What this series is

Watercooler has reached a baseline where it's useful, not just aspirational. So we're starting a short build-in-public series to produce a new 2-minute demo video (and replace the one on our landing page).

This isn't a "launch." It's an introduction—and a way to learn faster by showing real workflows, shipping iteratively, and letting feedback shape what comes next.

The market gap we're aiming at

Teams already have systems of record for outcomes: code, tickets, docs, deployments. And now there's a wave of AI tools that retrieve text: RAG over documents, chat history "memory," and embeddings everywhere.

But there's still a missing layer:

Operational context — the living trail of why decisions were made and how work actually moved across people, artifacts, and time.

Most systems capture:

the outcome ("we shipped X")
or the transcript ("we talked about X")

What's routinely lost is:

the reasoning path ("why X over Y")
the state at the time ("what we knew then")
the links between research → plan → implementation → checkpoints
continuity ("what changed, when, and why?")

Watercooler is built to make that middle layer durable.

What Watercooler is (in one breath)

Watercooler treats engineering threads as first-class, versioned artifacts (git-friendly source of truth), then builds a read-optimized memory layer (search + graphs + summaries) so humans and agents can reliably retrieve the relevant, distilled context later—with provenance and time.

A useful shorthand we'll repeat: Threads → memory graph → searchable "why."

The demo we're making

We're going to prove the idea across a realistic engineering arc:

1. Onboard a repo Ingest and conceptually segment a codebase so threads become a searchable map of functionality.

2. Research Attach docs/papers/links and connect supporting material to the right threads, with cross-references surfaced.

3. Plan Turn an objective into atomic tasks and a reasoned implementation plan, including gates (tests, review, acceptance criteria).

4. Execute Follow the plan with checkpoints and time-aware queries: what changed, when, and why.

The video will be lightweight (screen capture + voiceover). The point is clarity and proof, not polish.

We'll also export reusable cuts:

1 full demo video (~2 min)
4 micro-clips (15–25s each, one per phase)
1 looping "search → jump to thread" moment (the "proof" clip)

How we'll document it (the blog series)

Each post is short, screenshot/clip-heavy, and shippable. Each will link to the full demo, the repo, and the previous/next post in the series.

Planned posts:

Post 0: this post (intent + framing)
Post 1: storyboard + script + what we'll measure
Post 2: use case 1 — repo onboarding
Post 3: use case 2 — research augmentation
Post 4: use case 3 — planning
Post 5: use case 4 — execution + temporal continuity
Post 6: what shipped + what's next (memory backends as extensions)

What we'll measure

We're not chasing vanity metrics. We want signals that operational context is getting easier to recover:

Time-to-answer: "Where is auth handled and why did we choose this approach?"
Coverage: how many conceptual threads exist after onboarding
Cross-reference density: research ↔ plan ↔ implementation links
Usage odometer: searches run, threads opened (simple instrumentation)

How we'll engage while we build

We'll start with comments-first on LinkedIn and X (borrowed reach), then publish posts once we have artifacts worth linking.

We'll use the same rule everywhere:

be useful first
link lightly (only when it genuinely adds value)
always point to one canonical blog destination per topic

What to expect next

Next up is Post 1: the storyboard + the script, and the exact "proof moments" we're optimizing for.

If you've felt context decay—onboarding pain, incident follow-ups, "why did we choose X?" archaeology—tell us where it hurts most. That feedback will shape which workflows we emphasize first.

Follow along as we build in public.

Repo: github.com/mostlyharmless-ai/watercooler-cloud

Site: watercoolerdev.com