Week 1 recap — distribution, downloads, dedup questions (2026-05-15)

[manja316]

Week 1 in numbers — receipts, not narrative

Shipped pnl-truthteller Monday. Friday means recap. Posting numbers so next week's recap is comparable, not vibes.

Distribution shipped this week

• ✅ Mon: dev.to article published (methodology + slippage walkthrough)
• ✅ Mon: README Gumroad CTA + UTM attribution wired
• ✅ Tue–Thu: 4 GitHub Discussions opened (this is Week 1 recap — distribution, downloads, dedup questions (2026-05-15) #6) covering audit framing, dedup question, slippage index call, reconciliation methodology, and four failure modes
• ✅ Wed: Reddit r/algotrading draft staged (interactive session needed)
• ✅ Thu: Twitter thread drafted (Lucifer's @gmanjuu, posts manually)
• ✅ Fri: Show HN draft staged + this recap

Metrics (verifiable)

Source	Number	Δ vs 2026-05-10 baseline
pypi downloads/wk	28	-17 (drift down before tooling lift hits)
github stars	0	0
gumroad sales	0	0
dev.to reactions	TBD	n/a
operator questions (this repo)	1 (Discussion #2)	+1

All sources public: pypistats /api/packages/pnl-truthteller/recent, gh api repos/LuciferForge/pnl-truthteller --jq .stargazers_count.

What I'd flag honestly

1. pypi drift down before lift hits. Possible: pre-launch indexing crawl peaked at 45, real users hadn't started yet, now organic-only floor showing through. Will know by 2026-05-22 whether channel push moves the floor.
2. 0 stars is a real signal. Tool is useful but solo-operator-niche. Stars come from broader devs sharing, not single-operators using. Show HN is the first real test of that.
3. The dedup question (Discussion How are you deduping partial fills against order_id when CLOB reuses IDs across batches? #2) is the most valuable artifact. One real operator asking about order_id collision on sweep retries means the methodology is being read carefully, not skimmed.

What's next (week 2 — apps/ bundle)

Focus rotates Monday to regexai + jsonfix. Different audience (web devs). Same rule: ship, measure, recap Friday with the numbers, including the bad ones.

If you ran the tool this week, drop your slippage number in Discussion #3. n=5 → n=10 makes the Slippage Index actually defensible as data, not anecdote.