Score Marketplace deals against real eBay sale comps.
🎯 Latest find Herman Miller Aeron Size B — asking $385, eBay sold-comp median $602 Score: 87 · $215 below market · caught 14 minutes after the listing appeared
(Pinned example. Updated occasionally with real finds.)
Type a listing into /test and watch it scored against real eBay sold comps in real time.
- Free forever — 3 saved searches, adaptive Marketplace scanning, real comps, daily email digest. No trial clock, no card.
- Four-layer adaptive scanner — exponential rate-limit cooldown + slow-start ramp (60s → 20s floor) + half-open circuit breaker with cheap HTML probe + round-robin coordinator. 20-second cadence per watch on Pro (single watch), 5-minute cadence on Free. Scales naturally with watch count via the round-robin coordinator. Full math in the How polling works section below.
- Deterministic scoring — same listing, same comps, same score, every time. The math is plain Python in
appraisal/formula.py; no LLM in the scoring path. - Real eBay sold comps — Tukey-trimmed median + IQR from the eBay Browse API, last 90 days. Not "estimated value", not Marketplace-comparing-to-Marketplace.
- No Facebook login — Bullseye reads public Marketplace listings the same way an unauthenticated browser does. Your account is never touched, never bannable.
- Open source (AGPL-3.0) — every line is on GitHub. Read it, run it, fork it. The catch: if you host a derivative service for others, your modifications have to be open-source too.
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A new listing appears on Facebook Marketplace.
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Title is normalized via a small LLM (MiniMax) → clean eBay search term. This is the only LLM in the path; result is cached 12 hours.
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The cloud comp pipeline (Supabase Edge Function) fetches eBay sold comps from the Browse API for the last 90 days.
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Outliers are trimmed using Tukey fences (1.5 × IQR), and a trimmed median + IQR are computed.
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The asking price's percentile rank within the comp distribution drives the raw score:
score = (1 − percentile_rank) × 100Score 80 = cheaper than 80% of comparable sold listings. Score 50 = roughly at the median. Score 20 = priced 20% above the median.
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The raw score is then capped by:
- A confidence band (wider when the sample size is small or comps are dispersed).
- Honesty guards that prevent inflated scores: heterogeneous comp sets cap at 70 (e.g. "VEVOR Linear Actuator" matches every length+load class on eBay), sub-$30 listings cap at 80 (low signal-to-noise), low absolute savings (< $25) cap at 75 (a 20% discount on a $10 item isn't a "great deal").
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A condition adjustment shifts the score (e.g.
−15for "needs repair",+5for "excellent condition"). -
Score ≥ 80 → worth a manual look. Score ≥ 90 → act fast.
Full math, the trade-offs, and the version history are in the docstring at the top of formula.py. Tests live in test_formula.py.
Click to expand — four-layer adaptive scheduler (slow-start ramp, exponential cooldown, half-open circuit breaker, round-robin coordinator). HN engineers, this is for you.
The scheduler runs one round-robin coordinator job that ticks every COORDINATOR_TICK_S seconds (default 20s). Each tick picks the stalest watch from user_searches and runs the full per-listing pipeline for that one watch. So per-watch cadence ≈ tick × N active watches (round-robin).
Three gates can skip a tick before any FB request goes out:
Every time the scraper sees a Facebook rate-limit (HTTP 429, Cloudflare interstitial, "this content isn't available"), we record an fb_rate_limit event. The cooldown function counts those events in the last 30 minutes and computes:
cooldown_s = base × 2^min(n-1, 4) capped at 600
= 60s, 120s, 240s, 480s, 600s
Clock starts at the most recent rate-limit timestamp, with decorrelated jitter (0.85×–1.15×, seeded deterministically by the timestamp) so retries don't cluster on synchronized wall-clock offsets — and the dashboard's countdown timer doesn't oscillate on every refresh. Roll-off is 30 min — once we go that long without a rate-limit, the count resets to zero.
Starts at a conservative 60s effective interval and ramps down by 5s every 300s of clean polling, with a floor of 20s (SLOW_START_FLOOR_S). Any rate-limit during the window resets the ramp back to 60s — protects against ramping-up-into-a-block.
SLOW_START_INITIAL_S = 60 # cold-boot interval
SLOW_START_FLOOR_S = 20 # fastest we'll ever go
SLOW_START_HEALTHY_PERIOD_S = 300 # window we need clean to ramp
SLOW_START_STEP_S = 5 # interval reduction per ramp step
So a fresh boot takes about 40 minutes of clean polling to ramp from 60s → 20s (8 steps × 5min each). Ramp-direction events fire on the dashboard's slow-start panel so you can see exactly where in the curve you are.
If we had a rate-limit in the last 10 minutes AND cooldown just cleared, we don't trust the system enough to send a real GraphQL search yet. Instead we send a cheap HTML probe to a known-public Marketplace URL. Three outcomes:
- Probe returns clean HTML → close the circuit, proceed with the real poll
- Probe is blocked → record a synthetic
fb_rate_limitevent → cooldown re-arms longer (we don't burn search quota proving we're still flagged) - FB is down (5xx, timeout) → skip this tick → retry next
90s probe-cooldown so we don't re-probe every tick once we know we're blocked.
