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docs: design BrowserTrace launch growth plan
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‎docs/superpowers/specs/2026-05-09-browsertrace-7-day-launch-growth-design.md‎

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# BrowserTrace 7-Day Launch Growth Design
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Date: 2026-05-09
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Project: `aaronlab/browsertrace`
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Current audited stars: 3
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Target: more than 1000 GitHub stars
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## Objective
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Turn BrowserTrace from a launch-ready OSS repo into a visible AI developer tool
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through legitimate distribution. The goal is not complete until a current
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GitHub audit shows `aaronlab/browsertrace` has more than 1000 stars.
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## Operating Boundary
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The launch must not use fake stars, bought stars, bots, vote rings, spam, or
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deceptive accounts. Growth work should make the project easier to understand,
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try, discuss, and share.
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Codex can own repo-side work, copywriting, release assets, launch checklists,
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issue/discussion triage templates, and daily audits. The owner must still click
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publish or authenticate on platforms that require a personal account, including
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Hacker News, Product Hunt, X, LinkedIn, Reddit, Discord, WeChat, Jike, and
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similar communities.
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## Positioning
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Primary line:
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> Local flight recorder for AI browser agents.
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Expanded pitch:
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BrowserTrace records what a browser agent saw, clicked, decided, and returned at
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each step. When a Browser Use, Stagehand, Playwright + LLM, Skyvern, or
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computer-use run fails, developers can open a local timeline with screenshots,
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URLs, actions, model input/output, errors, and an exportable HTML report.
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The most important memory hook is the before/after:
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- Before: 1500 lines of logs and no idea what step 47 saw.
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- After: open localhost, jump to the failed step, inspect screenshot and model
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I/O.
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## Audience
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The first launch should focus on developers already feeling browser-agent
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debugging pain:
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- Browser Use, Stagehand, Playwright + LLM, Skyvern, and computer-use builders.
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- AI infra and observability developers who understand traces but need browser
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state, not just LLM call logs.
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- OSS/devtools audiences on Hacker News and Product Hunt.
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- Chinese AI builder communities on WeChat, Jike, X, and Xiaohongshu.
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The first audience is narrow by design. General AI audiences are too broad
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unless the post shows a concrete failure trace.
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## Growth Strategy
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Recommended approach: demo-first, community-paced launch.
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1. Improve the repo share surface before wide posting.
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2. Ship one excellent demo GIF and one zero-install exported HTML trace.
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3. Start with warm/owned communities to gather early feedback.
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4. Use feedback to tighten the README and launch copy.
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5. Run Show HN only when the project is easy to try and the owner can reply.
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6. Use Product Hunt after assets are strong enough for a broader maker audience.
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7. Spend the rest of the week replying, fixing sharp edges, and turning
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feedback into visible issues or PRs.
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This beats a one-day blast because each channel teaches the next one, and it
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reduces the risk of wasting Show HN or Product Hunt with weak first-run assets.
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## Channel Rules
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Hacker News Show HN is appropriate only when people can try what was made. The
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submission must not be a landing page, signup page, or fundraiser, and the owner
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should not ask friends to upvote or comment. BrowserTrace qualifies only if the
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demo and install path stay low-friction.
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Product Hunt should use a personal maker account. Product Hunt allows sharing
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the launch link, but the ask should be to visit, try, and comment, not to upvote.
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The launch should use prepared visuals, a clear tagline, a maker comment, and a
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demo link.
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Reddit should be treated as community engagement, not a posting surface. Avoid
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mass posting repeated links. Post only where self-promotion is allowed or where
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the post stands alone as a useful debugging write-up.
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## Deliverables
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Repo and demo assets:
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- Dynamic demo recorder that can record the latest failed run instead of a stale
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hardcoded run ID.
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- Fresh demo GIF or screenshot sequence showing the failed-step timeline.
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- GitHub social preview candidate at 1280 x 640 and under 1 MB.
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- Current release asset links and canonical `aaronlab` links.
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- README first screen with crisp hook, demo, and 60-second try path.
