DEV Community: Piyush Chauhan

Why big monorepos get slow, and what Rush.js + Sparo do about it

Piyush Chauhan — Fri, 16 Oct 2026 11:40:20 +0000

How Rush.js, PNPM workspaces, and Sparo's sparse checkout attack the three things that slow big monorepos: Git, install, and build

A big monorepo gets slow in three places that fail independently: Git, the dependency install, and the build. A fix for one does nothing for the others.
Sparse checkout made a test repo of 407 projects clone twice as fast, halved .git, and cut git status from 76 ms to 13 ms.
Sparse checkout did not speed up the install at all, because Rush cannot install from a checkout that is missing project folders.
Selection flags cut a leaf-file rebuild from 407 projects to 4. A change to a widely-used package still rebuilds everything, because that is what the graph says.
Most of this assumes you use npm, pnpm, or yarn. If you do not, the Rush half of the article does not apply to you.

The problem

Picture a new hire's first day. They run git clone and wait ten to forty minutes, watching the object counter climb past a million.

Then the install runs. A lockfile with tens of thousands of entries resolves, and node_modules grows past a gigabyte. By lunch they have not opened an editor.

On day two they change one line in one package. CI rebuilds the whole repository to check it.

Three separate costs produced that day, and each one scales with something different:

Git: the bytes of history, and the number of files in your working tree. Not which of those files you edit. You see it in clone time, pack size, git status, and checkout.
Install: the number of third-party packages, and the size of the dependency graph the package manager must resolve and write. You see it in node_modules size and install wall time.
Build: the number of projects, and whether you rebuild ones that did not change. You see it in "it rebuilt everything", and in CI minutes.

That is why one fix never covers all three. A sparse checkout does not make an install faster. A build cache does not make git status faster.

The repo this is grounded in

Everything below is checked against a small tutorial repo. It is a toy hotel-booking app with a NestJS API and a TanStack Start frontend. The domain logic is thin on purpose. The repo exists to demonstrate the tooling.

The repo has seven projects, all registered by hand in rush.json. Two are apps (@hotel/api and @hotel/web). Five are packages (@hotel/ui, @hotel/shared-types, @hotel/api-client, @hotel/eslint-config, and @hotel/typescript-config).

The runtime edges are web → ui, web → api-client, web → shared-types, api-client → shared-types, and api → shared-types. Each project also lists both config packages as dev dependencies. Dev-only edges therefore outnumber runtime edges two to one, which matters later when we select projects by what changed.

The toolchain is pinned: Rush 5.181.0, PNPM 11.5.1, and Node in the range >=20.9.0 <25.0.0.

The repo is 120 tracked files with a 1.1 MB .git directory. At that size sparse checkout buys you nothing, so this repo cannot tell you whether any of it works at scale. That is why the numbers later come from a synthetic scale-up instead.

Sparo: make Git deliver less

Sparo is a small wrapper around two Git features. To understand the wrapper, you need to know what it wraps.

Partial clone: download fewer objects

A normal git clone downloads every commit, every directory listing, and every version of every file. Most of that you will never read.

git clone --filter=blob:none asks the server for a subset. You still get all the commits and all the directory listings. What you skip is file contents. Git fetches a file's contents later, on demand, from a remote it marks as a promisor.

That deferral has a price. Git's own documentation says dynamic fetching "tends to be slow as objects are fetched one at a time". A git checkout fetches everything it needs in one batch, so a checkout is fine. A script that reads arbitrary files out of history is not.

Sparse checkout: write fewer files

Partial clone reduces what you download. Sparse checkout reduces what you write to disk. You give Git a list of directories, and Git leaves everything else out of the working tree.

The list goes in one of two formats. Cone mode is the default, and it takes directories: everything under a directory you name, plus the files at the top level. Non-cone mode takes gitignore-style patterns. It is deprecated, it costs O(N×M) pattern matches, and it does not work with a sparse index.

You can speed up the resulting tree further. The sparse index (index.sparse) stores whole directories as single entries instead of one entry per file. That shrinks the index and speeds up git status and git add. Git only writes it when you pass --sparse-index.

The file system monitor (core.fsmonitor) lets Git find modified files without checking every one on disk. It works natively on Windows and macOS. Linux support arrived in Git 2.55.0 in June 2026.

Two warnings about sparse checkout. It removes files that are already on disk, and it will refuse to proceed if a file it wants to remove has unstaged changes. Resolve those, then run git sparse-checkout reapply.

Profiles: name the set you want

Hand-writing sparse directory lists is worse than it sounds. The list changes as the repo grows, and every developer would maintain their own. Sparo replaces the list with a committed file.

sparo clone differs from git clone in four documented ways. It fetches only the default branch. It enables blobless partial clone. It checks out the skeleton folders. It configures cone mode. It also applies some Git settings for you, including fsmonitor, the untracked cache, feature.manyFiles, fetch.prune, and pull.rebase.

A profile lives at common/sparo-profiles/<name>.json and is committed to Git, so it is versioned with the branch it applies to. It has four fields: $schema, selections, includeFolders, and excludeFolders. Each entry in selections is a selector and an argument.

{
  "$schema": "https://developer.microsoft.com/json-schemas/sparo/v1/sparo-profile.schema.json",
  "selections": [{ "selector": "--to", "argument": "@hotel/web" }]
}

The selectors are Rush's project selectors. So --to, --from, and --impacted-by all work here, and you can list several. Multiple selections union together. excludeFolders beats includeFolders and beats selections.

Because the selector is Rush's, the profile above materializes exactly the projects that rush build --to @hotel/web would build: the app plus everything it depends on.

From a fresh clone the workflow is two commands: sparo clone <remote-url>, then sparo checkout --profile web. Two related commands are worth knowing. sparo checkout --to and --from add to the profiles already recorded instead of replacing them, and --no-profile clears the recording.

One part never gets stripped. Sparo calls it the skeleton, and it includes the whole common/ directory. That matters, because common/ holds Rush's config, its scripts, and its autoinstallers. No profile can leave you with a checkout that cannot install or build.

One caveat about the profiles in this repo. Both were written by hand, and their $schema points at a Microsoft-hosted URL. I checked that URL on 2026-10-16 and it returned HTTP 404. Sparo's own published schema at https://tiktok.github.io/sparo/schemas/sparo-profile.schema.json returned 200. The Microsoft URL is not an old location; it does not exist, and Sparo's docs never mention it. Run sparo init-profile --profile web to generate a template from your installed Sparo version.

What a sparse tree looks like day to day

I wrote this article inside a working copy created by sparo clone with a profile applied. That makes the mechanism inspectable instead of theoretical.

git ls-files | wc -l                                                      # 120
git ls-files | while read -r f; do [ -e "$f" ] && echo x; done | wc -l     # 118
git status --porcelain | wc -l                                             # 0
git config --get remote.origin.partialCloneFilter                          # blob:none
git config --get remote.origin.promisor                                   # true

Git tracks 120 files. Only 118 are on disk. git status calls the tree clean, because the two missing files are marked skip-worktree.

Those two files are docs/BANG_OPERATOR.md and .github/workflows/ci.yml. To read the workflow I had to run git show HEAD:.github/workflows/ci.yml, because it is not on disk in this clone. The sparse settings live in .git/config.worktree rather than .git/config, because git sparse-checkout set upgrades a repo to worktree-specific config.

That is the pitfall to plan around. A profile that selects --to @hotel/web excludes .github/, so your CI configuration disappears from the working tree. Everything that walks the tree sees a smaller repo than Git does. git grep returns fewer results, and git commit -a will not record an out-of-cone file as deleted.

If a tool needs a directory, name it:

{
  "selections": [{ "selector": "--to", "argument": "@hotel/web" }],
  "includeFolders": [".github"]
}

Three further problems come up in practice. The clone is blobless, so reading content Git has not fetched costs a network round trip. A repo-wide lint or a background IDE index will trigger exactly that. Sparo refuses to check out a profile that omits a folder with local edits, so stash or commit first. And sparo-ci checkout uses a treeless clone in CI rather than a blobless one. Anything that assumes directory listings are present will behave differently on a runner than on your laptop.

Sparse checkout fights the install

This is the result I didn’t expect, and it affects how you should plan.

A caveat on method: I built these three cases by hand with git sparse-checkout, not with sparo checkout --profile. That separates what Rush and PNPM require from what Sparo configures. Read the findings below as a property of Rush’s install, not as a measurement of Sparo’s own checkout.

Rush and PNPM need every registered project's package.json to be on disk. A profile that omits project folders therefore cannot install. I hit three failures in a row on the 407-project test fixture:

A cone set naming only the app and package folders leaves out common/, and rush update died in 0.29 s with MODULE_NOT_FOUND for common/scripts/install-run-rush.js. Cone mode includes root files and the directories you name, and nothing else.
Adding common/ was not enough. Rush still needs every project's package.json, so the same command failed with Could not find package.json for @hotel/eslint-config.
A non-cone set that materializes all 408 package.json files plus common/ does install, with 513 files on disk instead of 3,328. But rush build --to @hotel/ui then failed with TS6053: File '@hotel/typescript-config/react.json' not found, because the shared config packages were missing too.

A workable set is therefore narrower than "only the app I’m working on". Keep every package.json, common/, and the config packages, and sparsify only the source directories. That still takes you from 3,328 files to 513.

This recipe has a catch: it needs non-cone patterns. A cone set can only name directories, but the files you must keep sit at the root of every package. Non-cone mode is deprecated and slower. When you don’t need to install, cone mode is still the better choice.

The lever for install size is not the checkout. It is Rush's filtered install, which I cover in the next section. In the fixture, sparse checkout changed install time and install size by nothing measurable.

Rush: the graph, the install, the selection

Rush builds the dependency graph and delegates the install to PNPM. Four pieces of it do the work.

The project registry

rush.json lists every project by hand. There is no glob discovery and no directory scan.

From rush.json, abridged to two of the seven entries:

{
  "rushVersion": "5.181.0",
  "pnpmVersion": "11.5.1",
  "nodeSupportedVersionRange": ">=20.9.0 <25.0.0",
  "projects": [
    {
      "packageName": "@hotel/shared-types",
      "projectFolder": "packages/shared-types",
      "shouldPublish": false
    },
    {
      "packageName": "@hotel/ui",
      "projectFolder": "packages/ui",
      "shouldPublish": false
    }
  ]
}

All seven entries omit three optional fields: tags, versionPolicyName, and a truthy shouldPublish. Each omission costs you a feature. Without tags you cannot select projects by tag:<name>. Without a version policy nothing is lockstepped. With shouldPublish: false Rush tracks no change files.

Listing projects by hand costs you one line per project, and buys a shortcut everywhere else. Every command reads one small file instead of walking the tree for package.json. Adding a project becomes a one-line diff in the file that is obviously authoritative.

Install: one lockfile, and a way to install less

Rush delegates the install to PNPM in workspaces mode.

{
  "useWorkspaces": true,
  "minimumReleaseAgeMinutes": 1440,
}

Rush generates common/temp/pnpm-workspace.yaml listing every local project, then runs PNPM once for the whole repo. You get one lockfile at common/config/rush/pnpm-lock.yaml, one content-addressed store, and symlinks into each project's node_modules.

rush install performs an install but will not modify any file. It fails if the lockfile is out of date. rush update is the command that resolves and rewrites. Use update while working and install in CI. A CI job that runs update will accept a lockfile that no longer matches the manifests, which is the state you want CI to reject.

To install less, pass the same project selectors you would pass to rush build:

rush install --to @hotel/web

Rush still generates the full workspace. It then passes --filter to PNPM for each selection, so only the selected projects and their dependency closure get installed. Two constraints apply. The flags work only when useWorkspaces is true. And rush update rejects them, so this narrows the install and not the resolution.

It helps to know what ends up on disk. After a full install in this repo, common/temp holds 1.6 GB. The PNPM store accounts for 707 MB, node_modules for 344 MB, and Rush's own downloaded tooling under install-run for 336 MB. The rush-recycler cache adds 164 MB. About half a gigabyte is Rush managing itself.

The supply-chain age check

PNPM can refuse packages that were published too recently. Its setting is minimumReleaseAge, measured in minutes, added in PNPM 10.16.0 and defaulting to 1440 from PNPM 11. Rush surfaces the same value under its own key, minimumReleaseAgeMinutes. The check applies to transitive dependencies, not only the ones you name.

The reason to write the number down is where exemptions go. PNPM keeps them in minimumReleaseAgeExclude, inside pnpm-workspace.yaml. In a Rush repo that file is generated under common/temp, which is gitignored and dockerignored. An exemption added on one laptop therefore exists only on that laptop. Your local build keeps working while a fresh clone, CI, or docker compose build fails lockfile verification.

When you need a release younger than the window, the install fails with ERR_PNPM_MINIMUM_RELEASE_AGE_VIOLATION. Pin an older release, or add a deliberate exemption. Disabling the policy to land one patch is the third option, and the wrong one.

Bulk, global, and phased commands

common/config/rush/command-line.json defines six custom commands here. Three are bulk, which means they run once per selected project in dependency order: lint, typecheck, and seed. Three are global, which means they run once for the repo: dev, format, and format:check.

dev has to be global. Bulk commands buffer every project's output until that project finishes, and two watch servers never finish. The global command runs a small script that spawns each server in its own process group. Signalling the group tears down the whole rushx chain. Signalling only the leader leaves a server holding its port.

Rush also supports phased commands, which this repo does not use. Instead of one build script per project, a project declares operations such as _phase:build and _phase:test, along with their inputs and outputs, in rush-project.json. Phases are what make operation-level caching possible, because the cache key is computed per operation. A project can then cache its build and not its test. If you move to the build cache, this is the file you edit.

Subspaces are the other scaling feature this repo leaves switched off. A subspace is a group of projects with its own lockfile and its own install. That is how a monorepo of a few thousand projects splits resolution work by product group instead of resolving one enormous graph on every install. Cross-subspace dependencies are injected rather than symlinked in some configurations, so enabling subspaces is a deliberate step.

Selection: build only what changed

This is the half of Rush that keeps CI honest. Pass a project selector and Rush builds a subset.

rush build --to @hotel/web                 # @hotel/web and everything it depends on
rush build --from @hotel/shared-types      # shared-types, its dependents, and their dependencies
rush build --only @hotel/ui                # exactly this project, dependencies ignored
rush build --from git:origin/main          # everything this branch changed, closed over

Four properties matter if you build automation on top of these.

Selectors combine as a union. The same flag can repeat, as in --only a --only b, and no flag removes projects from the selection. To narrow a set, narrow the inputs.

Three flags are documented as unsafe: --only, --impacted-by, and --impacted-by-except. Each can select a project without its dependencies, so the build fails if those dependencies were never built. They suit a CI stage that runs after a build stage. They are the wrong choice for a cold checkout. --from is the safe form, because it pulls in the dependencies of everything it selects.

--from changed meaning in Rush 5.38.0. Today it means the project, its dependents, and the dependencies of that whole set. Before 5.38.0 it behaved like --impacted-by and added no dependencies, so older documentation describes the older behaviour. The git:<ref> selector is newer still, added in Rush 5.57.0.

There is no --from-except. Only --to-except and --impacted-by-except exist. --from-except shows up in generated CI configs and blog posts that were never run.

A selector can be a project name, . for the current project, git:<ref>, subspace:<name>, or tag:<name>. For the Git form, the documented shape is a single ref such as git:origin/main. Rush resolves it, and falls back to git merge-base -- HEAD <ref> when the ref is not a full commit hash. I found no documented support for a triple-dot range such as git:origin/main...HEAD, so I would compute the base commit and pass the hash.

Incremental builds, and the assumption under them

rush build is incremental and cannot be made otherwise. It hashes each project's inputs and skips any project whose inputs are unchanged since the last successful build. That state lives in <project>/.rush/temp/package-deps_build.json. It is why this repo's make clean deletes those files: the state tracks source hashes, not whether output still exists.

With the cache disabled, Rush uses output preservation. If no inputs changed, Rush assumes the output on disk is still correct. Delete apps/api/dist by hand and the assumption is now false. rush build reports everything up to date, emits nothing, and the failure surfaces later as a missing file at runtime.

The two build commands differ in a way that catches people out.

rush build reads from the cache and writes to it.
rush rebuild cleans and rebuilds every project. It does not read from the cache, and it does write to it.

So a CI job running rebuild gets no cache benefit while still filling the cache that other jobs read. If you adopt the cache, move CI to rush build. You can turn writes off with RUSH_BUILD_CACHE_WRITE_ALLOWED=0.

The cache itself is still documented as experimental, and it is off in this repo. That is why no build-cache directory exists under common/temp.

Rush and PNPM are not installed globally. mise.toml pins the toolchain for developers. Where mise is unavailable, as in the Dockerfiles, common/scripts/install-run-rush.js reads rushVersion from rush.json and runs that release.

Versioning and publishing

Every project here sets shouldPublish: false, and version-policies.json is empty, so this repo has no common/changes/ directory and publishes nothing. In a repo that does publish, rush change writes a per-change file, and rush change --verify fails CI when a public package changed without one. rush version --bump then applies the bumps, and rush publish writes the changelogs.

What the numbers look like

I built a fixture and measured it, because estimating would have been worse than saying nothing.

The fixture is this repo's working tree plus 400 generated packages. Each generated package has a package.json, a tsconfig.json, and six source files. The result is 407 registered projects and 3,328 tracked files, committed to a local bare remote of 1.7 MB, 5,785 objects, and a 1.32 MiB pack. The machine was an 8-core arm64 laptop running Node 24.22.0, Rush 5.181.0, and PNPM 11.5.1.

One setup detail cost me a wasted run. A bare remote needs uploadpack.allowFilter=true. Without it, git clone --filter=blob:none prints warning: filtering not recognized by server, ignoring and quietly produces a full clone. Every blobless number below comes from after that setting.

Baseline is a plain git clone plus a full rush rebuild. Optimised is git clone --filter=blob:none --sparse with a cone-mode sparse set, plus selection-based builds. Timings are nearest-rank percentiles over the stated run count.

Two caveats before you read the numbers. The clones run over file:// on one machine, so they measure Git's object and tree work rather than network transfer. Across a real network a sparse clone would look better than it does here, because the bytes it skips are the expensive part. And these measurements use three different trees, so each table names the tree it came from.

Checkout, measured on a cone-mode sparse set applied to a blobless clone:

Metric	Baseline	Optimised
Clone and sparse set (p50, n=10)	284 ms	160 ms
`.git` on disk	1,824 KiB	920 KiB
Files on disk	3,328	85
`git status` (p50 / p95, n=50)	76 / 80 ms	13 / 14 ms

Install and build. The install rows compare a full clone against the install-capable sparse tree, which holds 513 files and every package.json. Both build rows ran in the full clone.

