Swarm AI and minis.me

I’ve been prototyping minis.me—a tool that drafts the review your team is going to leave anyway. Think of it as a swarm of shadow teammates that know your codebase, your conventions, and the tone your org expects.

Why this matters

• Faster feedback loops mean fewer stuck branches.
• Consistency makes junior devs feel safer shipping.
• You catch the obvious stuff before it reaches people.

It’s not about replacing reviewers. It’s about letting humans spend their attention on design and architecture instead of nits and missing tests.

Shape of the system

• Local or private deployment options
• Repository‑aware memory with embeddings
• Styles tuned per‑team: tone, examples, conventions

Safety and privacy

Private repos stay private. I design for local or self‑hosted deployments first and add cloud options after. Logs exclude sensitive content by default. Review is a human act; the tool just helps you get ready for it.

What “predictive review” actually looks like

Given a PR, minis.me does a few simple, powerful things:

• Reads the diff and the surrounding files to understand the real change, not just the lines that moved.
• Cross‑checks against conventions it has learned from your repo (naming, patterns, lint rules, testing habits).
• Generates a first pass of comments, grouped by theme (tests, safety, readability, API shape), with suggested fixes where low risk.
• Adapts tone to match team norms—brief and direct for some orgs, detailed and teaching‑oriented for others.

Architecture in human terms

• Ingest: a lightweight worker pulls diffs, metadata, and a small context window from the repo.
• Memory: embeddings capture reusable patterns (think “how we name things,” not “what secrets live here”).
• Reasoning: a prompt chain that’s intentionally boring—clear, modular steps over magic prompts.
• Output: suggested comments and a single “review summary” a human can skim in 30 seconds.

No spooky claims. Just a pragmatic assistant for the parts of review that repeat.

Guardrails I care about

• Scope: never suggest changes outside the diff without clearly labeling them as future tasks.
• Privacy: process locally by default; if cloud is enabled, ship only what’s needed for the review.
• Slowness: target feedback under a minute; if it’s slower, it’s probably not worth doing for every PR.

Evaluating usefulness

I measure minis.me by how often humans keep or adapt its suggestions. If the tool’s comments frequently get turned into commits, it’s doing its job. If reviewers delete most of them, that’s a signal to adjust prompts or heuristics, not a reason to dump more tokens at the problem.

Where this is heading

• Personalization that gets specific: “this team likes tighter names and explicit error messages.”
• IDE integrations that surface “fix it now” hints before the PR is even created.
• A calmer review culture where humans spend time on design, not indentation wars.

Predictive review won’t replace code review; it will make the useful parts bigger and the tedious parts smaller.

Use cases I care about (and ones I don’t)

Good fits:

• Repos with established conventions that are teachable: clear naming, folder structure, test layout.
• Teams that value consistency and kindness in reviews—predictive comments become an empathy amplifier.
• Projects with lots of small PRs where cadence matters more than heroics.

Not great fits:

• Wildly experimental repos where every file invents a new pattern.
• PRs that are actually design proposals in code form; these deserve human conversation first.

An experiment log (how I evaluated it)

I ran a two‑week trial on a medium‑sized monorepo. Metrics:

• Time to first feedback (pre‑human): dropped from ~45 minutes to under 3.
• Human uptake of suggestions: 62% of comments resulted in commits within the same branch.
• Reviewer sentiment: less nitpicking; more time on API shape and long‑term fit.

We also learned where it misfired—niche test frameworks, deeply dynamic patterns that defy simple heuristics, and areas with ambiguous “house style.” The fix wasn’t “more AI,” it was writing down the style and teaching the tool once.

Human factors and ethics

I don’t want a tool that scolds. I want one that’s helpful. That means:

• Tone stays neutral and specific; suggestions include a reason and a quick path to fix.
• The tool never blocks merges; it supports humans rather than pretending to be a gatekeeper.
• It’s transparent about what it knows. If confidence is low, it says so.

The boring implementation details that matter

• Caching: don’t re‑read the entire repo; keep an index of stable patterns.
• Cost control: prefer small, well‑aimed prompts; batch across files when sensible.
• Diff hygiene: ignore churn (formatting noise) so comments point at what’s new.

Productizing the idea

If I ship minis.me, it will ship as a local‑first tool with a simple cloud option. No vendor lock‑in, no surprise data transfers, and a blunt, readable configuration file that teams can check in to source control. We’ll optimize for clarity over cleverness.

A closing loop

I think of predictive review as pre‑review. It clears the underbrush so humans can do the part only we can do: see the shape of an idea and decide if it fits the system we’re building. If minis.me does that, it succeeds.