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Launch plan — what to post, where, and when

Written 2026-07-16 against a survey of what actually worked and what died in this category. It is deliberately conservative: LLM-cost Show HNs are a graveyard, and the ones that landed had a specific measured number and an agent framing, not a platform pitch.

The evidence this is built on

Four days before this was written, "Show HN: LLM-spend — Audit your OpenAI/Anthropic API spend locally" got 1 point, 0 comments — and it was honest, well-written, and did a provider cross-check within 1%. Its peers: Costly 4 points, AgentCost 3, Cost-Xray 2, SpendScope 1. Meanwhile, in the same window:

Post Points Why it worked
Claude Code Usage Monitor 245 "dodge usage cut-offs" — a problem you feel today
Lowfat 156 "saved 91.8% of my tokens" — one number
CodeBurn 112 "$1,400/week with no visibility… 56% of spend was conversation turns, 21% actual coding"

The pattern is not "cost tool." It is a number that reframes something the reader already worries about.

The lede

Lead with the incident, because it is true, specific, and instantly recognizable:

A day of "cost-conscious" LLM work that I expected to cost ~$33 cost $149.76. Two causes: a price constant hardcoded at the old model's rate, and jobs running one item per request so a shared prompt was re-billed on every call. Neither showed up until the invoice.

Then the finding that generalizes — from our own data, and the one to lead the post with:

The gate's own pre-submit estimate is a ceiling: it assumes every request runs to its max_tokens. Measured against real completions, output fill is ~40–55% — so a batch that billed $0.60 was "estimated" at $16. Every cost tool I checked prints a number like that with no error bar. None of them publishes an accuracy figure at all.

That is the hook: your cost tooling is confidently wrong and nobody measures it.

The claim, and the proof that must ship with it

Post one measured claim and the harness that produced it. Lowfat posted 91.8% with no end-to-end benchmark and got dismantled in the comments; Forge (687 points) published its eval harness and went unchallenged.

Our claim: batch reconciles exact; realtime measured at +4.4% over two months — with ACCURACY.md linked in the post, including the reproduce-it commands and the explicit what we do not capture list. Never post a savings percentage without the equivalence method beside it.

What NOT to lead with

  • "Pre-spend gate." Providers now hard-cap natively at org/project level, every gateway does budgets, and an industry roundup already calls pre-request budget enforcement table stakes. Three other projects use this exact pitch — one of them owns the bare spendguard name on PyPI.
  • Subscription-value or coding-agent attribution. ccusage (~17.6k stars, npx, zero config) owns it, free, and does it deterministically. We keep the feature; we don't headline it.
  • "2,500 models." It's a free MIT file everyone syncs.
  • Anything that sounds like a platform. "Gate + pricing + reconcile + advisor + calibration + experiments + receipts + SaaS" reads as unfocused to a stranger and buries the one novel thing.

Sequence

  1. Ready first (all done as of v0.8.1): uvx --from llm-spendguard spendguard scan works with no key on a machine you don't own · spendguard run -- gates without writing startup hooks · ACCURACY.md published · README under 14K · repo has a description, topics, and a real --help.
  2. r/LocalLLaMA — disclose affiliation, no link in the title, lead with the 27× finding and the fill-rate data. This audience checks claims and rewards method.
  3. awesome-llmops PR (5.9k stars, maintained) — one line, accurate category.
  4. Show HN last, once 2–3 people who aren't you have run it. Title the finding, not the tool. Treat it as a 48-hour pulse: ~92% of the stars a post will generate arrive in that window, at roughly 1.4 stars per upvote — so a modest score is a normal outcome, not a verdict on the work.
  5. Sustained — the interesting posts are the reconciliation findings themselves ("here's what our ledger said vs what the invoice said, and why they differed"). That's a series only we can write.

Honest expectations

Most launches in this category get single-digit points. The realistic goal for week one is three strangers running scan and one filing a real issue — that is the signal that the first-run path works. Stars are a lagging vanity metric; a reconciliation bug report from someone else's account is the leading one.

The State of FinOps 2026 survey has granular AI spend monitoring at token/request/GPU level as its #1 most-requested capability, and AI spend management went from 31% to 98% of FinOps teams in two years. Demand is real. The framing is what's contested — so lead with the measurement nobody else publishes.