Metric	Baseline	Optimised
Cold `rush update` (p50, n=5)	46.5 s	41.9 s
`common/temp` after install	1.20 GiB	1.20 GiB
Build after a leaf-file change	24.60 s, 407 projects	1.04 s, 4 projects
Build after a hub-file change	24.60 s, 407 projects	24.74 s, 401 projects

Four things in those tables are worth reading twice.

Blobless clone on its own is worse than useless

--filter=blob:none alone cloned in 341 ms with a 1,972 KiB .git, both worse than the plain clone. Checking out HEAD materializes every blob anyway, and the deferred ones land in a second, unconsolidated pack. The win comes from the sparse set that stops those files being written.

Order matters too. Applying the sparse set after a blobless clone cost 561 ms and left the largest .git of any mode at 1,988 KiB. Cloning with --sparse first and setting the cone afterwards took 160 ms and half the .git size.

The `git status` number is the one you feel

76 ms to 13 ms. Git stops checking 3,242 files that are no longer on disk. That is the difference between a command you do not notice and one you do.

The install does not move

Sparse checkout cut the working tree by 97% and changed install time by nothing.

Read the spread rather than the median. The full clone gave 39.6, 41.8, 46.5, 49.3, and 52.9 seconds. The install-capable sparse tree gave 36.6, 40.5, 41.9, and 60.1 seconds, plus one run that stalled at 330.6 seconds on the registry. Two of the five sparse runs beat every full-clone run, and the sparse distribution is the wider one. That is what "no effect" looks like when the metric is dominated by network and registry latency: the two distributions overlap, and neither ordering is stable. common/temp came out identical at 1.20 GiB.

Install cost is the resolution and materialization of the dependency graph, and that graph is the same however many project folders you checked out. The three failures in the previous section explain why you cannot sparsify your way out of it.

Selection depends on the graph, not on the flag

A file with no dependents rebuilt 4 projects in 1.04 s, which is 4% of the full 24.60 s. A file in a package that 400 others depend on rebuilt 401 projects in 24.74 s, which is a full rebuild plus bookkeeping.

Selection is not magic. It is a function of your dependency graph, and a widely-used shared package will always be expensive to change. The fixture wires all 400 generated packages downstream of that hub package, so the second row is the worst case rather than a rigged one.

The cache was off in this fixture, and a grep across all 30 build and install logs found no cache messages. The only skip reported anywhere came from Rush's incremental change detection.

CI and CD

The workflow this repo commits

The repo commits one workflow, and its ordering encodes two lessons. From .github/workflows/ci.yml:

name: CI
on:
  push:
    branches: ["main"]
  pull_request:
    branches: ["main"]
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 2
      - uses: actions/setup-node@v4
        with:
          node-version: 22
      - run: node common/scripts/install-run-rush.js install
      - run: node common/scripts/install-run-rush.js format:check
      - run: node common/scripts/install-run-rush.js lint
      - run: node common/scripts/install-run-rush.js typecheck
      - run: node common/scripts/install-run-rush.js rebuild --verbose
      - run: git diff --exit-code -- apps/web/src/routeTree.gen.ts

The first lesson is that typecheck runs before build. Vite does not type-check during build, so rush build can succeed on a web app that has type errors in it. Type-checking has to be its own pass.

The second lesson follows from the first. Because typecheck runs before anything is built, apps/web/src/routeTree.gen.ts has to be committed. TanStack Start's Vite plugin generates that file, and src/router.tsx imports it, but at typecheck time nothing has produced it yet. The last step re-runs the generator during the build and diffs the file, so drift fails the job.

Two smaller choices are deliberate. CI uses rebuild rather than build, because it wants a clean full build. And --production is absent, because it is not a built-in Rush parameter. rush.json does not declare it, and Rush 5 rejects the whole command with Unrecognized arguments: --production.

This job installs everything and builds everything, so it scales linearly with the repo. That is the right default at this size. The next section is the shape it should take at a large one. None of it is implemented in this repository.

The workflow a large repo wants

In a large repo the checkout is the first thing to shrink.

What follows is a design, not a pipeline I have run. I tested the pieces it depends on: the selectors, the filtered install, and the cache behaviour. I did not run this workflow end to end, so treat it as something to verify rather than a recipe.

The rule that matters most is to use one selector for every step. If the checkout materializes a different set of projects than the later steps select, some step ends up running against a project that is not on disk.

Call the file ci-affected.yml:

- uses: actions/checkout@v4
  with:
    fetch-depth: 0
- run: git fetch origin main
- run: npx sparo-ci checkout --from git:origin/main
- run: node common/scripts/install-run-rush.js install --from git:origin/main
- run: node common/scripts/install-run-rush.js format:check
- run: node common/scripts/install-run-rush.js lint --from git:origin/main
- run: node common/scripts/install-run-rush.js typecheck --from git:origin/main
- run: node common/scripts/install-run-rush.js build --from git:origin/main
- run: git diff --exit-code -- apps/web/src/routeTree.gen.ts

Some choices there are deliberate. It uses --from rather than --impacted-by, because --impacted-by selects dependents without their dependencies and fails on a cold worker. It keeps typecheck before build. It uses build rather than rebuild, because only build reads the cache. And it uses a filtered install, which is the one place install time drops.

Four parts of that snippet I have not verified, and would check first on a real repo.

fetch-depth: 0 fights the point of a sparse checkout. Selection needs the merge-base, and that needs the base commit locally, so a shallow clone breaks it. Cloning full history is the blunt fix. --filter=blob:none makes that history cheap, or you can fetch just the target branch.
format:check cannot be scoped. It is a global command, so it takes no selector, and on a sparse tree it only sees the files that were materialized. It quietly checks less than it did before.
The route-tree drift check only means something when the web app is in the selected set. On a change that does not touch apps/web, that step compares a file nothing regenerated.
The install step may fail outright. This is the constraint from the previous section: Rush needs every registered project's package.json on disk. If the checkout does not keep them, the install fails however the rest is arranged.

Four ways affected selection goes wrong

A shallow clone breaks merge-base. This repo's fetch-depth: 2 is enough for its route-tree diff and not enough for selection. Rush resolves a git:<ref> selector through Git, and falls back to git merge-base -- HEAD <ref> when the ref is not a full commit hash. That needs the base commit locally. On a shallow clone the merge-base is missing, or it is the shallow boundary rather than the real one. Either way the selected set silently comes out too small.
A stale cache returns wrong output. With buildCacheEnabled: true, cache restoration replaces output preservation, so a project's output folders are always replaced by a restore. The cache key covers the project's source files, plus the sources of its workspace dependencies. It also covers the versions of external npm dependencies including indirect ones, and the Rush command-line parameters. It does not cover environment variables or the Node.js version unless you opt in through operationSettings[].dependsOnEnvVars and dependsOnNodeVersion. No documentation states that Rush's or PNPM's own version invalidates an entry. A build that reads process.env.SOMETHING will therefore restore wrong output with full confidence. Rush's docs point at rush-audit-cache-plugin for finding inputs you failed to declare.
Cache reads and writes go the wrong way round. isCacheWriteAllowed defaults to false in the cloud provider blocks. The documented pattern is read-only on developer machines and RUSH_BUILD_CACHE_WRITE_ALLOWED=1 on CI. Get it backwards and developer machines overwrite entries while CI reads nothing useful.
Flaky tests block merges. A test that fails one run in twenty is worse than a slow test, because it teaches people to re-run until green. Select affected projects for speed, but do not make the merge decision depend on a suite you do not trust.

Do not copy this repo's Node pinning

This repo runs three different Node versions and gets away with it. mise.toml pins Node 24 for developers. The workflow above uses Node 22. Both Dockerfiles use node:22-slim.

It works because rush.json enforces a range rather than an exact version, and all three sit inside it. It also works because Rush and PNPM are pinned exactly, so the tool that resolves the graph is the same everywhere.

I would not copy it. A range is a tolerance, not a pin, and the failure mode of drift is a build that reproduces on one machine and not another. Pick one Node version and use it in all three places.

Deployment

Deployment starts from the same problem. Two of the seven projects produce runnable artifacts, and both need the shared workspace to build.

The Dockerfiles are single-stage and deliberately pragmatic. Each is node:22-slim, copies the whole repo, installs with the install-run bootstrap, and builds one selection. From apps/api/Dockerfile:

FROM node:22-slim
WORKDIR /repo
COPY . .
RUN node common/scripts/install-run-rush.js install
RUN node common/scripts/install-run-rush.js build --to @hotel/api
CMD ["node", "dist/main.js"]

The web variant carries one detail worth copying to any Vite app. VITE_API_URL is a build ARG and not a runtime environment variable, because Vite inlines import.meta.env.* at build time. Server-side rendering needs a different URL from the browser. It runs inside the container, so it must reach the API by its Compose service name rather than localhost.

The single-stage choice trades image size for reliability. Rush and PNPM link packages through symlinks into common/temp, and copying a pruned subset of that layout across build stages is fragile.

rush deploy is the improvement, and it is not implemented here. It copies the selected project plus its regular dependencies, npm packages and local projects alike. Dev dependencies are left out by default. Its config lives at common/config/rush/deploy.json, which you create with rush init-deploy --project <name> and commit. Output goes to common/deploy/ unless you pass --target-folder, and --create-archive produces a zip.

The setting that needs care is linkCreation, which accepts default, script, or none. With script you get a create-links.js to run on the server, and node_modules/.bin binaries are not generated yet.

Per-app pipelines fall out of the same selection machinery as CI. Trigger the API pipeline when --from says the API's closure changed, and the web pipeline on the same test for the web closure.

Ordering is where deployment differs from affected selection. Migrations run before the API deploy, because the new code expects the new schema. The API deploys before the web app, because the running frontend may be calling an API one version ahead of it or behind it. Keep that skew window as short as your API contract tolerates.

How Rush and Sparo compare with the alternatives

	Rush + Sparo	Nx / Turborepo	Bazel / Buck2	Scalar, Sapling / EdenFS
Unit of work	A project in `rush.json`	A workspace project	A `BUILD` file target	A path in the repo
Caching	Per-project hashing	Task hashing, remote cache	Content-addressed, sandboxed	None
Git scaling	Sparse checkout and blobless clone	Not addressed	Not addressed	The whole point
Migration cost	Register packages, one lockfile	A config file, keep your layout	Highest: build files, hermetic tools	Low for Scalar; a new client for Sapling

Nx and Turborepo optimise task execution on top of whatever install you already have. Turborepo explicitly does not manage dependencies. Bazel and Buck2 buy isolation and reproducibility at the cost of modelling every build. Buck2's own list of ready languages runs to nine, including Haskell and OCaml.

Scalar, Sapling, and EdenFS attack Git's I/O rather than your build graph, and Scalar ships inside upstream Git. That group combines with any of the first three, since a sparse checkout is orthogonal to how you cache builds.

When not to use this

Skip all of it if your repo holds a few dozen packages and one team.

The setup cost is real. You maintain a registry, agree on a lockfile policy, keep profiles current, and understand cache invalidation. Below that size the tooling is a bigger maintenance surface than the problem.

Rush is the wrong pick if you are not on the npm family of package managers, because its value is owning the npm lockfile and install strategy.

Skip the build cache until your projects declare accurate inputs and outputs. A cache that restores wrong output is worse than no cache, because it is fast and wrong. Skip it as well if your builds touch state outside the project folder, or read environment variables you have not declared in rush-project.json.

Skip sparse checkout if the whole repo already checks out in a second, or if your tools assume a full tree and you cannot change them. The docs/ and .github/ experience above is what that looks like, and it applies to search tools and CI scripts as much as to IDEs.

Finally, do not adopt any of this on instinct. If you cannot state your current clone time, install time, and CI p50, you cannot tell whether a change helped.

How to measure your own repo

Baseline with four commands:

du -sh .git                  # total Git footprint on disk
git count-objects -vH        # object count and pack size
git ls-files | wc -l         # index size: what Git knows about
time git status              # the command you run a hundred times a day

count-objects -vH prints in-pack and size-pack, which are the numbers that move when you go blobless. Compare git ls-files | wc -l against the files actually on disk to see what a profile removed.

Then measure the two things those commands cannot tell you. Time an install cold and warm. Count the projects selected for the change you make most often. Run each timing five times and report p50 and p95 rather than the mean. Clone and install times are heavy-tailed, so the mean is dominated by whichever run was slow.

What this tutorial does not cover

The Dockerfiles are single-stage, and rush deploy is a follow-up rather than implemented. The build cache is off, so nothing here exercises cache keys. There is no Bazel or Buck2 depth, and that table row summarises each project's own documentation.

The two Sparo profiles were written by hand, their $schema URL does not resolve, and neither has been verified with sparo init-profile. CI installs and rebuilds everything. Subspaces and phased commands are not configured. And there is no real test suite: every test script is echo "no tests". The flaky-test failure mode is therefore described from the design side rather than observed.

Resources

Rush, with project selection and the build cache
rush deploy and deploy.json
Sparo, with the profile schema
Git: sparse checkout, partial clone, and Scalar
PNPM settings, including minimumReleaseAge
Github

A type-safe decision engine in ReScript

Piyush Chauhan — Sat, 10 Oct 2026 07:27:05 +0000

How a ReScript + Next.js demo runs experiments and rules through one validated registry, with deterministic bucketing and provenance on every decision

Experiments, rules, and preview overrides through one validated registry, with provenance on every value.

TL;DR: every variant question becomes one typed decision computed on the server, and every value remembers which layer wrote it. Two experiments cannot write the same field without failing loudly. The engine lives in the Elsewhere demo, a hotel site built with ReScript and Next.js.

Why stringly-typed flags keep breaking

A flag pipeline starts cheap. Someone puts hero_layout = split in a config table, reads it inside a component, and branches on the string. Nothing connects the read to the write: no compiler knows that splti is a typo, and no test knows which component owns the field.

Then the experiments multiply. Two of them write the same field, one silently wins, and the bug report says the hero layout flips between requests. Six months later nobody can answer why a particular visitor saw compact, because the value is in a payload and the reason is nowhere. One registry plus one resolve step fixes both problems, and that is the rest of this post.

The demo: Elsewhere, a hotel site with experiments baked in

I built the demo to test one architecture end to end. It is a hotel and destinations site that renders on the server, reads a SQLite catalog, and ships a development-only inspector. Everything runs from the Elsewhere repository.

The code is split three ways, and the dependency direction is the point:

The engine owns seven decision paths. Nothing else is adjustable, by design:

Path	Type	What it changes
`hero.layout`	`immersive` / `split`	hero image treatment
`search.layout`	`overlay` / `inline`	destination search placement
`destinationCard.layout`	`image` / `compact`	which card component renders
`destinations.columns`	`three` / `two`	destination grid density
`offers.visible`	`bool`	seasonal offers section
`planningGuide.visible`	`bool`	"PLAN AHEAD" prompts
`planningGuide.detail`	`brief` / `expanded`	prompt wording

Those seven fields are the only surface any experiment or rule may touch, which is what keeps ownership checkable.

The request path, end to end

A request crosses four layers before any HTML exists. The proxy handles identity, the page route validates and calls the engine, the engine returns a snapshot, and the ReScript React shell renders from that snapshot without re-deriving anything.

sequenceDiagram
    participant B as Browser
    participant P as Next proxy (middleware)
    participant R as Page route
    participant C as @repo/core (decision engine)
    participant U as @repo/ui (ReScript React)
    B->>P: GET / (no visitorId)
    P->>P: verify or issue signed cookie
    P-->>B: Set-Cookie: visitorId=<uuid>.<hmac>
    B->>R: GET / (with cookie)
    R->>C: resolveDecisionRequest(page, search, visitorId, rollout)
    C->>C: validate search, build context, allocate, resolve
    C-->>R: decisionSnapshot {values, provenance, assignments, ignored}
    R-->>U: snapshot + catalog + search
    U-->>B: HTML (SSR)

Decisions are types, not strings

Every decision value is a variant. heroLayout is #immersive or #split, destinationCardLayout is #image or #compact, and a renderer that handles one case but not the other fails compilation. That property is why the engine is written in ReScript rather than TypeScript.

Strings still exist, but only at the wire boundary. @as("hero.layout") keeps the JSON key dotted while the value stays a variant, and @tag("source") labels every provenance case with the layer that produced it.

@genType type page = [#home | #destinations]
@genType type variant = [#control | #treatment]
@genType type country = [#IN | #US]
@genType type heroLayout = [#immersive | #split]
@genType type searchLayout = [#overlay | #inline]
@genType type cardLayout = [#image | #compact]
@genType type columns = [#three | #two]
@genType type guideDetail = [#brief | #expanded]

@genType @unboxed
type path =
  | @as("hero.layout") HeroLayout
  | @as("search.layout") SearchLayout
  | @as("destinationCard.layout") DestinationCardLayout
  | @as("destinations.columns") DestinationsColumns
  | @as("offers.visible") OffersVisible
  | @as("planningGuide.visible") PlanningGuideVisible
  | @as("planningGuide.detail") PlanningGuideDetail

@genType
type decisionValues = {
  @as("hero.layout") mutable heroLayout: heroLayout,
  @as("search.layout") mutable searchLayout: searchLayout,
  @as("destinationCard.layout") mutable destinationCardLayout: cardLayout,
  @as("destinations.columns") mutable destinationsColumns: columns,
  @as("offers.visible") mutable offersVisible: bool,
  @as("planningGuide.visible") mutable planningGuideVisible: bool,
  @as("planningGuide.detail") mutable planningGuideDetail: guideDetail,
}
// Sparse fields are read through the validated string-keyed boundary codec.
@genType
type decisionPatch = {
  @live @as("hero.layout") heroLayout?: heroLayout,
  @live @as("search.layout") searchLayout?: searchLayout,
  @live @as("destinationCard.layout") destinationCardLayout?: cardLayout,
  @live @as("destinations.columns") destinationsColumns?: columns,
  @live @as("offers.visible") offersVisible?: bool,
  @live @as("planningGuide.visible") planningGuideVisible?: bool,
  @live @as("planningGuide.detail") planningGuideDetail?: guideDetail,
}
@genType @tag("source")
type provenance =
  | @as("default") Default({})
  | @as("rule") Rule({id: string})
  | @as("experiment") Experiment({id: string, variant: variant})

The codec helpers below that block (cast, dictionary, objectValue, fail, defaults, validPatchValue, and decodePatch) validate the wire boundary, rejecting unknown paths and out-of-range values before any write happens.

One registry owns every experiment and rule

Experiments and rules are declared as data, and createDecisionRegistry validates the whole declaration at module load. Duplicate IDs, overlapping allocations, and two owners claiming the same path all throw at startup, so a broken registry never serves a request.