The slow-start floor of 20s is then clamped against the license-tier minimum:
| Tier | License floor | Effective per-watch cadence |
|---|---|---|
| Free (3 watches max) | 300s | ~5 min/watch |
| Pro (cold) | 20s | 60s/watch (single, ramping) |
| Pro (warm, 1 watch) | 20s | 20s |
| Pro (warm, 5 watches) | 20s | ~100s (1.7 min) |
| Pro (warm, 45 watches) | 20s | ~15 min |
This is the only difference between Free and Pro polling: Pro lets the slow-start system run all the way to its 20s floor, while Free clamps at 5 min so the difference is meaningful. Both tiers run the exact same four-layer scheduler.
def coordinator_tick():
if _should_skip_tick_for_backoff(): return # Layer 1: cooldown
if _slow_start_should_skip(): return # Layer 2: slow-start
if _circuit_breaker_should_skip(): return # Layer 3: half-open probe
sid = pick_next_watch_to_poll() # license floor in pick
poll_search(sid) # actual FB requestThe desktop app shows the actual measured average gap between consecutive polls of each watch on the saved-searches page ("polled every 1m 30s, 24h avg") and a global average on the home dashboard. That's the truth — what we advertise — not the theoretical floor.
Source: scheduler/jobs.py (gate logic) and scheduler/main.py (APScheduler wiring).
Download Bullseye-Setup.exe — ~32 MB, Windows 10/11.
⚠️ SmartScreen warning? Click More info → Run anyway. Yes, it's safe — the warning is reputation-based, not a malware signal. Chrome and Windows SmartScreen flag every brand-new Windows app until enough people install it. Bullseye is fully open-source on GitHub, so you can audit the source, build from source, or verify the SHA-256 hash of the binary before running it.
Want to verify the binary? Compare the SHA-256 hash:
# Windows
Get-FileHash Bullseye-Setup.exe -Algorithm SHA256
# macOS / Linux
shasum -a 256 Bullseye-Setup.exeThe current release hash is on the GitHub releases page — match it before running.
macOS is on the roadmap. Join the waitlist to get notified when the .dmg ships.
| Free | Pro | |
|---|---|---|
| Active saved searches | 3 | Unlimited |
| Marketplace scanning | Adaptive · ~5 min/watch | Adaptive · ~20 sec/watch (single watch) — scales with watch count |
| Email alerts | Daily 8am digest | Instant (60s batched) |
| Desktop notifications | Yes | Yes |
| Score breakdown | Score + savings | Full (percentile rank, sample size, confidence band, outliers, condition flags) |
| Live observability dashboard | No | Yes |
| Price | Free forever | $9.99 / mo or $99 / yr · 7-day trial, no card |
The free tier is genuinely free, not a crippled trial. Pro is for resellers and power users who want unlimited watches plus instant alerts.
| Layer | What |
|---|---|
| Desktop app | Python 3.12 · Flask · pywebview (WebView2) · SQLite |
| Installer | PyInstaller + Inno Setup |
| Cloud | Supabase Postgres (auth + storage) · Supabase Edge Functions (Deno / TypeScript) |
| Comp data | eBay Browse API |
| Title normalization | MiniMax LLM (cached 12 h) |
| Resend | |
| Payments | Stripe Checkout + Customer Portal |
| Landing | GitHub Pages (custom domain via name.com) |
The polling scraper runs on your machine, not on a central server. Each install hits Facebook from the user's home IP at the cadence of a normal browser session — there is no central scraper IP for Facebook to block, and no Facebook account ever gets touched.
desktop/— Python desktop app (UI, scheduler, local scraper, SQLite, installer build).cloud/— Supabase migrations + Edge Functions (comp pipeline, license, billing, email, telemetry, achievements).landing/— Static landing page (getbullseye.app).LICENSE— AGPL-3.0.
- Windows only for v0.1. macOS is next; Linux is on the maybe pile.
- Unsigned installer triggers Windows SmartScreen on first run — see the install notes above.
- Cars and heavy vehicles: eBay sold-listing volume for used vehicles is thin, so comp-based scoring is less reliable there. The app applies a wider minimum confidence band on the vehicles category, but treat car scores as a starting point, not a verdict.
- Facebook redesigns can break the parser. Has happened twice during alpha; both times patched within ~24 h. Open-source means anyone can submit the fix.
- No proxies, no headless Chrome. Bullseye polls public listing endpoints from your home IP. If Facebook ever rate-limits a residential IP that runs Bullseye, that user is paused, not the entire userbase.
# Windows, from the repo root
cd desktop
python -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install -r requirements.txt
# Run the app from source
python -m src.main
# Build a fresh installer (requires Inno Setup 6 installed)
cd build
.\build_windows.batYou'll need a .env with the cloud-side keys (Supabase, Stripe, Resend, eBay, MiniMax) for full functionality. See .env.example.
Tests:
cd desktop
python -m pytestBullseye is the third iteration of a marketplace deal-finder I've been building since April 2026:
- salvage-radar — Craigslist + heuristic scoring + Claude-subagent appraisal (archived)
- bullseye — FB Marketplace + percentile-rank scoring + local Ollama / Postgres (archived)
- bullseye-app — the shipped product. Windows installer, eBay sold-comp pipeline, cloud edge functions.
AGPL-3.0 — read it, run it, fork it for personal use. If you host a derivative service for others, your modifications also have to be open-source under AGPL. Same license as Plausible, Cal.com, Sentry, and Grafana, for the same reason: keeps the ecosystem honest.
Bullseye is solo-built right now, but PRs are welcome. Open an issue first for anything bigger than a typo so we can talk through the approach before you write code.
By submitting a PR, you agree to license your contribution under AGPL-3.0.
Built in Vancouver, BC · Questions: hello@getbullseye.app