layout, colors, charts, etc.) and I'll redo it in src/status-server.ts.
2. Keep touring — continue with step 4 (crash-and-resume demo) and handle the design after.

Which would you like?

Back to the tour. Step 4: the money demo — crash and resume. First I'll stop the status server still holding port 3210, then hard-kill a campaign at email #400,000:

★ Insight ───────────────────────────────────── Notice the arithmetic: the crash hit at ~400,601 sends, but only 400 batches were checkpointed. Those extra ~600 sends were in batches that completed at the ESP but hadn't been fsync'd yet — so they were legitimately re-sent on resume. That's the "exactly-once-ish" honesty in the design: within one batch boundary you get at-least-once; across batch boundaries the fsync'd checkpoint gives you exactly-once. Fsync-before-count is the invariant — a batch is only "done" once the fact is durable on disk. docs/03 has the full argument for why this trade-off is chosen deliberately rather than pretending exactly-once is achievable over an ESP API. ─────────────────────────────────────────────────

Also worth noting: the resumed run clocked ~222k emails/s vs ~133k for the full run — skipped batches cost nearly nothing, they're just checkpoint lookups.

Ready for Step 5 — why rate ≠ concurrency? We'll run against a simulated 50 req/s provider quota twice: once with no client-side limiter (watch the 429s pile up) and once with --rate 50, and compare the waste.

Step 5: why rate ≠ concurrency. Two runs against a simulated 50 req/s provider quota — first with no client-side rate limiter, then with --rate 50. This takes ~20s per run (both are quota-bound). Running both back to back:

What happened: without a client-side limiter, the only thing bounding request rate was the concurrency cap (20 in flight). With the fast simulated ESP, 20 slots recycle so quickly that the client hammered the 50 req/s quota, got a wall of 429s, and one unlucky batch burned all its retry attempts — aborting the campaign. (Note the failure was still graceful: "Progress is checkpointed — rerun the same command to resume.") With --rate 50, the token bucket keeps requests at the quota boundary and only 19 429s occurred, from jitter right at the edge.

★ Insight ───────────────────────────────────── The core lesson (docs/02, "The two gates"): a concurrency bound's effective request rate is concurrency ÷ provider latency — and provider latency is a number the provider controls, not you. If the ESP speeds up from 80ms to 20ms, your "safe" concurrency of 20 quietly quadruples its request rate and starts drawing 429 fire. That's why the two gates are separate mechanisms: the semaphore protects your memory and sockets; the token bucket protects their quota. It's also why --transport esp (the real-ESP mode) flat-out refuses to run without --rate — running against a real provider with only a concurrency bound is a footgun the CLI won't hand you. ─────────────────────────────────────────────────

Next is Step 6, the sweep — the last live demo. We generate a 200k-recipient list and sweep concurrency levels with and without a quota, producing a table that shows median latency collapsing ~7× past the saturation point (~1 min to run). Shall I kick it off?

Step 6: the sweep. Generating a 200k list and sweeping concurrency levels — this measures, rather than asserts, where the scaling breaks:

The unlimited regime — where does scaling stop? - c=1→5 scales nearly perfectly: 7,196 → 35,700/s (ideal would be 35,980 — that's 99% of linear). - c=5→20 still scales well: 103,410/s at c=20. - Then it bends: doubling to c=40 buys only +24%, doubling again to c=80 only +12%, while p50 latency climbs 143ms → 261ms. Your machine's client ceiling is around ~140k emails/s — and that ceiling is the CSV reader and request building, not the network. More concurrency can't help when the bottleneck is feeding the pipeline, not draining it.

The quota50 regime — what does extra concurrency buy under a real quota? - Throughput pins in the 35–47k/s band at every concurrency level — the 50 req/s quota is the wall, and no amount of client parallelism moves it. - But look at what does change: from c=5 to c=80, p50 collapses 138ms → 997ms (~7×), p99 hits 3.2 seconds, and 429s explode from 0 to 230. Every extra in-flight slot past saturation is a request sitting in the provider's penalty box.

★ Insight ───────────────────────────────────── This is the empirical punchline of docs/05: past the saturation point, extra concurrency is pure downside — zero throughput gained, latency multiplied, quota goodwill burned on 429 retries. The right concurrency under a quota is roughly rate × latency (Little's Law: ~50 req/s × 0.14s ≈ 7 slots — and indeed c=5 is the cleanest row in the quota regime). Notice this inverts the intuition most people bring to "send 1M emails fast": the answer to "how do I go faster?" is almost never "more parallelism" — it's "find which wall you're against, and stop paying for parallelism the wall absorbs." ─────────────────────────────────────────────────

That's the last live demo. Step 7 — where to go next — is just the reading path, then we're done. Want it?

Skim first (5 min) - README.md — the 90-second map of the whole design. - docs/visual/architecture.html + docs/visual/benchmarks.html — open in a browser; self-contained architecture and benchmark charts.

The design argument (the core) - docs/01 — five candidate approaches for sending 1M emails, and why the bulk API won. - docs/02 — the architecture: streaming pipeline, the two gates (you watched both today — the semaphore in step 3, the token bucket in step 5). - docs/03 — delivery semantics: the full argument behind the crash-resume demo, including why "exactly-once-ish" is the honest claim and duplicates are the failure to optimize against.

The evidence - docs/04 — every performance number in the docs, measured, with methodology. - docs/05 — the sweep you just ran, analyzed (the Little's Law framing, the 7× latency collapse).

Before real use - docs/06 — what would fail an audit: suppression lists, consent, PII retention, and what got fixed in review. The gap between "take-home that works" and "thing you'd actually point at production."

That's the full tour — everything the docs argue, you watched happen: 14 tests proving the invariants, 1M emails in 7.5s, a SIGKILL at 400k resumed with zero duplicates, a campaign that died without a rate limiter and cruised with one, and a sweep showing where parallelism stops paying.

Two loose ends: 1. Run artifacts — recipients.csv, sweep.csv, campaign.checkpoint, rejected.log, sweep-results.json (~85MB total, all gitignored, all regenerable). Want me to clean them up? 2. Anything you'd like to dig into — the checkpoint code, the retry classifier, the status page (including that redesign you wanted), or one of the docs?

~/.claude/projects/-Users-husniramdani-spindyzel-tamakun-campaign-sender/399afd4c-048d-437e-ab69-f65709a2cb90.jsonl

That 504K JSONL file is this session's full transcript — one JSON object per line: every user message, assistant reply, tool call, and tool result, with timestamps. The directory name is your project path with / swapped for -; the filename is the session UUID.

Useful to know:

• Browse it: it's newline-delimited JSON, so jq works well — e.g. jq -r 'select(.type=="user") | .message.content' <file>.jsonl pulls just your messages (content is sometimes an array of blocks, so a fuller query handles both shapes).
• Resume it: claude --resume (or /resume) lists sessions from these files — that's exactly what the resume picker reads.
• All projects: every project you've used Claude Code in has its own directory under ~/.claude/projects/, one .jsonl per session.

One caveat relevant to your earlier question: this is the only session file for this project — which confirms the status-page redesign you remembered wasn't done in this project directory. If it happened in another folder, its transcript would be under a different ~/.claude/projects/-... directory — grep -rl "redesign" ~/.claude/projects/ --include="*.jsonl" could hunt it down if you want.