@genType
let experiments: array<experiment> = [
  {
    id: "arrival-flow",
    surface: "arrival-flow",
    enabled: true,
    allocation: (0, 10000),
    owns: [HeroLayout, SearchLayout, DestinationCardLayout],
    variants: {
      control: {},
      treatment: {heroLayout: #split, searchLayout: #inline, destinationCardLayout: #compact},
    },
  },
  {
    id: "destination-density",
    surface: "destination-density",
    enabled: true,
    allocation: (0, 10000),
    owns: [DestinationsColumns],
    variants: {control: {}, treatment: {destinationsColumns: #two}},
  },
  {
    id: "planning-guide-detail",
    surface: "planning-guide-detail",
    enabled: true,
    allocation: (0, 10000),
    owns: [PlanningGuideDetail],
    variants: {control: {}, treatment: {planningGuideDetail: #expanded}},
  },
]

Ownership is the strictest of those checks, because it is the one that catches a logic mistake rather than a typo:

      let conflict = switch previous {
      | Some(previous) => surface == None || previous.surface != surface
      | None => false
      }
      if own(local, name) || conflict {
        let previousOwner = switch previous {
        | Some(p) => p.owner
        | None => owner
        }
        fail("Ownership conflict on " ++ name ++ ": " ++ previousOwner ++ " and " ++ owner)

That gives a registry with five members:

Kind	id	owns	effect
experiment	`arrival-flow`	`hero.layout`, `search.layout`, `destinationCard.layout`	treatment sets `split` / `inline` / `compact`
experiment	`destination-density`	`destinations.columns`	treatment sets `two`
experiment	`planning-guide-detail`	`planningGuide.detail`	treatment sets `expanded`
rule	`india-seasonal-offers`	`offers.visible`	when `seasonal-offers` flag AND country `IN`, sets `offers.visible = true`
rule	`planning-guide-flag`	`planningGuide.visible`	when `planning-guide` flag, sets `planningGuide.visible = true`

Allocation is (0, 10000) on each surface, which is the full slot range: one experiment owns the whole surface and every visitor lands on one of its two variants.

Deterministic assignment without server state

Assignment is a pure function of the visitor ID plus a namespace string. FNV-1a hashes the concatenation, and the result is reduced twice: modulo 100 picks the 50/50 variant, modulo 10000 picks the slot inside a surface. There is no assignment table, no database write, and no session store.

That makes assignment replayable. The same cookie and the same experiment ID produce the same variant on a cold server, after a restart, or behind a different edge node, which is the whole reason the engine can run without shared state.

/** FNV-1a hashes JavaScript UTF-16 code units, including surrogate pairs. */
@genType let fnv1a32 = (input: string): float => {
  let hash = ref(-2128831035)
  for index in 0 to length(input) - 1 {
    hash.contents = imul(Int.bitwiseXor(hash.contents, charCodeAt(input, index)), 16777619)
  }
  unsigned(hash.contents)
}
@genType
let bucketForExperiment = (visitorId: string, experimentId: string): int =>
  Int.fromFloat(fnv1a32(visitorId ++ ":" ++ experimentId) % 100.)
@genType
let bucketForSurface = (visitorId: string, surface: string): int =>
  Int.fromFloat(fnv1a32(visitorId ++ ":surface:" ++ surface) % 10000.)
@genType
let assignVariant = (visitorId: string, experimentId: string): DecisionTypes.variant =>
  if bucketForExperiment(visitorId, experimentId) < 50 {
    #control
  } else {
    #treatment
  }

The four externals and the unsigned helper I left out of that snippet bind Math.imul, charCodeAt, length, and the >>> 0 unsigned coercion.

Both reductions are visible in the geometry of one experiment:

Slots live in a sorted table per surface, so picking one is a binary search:

let findSlot = (table: array<Experiments.experiment>, bucket) => {
  let low = ref(0)
  let high = ref(Belt.Array.length(table))
  while low.contents < high.contents {
    let middle = (low.contents + high.contents) / 2
    let (start, _) = Belt.Array.getUnsafe(table, middle).allocation
    if start <= bucket {
      low.contents = middle + 1
    } else {
      high.contents = middle
    }
  }
  if low.contents == 0 {
    None
  } else {
    let candidate = Belt.Array.getUnsafe(table, low.contents - 1)
    let (_, end) = candidate.allocation
    if bucket < end {
      Some(candidate)
    } else {
      None
    }
  }
}

The whole path from cookie to variant is one hash, one modulo, and one comparison:

flowchart LR
    A["visitorId + ':surface:' + surface"] --> B[FNV-1a 32-bit hash]
    B --> C["mod 10000 → bucket 0..9999"]
    C --> D[Binary search sorted allocation table]
    D --> E[Experiment owns that slot]
    E --> F["bucket % 100 < 50 → control / treatment"]

Rules: eligibility and effects

Experiments and rules both end in a patch, but they get there differently. An experiment is picked by slot and then filtered by eligibleWhen, so a visitor can be in a slot and still hold out. A rule has no allocation at all: its when clause is the entire gate, and it applies whenever that clause is true.

@genType @tag("type")
type rec rule =
  | @as("all") All({rules: array<rule>})
  | @as("any") Any({rules: array<rule>})
  | @as("not") Not({rule: rule})
  | @as("eq") Eq({@live field: string, value: country})
  | @as("flag") Flag({name: string})

Predicates are data too, and the demo ships two of them:

@genType
let ruleEffects: array<ruleEffect> = [
  {
    id: "india-seasonal-offers",
    owns: [OffersVisible],
    when_: All({
      rules: [Flag({name: "seasonal-offers"}), Eq({field: "visitor.country", value: #IN})],
    }),
    patch: {offersVisible: true},
  },
  {
    id: "planning-guide-flag",
    owns: [PlanningGuideVisible],
    when_: Flag({name: "planning-guide"}),
    patch: {planningGuideVisible: true},
  },
]

decodeRule validates the JSON shape of a predicate and rejects unknown fields, flags, and rule nodes, so evaluateValidatedRule can walk the tree without re-checking anything.

Resolution and provenance

Resolution applies patches in a fixed order: the selected experiments first, then the rules. A second write to the same path is a failure, not an overwrite, and every write records its source. That pairing is what makes the inspector honest, because a value and its reason are produced by the same line of code.

  let apply = (patch: decisionPatch, owner, source) => {
    // Registry codecs have validated these wire keys and values already.
    let entries: array<(string, unknown)> = Dict.toArray(cast(patch))
    entries->Belt.Array.forEach(((name, value)) => {
      switch getOwn(writtenBy, name) {
      | Some(previous) =>
        fail("Decision write conflict on " ++ name ++ ": " ++ previous ++ " and " ++ owner)
      | None => ()
      }
      Dict.set(writtenBy, name, owner)
      switch decodePath(cast(name)) {
      | HeroLayout =>
        values.heroLayout = cast(value)
        provenance.heroLayout = source
      | SearchLayout =>
        values.searchLayout = cast(value)
        provenance.searchLayout = source
      | DestinationCardLayout =>
        values.destinationCardLayout = cast(value)
        provenance.destinationCardLayout = source
      | DestinationsColumns =>
        values.destinationsColumns = cast(value)
        provenance.destinationsColumns = source
      | OffersVisible =>
        values.offersVisible = cast(value)
        provenance.offersVisible = source
      | PlanningGuideVisible =>
        values.planningGuideVisible = cast(value)
        provenance.planningGuideVisible = source
      | PlanningGuideDetail =>
        values.planningGuideDetail = cast(value)
        provenance.planningGuideDetail = source
      }
    })
  }

A snapshot for one visitor (country IN, offers=on, arrival-flow treatment) carries a badge per value:

The order of operations, from query string to provenance:

flowchart LR
    A[Search params] --> B[validatePageSearch allowlist]
    B --> C[Rule context: country + flags]
    C --> D[Allocate: one experiment per surface]
    D --> E[Resolve: apply experiment then rule patches]
    E --> F[decisionSnapshot]
    F --> G["provenance: default | rule | experiment"]

Preview overrides and the decision inspector

In development, query params force a variant: ?exp.arrival-flow=treatment. The allowlist decides what survives the trip. An unknown key is dropped, and a recognized key whose value is malformed stays in the snapshot as a diagnostic instead of disappearing.

let recognized = key =>
  key == "country" ||
  key == "offers" ||
  key == "guide" ||
  (String.startsWith(key, "exp.") &&
  own(Experiments.decisionRegistry.experimentById, String.slice(key, ~start=4)))

@genType
let validatePageSearch = (input: unknown, production: bool): pageSearch => {
  let result = dictionary()
  switch JSON.Decode.object(cast(input)) {
  | None => ()
  | Some(source) =>
    Dict.toArray(source)->Belt.Array.forEach(((key, value)) => {
      if (
        (key == "destination" || (!production && recognized(key))) && Type.typeof(value) == #string
      ) {
        Dict.set(result, key, (cast(value): string))
      }
    })
  }
  result
}

The decision inspector is a development-only panel that prints every resolved value next to its provenance, lists ignored overrides with their raw values, and lets you flip country, flags, and individual experiments to re-render the page from a new snapshot.

Signed cookies and rollout flags

Identity is a cookie. In production that cookie is a UUID plus an HMAC-SHA-256 signature: the edge proxy verifies it or issues a new one, and the page verifies it again before the engine sees it. A missing, tampered, or unsigned cookie is treated as no identity at all.

function signature(id: string, secret: string): string {
  return createHmac("sha256", secret).update(id).digest("hex");
}
export function visitorFromCookie(
  cookie: string | undefined,
  secret: string | undefined,
): string | undefined {
  if (!secret) {
    return cookie && uuid.test(cookie) ? cookie : undefined;
  }
  if (!cookie) {
    return undefined;
  }
  const match = /^([0-9a-f-]{36})\.([0-9a-f]{64})$/.exec(cookie);
  if (!match || !uuid.test(match[1])) {
    return undefined;
  }
  return timingSafeEqual(
    Buffer.from(match[2], "hex"),
    Buffer.from(signature(match[1], secret), "hex"),
  )
    ? match[1]
    : undefined;
}
export function visitorCookie(id: string, secret: string | undefined): string {
  return secret ? id + "." + signature(id, secret) : id;
}

I left out the uuid regex constant above those functions, which checks the UUID shape before any signature comparison happens.

Rollout configuration is validated the same way, eagerly, at startup. Flag values accept only on, empty, or absent, and anything else throws before the server listens:

function flag(name: string): boolean {
  const value = process.env[name];
  if (value !== undefined && value !== "" && value !== "on") {
    throw new Error(`Invalid ${name}: expected on or empty`);
  }
  return value === "on";
}

The disabled-experiment list and the production cookie secret get the same treatment, both inside readRollout:

export function readRollout(): rollout & { cookieSecret: string | undefined } {
  const known = new Set(experiments.map((experiment) => experiment.id));
  const disabledIds = [
    ...new Set(
      (process.env.DECISION_DISABLED_EXPERIMENTS ?? "")
        .split(",")
        .map((v) => v.trim())
        .filter(Boolean),
    ),
  ];
  for (const id of disabledIds) {
    if (!known.has(id)) {
      throw new Error(`Invalid DECISION_DISABLED_EXPERIMENTS: unknown experiment ${id}`);
    }
  }
  let cookieSecret: string | undefined;
  if (process.env.NODE_ENV === "production") {
    cookieSecret = process.env.DECISION_COOKIE_SECRET;
    if (!cookieSecret || Buffer.byteLength(cookieSecret, "utf8") < 32) {
      throw new Error("Invalid DECISION_COOKIE_SECRET: at least 32 UTF-8 bytes required");
    }
  }

seasonalOffers and planningGuide are read with the same flag() call, so both accept only absent, empty, or on, and off fails startup.

Crossing the ReScript ↔ TypeScript boundary

genType emits a .gen.tsx and a .res.mjs per module, which is how the RegistryCore and RegistryUi namespaces become importable TypeScript. @live, @as, and @tag carry the wire contract across, so the TS side sees the same strings the JSON has.

The React shell needs one context and one hook, and that is the entire UI-side integration:

type values = RegistryCore.DecisionTypes.decisionValues
let context: React.Context.t<option<values>> = React.createContext(None)

module Provider = {
  let make = React.Context.provider(context)
}

@react.component
let make = (~values: values, ~children) => <Provider value={Some(values)}> {children} </Provider>

let useDecision = (selector: values => 'a): 'a => {
  switch React.useContext(context) {
  | Some(values) => selector(values)
  | None => JsError.throwWithMessage("useDecision requires a DecisionProvider")
  }
}

A page component then reads a single field through that hook, and the field name is checked by the ReScript compiler:

    let heroLayout = DecisionProvider.useDecision(values => values.heroLayout)

One more adaptation lives in apps/web/src/server/page-data.ts, where resolved, assignments, and ignored are spread into plain objects before they cross into React Flight, because null-prototype dictionaries are not accepted there.

What this cost us

Registering an experiment is ceremony. Every new one needs an id, a surface, an allocation, an owns list, and both variants declared before anything renders. The registry catches the mistakes at startup, but I still wrote that record by hand each time.

ReScript is a second compiler and a second syntax. The OCaml heritage shows up in ref, Belt.Array, and pipe-first argument order, and the exhaustiveness that makes the engine safe also means every new path case touches every consumer.

The wire still speaks strings. hero.layout appears as a string in the JSON, in the serialized snapshot, and in the generated TypeScript types. The variants only exist after validation, which is a real cost in a codebase that is mostly TypeScript.

Identity is one sticky cookie. Anyone who clears cookies or switches devices is a new visitor to the engine, and there is no cross-device merge.

Nothing tracks exposure or conversions. The inspector panel states that in one line, and it is accurate: the engine assigns deterministically and reports provenance, and analytics are out of scope for the demo.

Run it yourself

Node >= 22.12 and pnpm 12.4.2 are the only prerequisites.

pnpm install
pnpm exec playwright install chromium
export DB_FILE_NAME=/absolute/path/to/catalog.db
pnpm db:setup
pnpm dev

Open http://127.0.0.1:3000, then try /?exp.arrival-flow=treatment&country=IN&offers=on to see a forced variant, a forced country, and the India offers rule land in the same snapshot. All of it is in the Elsewhere repository.

FAQ

Why ReScript instead of TypeScript?

Exhaustive matching turns an omitted variant into a compile error, and this engine leans on that harder than on any other language feature: a new path case cannot be added without updating every consumer. The typed core is roughly 900 lines of .res across eight modules, with the whole domain expressed as variants.

How do I add a new experiment?

Add one record to the experiments array in packages/core/src/Experiments.res with an id, a surface, an allocation, an owns list, and both variants. createDecisionRegistry validates it at startup: duplicate IDs, overlapping allocations, and paths already owned by another experiment or rule all throw before a request runs.

How do I kill an experiment in production?

Set DECISION_DISABLED_EXPERIMENTS to a comma-separated list of registered IDs. Unknown IDs reject startup, and the disabled set is applied to allocation as well as resolution, so a disabled experiment stops writing even if a preview URL forces it.

Does it track conversions or metrics?

No. The engine assigns a variant deterministically and records which layer wrote each value, and that is all it does. Exposure logging, conversion events, and analysis are out of scope for the demo, which the inspector says in one line.

How I Built a .NET + React Islands Site With a Playwright Snapshot Worker

Piyush Chauhan — Sat, 26 Sep 2026 15:35:31 +0000

Razor owns the pages. React owns a few interactions. A separate browser publishes the finished HTML before anyone visits.

The problem: interactive pages without browser rendering per visitor

Imagine opening /hotels/casa-aurelia. You should get the hotel's description, prices, photos, and metadata in the HTML response. Then, if you click a photo, a gallery dialog should open. A client-only SPA that initially sends a nearly empty shell makes discovery of that content depend on executing JavaScript; rendering Chromium on every visitor request would solve a different problem at a high operational cost. I wanted crawlable first-response HTML and hydration, without either compromise.

This project is a hotel-and-editorial site built with ASP.NET Core 10 Razor Pages, React islands, PostgreSQL, and a Node/Playwright worker. The public site has catalog pages, a journal, search, and a stay inquiry flow—not a reservation or payment system. Its editors have a separate protected React admin. The awkward part is that the public gallery and other islands begin as empty markers in Razor: rendering only Razor would ship the page content but not the gallery's initial HTML.

My solution was to move browser execution to publication time. Razor generates the source document; a background Chromium process mounts its islands, validates the result, and saves the completed HTML in PostgreSQL. The public gateway serves that saved document. The visitor's browser hydrates the already populated islands for interaction. This is browser-rendered React at publication time, not React server-side rendering in .NET, and Chromium is not on the visitor request path.

Who owns what?

The architecture becomes easier to reason about when each layer has a narrow job:

Owner	Responsibility	What it does not own
ASP.NET Core Razor Pages	Public routes, catalog and article content, HTML shell, SEO title, description, canonical links, structured data, and no-JS results	React's interactive state
React islands	Search/date controls, gallery, mobile navigation, and inquiry form interaction	Public-page routing or the catalog's canonical HTML
React Router admin	Protected editor UI behind the server's document guard and APIs	Public visitor routes
PostgreSQL	Catalog, snapshot jobs, and published HTML	Browser rendering
Playwright worker	Off-request source-page capture, validation, and conditional publication	Serving a visitor request

The distinction between content and controls matters. Razor writes the hotel heading, text, cards, and inquiry link. It also renders search results and GET-based sort/pagination so those remain useful without JavaScript. React adds the gallery dialog and richer search controls; those controls do need JavaScript. The admin is deliberately different: it is a protected React Router SPA, not an island pretending to be a public page.

This arrangement does not claim that every part of the site works without JavaScript. It makes the public content and search-results path independently useful while treating interactive controls as an enhancement with an explicit dependency.

One request, two paths

A canonical public GET or HEAD—such as /hotels/casa-aurelia, a journal post, or bare /search—passes through the snapshot gateway. It looks up a published HTML document by path. The document already includes Razor content and the gallery markup produced by Chromium. Only after delivery does the visitor's React runtime attach behavior.

flowchart LR
    V[Visitor GET /hotels/casa-aurelia] --> G[ASP.NET snapshot gateway]
    G --> P[(PostgreSQL published HTML)]
    P --> H[HTML with populated islands]
    H --> B[Visitor browser hydrates controls]
    Q[Filtered /search?..., inquiry, admin] --> L[Live private server route]
    L --> R[Dynamic response, not public snapshot]

The second path is not an exception hidden behind a cache key. /search?destination=... is a live Razor result page with private, no-store and noindex,follow; the bare /search page is eligible for a canonical snapshot. Inquiry pages and submissions, admin documents, and APIs are live/private, not publicly cached snapshot variants. This keeps a filter query, a guest's inquiry, or an editor session out of the shared HTML store.

The gateway returns persisted HTML with Cache-Control: public, max-age=60, stale-while-revalidate=300. That policy also sets the limit on how quickly an already cached public page may reflect an edit. It is a deliberate tradeoff, not instant global invalidation. In non-snapshot development mode the same canonical Razor pages can be served live; the diagram describes the snapshot-enabled delivery path.