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Launch assets:
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- `LAUNCH.md` as the owner-facing launch control room.
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- Channel-specific copy for HN, Product Hunt, X, LinkedIn, Reddit, WeChat, Jike,
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and Xiaohongshu.
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- Response templates for praise, bug reports, skeptical comments, comparison
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questions, and cloud/security questions.
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- Outreach target list with one relevant ask per community.
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- Daily metrics checklist: stars, visitors if available, issues, discussions,
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comments, forks, release downloads, and recurring objections.
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## 7-Day Schedule
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Day 0: asset hardening
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- Fix demo recorder.
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- Generate or refresh demo media.
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- Verify release, README, badges, discussions, and canonical links.
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- Prepare final post copy and response templates.
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Day 1: warm launch
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- Publish X/LinkedIn and Chinese community posts.
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- Ask for feedback, not stars.
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- Create issues for repeated requests within the same day.
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Day 2: Show HN
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- Submit `Show HN: BrowserTrace - record and replay AI browser-agent runs to
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find bugs` if Day 1 feedback shows the first-run path is understandable.
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- Owner stays available for comments.
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- Reply technically and briefly; never paste generated comments.
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Day 3: targeted communities
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- Share a value-first debugging write-up in one or two relevant communities.
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- Prefer Browser Use, Stagehand, Playwright, Skyvern, or local AI-agent groups
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over generic startup communities.
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Day 4: Product Hunt prep
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- Prepare listing images, tagline, first maker comment, and demo link.
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- Schedule only if the visual assets are strong and the owner can monitor the
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launch.
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Day 5: tutorial content
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- Publish a practical post: "How to debug a browser agent failure with
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screenshots and model I/O."
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- Link BrowserTrace as the reproducible example.
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Day 6: integration outreach
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- Open or comment on relevant integration issues where welcome.
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- Invite adapter PRs for Skyvern and other browser-agent frameworks.
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Day 7: retrospective and second iteration
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- Audit stars, comments, issues, and drop-off points.
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- Ship one visible improvement from launch feedback.
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- Decide whether to run Product Hunt, defer it, or use another dev community.
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## Success Metrics
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Primary metric:
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- Current GitHub `stargazerCount > 1000` for `aaronlab/browsertrace`.
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Leading indicators:
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- 50+ stars: first messaging works outside warm network.
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- 100+ stars: Show HN or a community post resonated.
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- 250+ stars: repo is shareable without personal explanation.
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- 500+ stars: broader AI/devtools audience understands the category.
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- 1000+ stars: launch plus follow-up loops have reached durable visibility.
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Behavioral indicators:
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- Users open issues with concrete framework requests.
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- Users ask for cloud/share/team workflow.
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- Users compare BrowserTrace to Langfuse, LangSmith, Browserbase, or Laminar.
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- Maintainers or framework users mention integrations.
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## Risks
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Weak demo:
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- Mitigation: record a simple, deterministic failure first. A polished but fake
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scenario is less valuable than a clear trace of what broke.
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Over-broad audience:
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- Mitigation: lead with browser-agent failure pain, not generic AI observability.
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Spam perception:
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- Mitigation: one post per channel, tailored copy, no mass DM, no direct upvote
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asks, and real technical replies.
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Owner availability:
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- Mitigation: prepare response templates and avoid submitting to HN/Product Hunt
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unless the owner can monitor the thread.
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PyPI not published:
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- Mitigation: keep GitHub install path prominent until credentials are available.
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If PyPI becomes available, update install commands immediately.
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## Verification
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Before each public launch step:
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- `gh repo view aaronlab/browsertrace --json owner,url,homepageUrl,stargazerCount`
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shows canonical `aaronlab` ownership and current stars.
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- `uv run --python 3.11 --extra dev pytest -q` passes.
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- README install and demo commands work from a clean checkout.
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- Release asset link opens without authentication.
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- Demo media is present and renders in the README.
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- No launch copy asks directly for upvotes or stars.
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The active objective remains incomplete until the primary metric is achieved.

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