How an island becomes published HTML

Razor emits an island marker and JSON props. Here is the gallery marker excerpt from the hotel page:

<div data-island="Gallery" data-hydrate="visible"
     data-props="@(PublicPageModel.IslandProps(new
     {
         name = hotel.Name,
         items = stay.Gallery.Select(x => new
         {
             x.Id, x.Src, x.Alt, x.Caption, x.Category, x.RoomId
         })
     }))"></div>

_Layout.cshtml likewise supplies the shared MobileNav marker and the runtime script, and owns the document's metadata. The marker above is empty in the Razor source. data-island selects a dynamically imported component; data-props passes its serialized input; data-hydrate="visible" tells the visitor runtime that it can defer mounting until near the viewport.

The worker visits /_snapshot-source?path=/hotels/casa-aurelia with a private token. That route only delegates to Razor for an allowed canonical path that has a job or an existing snapshot. On the protected source page, the island runtime mounts all markers, even normally deferred ones. When every mount's effect has run, it sets window.__SNAPSHOT_READY__. Playwright waits for that signal and for successful browser execution before serializing the document. It rejects invalid documents rather than saving a partial render: among its checks are the canonical path, exactly one h1, populated islands, the expected runtime script, absence of private-form inputs and the token, and browser errors. It also strips Chromium-added Vite modulepreload hints and inserts separators between adjacent island text nodes so the saved HTML can hydrate correctly.

On a visitor's request, this HTML already contains a gallery grid inside the same marker. The runtime checks whether the marker has child markup. Its essential choice is:

// Excerpt from the public island runtime; Ready wraps the imported component.
if (marker.children.length > 0) hydrateRoot(marker, createElement(Ready))
else createRoot(marker).render(createElement(Ready))

hydrateRoot attaches to captured markup; createRoot fills an empty marker in live Razor development or another uncaptured context. Visitors defer visible islands with IntersectionObserver (with a near-viewport margin), whereas the source-page capture mounts them eagerly. There is no duplicate static gallery fallback sitting beside a second React gallery. Without JavaScript, the published gallery grid and Razor text are still present, but its dialog and other island controls will not work.

This is testable as behavior, not just a design sketch. The repository's tests/e2e/hydration.spec.ts reads the published hotel HTML and finds one .gallery-grid inside the gallery island with no duplicate fallback. It then loads that hotel page, opens a gallery image, advances to the next photo, and closes the dialog. The static representation survives delivery and the same representation gains interaction.

Publishing without serving stale work

The worker needs a precise answer to a race: what if an editor changes the hotel again while Chromium is rendering the first change? Saving that first render afterward would roll the public page backward.

flowchart LR
    A[Admin edits catalog] --> T[One DB transaction: content + affected jobs]
    T --> J[(Versioned snapshot jobs)]
    J --> W[Worker leases due job]
    W --> S[Token-protected Razor source]
    S --> C[Chromium mounts islands; waits; validates]
    C --> X{Job version still current?}
    X -- Yes --> P[Atomic HTML upsert + job removal]
    X -- No --> D[Discard obsolete capture]
    P --> G[Gateway serves published HTML]

Admin publication and affected-route invalidation share a database transaction. Editing a hotel can affect its own route, destination listing/detail, bare search, home page, offer pages, and published articles that embed catalog records. Renames include old paths. A route that is no longer published loses its snapshot and job immediately rather than retaining discoverable old HTML. Valid published routes get a new desired job version.

The worker leases due jobs using PostgreSQL FOR UPDATE SKIP LOCKED, renders the protected Razor source in its own Chromium context, and checks the job's current desired version under a row lock before inserting/updating the HTML and deleting the job in one transaction. If the version changed, it discards the obsolete capture. Errors never publish a partial document: the job is released and retried with exponential backoff. The old complete snapshot stays available while a published route is regenerated.

A first-time canonical path has no old document to serve. If it is queued, the gateway responds 503 with Retry-After: 5 until publication succeeds. Unknown or unpublished detail routes return 404 (the fixed canonical index routes can return 503 while their first capture is pending). Once a route is unpublished or renamed, its persisted HTML is removed immediately; already cached copies may still live for the declared cache window, so publication and caching policy must be understood together.

What must never be snapshotted

The easiest way to leak private data would be to treat the snapshot store as a generic response cache. It is not. Its key is a canonical public path, not arbitrary combinations of query string, cookies, authentication, or POST body. Filtered search remains a live private response. The inquiry flow carries personal input, is CSRF-protected, and must remain private. Admin sessions and protected API responses cannot become public snapshots either.

The source endpoint is also a security boundary. It requires a sufficiently long SNAPSHOT_INTERNAL_TOKEN, compares the supplied header, rejects unrecognized canonical routes, sends private, no-store and noindex, and should be blocked from the public ingress as well. A robots directive alone would not secure it. The worker restricts fetched resources to its configured API origin and validates the captured result before publication; neither check replaces proper network isolation and authentication.

The worker rejects a source document containing password or antiforgery inputs, a token string, unexpected executable scripts, missing canonical metadata, unrendered islands, or browser errors. These checks are a publishing guard, not a blanket proof that arbitrary application data is safe to cache. The safe baseline remains an explicit allowlist of canonical public routes and a separate live/private path for anything user-specific.

The SEO advantage over a client-only SPA

A client-only SPA can be indexed by search engines that render JavaScript, but its initial HTML may contain little more than an app root. Content discovery then depends on fetching and running scripts successfully; rendering can be delayed or missed by crawlers that do not execute them. A React SPA with SSR or prerendering does not have that limitation by definition. The comparison here is with a client-rendered shell, not with all SPAs.

For a published hotel URL, a crawler's first HTML response already contains the hotel heading, description, links, gallery markup, and metadata. It does not have to execute React to discover the principal content. Razor provides the page-specific title and description, a canonical URL, and Hotel JSON-LD; the worker preserves these in the published document. The gateway generates robots.txt and an XML sitemap of catalog-published canonical routes. When a route is unpublished, it is removed from that sitemap and its stored HTML is deleted. Bare /search is canonical; filtered search URLs specify noindex,follow to avoid indexing many query variants. Inquiry and admin documents are marked noindex and are not public snapshots.

This is the outcome I like about prerender-then-hydrate designs: content is present before client JavaScript, while controls become interactive after hydration. It shares that goal with several other rendering strategies, but the publication boundary here is different.

The concrete SEO benefit is a more reliable path for crawlers to discover and assess canonical content, links, and metadata. It is not a guaranteed ranking. A sitemap is a discovery hint, structured data does not guarantee a rich result, and an indexable response does not mean a search engine will index or rank it. A newly queued URL returns 503 until its first capture; finish publication before directing production traffic or crawlers to it. After an origin change, wait for republishing and check the actual canonical URLs in the returned HTML: PUBLIC_ORIGIN is embedded at capture time. Old cached pages can also persist briefly after an edit or removal.

Lighthouse audits the loaded page, so it is not a direct test of what a no-JavaScript crawler sees. Here, the first response already exposes content and metadata, while off-request publication avoids Chromium work on the visitor path; both are useful starting conditions. But a high Lighthouse performance score is not automatic: React chunks, CSS, images, fonts, server/cache behavior, hydration work, and audit settings still affect LCP, CLS, and interactivity. Deferred visible islands and removal of captured modulepreload hints can reduce unnecessary upfront work. Audit the published snapshot, not Vite HMR: the repository documents make lighthouse-https for a local HTTPS run and pnpm lighthouse for its desktop preset. Compare repeated runs under the same conditions and inspect the generated HTML, canonical, robots directives, sitemap, and metric breakdown rather than quoting a single score.

How SSR, SSG, ISR, and PPR compare

These names describe when and where HTML is produced, not whether React can hydrate it. Each can ship crawlable HTML when configured to render the actual public content. The useful question for this site was where to pay the rendering cost and how to update edited catalog pages.

Strategy	When HTML is produced	Advantage	Cost or mismatch for this site
SSR (server-side rendering)	On each request, potentially cached afterward	Fresh request-specific data; a natural fit for personalized search or inquiry pages	Uncached rendering and data access add request-path work; cache keys and private data need care
SSG (static site generation)	At build time	Simple, fast delivery of already complete public HTML	Catalog edits or new pages need a rebuild and redeploy unless another regeneration mechanism is added
ISR (incremental static regeneration)	Initially prerendered or generated on demand, then regenerated after revalidation	Reuses HTML between visits and can refresh without rebuilding the whole site	Refresh policy and stale windows must be managed; in the common request-triggered model, regeneration waits for traffic
PPR (Partial Prerendering)	Static shell prepared ahead of time; dynamic regions render and stream at request time	Static content can arrive early while request-specific regions stay dynamic	More per-request rendering and cache-boundary coordination than these wholly public canonical documents need
This site's snapshot publication	Razor source plus browser-mounted islands captured for queued paths after catalog edits or release changes, then stored in PostgreSQL	Full public HTML and hydrated controls, with no Chromium on visitor requests; versioned jobs can target affected routes	Publication delay, first-capture `503`, and a database/browser worker to operate; private routes still need live rendering

The closest analogy is ISR's last-good HTML while regenerating, not an identical implementation. This worker queues affected paths when editors publish, captures eagerly in the background, and discards a capture if its desired version changed. It does not wait for a visitor or a timer to trigger regeneration. PPR is also not what happens here: the canonical snapshot is a complete shared document, not a static shell with request-time streamed islands. The project's filtered search and inquiry pages use live Razor responses instead. Next.js describes ISR's request-triggered revalidation and its static-shell/streamed-region model; those are comparisons, not dependencies of this .NET implementation.

Tradeoffs and running it

The split removes Chromium from visitor latency, but adds a publication pipeline. An edit can take time to appear; a new page waits for its first successful capture. If the worker, Chromium, or PostgreSQL is unavailable, publication stalls even while an old complete snapshot may continue to be served. No performance score or SEO result follows automatically from this architecture: measure the deployed site and audit the generated document.

Locally I use two different modes from the repository root:

pnpm dev             # Live Razor pages and Vite HMR; no snapshot worker required
pnpm dev:snapshots   # Build Vite manifest; run .NET plus continuous worker

The production-like mode needs PostgreSQL, a valid internal token, and Playwright Chromium; the quick-start guide covers initial setup and seeding. When it first starts, a new canonical route may answer 503 until captured. Failed jobs back off rather than writing half a page, so a persistent 503 is a reason to inspect worker logs and the snapshot job table—not to bypass the gateway.

Deploy the API and worker from the same release: the worker's asset fingerprint includes the Vite manifest, relevant source files, and PUBLIC_ORIGIN. A changed fingerprint requeues stored pages; changing PUBLIC_ORIGIN matters because canonical links are embedded in HTML. Keep old hashed assets available while old snapshots still reference them, and restart a running API after rebuilding its manifest in snapshot mode. Otherwise an intact stored page can point at a chunk the server no longer serves. These are operational costs of publishing HTML with client-side hydration rather than computing every page on demand.

The design rule I kept

Give the public document a single owner and give interactivity a clear boundary. Here, Razor defines the public page and its metadata; a background browser completes its islands once for publication; React hydrates that exact markup for visitors. The protected admin and user-specific flows never cross into the public snapshot store.

That buys immediately useful content without putting Chromium on each request. It also makes the limitations visible: editorial publication delay, a PostgreSQL-and-browser worker to operate, JavaScript required for controls, and strict route/cache/security boundaries. For this site, those are easier to reason about than making the entire public experience depend on a client-rendered shell or hiding a browser render in the request path.

Github: https://github.com/piyushchauhan2011/dotnet-islands

Integration Is the Product: Platform Engineering for Fast Frontend Teams

Piyush Chauhan — Sat, 12 Sep 2026 13:26:14 +0000

How local composition, exact artifacts, and versioned releases turn a dependency queue into continuous feedback.

The useful measure of team autonomy is not “can it publish?” It is “how quickly can the team see its exact change running in a realistic product?”

A frontend package can compile, test, and publish successfully—and still break when the product shell owns routing, authentication, server-side rendering (SSR), hydration, global styles, middleware, or a singleton dependency such as React.

I learned this through a familiar local loop:

build package → pnpm pack → edit the shell's package.json → install → run → discover → repeat

That loop proves the product boundary matters. It is also a warning: integration has become manual, temporary state leaks into committed files, and every cross-team change waits for the central application to catch up.

The answer is not “never integrate” or “put everything in a monorepo.” The answer is to make integration a self-service product capability: composable locally, verified as an artifact, previewable in a pinned product, and promotable as an immutable release.

The model in one picture

Each loop answers a different question. Do not make the slowest one the default.

Loop	Question it answers	Default use	Evidence it produces
1. Domain workbench	Does the domain feature behave correctly?	Daily implementation	Fast, deterministic UI and contract feedback
2. Local product composition	Does my source work in the real shell?	Host-boundary changes	Routing, auth, SSR, hydration, styling feedback
3. PR product preview	Does the artifact I intend to ship work in a clean product?	Before merge	A reviewable URL using exact candidate bytes
4. Versioned release	Is this selected composition healthy under shared and production conditions?	Promotion	Canary and operational evidence

The important design rule:

A failure found in an outer loop should improve the cheapest inner loop that can catch it next time.

Some failures—real traffic, irreversible migrations, external side effects—belong in the outer loop. Pretending every risk can move left makes local feedback less trustworthy, not more.

A concrete example: the Booking module

Assume a Booking frontend module mounted in a product shell. It calls Hotel APIs, participates in auth and routing, emits booking events, and server-renders UI.

Here are the four boundaries where its “green” isolated build can still fail:

┌──────────────────┐    ┌────────────────────────┐
│ Booking package  │───▶│ Product shell          │
│ exports, assets, │    │ routing, auth, SSR,    │
│ peer dependencies│    │ hydration, CSS         │
└────────┬─────────┘    └──────────┬─────────────┘
         │                         │
         ▼                         ▼
┌──────────────────┐    ┌────────────────────────┐
│ Domain contracts │    │ Environment and data   │
│ Hotel API/events │    │ pinned versions, known │
│ errors, timing   │    │ scenarios, replayable  │
└──────────────────┘    └────────────────────────┘

1. Build and artifact boundary

Local linking can succeed while the published tarball omits types, CSS, assets, compiled files, or an export-map entry. It can also hide bad peer-dependency resolution.

A particularly expensive frontend failure is a duplicate singleton: Booking bundles React or framework context while the shell expects to own it. Hooks, contexts, or hydration then fail only in the composed application.

Design consequence: support fast local source overrides and install the packed artifact into a clean shell in CI. Neither replaces the other.

2. Platform and runtime boundary

A mini-shell is valuable, but it is not the product. The real shell may resolve auth asynchronously, evaluate flags on the server, enforce CSP, select locale, apply global CSS, and hydrate browser markup.

A synchronous fake user can hide a lifecycle race. A window access can pass locally and fail during SSR. A global selector can quietly override the product header.

Design consequence: teams need an easy route into the real shell before release—not a handoff to another team.

3. Domain contract boundary

Types alone are not contracts. A Hotel response can make a field optional; an event can be duplicated; a shared package can preserve its type while changing runtime behavior.

Design consequence: contracts need examples for absence, errors, timeout, compatibility, and delivery semantics. Providers own valid fakes; consumers own the assumptions they make.

4. Environment and data boundary

A shared environment called dev is usually a location, not a reproducible state. It may contain yesterday’s Hotel build, today’s Payment candidate, mutable test accounts, and an unannounced shell change.

Design consequence: pin composition and provide named, deterministic data scenarios. A passing result should be attributable; a failure should be replayable.

The paved road: two local modes, one composition contract

The productive workflow is not “fake everything” versus “run the whole company.” It offers two intentionally different local modes.

Mode A: domain workbench

The workbench mounts local Booking with the production rendering mode, design system, route contract, and named auth, flag, and locale scenarios. Hotel and Payment provide provider-owned fakes; inexpensive, behaviorally important infrastructure can run locally.

platform dev

Use it for focused work. It should be fast, deterministic, usable offline where practical, and let an engineer select success, absence, timeout, error, and authorization states in one command.

Mode B: real-shell composition

The real product shell runs with released dependencies while the developer substitutes only local Booking source and gets HMR.

platform dev --composition=integration-stable

Use it when the change touches routing, auth lifecycle, SSR/hydration, global CSS, middleware, or framework ownership. It removes the pnpm pack / edit-manifest / reinstall loop without claiming to prove the tarball.

Both modes resolve the same composition contract. The interface can be a CLI, package-manager feature, dev server, container stack, or managed environment; the durable part is the input and output:

composition: integration-stable@sha256:8f1e...
shell: product-shell@13.2.1
modules:
  booking:
    mode: local-source
    path: ../booking
    watch: true
  hotel:
    mode: integration-stable
    version: 5.8.2
  payment:
    mode: fake
    scenario: authorized
scenarios:
  auth: signed-in-customer
  locale: en-GB
data:
  booking: local-isolated

A good platform dev command does four things before it starts:

Resolves peer, shell-API, rendering-mode, and contract compatibility.
Leaves the application’s normal package.json and lockfile untouched.
Watches only explicit local overrides; released components remain immutable.
Prints the resolved versions, modes, paths, scenario IDs, and manifest digest.

That last point matters. “Works on my machine” is useful only when “it” has an identity another developer or CI job can reproduce.

Useful companion commands are straightforward:

platform preview  # create or show this change's pinned preview
platform trace    # inspect correlated diagnostics
platform doctor   # explain peers, credentials, ports, versions, dependencies
platform reset    # remove generated state; never normal project files

Let contracts unblock implementation—not conversation

Composition accelerates feedback after code exists. Contracts solve the earlier scheduling problem: how can Booking start when Hotel is not finished?

The teams still need to agree on meaning. The goal is to turn that agreement into a versioned candidate someone can consume immediately.

For a cancellable reservation hold, the sequence is:

Agree on the smallest useful contract. Request, response, errors, idempotency, timeout expectations, and representative examples.
Publish a provider-owned candidate. Hotel ships the contract plus valid fixtures, fake adapter, or mock server under an immutable candidate ID.
Implement the consumer against it. Booking exercises success, absence, timeout/error, and compatibility scenarios in the workbench.
Verify the provider against the same agreement. Hotel’s CI verifies the implementation. Consumer-driven contracts can run concrete consumer expectations before merge; Pact is one option.
Compose both candidates. The preview pins Booking and Hotel candidates for product-level review.
Keep a declared compatibility window. Support current and previous versions unless an exception is explicit and time-bounded.

semantic decision
        │
        ▼
provider contract candidate ──────▶ consumer implementation
        │                                      │
        └──── provider verification ◀──────────┘
                         │
                         ▼
               pinned combined preview

This separates a necessary semantic dependency from an avoidable implementation dependency. Contract verification does not prove assembled-product behavior, so the combined preview remains necessary.

Test the bytes you will release

Source composition is for speed. Artifact verification is for release confidence.

Before merge, CI should:

build once → pack once → attach commit and provenance → clean install into pinned shell → test → preview

For a Node package, pnpm pack produces the tarball under test. The clean shell install should verify exports, included files, types, assets, lifecycle behavior, package metadata, SSR/hydration where relevant, and only the critical product journeys affected by the module.

The artifact that passes is the artifact that moves forward. Rebuilding for staging or production invalidates prior evidence.

The shell owns singleton dependencies such as React, framework runtimes, and shared contexts. Modules declare supported ranges through peerDependencies rather than bundling private copies. A compatibility manifest adds constraints package resolution cannot express—such as supported shell APIs, rendering modes, or contract generations—and fails an invalid composition with an explanation before it runs.

Provenance answers a different question: which source revision and CI build produced these tested bytes? It complements, rather than replaces, compatibility testing.

Preview a product, not an isolated copy of the company

A PR preview should be a hybrid composition:

dedicate the changed Booking candidate and shell where needed;
reuse known-stable Hotel, Payment, and other remote services;
pin every reused version;
expose the final composition and artifact IDs in the preview.

This gives QA, design, and product a reviewable URL without creating a full company clone for every pull request. It catches artifact omissions, clean-install failures, selected cross-domain behavior, and visual product issues. It still has bounded data, traffic, scale, and external integrations—so it is strong evidence, not a production guarantee.

GitOps gives promotion a memory

A composition is already declarative, so GitOps is useful connective tissue between verified bytes and running environments. It does not “solve integration”; it records, applies, and reconciles a selected product state.

composition: booking-canary-2026-09-12
shell: product-shell@13.2.1
components:
  booking:
    artifact: registry.example/booking@sha256:4d8c...
    version: 7.5.0
    sourceCommit: 91ac...
    buildProvenance: attestations/booking/4d8c...
  hotel:
    artifact: registry.example/hotel@sha256:70be...
    version: 5.8.2

candidate artifact → composition-manifest PR → verification + preview
    → merge → reconciled environment → canary → manifest revert if unhealthy

This aligns with the OpenGitOps principles: desired state is declarative, versioned and immutable, automatically pulled, and continuously reconciled.

Keep environment semantics distinct:

Environment	Purpose	What may change it?
`integration-latest`	Early compatibility signal from accepted updates	Accepted component updates
`integration-stable`	Dependable baseline for local hybrid development	Deliberate known-compatible promotion
staging / release candidate	Release evidence	Deliberately selected manifest only
production	Real customer and traffic evidence	Canary promotion of a reviewed manifest

Routine deployments must not mutate staging. Otherwise it becomes contested shared workspace exactly when it needs to represent a release candidate.

A manifest revert restores the prior artifact digest. It cannot reverse destructive migrations or external side effects, so every module still needs a forward- and backward-compatible migration strategy.

Make failure attributable in one screen

Pinning versions is necessary but incomplete. Reproducibility also needs data and diagnostics.

A provisioned Booking scenario should have a testRunId, TTL, and cleanup policy. Records across Booking, Hotel, and Payment carry that identifier where permitted. Stable fixtures are immutable or automatically reset; tests needing mutation receive isolated data.

Requests and events should propagate trace and request IDs plus:

module version · product composition ID · contract version · testRunId

OpenTelemetry provides the trace and context-propagation model. Adding composition context makes a trace actionable integration evidence.

A failed Booking request should answer:

Which Booking artifact and product composition handled it?
Which Hotel contract and service version were selected?
Which test scenario supplied the data?
Where did time or failure enter the trace?
Which team owns the failing capability and what changed last?

Ownership then follows the boundary: Booking triages a failure in its artifact; Hotel owns a published-contract violation; the platform team owns a composition engine rejection of a valid declared configuration. A bare failure routed to a team channel recreates the integration queue.

Start small: improve one developer journey

Do not begin by building a universal portal. Begin with the most painful edit-to-integrated-feedback loop and give it a measurable exit condition.

Stage	Deliverable	Exit condition
1. Make composition explicit	Pinned shell/module versions, clean artifact install	Any authorized engineer can reproduce a candidate from clean state without editing `package.json`.
2. Shorten local feedback	Workbench, named scenarios, real-shell local overrides	One command reaches a usable shell while the normal lockfile remains unchanged.
3. Move compatibility before merge	Candidate contracts, packed-artifact CI, hybrid preview	CI installs exact candidates into a clean pinned composition and reviewers can identify every changed/reused component.
4. Close the production loop	GitOps manifests, canary, trace metadata, ownership routing	The PR-tested artifact reaches canary/production unchanged and a safe revert restores the prior compatible version.

Measure the wait, not the tooling:

median edit-to-feedback time, separately for workbench and real shell;
percentage of PRs verified with the exact artifact later promoted;
local composition startup success rate, not just process starts;
preview request-to-ready and recovery time;
failures found before versus after merge, classified by boundary;
dependency-update lead time and blocked days caused by cross-team dependencies;
deployment failure/recovery outcomes and observed developer task success.

DORA’s delivery metrics are useful alongside these leading indicators. Do not turn them into a cross-team leaderboard: trends for one product journey reveal more than a blended organizational number.

Integration is the platform product

A weak platform makes teams publishable, then centralizes the moment of truth. A healthy platform makes the product boundary continuously available to the team changing it.

For Booking, that means one connected path:

local source in real shell
  → exact packed candidate in clean CI
  → pinned PR preview
  → reviewed composition manifest
  → canary with traceable identity
  → manifest revert when healthy evidence disappears

Packages, separate repositories, and independent releases can all support this model. The decisive capability is faster, self-service feedback at progressively realistic levels—not the repository topology or a portal.

Integration is not the gate after development. Integration is the product the platform team provides.

References

Graph Coloring: A Simple Implementation with JavaScript

Piyush Chauhan — Tue, 08 Sep 2026 17:04:14 +0000

Introduction

Graph coloring is a fundamental problem in computer science and graph theory, often used to solve various real-world problems such as scheduling tasks, register allocation in compilers, and solving map coloring puzzles. In this article, we will explore how to implement a simple graph coloring algorithm using JavaScript.

What is Graph Coloring?

Graph coloring involves assigning colors to the vertices of a graph such that no two adjacent vertices share the same color. The goal is to use the minimum number of colors required to achieve this condition.

Example: Map Coloring

A classic example is map coloring, where countries (vertices) are colored so that no two adjacent countries have the same color. This ensures that maps can be visually distinguished and avoids confusion between neighboring regions.

Implementation in JavaScript

We will implement a graph coloring algorithm using JavaScript objects to represent the graph structure and perform depth-first search (DFS) for coloring.

Step-by-Step Guide

Graph Representation:
- We will use an adjacency list representation of the graph.
Color Assignment:
- We will use DFS to assign colors while ensuring no two adjacent vertices share the same color.
Conflict Detection:
- We will detect conflicts and return false if a valid coloring cannot be achieved.

JavaScript Code

Here's the complete implementation:

// Function to perform the graph coloring using DFS
function dfs(graph, vertex, visited, colorMap) {
    // If the vertex is already colored, return true
    if (colorMap[vertex] !== undefined) return true;

    // Initialize with a default color
    colorMap[vertex] = 0;

    for (let neighbor of graph[vertex]) {
        if (!visited[neighbor]) {
            visited[neighbor] = true;
            if (!dfs(graph, neighbor, visited, colorMap)) {
                return false; // Conflict found
            }
        } else if (colorMap[neighbor] === colorMap[vertex]) {
            return false; // Conflict found
        }
    }

    // Assign the next available color
    colorMap[vertex] = 1;

    return true;
}

// Function to perform graph coloring on the entire graph
function graphColoring(graph) {
    const visited = {};
    const colorMap = {};

    for (let vertex of Object.keys(graph)) {
        if (!visited[vertex]) {
            visited[vertex] = true;
            if (!dfs(graph, vertex, visited, colorMap)) {
                return false; // Conflict found
            }
        }
    }

    console.log("Coloring scheme:", colorMap);
    return true;
}

// Example usage:
const graph = {
    A: ['B', 'C'],
    B: ['A', 'D'],
    C: ['A', 'D', 'E'],
    D: ['B', 'C', 'E'],
    E: ['C', 'D']
};

if (graphColoring(graph)) {
    console.log("Graph is colored successfully!");
} else {
    console.log("Conflict found in graph coloring.");
}

Explanation

dfs Function:
- This function performs a depth-first search to assign colors.
- If the vertex has already been assigned a color, it returns true.
- It initializes the color map for unvisited vertices and recursively assigns the next available color while checking for conflicts.
graphColoring Function:
- Initializes visited and colorMap arrays.
- Iterates over all vertices in the graph and calls dfs for unvisited vertices.
- If a conflict is found during the DFS, it returns false; otherwise, it prints the color map.

Example Graph

Let's consider the example graph:

Vertices: A, B, C, D, E
Edges: A-B, A-C, B-D, C-D, C-E, D-E

The output will be a valid coloring scheme for this graph if one exists.

Conclusion

In this article, we explored how to implement a simple graph coloring algorithm using JavaScript. By representing the graph as an adjacency list and performing depth-first search, we can efficiently assign colors while ensuring no two adjacent vertices share the same color.

Navigating Python Development: A Step-by-Step Guide to Using Poetry

Piyush Chauhan — Tue, 08 Sep 2026 17:03:46 +0000

What is Poetry?

Poetry is an opinionated package management system for Python projects. It helps you:

Set up your project with all the necessary files.
Manage dependencies easily.
Isolate environments to ensure that different projects have separate dependencies.
Resolve dependency conflicts automatically.

Why Use Poetry?

Simplifies Project Setup: Automatically generates pyproject.toml and poetry.lock files, making it easier to share your project with others.
Environment Management: Creates isolated virtual environments for each project, which helps avoid version conflicts.
Dependency Resolution: Automatically handles dependencies and their versions, reducing the risk of issues related to dependency mismatches.

Installing Poetry

You can install Poetry using pip:

pip install poetry

Creating a New Project with Poetry

Open your terminal or command prompt.
Navigate to the directory where you want to create your project.
Run the following command to create a new Python project:

   poetry new my_project

This will create a new directory named my_project with the following structure:

my_project/
├── pyproject.toml
└── src/
    └── my_project/
        ├── __init__.py
        └── main.py

Managing Dependencies

Add a Dependency: You can add dependencies to your project using the add command:

   poetry add requests

List Installed Dependencies: To see all installed dependencies, use:

   poetry show

Update a Dependency: To update an existing dependency, use:

   poetry update requests

Remove a Dependency: To remove a dependency, use:

   poetry remove requests

Managing Environments

Activate the Environment: Run the following command to activate your project's virtual environment:

   poetry shell

Deactivate the Environment: Simply type exit in the activated shell.

Running Scripts

You can run scripts from within a Poetry-managed environment by using the poetry run command:

poetry run python src/my_project/main.py

This runs main.py with the virtual environment's Python interpreter.

Summary

Poetry is a powerful tool for managing Python projects. It simplifies dependency management, ensures isolated environments, and helps resolve version
conflicts. By following these steps, you can effectively use Poetry to manage your Python projects.

The browser only talks to one server — composing Marko, React, and Riot into one hotel page

Piyush Chauhan — Mon, 07 Sep 2026 12:32:03 +0000

You open a hotel page. It looks like one product: a search grid, a featured stay, local highlights, reviews, a sticky trip summary.

Under the hood it is eight HTTP servers and three UI runtimes.

That is the experiment behind HarborStay, a demo booking app I built to answer a stubborn question:

Can independent teams ship independent UI, in independent frameworks, and still give the browser a single, paint-ready HTML page?

The punchline: yes — if the shell never imports a component. It only fetches HTML.

The one rule

The browser never talks to a fragment.

It talks to the composer on port 3100. The composer owns routes, layout, and the booking flow. Everything else is a fragment server that returns a chunk of HTML.

flowchart LR
  Browser["Browser"] --> Composer["Composer :3100"]

  subgraph fragments["Fragment servers"]
    Nav["Navigation Marko :3101"]
    Search["Hotel search Marko :3102"]
    Details["Hotel details Marko :3103"]
    Reviews["Reviews Marko :3104"]
    Recs["Recommendations Marko :3105"]
    Highlights["Local highlights React :3106"]
    Disco["Experiences discovery Riot :3107"]
    Itin["Experiences itinerary Riot :3108"]
  end

  Composer --> Nav
  Composer --> Search
  Composer --> Details
  Composer --> Reviews
  Composer --> Recs
  Composer --> Highlights
  Composer --> Disco
  Composer --> Itin
  Composer --> CDN["CDN :3200"]

This is the opposite of the usual microfrontend story (Module Federation, shared React, a host that import()s widgets). HarborStay is HTML composition. The shell does not know whether a fragment was rendered by Marko, React, or a hand-rolled Riot string. It only knows a URL.

That one constraint buys a lot:

Fragment teams can pick a runtime without asking the shell.
A fragment outage becomes a fallback box, not a blank page.
You can deploy search without redeploying reviews.

It also forces honesty. If two fragments need to share a Redux store, the architecture is already leaking.

What the user actually sees

HarborStay models a small premium catalog: Harbor View Lodge in Lisbon, Copper Hill Suites in Seville, Sunset Terrace House in Barcelona, Moss & Lantern in Copenhagen.

Routes the composer owns:

Route	What you get
`/` and `/hotel/:hotelId`	Streaming stays page
`/experiences/:hotelId`	Neighborhood explorer + day plan
`/booking/confirm/:hotelId`	Confirmation (I got `HS-470455` for Moss & Lantern)

On /hotel/moss-lantern the page is not “a React tree.” It is a layout of slots:

flowchart TB
  subgraph page["One HTML page from the composer"]
    Topbar["Topbar: HarborStay + Stays / Experiences"]
    subgraph main["Main column"]
      Search["Search island: Where to next?"]
      Details["Details island: Featured stay"]
      Highlights["Highlights island: What to do in Copenhagen"]
      Reviews["Reviews island: What travelers say"]
      Recs["Recommendations island: More stays nearby"]
    end
    Sidebar["Sticky trip summary + Book this stay"]
  end

Click Luxury on the search chips and the grid collapses to Harbor View Lodge and Moss & Lantern. The reviews below do not re-fetch. They never heard about the filter. That is not a bug — that is the point.

Island rule: each fragment owns its own interactivity. Search filters are Marko state. Highlight selection is React state. Experience categories are Riot state. The composer does not have a global store.

A tiny protocol instead of a shared SDK

Every fragment speaks harborstay-fragment/v1.

GET /?hotelId=moss-lantern
GET /manifest
GET /assets/<name>.css
GET /assets/<name>.js

And a few provenance headers:

X-Fragment-Name: search
X-Fragment-Hotel-Id: moss-lantern
X-Fragment-Protocol: harborstay-fragment/v1
X-Fragment-Css: http://localhost:3200/assets/search.css
X-Fragment-Js:  http://localhost:3200/assets/search.js

The composer reads the HTML body. Asset URLs are informational. In this demo, CSS and JS are served from a tiny CDN app on :3200 that copies fragment assets on startup.

That split is the real-world shape: SSR lives with the team that owns the UI. Bytes live on a CDN.

Assets belong to the fragment that created them, not to the shell:

apps/hotel-search/src/assets/search.css
apps/hotel-search/src/build-client.js   → cdn/public/assets/search.js

If search wants a new filter chip, reviews does not rebuild.

Three runtimes, one HTML contract

This is the part people assume is impossible.

Runtime	Fragments	Server render	Client
Marko	navigation, search, details, reviews, recommendations	`@marko/compiler` → `template.render().toString()`	Marko DOM runtime mounts into a root
React 19	local highlights	`react-dom/server` `renderToStaticMarkup`	`hydrateRoot`
Riot	experiences discovery + itinerary	hand-written `renderSsrMarkup()`	`riot.component()` after clearing the SSR root

The composer does not care. All three return strings.

flowchart TB
  subgraph marko["Marko :3102"]
    M1["hotel-search.marko"] --> M2["HTML string"]
  end
  subgraph react["React :3106"]
    R1["HighlightsView.jsx"] --> R2["HTML string"]
  end
  subgraph riot["Riot :3107"]
    T1["renderSsrMarkup()"] --> T2["HTML string"]
  end
  M2 --> Composer["Composer stitches slots"]
  R2 --> Composer
  T2 --> Composer
  Composer --> Browser["Browser paints HTML before any island JS"]

Why SSR in a microfrontend at all?

The first paint does not wait for JavaScript.
HTML is the only lingua franca three frameworks share.
If experiences-discovery.js fails, the user still sees the cards.

That last one is Islands architecture in the small: static HTML with pockets of JS, not a SPA that hydrates the entire document.

How data crosses the SSR/client gap

Every interactive fragment embeds props as JSON, not data- attributes:

<script type="application/json" id="hotel-search-fragment-data">
  {"hotels":[...],"hotelId":"moss-lantern"}
</script>
<div id="hotel-search-fragment-root">
  <!-- SSR HTML -->
</div>

The client bundle reads that script and mounts. Before embedding, fragment servers escape < as \u003c so a hotel description cannot break out of the script tag.

data-hotels='[{...}]' looks simpler. It is worse: large JSON in attributes bloats the DOM, needs ugly escaping, and is visible to everything that walks the tree.

Hydration is not one word

Walking the live app made this concrete.

Marko search hydrates into #hotel-search-fragment-root. Luxury / Value / Top rated re-render the grid without a navigation. The client template must match the server template or you get a flash.

React highlights hydrates with hydrateRoot. On Moss & Lantern you get Garden loop, Sauna ritual, Harbor market. Click a card and only that island updates.

Riot cannot hydrate the way React does. There is no reconcile-existing-DOM API in this setup, so the client wipes the SSR HTML and remounts:

mountNode.innerHTML = '';
component(DiscoveryTag)(mountNode, props);

You still get a fast first paint. You also get a tiny risk of visual flash if the Riot template and renderSsrMarkup() drift. Keep class names, element types, and copy identical.

On /experiences/moss-lantern I clicked Food: three cards became one tasting session. I clicked Day 2: breakfast / walk / dinner became scenic run / museum / sunset lounge. Two Riot islands, two state machines, zero shared store.

sequenceDiagram
  participant U as User
  participant D as Discovery Riot
  participant I as Itinerary Riot
  U->>D: Click Food
  D->>D: state.activeCategory = Food
  Note over I: Itinerary does not care
  U->>I: Click Day 2
  I->>I: state.selectedDay = 2
  Note over D: Discovery does not care

Streaming: do not wait for the slowest fragment

The stays page does not await Promise.all then send one blob.

streamPage() is an async function* . It yields the <head> and shell immediately, then races fragment promises and flushes HTML as each one finishes.

yield headPrefix;

const pending = new Map(
  fragmentOrder.map((name) => [name, safeRenderFragment(name, hotelId, delay)])
);

while (pending.size > 0) {
  const next = await Promise.race(
    [...pending.entries()].map(([name, promise]) =>
      promise.then((fragment) => ({ name, fragment }))
    )
  );
  pending.delete(next.name);
  yield next.fragment.html;
}

Fastify wraps that in Readable.from(...). The response header x-ssr-stream: true is a breadcrumb that this is a streamed document.

gantt
  title Streaming vs waiting for everyone
  dateFormat  X
  axisFormat  %s ms
  section Buffered
  Wait for slowest fragment     :a1, 0, 860
  First paint                   :a2, 860, 50
  section Streamed
  Head + shell                  :b1, 0, 40
  Search HTML                   :b2, 160, 40
  Recommendations HTML          :b3, 300, 40
  Details HTML                  :b4, 540, 40
  Reviews HTML                  :b5, 860, 40

The demo even fakes latency so you can see streaming:

const FRAGMENT_STREAM_DELAY_MS = {
  search: 160,
  recommendations: 300,
  details: 540,
  reviews: 860,
};

In production you delete those delays. Keep the race. First Contentful Paint becomes “when the shell arrives,” not “when reviews finally woke up.” Skeletons (.loading-fragment) hold layout so later chunks do not shove the page around.

If a fragment is restarting, safeRenderFragment catches the error and yields:

reviews fragment is warming up.

Independent deploys stop being theoretical.

JavaScript budget is a route problem

A composed page can accidentally ship four frameworks to every visitor. HarborStay avoids that with page splits, not heroics.

pnpm dev:home runs composer + CDN + stays fragments.
pnpm dev:experiences runs composer + CDN + the two Riot apps.
The stays page builder never emits experiences-discovery.js.
The experiences page never emits search.js.

That is the cheapest performance win in the repo. Intersection Observer hydration, click-to-hydrate, and requestIdleCallback are next — the docs already sketch them — but route-level omission beats all of them.

flowchart LR
  StayPage["/hotel/:id"] --> StayJS["search.js + highlights.js"]
  ExpPage["/experiences/:id"] --> ExpJS["discovery.js + itinerary.js"]
  StayPage -.-> NoRiot["Riot bundles not referenced"]
  ExpPage -.-> NoMarko["Search bundle not referenced"]

State: URL, local, or an event — pick one

Shared mutable global state is how microfrontends become a distributed monolith.

HarborStay’s rules:

Ephemeral UI state stays in the island. Filter chips, selected highlight, active day.
Shareable state lives in the URL. /hotel/moss-lantern is the source of truth for “which stay.” Full page reload is intentional in this demo.
Cross-fragment talk is an event, not a store import.

document.dispatchEvent(new CustomEvent('harborstay:hotel-selected', {
  bubbles: true,
  detail: { hotelId: 'moss-lantern' },
}));

Do not do this:

import { store } from '@harborstay/store'; // two bundles = two stores

Two IIFE bundles that “import the same store” get two closures. They will not share state. They will gaslight you.

BroadcastChannel is the right tool only when tabs or iframes must stay in sync. localStorage is for values that must survive a round trip. Neither belongs in the first version.

How this would ship

Each fragment is a Node HTTP server. DNS for users points at the composer. Fragments live on internal hostnames. Static assets go to a real CDN.

flowchart TB
  User["User"] --> LB["Load balancer / CDN"]
  LB --> Composer["Composer"]
  Composer --> F1["mfe-hotel-search"]
  Composer --> F2["mfe-reviews"]
  Composer --> F3["mfe-local-highlights"]
  Composer --> F4["other fragments"]
  Assets["cdn.example.com"] -.-> User

Rolling deploy order:

Publish content-hashed assets.
Roll fragment servers (any order — fallbacks cover restarts).
Roll the composer last.

Turborepo is the monorepo glue: pnpm dev:home fans out with persistent dev tasks, and turbo run build only rebuilds what changed.

This is a demo, not a production platform. The goal is a shape you can reason about: one contract, many runtimes, progressive HTML.

What I would do next

The interesting unfinished work is not “add another framework.” It is:

Soft navigation — history.pushState + swap only the fragments that changed.
True streaming inside fragments — Marko template.stream(), React renderToPipeableStream.
Riot hydrate() when the API is stable, so we stop wiping SSR DOM.
Edge composer — the generator already looks like a Web Streams mental model.
SWR cache of fragment HTML at the composer for anonymous hotel pages.

Run it

pnpm install
pnpm dev:home          # stays flow
pnpm dev:experiences   # experiences flow
pnpm dev:all           # everything

Then open http://localhost:3100.

If you want a mental model to steal, steal this one:

Compose HTML, not components. Hydrate islands, not pages. Keep state with the team that owns the UI. Let the slowest fragment arrive last without blocking the first paint.

The user still sees one hotel page. The architecture is allowed to be many rooms behind one door.

GITHUB: https://github.com/piyushchauhan2011/marko-mfe

Why Your JavaScript Sums Are Wrong (And How to Fix Them)

Piyush Chauhan — Wed, 05 Aug 2026 07:24:22 +0000

You know that Array.prototype.reduce you've used a thousand times to sum numbers? It's lying to you.

1. A One-Liner That Should Work

const nums = Array.from({ length: 1000 }, () => 0.1);
const sum = nums.reduce((a, b) => a + b, 0);

console.log(sum);        // 99.9999999999986
console.log(sum === 100); // false (!)

A thousand tenths should equal one hundred. But in JavaScript — and in virtually every other mainstream language — it doesn't. Not exactly.

If you've ever shrugged off a 0.0000000000001 discrepancy, this article is for you. Because at scale, that "tiny" error isn't tiny at all.

2. The Problem: Why Floats Betray Us

JavaScript uses IEEE 754 double-precision floating-point for all numbers. In plain English: your numbers are stored as binary fractions, and most decimal fractions (like 0.1) have no exact binary representation.

When you add 0.1 + 0.2, the computer isn't adding "one-tenth plus two-tenths." It's adding the closest binary approximations it can store. The result? 0.30000000000000004.

Now multiply that imperfection across a thousand, a million, or a billion additions. The error doesn't stay small — it accumulates.

The Core Issue

When you sum left-to-right with a running total, you eventually reach a point where the running total is huge and the next number is tiny. In floating-point arithmetic, adding a tiny number to a huge number often results in no change at all — the tiny number gets "swallowed."

Imagine pouring water into a bucket with a small hole. Each pour loses a few drops. After 1,000 pours, you're missing a puddle.

3. The Fix: Kahan Summation

In 1965, William Kahan published an algorithm that tracks and recovers the low-order bits lost in each addition. The trick? A compensation variable that remembers what got rounded away.

The Intuition

Instead of just keeping a running total, Kahan summation keeps two values:

sum — the best approximation so far
c (compensation) — the error that was discarded in the last step

Before each new addition, the compensation from the previous step is added back in. After the addition, the new error is computed and stored for the next iteration.

The Algorithm

Input: x
       │
       ▼
    x + c  ◄── compensation from last step
       │
       ▼
   t = sum + (x + c)
       │
       ▼
   c = (t - sum) - (x + c)  ← the "recovered" error
       │
       ▼
   sum = t

JavaScript Implementation

function kahanSum(numbers) {
  let sum = 0;
  let c = 0; // compensation for lost low-order bits

  for (let i = 0; i < numbers.length; i++) {
    const y = numbers[i] - c;  // subtract previous error
    const t = sum + y;         // tentative sum
    c = (t - sum) - y;         // new error: (what we added) - (what we meant to add)
    sum = t;
  }

  return sum;
}

Line-by-Line Breakdown

Line	What It Does
`y = numbers[i] - c`	Add back the error from the previous step
`t = sum + y`	Perform the actual addition
`c = (t - sum) - y`	Calculate what got lost in rounding
`sum = t`	Commit the new sum

The magic is in (t - sum) - y. Because of how floating-point subtraction works, this expression recovers the bits that were rounded away during sum + y.

4. Live Comparison: Naive vs. Kahan

Let's run both algorithms on the same pathological dataset:

// 10 million copies of 0.0001
// True answer: exactly 1000
const nums = Array.from({ length: 10_000_000 }, () => 0.0001);

console.time('naive');
const naive = nums.reduce((a, b) => a + b, 0);
console.timeEnd('naive');

console.time('kahan');
const kahan = kahanSum(nums);
console.timeEnd('kahan');

console.log('Naive: ', naive);  // 999.9998999965879
console.log('Kahan: ', kahan);  // 1000.0000000000001

The naive sum is off by 0.0001. That might not sound like much, but in financial systems processing millions of transactions, "small" errors compound into real money.

Kahan's error? 1.1e-13 — effectively zero for all practical purposes.

5. The Middle Ground: Pairwise Summation

Before we crown Kahan the winner, there's a third contender that often gets overlooked: pairwise summation (also called cascade summation).

The idea is beautifully simple: instead of accumulating left-to-right, recursively split the array in half, sum each half, then add the two results. This ensures you're always adding numbers of roughly the same magnitude.

The Intuition

Imagine balancing a scale. Naive summation keeps piling weights on one side until the running total dwarfs every new addition. Pairwise summation weighs equal-sized groups against each other at every step.

[1, 2, 3, 4, 5, 6, 7, 8]
    /              \
[1,2,3,4]      [5,6,7,8]
  /    \          /    \
[1,2] [3,4]    [5,6] [7,8]
 |     |        |     |
 3     7        11    15
   \   /          \  /
    10             26
         \        /
           36

Recursive Implementation

function pairwiseSum(arr, start = 0, end = arr.length) {
  const len = end - start;
  if (len === 0) return 0;
  if (len === 1) return arr[start];

  const mid = start + Math.floor(len / 2);
  return pairwiseSum(arr, start, mid) + pairwiseSum(arr, mid, end);
}

⚠️ Caution: For very large arrays (> ~10,000 items), the recursive version will blow the call stack. Here's a stack-safe iterative version:

🔧 Stack-safe iterative version

function pairwiseSumIterative(arr) {
  if (arr.length === 0) return 0;

  let queue = arr.slice();

  while (queue.length &gt; 1) {
    const next = [];
    for (let i = 0; i &lt; queue.length; i += 2) {
      next.push(queue[i] + (queue[i + 1] || 0));
    }
    queue = next;
  }

  return queue[0];
}

6. The Heavyweight: decimal.js

Sometimes you need exact decimal arithmetic, not just "less error." That's where decimal.js comes in. It represents numbers as strings internally and performs arbitrary-precision math.

Installation

npm install decimal.js

Usage

const Decimal = require('decimal.js');

function decimalSum(numbers) {
  return numbers
    .reduce((sum, n) => sum.plus(new Decimal(n.toString())), new Decimal(0))
    .toNumber();
}

💡 Pro tip: Always pass numbers through .toString() before constructing a Decimal. new Decimal(0.1) still inherits floating-point imprecision because 0.1 is already corrupted by the time it reaches the constructor.

7. The Showdown: Four-Way Comparison

Let's put them all to the test with a pathological dataset:

// 10 million copies of 0.0001
// True answer: exactly 1000
const nums = Array.from({ length: 10_000_000 }, () => 0.0001);

Method	Result	Error	Relative Speed
Naive `reduce`	`999.9998999965879`	`~1.0e-4`	1.0x (baseline)
Pairwise	`999.9999999999123`	`~8.8e-11`	~1.5x slower
Kahan	`1000.0000000000001`	`~1.1e-13`	~3.5x slower
decimal.js	`1000` (exact)	`0`	~40-80x slower

What the Numbers Tell Us

Naive is fast but wrong at scale. The error is small in absolute terms, but in financial systems, "small" errors compound into real money.
Pairwise dramatically reduces error with minimal overhead. Its error grows logarithmically O(log n) instead of linearly.
Kahan achieves near-constant error O(ε) — the gold standard for floating-point summation.
decimal.js is exact but slow. It's not really a summation algorithm; it's a different number system entirely.

8. The Decision Framework

Here's a quick cheat sheet for choosing the right tool:

Scenario	Recommendation	Why
Small arrays (< 1,000 items)	Naive `reduce`	Error is negligible; don't over-engineer
Large arrays, need speed, "good enough" accuracy	Pairwise	Logarithmic error growth, parallelizable
Financial/scientific computing, must minimize error	Kahan	Constant error bound, no extra dependencies
Accounting, currency, must be exact	decimal.js	Arbitrary precision eliminates rounding entirely
GPU/Web Worker parallel processing	Pairwise	Perfectly parallelizable; Kahan is inherently sequential

The Trade-off Triangle

        Speed
         /\
        /  \
       /    \
      /      \
     /   ⚠️   \
    /__________\
 Accuracy    Simplicity

You can pick two:

Fast + Simple → Naive
Fast + Accurate → Pairwise
Accurate + Simple → Kahan
Exact → decimal.js (sacrifices speed)

9. When NOT to Use Kahan

Kahan summation is great, but it's not always the right call:

Integers only — If your array contains only integers representable in 53 bits, naive summation is exact until you exceed Number.MAX_SAFE_INTEGER.
Already using a decimal library — If you've bought into decimal.js or big.js, you don't need Kahan; you're already exact.
Real-time graphics/animation — A 3-4x slowdown for a pixel-perfect particle position is rarely worth it.
Very small datasets — The error on [0.1, 0.2, 0.3] is 5.55e-17. Your users don't care.

10. One-Liners for Your Toolkit

For the copy-paste engineers:

// Kahan (functional, but slightly slower due to array destructuring)
const kahanSum = (a) => a.reduce(([s, c], x) => {
  const y = x - c;
  const t = s + y;
  return [t, (t - s) - y];
}, [0, 0])[0];

// Pairwise (iterative, stack-safe)
const pairwiseSum = (a) => {
  let q = [...a];
  while (q.length > 1) {
    const n = [];
    for (let i = 0; i < q.length; i += 2) n.push(q[i] + (q[i+1] || 0));
    q = n;
  }
  return q[0] || 0;
};

Conclusion

Floating-point summation is one of those problems that seems trivial until it isn't. The good news: JavaScript gives you options.

Reach for pairwise summation when you need a quick accuracy boost without a performance cliff.
Reach for Kahan summation when precision is paramount and you're working with raw floats.
Reach for decimal.js when you're dealing with money, tax calculations, or anything where "close enough" isn't in your vocabulary.

And sometimes? Just use reduce. Not every array needs a PhD.

What's your go-to strategy for numerical accuracy in JavaScript? Drop your war stories in the comments below. 👇

Backtracking Unveiled: Mastering Depth-First Search and Pruning Techniques

Piyush Chauhan — Sat, 22 Feb 2025 09:04:27 +0000

Backtracking is a systematic way to iterate through all possible configurations of a problem. It is often used to solve problems where a sequence of decisions must be made, and each decision leads to a new set of possibilities. Backtracking uses depth-first search (DFS) to explore the state space, and it employs pruning functions to eliminate invalid or suboptimal paths early in the search process.

Components of Backtracking Problems

Initial State: The starting point of the problem.
Target/Goal State: The desired solution.
Intermediate States: States generated while moving from the initial state to the target state.
Path: The sequence of states from the initial state to the target state.
Operators: Rules or actions that transform one state into another.
Pruning Function: A heuristic to eliminate invalid or suboptimal states early.

Backtracking Algorithm

Start with the initial state.
Use DFS to explore all possible states.
If a state is invalid (based on the pruning function), backtrack and try another path.
If a state is the goal state, return the solution.
Repeat until all possible states are explored or a solution is found.

Example Problems Solved Using Backtracking

1. 3-Digit Lock Problem

Initial State: "111"
Target State: The correct combination (e.g., "352")
Operators: Increment each digit by 1 (from 1 to 9).
Pruning Function: If a digit is correct (e.g., produces a "click"), fix it and move to the next digit.

Implementation:

function findLockCombination(target) {
    function backtrack(combination, index) {
        if (combination === target) {
            console.log("Found combination:", combination);
            return true;
        }
        if (index >= 3) return false;

        for (let i = 1; i <= 9; i++) {
            const newCombination = combination.slice(0, index) + i + combination.slice(index + 1);
            if (newCombination[index] === target[index]) {
                console.log("Click at position", index + 1, "with digit", i);
                if (backtrack(newCombination, index + 1)) return true;
            }
        }
        return false;
    }

    backtrack("111", 0);
}

const targetCombination = "352";
findLockCombination(targetCombination);

2. Monks and Demons Problem

Initial State: 3 monks, 3 demons, and the boat on the left shore.
Target State: All monks and demons on the right shore.
Operators: Move 1 or 2 people in the boat.
Pruning Function: Ensure that monks are never outnumbered by demons on either shore.

Implementation:

function solveMonksAndDemons() {
    const initialState = { left: { monks: 3, demons: 3 }, right: { monks: 0, demons: 0 }, boat: "left" };
    const visited = new Set();

    function isSafe(state) {
        const { left, right } = state;
        return (
            (left.monks === 0 || left.monks >= left.demons) &&
            (right.monks === 0 || right.monks >= right.demons)
        );
    }

    function backtrack(state, path = []) {
        const key = JSON.stringify(state);
        if (visited.has(key)) return false;
        visited.add(key);

        if (state.left.monks === 0 && state.left.demons === 0) {
            console.log("Solution found:", path);
            return true;
        }

        const { left, right, boat } = state;
        const moves = [
            { monks: 1, demons: 0 },
            { monks: 2, demons: 0 },
            { monks: 0, demons: 1 },
            { monks: 0, demons: 2 },
            { monks: 1, demons: 1 },
        ];

        for (const move of moves) {
            if (
                (boat === "left" && left.monks >= move.monks && left.demons >= move.demons) ||
                (boat === "right" && right.monks >= move.monks && right.demons >= move.demons)
            ) {
                const newLeft = {
                    monks: left.monks - (boat === "left" ? move.monks : -move.monks),
                    demons: left.demons - (boat === "left" ? move.demons : -move.demons),
                };
                const newRight = {
                    monks: right.monks - (boat === "right" ? move.monks : -move.monks),
                    demons: right.demons - (boat === "right" ? move.demons : -move.demons),
                };
                const newState = {
                    left: newLeft,
                    right: newRight,
                    boat: boat === "left" ? "right" : "left",
                };

                if (isSafe(newState) && backtrack(newState, [...path, newState])) {
                    return true;
                }
            }
        }
        return false;
    }

    backtrack(initialState);
}

solveMonksAndDemons();

3. Farmer, Goat, Cabbage, and Wolf Problem

Initial State: Farmer, goat, cabbage, and wolf on the left shore.
Target State: All on the right shore.
Operators: Farmer can take one item (goat, cabbage, or wolf) in the boat.
Pruning Function: Ensure the goat is not left alone with the cabbage, and the wolf is not left alone with the goat.

Implementation:

function solveFarmerProblem() {
  const initialState = {
    left: ["farmer", "goat", "cabbage", "wolf"],
    right: [],
    boat: "left",
  };
  const visited = new Set();

  function isSafe(state) {
    const unsafe = (side) =>
      (side.includes("goat") &&
        side.includes("cabbage") &&
        !side.includes("farmer")) ||
      (side.includes("wolf") &&
        side.includes("goat") &&
        !side.includes("farmer"));

    return !(unsafe(state.left) || unsafe(state.right));
  }

  function stateKey(state) {
    return JSON.stringify(state);
  }

  function backtrack(state, path = []) {
    if (state.left.length === 0) {
      console.log("Solution found:", path);
      return true;
    }

    const key = stateKey(state);
    if (visited.has(key)) return false;
    visited.add(key);

    const { left, right, boat } = state;
    const fromShore = boat === "left" ? left : right;

    // Possible moves: Farmer alone or Farmer + one item
    for (const item of [...fromShore, null]) {
      if (item === null || item !== "farmer") {
        let newLeft = [...left];
        let newRight = [...right];

        if (boat === "left") {
          newLeft = newLeft.filter((x) => x !== "farmer" && x !== item);
          newRight = [...newRight, "farmer"];
          if (item) newRight.push(item);
        } else {
          newRight = newRight.filter((x) => x !== "farmer" && x !== item);
          newLeft = [...newLeft, "farmer"];
          if (item) newLeft.push(item);
        }

        const newState = {
          left: newLeft,
          right: newRight,
          boat: boat === "left" ? "right" : "left",
        };

        if (isSafe(newState) && backtrack(newState, [...path, newState])) {
          return true;
        }
      }
    }

    return false;
  }

  backtrack(initialState);
}

solveFarmerProblem();

4. Water Jug Problem

Initial State: Both jugs are empty.
Target State: 2 gallons in the 4-gallon jug.
Operators: Fill, empty, or pour water between jugs.
Pruning Function: Avoid revisiting states.

Implementation:

function solveWaterJug() {
    const initialState = { jug4: 0, jug3: 0 };
    const target = 2;
    const visited = new Set();

    function backtrack(state, path = []) {
        const key = `${state.jug4},${state.jug3}`;
        if (visited.has(key)) return false;
        visited.add(key);

        if (state.jug4 === target) {
            console.log("Solution found:", path);
            return true;
        }

        const { jug4, jug3 } = state;
        const moves = [
            { jug4: 4, jug3 }, // Fill jug4
            { jug4, jug3: 3 }, // Fill jug3
            { jug4: 0, jug3 }, // Empty jug4
            { jug4, jug3: 0 }, // Empty jug3
            { jug4: Math.max(0, jug4 - (3 - jug3)), jug3: Math.min(3, jug3 + jug4) }, // Pour jug4 to jug3
            { jug4: Math.min(4, jug4 + jug3), jug3: Math.max(0, jug3 - (4 - jug4)) }, // Pour jug3 to jug4
        ];

        for (const move of moves) {
            if (backtrack(move, [...path, move])) {
                return true;
            }
        }
        return false;
    }

    backtrack(initialState);
}

solveWaterJug();

Key Points:

Backtracking is a systematic way to explore all possible solutions.
It uses depth-first search and pruning functions to eliminate invalid paths.
It is useful for solving NP-hard problems like puzzles, constraint satisfaction problems, and combinatorial optimization.

A* Search Algorithm and Iterative Deepening A* in JavaScript

Piyush Chauhan — Sat, 08 Feb 2025 05:44:55 +0000

1. Introduction

Search algorithms play a crucial role in solving problems across computer science, robotics, game development, and beyond. Among them, heuristic-based search algorithms stand out for their ability to efficiently find solutions by leveraging additional knowledge about the problem domain. This article delves into two such algorithms: the widely used A* search algorithm and its memory-efficient counterpart, Iterative Deepening A* (IDA*).

By the end of this article, you will:

Understand the core principles of A* and IDA*.
Learn how these algorithms work through code implementations in JavaScript.
Explore their mathematical underpinnings and use cases.

**2. Understanding A* Search Algorithm**

What is A*?

A* is a best-first search algorithm that aims to find the shortest path from a start node to a goal node. It uses an evaluation function:

f (n) = g (n) + h (n)

Where:

$g (n)$ : The cost of the path from the start node to the current node $n$ .
$h (n)$ : A heuristic estimate of the cost to reach the goal node from $n$ .

The algorithm prioritizes nodes with the smallest $f (n)$ value, making it both optimal and complete, provided the heuristic $h (n)$ is admissible (never overestimates the true cost).

Properties of A*

Completeness: Guaranteed to find a solution if one exists.
Optimality: Finds the shortest path with an admissible heuristic.
Admissibility and Consistency: Ensures the heuristic is reliable for optimal solutions.

Wikipedia Pseudocode for A*

function reconstruct_path(cameFrom, current)
    total_path := {current}
    while current in cameFrom.Keys:
        current := cameFrom[current]
        total_path.prepend(current)
    return total_path

// A* finds a path from start to goal.
// h is the heuristic function. h(n) estimates the cost to reach goal from node n.
function A_Star(start, goal, h)
    // The set of discovered nodes that may need to be (re-)expanded.
    // Initially, only the start node is known.
    // This is usually implemented as a min-heap or priority queue rather than a hash-set.
    openSet := {start}

    // For node n, cameFrom[n] is the node immediately preceding it on the cheapest path from the start
    // to n currently known.
    cameFrom := an empty map

    // For node n, gScore[n] is the currently known cost of the cheapest path from start to n.
    gScore := map with default value of Infinity
    gScore[start] := 0

    // For node n, fScore[n] := gScore[n] + h(n). fScore[n] represents our current best guess as to
    // how cheap a path could be from start to finish if it goes through n.
    fScore := map with default value of Infinity
    fScore[start] := h(start)

    while openSet is not empty
        // This operation can occur in O(Log(N)) time if openSet is a min-heap or a priority queue
        current := the node in openSet having the lowest fScore[] value
        if current = goal
            return reconstruct_path(cameFrom, current)

        openSet.Remove(current)
        for each neighbor of current
            // d(current,neighbor) is the weight of the edge from current to neighbor
            // tentative_gScore is the distance from start to the neighbor through current
            tentative_gScore := gScore[current] + d(current, neighbor)
            if tentative_gScore < gScore[neighbor]
                // This path to neighbor is better than any previous one. Record it!
                cameFrom[neighbor] := current
                gScore[neighbor] := tentative_gScore
                fScore[neighbor] := tentative_gScore + h(neighbor)
                if neighbor not in openSet
                    openSet.add(neighbor)

    // Open set is empty but goal was never reached
    return failure

**3. Implementing A* in JavaScript**

Below is a JavaScript implementation of A* for a grid-based shortest path problem:

const assert = require("assert");

class PriorityQueue {
  constructor() {
    this.elements = [];
  }

  enqueue(item, priority) {
    this.elements.push({ item, priority });
    this.elements.sort((a, b) => a.priority - b.priority);
  }

  dequeue() {
    return this.elements.shift().item;
  }

  isEmpty() {
    return this.elements.length === 0;
  }
}

function aStar(start, goal, grid, heuristic) {
  const rows = grid.length;
  const cols = grid[0].length;

  const openSet = new PriorityQueue();
  openSet.enqueue(start, 0);

  const cameFrom = {};
  const gScore = Array.from({ length: rows }, () => Array(cols).fill(Infinity));
  gScore[start[0]][start[1]] = 0;

  const fScore = Array.from({ length: rows }, () => Array(cols).fill(Infinity));
  fScore[start[0]][start[1]] = heuristic(start, goal);

  while (!openSet.isEmpty()) {
    const current = openSet.dequeue();
    const [x, y] = current;

    if (current[0] === goal[0] && current[1] === goal[1]) {
      return reconstructPath(cameFrom, current);
    }

    for (const [dx, dy] of [
      [0, 1],
      [1, 0],
      [0, -1],
      [-1, 0],
    ]) {
      const neighbor = [x + dx, y + dy];
      const [nx, ny] = neighbor;

      if (nx >= 0 && nx < rows && ny >= 0 && ny < cols && grid[nx][ny] === 0) {
        const tentative_gScore = gScore[x][y] + 1;

        if (tentative_gScore < gScore[nx][ny]) {
          cameFrom[neighbor] = current;
          gScore[nx][ny] = tentative_gScore;
          fScore[nx][ny] = tentative_gScore + heuristic(neighbor, goal);

          openSet.enqueue(neighbor, fScore[nx][ny]);
        }
      }
    }
  }
  return "No path found";
}

function heuristic([x1, y1], [x2, y2]) {
  return Math.abs(x1 - x2) + Math.abs(y1 - y2);
}

function reconstructPath(cameFrom, current) {
  const path = [current];
  while (cameFrom[current]) {
    current = cameFrom[current];
    path.unshift(current);
  }
  return path;
}

// Test A* Algorithm
const grid = [
  [0, 0, 0, 0],
  [1, 1, 1, 0],
  [0, 0, 0, 0],
  [0, 1, 1, 1],
  [0, 0, 0, 0],
];

const start = [0, 0];
const goal = [4, 3];
const path = aStar(start, goal, grid, heuristic);
console.log("Found Path:", path);
assert.notStrictEqual(path, "No path found");
assert.strictEqual(Array.isArray(path), true);

// Test for no path
const blockedGrid = [
  [0, 1, 0],
  [1, 1, 0],
  [0, 1, 0],
];
const noPathResult = aStar([0, 0], [2, 2], blockedGrid, heuristic);
assert.strictEqual(noPathResult, "No path found");

4. Mathematical Proofs and Analysis

Proof of Optimality

A* guarantees the shortest path if the heuristic $h (n)$ is admissible and consistent.

Admissibility: $h (n)$ never overestimates the true cost to the goal. This ensures that A* will not bypass the optimal path.
Consistency: For every pair of nodes $n$ and $n^{'}$ , the heuristic satisfies: $h (n) \leq c (n, n^{'}) + h (n^{'})$ where $c (n, n^{'})$ is the cost of transitioning from $n$ to $n^{'}$ . This property ensures that the $f (n)$ values along a path are non-decreasing.

Time and Space Complexity

Time Complexity: In the worst case, A* explores all nodes with $f (n)$ less than the cost of the optimal solution. This can grow exponentially with the branching factor $b$ and the depth $d$ of the solution: $O (b^{d})$ .
Space Complexity: A* stores all explored nodes in memory, leading to $O (b^{d})$ space usage.

5. Limitations of A*

While A* is highly efficient for many problems, it has significant limitations:

Memory Requirements: Storing all explored nodes can become infeasible for large graphs.
Performance: On graphs with high branching factors or inaccurate heuristics, A* may perform poorly.

**6. Iterative Deepening A* (IDA*)**

Why IDA*?

IDA* addresses A*'s memory limitations by combining the benefits of A*'s heuristic-guided search with the memory efficiency of depth-first search. Instead of maintaining a priority queue, IDA* performs iterative depth-first searches with increasing $f$ -cost thresholds.

How It Works

Initialize the threshold to $h (s t a r t)$ .
Perform a depth-first search, pruning paths with $f (n) >$ threshold.
If no solution is found, increase the threshold to the smallest $f (n)$ value that exceeded the current threshold.
Repeat until the goal is reached.

Wikipedia Pseudocode for IDA*

path              current search path (acts like a stack)
node              current node (last node in current path)
g                 the cost to reach current node
f                 estimated cost of the cheapest path (root..node..goal)
h(node)           estimated cost of the cheapest path (node..goal)
cost(node, succ)  step cost function
is_goal(node)     goal test
successors(node)  node expanding function, expand nodes ordered by g + h(node)
ida_star(root)    return either NOT_FOUND or a pair with the best path and its cost

procedure ida_star(root)
    bound := h(root)
    path := [root]
    loop
        t := search(path, 0, bound)
        if t = FOUND then return (path, bound)
        if t = ∞ then return NOT_FOUND
        bound := t
    end loop
end procedure

function search(path, g, bound)
    node := path.last
    f := g + h(node)
    if f > bound then return f
    if is_goal(node) then return FOUND
    min := ∞
    for succ in successors(node) do
        if succ not in path then
            path.push(succ)
            t := search(path, g + cost(node, succ), bound)
            if t = FOUND then return FOUND
            if t < min then min := t
            path.pop()
        end if
    end for
    return min
end function

**7. Implementing IDA* in JavaScript**

function idaStar(start, goal, grid, heuristic) {
  let threshold = heuristic(start, goal);

  while (true) {
    const cameFrom = {};
    const result = dfs(
      start,
      0,
      threshold,
      goal,
      grid,
      heuristic,
      cameFrom,
      new Set()
    );
    if (result.found) {
      return reconstructPath(cameFrom, goal);
    }
    if (result.threshold === Infinity) {
      return "No path found";
    }
    threshold = result.threshold;
  }
}

function dfs(node, g, threshold, goal, grid, heuristic, cameFrom, visited) {
  const f = g + heuristic(node, goal);
  if (f > threshold) return { threshold: f, found: false };
  if (node[0] === goal[0] && node[1] === goal[1])
    return { found: true, cameFrom };

  visited.add(node.toString());
  let minThreshold = Infinity;
  for (const [dx, dy] of [
    [0, 1],
    [1, 0],
    [0, -1],
    [-1, 0],
  ]) {
    const neighbor = [node[0] + dx, node[1] + dy];
    const [nx, ny] = neighbor;

    if (
      nx >= 0 &&
      nx < grid.length &&
      ny >= 0 &&
      ny < grid[0].length &&
      grid[nx][ny] === 0 &&
      !visited.has(neighbor.toString())
    ) {
      cameFrom[neighbor] = node;
      const result = dfs(
        neighbor,
        g + 1,
        threshold,
        goal,
        grid,
        heuristic,
        cameFrom,
        visited
      );
      if (result.found) return result;
      if (result.threshold < minThreshold) minThreshold = result.threshold;
    }
  }
  visited.delete(node.toString());
  return { threshold: minThreshold, found: false };
}

const grid = [
  [0, 0, 0, 0],
  [1, 1, 1, 0],
  [0, 0, 0, 0],
  [0, 1, 1, 1],
  [0, 0, 0, 0],
];

const start = [0, 0];
const goal = [4, 3];

const pathIdaStar = idaStar(start, goal, grid, heuristic);
console.log("IDA* Found Path:", pathIdaStar);
assert.notStrictEqual(pathIdaStar, "No path found");
assert.strictEqual(Array.isArray(pathIdaStar), true);

const blockedGrid = [
  [0, 1, 0],
  [1, 1, 0],
  [0, 1, 0],
];

const noPathResultIdaStar = idaStar([0, 0], [2, 2], blockedGrid, heuristic);
assert.strictEqual(noPathResultIdaStar, "No path found");

**8. Comparing A* and IDA***

Feature	A*	IDA*
Memory Usage	High (stores all nodes)	Low (depth-first search)
Speed	Faster for smaller graphs	Slower due to iterative nature
Implementation Complexity	Moderate	Higher
Suitability for Large Graphs	Limited	More suitable

9. Use Cases

Game Development: Pathfinding for characters and AI in games (e.g., NPC movement).
Robotics: Navigation for autonomous robots in real-world environments.
Network Routing: Finding the shortest paths in network graphs.
Puzzle Solving: Solving puzzles like the 8-puzzle or 15-puzzle efficiently.

10. Common Pitfalls and Best Practices

When implementing and applying A* or IDA*, it’s essential to avoid common mistakes and adopt best practices to ensure optimal performance.

1. Choosing Heuristics Wisely

Admissibility: Ensure that the heuristic never overestimates the cost to reach the goal. This guarantees that A* will find the optimal solution.
Consistency (Monotonicity): Verify that the heuristic satisfies the triangle inequality: $h (n) \leq c (n, n^{'}) + h (n^{'})$ . This ensures that the algorithm’s $f (n)$ values are non-decreasing.
Common heuristics include:
- Manhattan Distance: Suitable for grid-based problems where movement is restricted to horizontal or vertical directions.
- Euclidean Distance: Best for problems allowing diagonal or free movement in a continuous space.
- Domain-Specific Heuristics: Tailor heuristics based on specific problem knowledge to improve performance.

2. Balancing $g (n)$ and $h (n)$

Assign appropriate weights to $g (n)$ (path cost so far) and $h (n)$ (estimated cost to goal).
Overemphasizing $h (n)$ might lead to suboptimal solutions or longer paths.
Relying heavily on $g (n)$ might degrade performance, as the algorithm explores more nodes unnecessarily.

3. Debugging and Visualization Techniques

Visualization: Tools like heatmaps or animations of the search process can help debug and optimize the algorithm.
Logging: Track $f (n)$ , $g (n)$ , and $h (n)$ values for each node to identify anomalies.
Unit Testing: Validate heuristic functions and graph structures with simple test cases to ensure correctness.
Benchmarking: Test the implementation on different datasets to measure performance and identify bottlenecks.

11. Advanced Topics

1. Variants of A*

A* has inspired several variants tailored to specific scenarios or performance needs:

Weighted A*:
- Modifies the evaluation function to $f (n) = g (n) + w \cdot h (n)$ , where $w$ (weight) adjusts the heuristic's influence.
- Used to trade optimality for speed by biasing the search toward the goal.
Memory-Bounded A*:
- Examples include Simplified Memory-Bounded A* (SMA*) and Recursive Best-First Search (RBFS).
- These variants limit memory usage by discarding or revisiting nodes as needed.
Hierarchical A*:
- Splits large graphs into smaller subgraphs, solves each independently, and combines results.
- Ideal for complex environments like city navigation.

2. Integration with Machine Learning

Machine learning can enhance A* by dynamically adjusting heuristics or guiding the search process:

Dynamic Heuristics: Use neural networks or reinforcement learning to predict better heuristic values for each state.
Adaptive Pathfinding: Train models to optimize paths based on historical data or changing environments.
Hybrid Models: Combine A* with ML-driven approaches for problems like real-time strategy games or robotics.

3. Parallelizing A* for Performance Optimization

Parallelization can significantly speed up A* for large graphs:

Parallel Open Set: Divide the open set among multiple threads or processes, each exploring a subset of nodes.
Distributed Search: Use distributed systems to partition the graph and execute search concurrently.
GPU Acceleration: Leverage GPUs for massive parallelism, especially for heuristic computations and large-scale graph traversal.

12. Conclusion

The A* search algorithm and Iterative Deepening A* are powerful tools for solving pathfinding and graph traversal problems. While A* excels in speed and guarantees optimality with sufficient memory, IDA* offers a memory-efficient alternative for large graphs. By understanding their principles, properties, and implementations, you can leverage these algorithms to tackle a wide range of computational problems effectively.

How I Use AI, Sticky Notes, and Pomodoro to Stay Productive as a Software Engineer

Piyush Chauhan — Fri, 24 Jan 2025 15:43:44 +0000

As a software engineer juggling frontend and backend tasks, I’ve found that staying organized and focused is key to delivering quality work without burning out. Over time, I’ve developed a workflow that combines AI assistance, sticky notes, and the Pomodoro Technique to break down tasks, track progress, and maintain my sanity. In this article, I’ll share my process and how you can adapt it to your own workflow.

The Problem: Overwhelming Tasks and Context Switching

When working on a new feature or story, I often start with a vague set of requirements. Breaking these down into actionable steps can be overwhelming, especially when I’m still figuring out the details. Add in the constant context switching between frontend and backend work, and it’s easy to lose track of what’s done and what’s left.

To tackle this, I needed a system that:

Helps me break down tasks into manageable chunks.
Keeps me aware of my progress.
Allows me to adapt as I learn more about the problem.
Ensures I stay focused and hydrated (because, let’s be honest, we all forget to drink water sometimes).

My Workflow: AI, Sticky Notes, and Pomodoro

Here’s how I’ve structured my workflow to address these challenges:

1. Generate an Initial To-Do List with AI

When I start a new subtask, I use AI tools (like Perplexity.ai or Continue.dev with Codellama) to generate an initial to-do list based on the requirements. For example, if I’m working on a user login feature, I might ask:

"Generate a to-do list for implementing a user login feature with email and password authentication. Include frontend and backend tasks."

The AI might suggest:

Set up login API endpoint.
Create frontend login form.
Add input validation for email and password.
Implement error handling for invalid credentials.

This gives me a solid starting point with 2–5 high-level tasks.

2. Create a Feature Branch

Once I have my initial to-do list, I create a feature branch for the subtask. For example:

git checkout -b feature/user-login

This keeps my work isolated and makes it easier to manage changes.

3. Work Incrementally and Extend the To-Do List

I start by tackling the first task on my list. As I work, I often uncover additional subtasks or edge cases. This is where AI comes in again. I’ll share my current progress and ask for suggestions:

"I’m working on implementing the login API. Here’s what I’ve done so far: [details]. What additional tasks should I consider?"

The AI might suggest:

Add rate limiting to prevent brute force attacks.
Log failed login attempts for security monitoring.
Write unit tests for the login functionality.

I add these tasks to my to-do list and continue working incrementally.

4. Track Progress with Sticky Notes

I’m a big fan of sticky notes for tracking progress. Whether physical or digital, they give me a visual representation of what’s done and what’s left.

Physical Sticky Notes: I place them on my desk or monitor, moving completed tasks to a "Done" section.
Digital Tools: If I’m working remotely, I use tools like Trello, Notion, or Todoist to manage my to-do list.

Crossing off tasks gives me a sense of accomplishment and keeps me motivated.

5. Stay Focused with the Pomodoro Technique

To maintain focus, I use the Pomodoro Technique:

Work for 25 minutes, then take a 5-minute break.
After four Pomodoros, take a longer break (15–30 minutes).

I assign specific to-do items to each Pomodoro session. For example:

Pomodoro 1: Set up login API endpoint.
Pomodoro 2: Add input validation for email and password.

During breaks, I make sure to hydrate, stretch, or take a short walk.

6. Create a Pull Request and Request Review

Once all tasks are completed, I create a pull request (PR) for my feature branch. I write a detailed PR description summarizing:

What was implemented.
Any challenges faced.
Testing performed.

I then request a review from my team and iterate based on feedback.

Tools I Use

Here are some tools and methods that have been invaluable in my workflow:

AI Tools

Perplexity.ai: For generating initial to-do lists and brainstorming solutions.
Continue.dev: For seamless integration with my IDE and local development environment.
Local AI Models: I use Qwen2.5-Coder 7B and CodeLlama 7B for code completion, debugging, and extending my to-do lists as I work. Running these models locally ensures privacy and customization.

Task Management

Trello: For visual task tracking with boards, lists, and cards.
Notion: For documenting requirements, progress, and reference materials.
JIRA: For aligning my tasks with team-wide project management and sprint planning.

Pomodoro Timers

Manual iPhone Timer: I use the built-in timer on my iPhone for Pomodoro sessions. It’s simple and effective.
Paper Checklist: I keep a handwritten checklist on paper to track tasks for each Pomodoro session. There’s something satisfying about physically crossing off completed tasks.

Hydration Tracking

Manual iPhone Timer: I set recurring reminders to drink water throughout the day.
Paper Drawing: I draw circles on paper and color them in to track my hydration progress. Each circle represents a glass of water, and filling them in gives me a visual sense of accomplishment.

Why This Works for Me

This workflow works because it:

Breaks down complex tasks into manageable chunks.
Adapts to new information as I learn more about the problem.
Keeps me focused with the Pomodoro Technique.
Provides a visual representation of progress with sticky notes.
Ensures I stay hydrated and energized throughout the day.

Example Workflow in Action

Scenario: Implementing a User Login Feature

Initial To-Do List (AI-Generated):
- Set up login API endpoint.
- Create frontend login form.
- Add input validation for email and password.
- Implement error handling for invalid credentials.
Feature Branch: feature/user-login
Incremental Work:
- Start with "Set up login API endpoint."
- Commit: git commit -m "Add login API endpoint with email/password validation"
- Ask AI: "What else should I consider for the login API?"
- AI suggests: Add rate limiting, log failed attempts, and write unit tests.
- Add these tasks to your to-do list.
Sticky Notes:
- Move "Set up login API endpoint" to "Done."
- Add new tasks: "Add rate limiting," "Log failed attempts," "Write unit tests."
Pomodoro Sessions:
- Session 1: Work on "Add rate limiting."
- Session 2: Work on "Log failed attempts."
PR and Review:
- Create PR with title "feat: implement user login" and description: "Implemented user login feature with email/password authentication. Added rate limiting and logging for security. Unit tests included."
- Request review and iterate based on feedback.

Key Benefits of This Workflow

Clarity: Breaking tasks into smaller chunks makes complex work manageable.
Focus: Pomodoro sessions and sticky notes help maintain focus and track progress.
Adaptability: AI assistance allows you to dynamically adjust your plan as you learn more.
Wellness: Regular breaks and hydration keep you energized and productive.

By formalizing and refining this workflow, you can enhance your productivity, reduce stress, and deliver high-quality work consistently.

Try It Out!

If you’re feeling overwhelmed by your tasks or struggling to stay focused, give this workflow a try. Start small—use AI to generate a to-do list, create a feature branch, and work incrementally. Experiment with sticky notes and the Pomodoro Technique to see what works best for you.

I’d love to hear how you stay productive in your day-to-day work. Share your tips and tools in the comments below!

Happy coding! 🚀

Introduction to WebAssembly (WASM)

Piyush Chauhan — Fri, 10 Jan 2025 16:21:06 +0000

WebAssembly (WASM) is a binary instruction format for a stack-based virtual machine, designed as a portable target for high-performance applications. In this article, we'll explore how to compile a simple C program to WebAssembly, load it into a web browser, and interact with it using JavaScript. We'll also explore some useful tools and commands for working with WASM outside the dev container environment.

Setting Up the Development Environment

Create the necessary folder structure and files for your WebAssembly project.

Create Project Folder:

Begin by creating a new directory for your project. Inside this folder, you'll add the necessary files and configurations.

mkdir wasm-web-example
cd wasm-web-example

Set Up Dev Container:

In the wasm-web-example directory, create the .devcontainer folder to store the dev container configuration files. These files will set up a container with Emscripten installed to compile C code into WebAssembly.

Inside the .devcontainer folder, create the following files:

devcontainer.json: The devcontainer.json file configures VSCode to use the Docker container with the necessary extensions and environment settings.

{
    "name": "Emscripten DevContainer",
    "build": {
        "dockerfile": "Dockerfile"
    },
    "customizations": {
        "vscode": {
            "settings": {
                "terminal.integrated.shell.linux": "/bin/bash",
                "C_Cpp.default.configurationProvider": "ms-vscode.cmake-tools",
                "C_Cpp.default.intelliSenseMode": "gcc-x64"
            },
            "extensions": [
                "ms-vscode.cpptools",
                "ms-vscode.cmake-tools"
            ]
        }
    },
    "postCreateCommand": "emcc --version"
}

Dockerfile: The Dockerfile will set up the Emscripten environment. Here's the content for that file:

# Use the official Emscripten image
FROM emscripten/emsdk:3.1.74

# Set the working directory
WORKDIR /workspace

# Copy the source code into the container
COPY . .

# Install any additional packages if necessary (optional)
# Ensure to clean up cache to minimize image size
RUN apt-get update && \
    apt-get install -y build-essential && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*

Create VSCode Settings:

In the root of your project, create a .vscode folder with the following files:

c_cpp_properties.json: This file configures the C++ IntelliSense and include paths for your project.

{
    "configurations": [
        {
        "name": "Linux",
        "includePath": [
            "${workspaceFolder}/**",
            "/emsdk/upstream/emscripten/system/include"
        ],
        "defines": [],
        "compilerPath": "/usr/bin/gcc",
        "cStandard": "c17",
        "cppStandard": "gnu++17",
        "configurationProvider": "ms-vscode.cmake-tools"
        }
    ],
    "version": 4
}

settings.json: This file includes specific VSCode settings for language associations.

{
    "files.associations": {
        "emscripten.h": "c"
    },
    "[javascript]": {
        "editor.defaultFormatter": "vscode.typescript-language-features"
    },
    "[typescript]": {
        "editor.defaultFormatter": "vscode.typescript-language-features"
    },
    "[jsonc]": {
        "editor.defaultFormatter": "vscode.json-language-features"
    },
    "[json]": {
        "editor.defaultFormatter": "vscode.json-language-features"
    },
    "[html]": {
        "editor.defaultFormatter": "vscode.html-language-features"
    }
}

Create C, JavaScript, and HTML Files:

Now, create the following files for your project:

test.c: This C file contains the simple function that will be compiled to WebAssembly.

// test.c
int add(int lhs, int rhs) {
    return lhs + rhs;
}

test.html: This HTML file will load the WebAssembly module using JavaScript.

<!DOCTYPE html>
<html lang="en">

<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>WebAssembly Example</title>
</head>

<body>
  <h1>WebAssembly Example</h1>
  <div id="output"></div>

  <script src="test.js"></script>
</body>

</html>

test.js: This JavaScript file will fetch the WebAssembly module and call the exported function.

// test.js
// Path to the .wasm file
const wasmFile = 'test.wasm';

// Load the WebAssembly module
fetch(wasmFile)
  .then(response => {
    if (!response.ok) {
      throw new Error(`Failed to load ${wasmFile}: ${response.statusText}`);
    }
    return response.arrayBuffer();
  })
  .then(bytes => WebAssembly.instantiate(bytes))
  .then(({ instance }) => {
    // Access exported functions
    const wasmExports = instance.exports;
    console.log({ wasmExports })

    // Example: Call a function exported from the WebAssembly module
    if (wasmExports.add) {
      const result = wasmExports.add(5, 3); // Example function call
      document.getElementById('output').textContent = `Result from WebAssembly: ${result}`;
    } else {
      document.getElementById('output').textContent = 'No "add" function found in the WebAssembly module.';
    }
  })
  .catch(error => {
    console.error('Error loading or running the WebAssembly module:', error);
    document.getElementById('output').textContent = 'Error loading WebAssembly module.';
  });

Now that you've set up all the necessary files and configurations, you can move on to compiling and interacting with WebAssembly.

The project structure looks like following now:

➜  wasm-web-example: tree . -a
.
├── .devcontainer
│   ├── Dockerfile
│   └── devcontainer.json
├── .vscode
│   ├── c_cpp_properties.json
│   └── settings.json
├── test.c
├── test.html
├── test.js

Compiling C Code to WebAssembly Using Emscripten

Basic C Program:

The file test.c contains a simple function add that adds two integers. We will compile this C function into WebAssembly using Emscripten.

// test.c
int add(int lhs, int rhs) {
    return lhs + rhs;
}

Emscripten Command:

Inside the dev container, open the terminal (use cmd+j in VSCode) and run the following Emscripten command to compile the C code to WebAssembly:

emcc test.c -O3 -s STANDALONE_WASM -s EXPORTED_FUNCTIONS='["_add"]' --no-entry -o test.wasm

Breakdown of the Command

emcc: This is the Emscripten C/C++ compiler command. It compiles C/C++ source files into WebAssembly or asm.js.
test.c: This specifies the input C source file that you want to compile.
-O3: This flag enables aggressive optimizations during the compilation process. The -O3 optimization level is typically used for performance-critical applications, as it applies various optimization techniques that can significantly improve runtime performance.
-s STANDALONE_WASM: This option instructs Emscripten to generate a standalone WebAssembly module. A standalone WASM module does not depend on any JavaScript glue code and can be executed independently in environments that support WebAssembly.
-s EXPORTED_FUNCTIONS='["_add"]': This flag specifies which functions from the C code should be exported and made available for calling from JavaScript. In this case, the function named _add will be accessible in the resulting WASM module.
--no-entry: This option tells the compiler that there is no entry point (like a main() function) in the program. This is useful for libraries or modules that are intended to be used by other code rather than executed directly.
-o test.wasm: This specifies the output file name for the compiled WebAssembly module. In this case, it will create a file named test.wasm.

This command will generate test.wasm, the WebAssembly binary, and ensure that the add function is exported for use in JavaScript.

Loading and Interacting with WebAssembly in the Browser

HTML Setup:

The file test.html contains a simple HTML page that loads the WebAssembly binary using JavaScript. The JavaScript code in test.js fetches the .wasm file and instantiates it.

<!-- test.html -->
<!DOCTYPE html>
<html lang="en">

<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>WebAssembly Example</title>
</head>

<body>
  <h1>WebAssembly Example</h1>
  <div id="output"></div>

  <script src="test.js"></script>
</body>

</html>

JavaScript Setup:

The JavaScript file test.js loads the test.wasm file and calls the exported add function:

// test.js
// Path to the .wasm file
const wasmFile = 'test.wasm';

// Load the WebAssembly module
fetch(wasmFile)
  .then(response => {
    if (!response.ok) {
      throw new Error(`Failed to load ${wasmFile}: ${response.statusText}`);
    }
    return response.arrayBuffer();
  })
  .then(bytes => WebAssembly.instantiate(bytes))
  .then(({ instance }) => {
    // Access exported functions
    const wasmExports = instance.exports;
    console.log({ wasmExports })

    // Example: Call a function exported from the WebAssembly module
    if (wasmExports.add) {
      const result = wasmExports.add(5, 3); // Example function call
      document.getElementById('output').textContent = `Result from WebAssembly: ${result}`;
    } else {
      document.getElementById('output').textContent = 'No "add" function found in the WebAssembly module.';
    }
  })
  .catch(error => {
    console.error('Error loading or running the WebAssembly module:', error);
    document.getElementById('output').textContent = 'Error loading WebAssembly module.';
  });

This will display the result of the add function in the HTML page when the module is loaded successfully.

Using External Tools on macOS

Outside the dev container, there are several useful commands you can run to work with WebAssembly on your Mac.

Install `wabt`:

wabt (WebAssembly Binary Toolkit) provides utilities for working with WebAssembly, including converting .wasm files to the human-readable WAT (WebAssembly Text) format. Install it via Homebrew:

brew install wabt

Convert WASM to WAT:

Once wabt is installed, you can use the wasm2wat tool to convert your WebAssembly binary (test.wasm) to the WAT format:

wasm2wat test.wasm

This will output a text representation of the WebAssembly module that you can read and inspect.

(module
  (type (;0;) (func))
  (type (;1;) (func (param i32 i32) (result i32)))
  (type (;2;) (func (param i32)))
  (type (;3;) (func (result i32)))
  (func (;0;) (type 0)
    nop)
  (func (;1;) (type 1) (param i32 i32) (result i32)
    local.get 0
    local.get 1
    i32.add)
  (func (;2;) (type 2) (param i32)
    local.get 0
    global.set 0)
  (func (;3;) (type 3) (result i32)
    global.get 0)
  (table (;0;) 2 2 funcref)
  (memory (;0;) 258 258)
  (global (;0;) (mut i32) (i32.const 66560))
  (export "memory" (memory 0))
  (export "add" (func 1))
  (export "_initialize" (func 0))
  (export "__indirect_function_table" (table 0))
  (export "_emscripten_stack_restore" (func 2))
  (export "emscripten_stack_get_current" (func 3))
  (elem (;0;) (i32.const 1) func 0))

Serve the HTML Page:

To view the HTML page that interacts with the WebAssembly module, you can use Python’s simple HTTP server:

python -m http.server

This command will start a local web server on http://localhost:8000, where you can open index.html in your browser to see the WebAssembly module in action.

Conclusion

By following the steps outlined in this article, you can set up a development environment to compile C code to WebAssembly, interact with it using JavaScript, and convert the resulting WebAssembly binary to the WAT format for inspection. The use of external tools like wabt and Python’s HTTP server makes it easier to manage and explore WebAssembly modules on your macOS system.

DEV Community: Piyush Chauhan

Why big monorepos get slow, and what Rush.js + Sparo do about it

The problem

The repo this is grounded in

Sparo: make Git deliver less

Partial clone: download fewer objects

Sparse checkout: write fewer files

Profiles: name the set you want

What a sparse tree looks like day to day

Sparse checkout fights the install

Rush: the graph, the install, the selection

The project registry

Install: one lockfile, and a way to install less

The supply-chain age check

Bulk, global, and phased commands

Selection: build only what changed

Incremental builds, and the assumption under them

Versioning and publishing

What the numbers look like

Blobless clone on its own is worse than useless

The git status number is the one you feel

The install does not move

Selection depends on the graph, not on the flag

CI and CD

The workflow this repo commits

The workflow a large repo wants

Four ways affected selection goes wrong

Do not copy this repo's Node pinning

Deployment

How Rush and Sparo compare with the alternatives

When not to use this

How to measure your own repo

What this tutorial does not cover

Resources

A type-safe decision engine in ReScript

Why stringly-typed flags keep breaking

The demo: Elsewhere, a hotel site with experiments baked in

The request path, end to end

Decisions are types, not strings

One registry owns every experiment and rule

Deterministic assignment without server state

Rules: eligibility and effects

Resolution and provenance

Preview overrides and the decision inspector

Signed cookies and rollout flags

Crossing the ReScript ↔ TypeScript boundary

What this cost us

Run it yourself

FAQ

Why ReScript instead of TypeScript?

How do I add a new experiment?

How do I kill an experiment in production?

Does it track conversions or metrics?

How I Built a .NET + React Islands Site With a Playwright Snapshot Worker

The problem: interactive pages without browser rendering per visitor

Who owns what?

One request, two paths

How an island becomes published HTML

Publishing without serving stale work

What must never be snapshotted

The SEO advantage over a client-only SPA

How SSR, SSG, ISR, and PPR compare

Tradeoffs and running it

The design rule I kept

Integration Is the Product: Platform Engineering for Fast Frontend Teams

The model in one picture

A concrete example: the Booking module

1. Build and artifact boundary

2. Platform and runtime boundary

3. Domain contract boundary

4. Environment and data boundary

The paved road: two local modes, one composition contract

Mode A: domain workbench

Mode B: real-shell composition

Let contracts unblock implementation—not conversation

Test the bytes you will release

Preview a product, not an isolated copy of the company

GitOps gives promotion a memory

Make failure attributable in one screen

Start small: improve one developer journey

The `git status` number is the one you feel