Getting Uncomfortable With Complexity Is the Only Way Forward
I spent three years trying to simplify how teams shipped small feature changes before I realized the problem wasn't the tools. It was the workflow itself. Making Hacks Simple is a methodology and accompanying toolkit I put together to help developers and technical teams ship small, disposable code changes without the usual overhead. The name is a little self-explanatory but not in a cute way. Most people think of "hacks" as bad code you leave behind. They are wrong. A hack is just an experiment with incomplete information. The problem is that every experiment currently requires either a full deployment pipeline or a painful local setup that eats half your day. Making Hacks Simple removes that friction by creating a lightweight, isolated environment where you can prototype, test, and kill changes in under 10 minutes instead of 45 to 90. Here is how the process actually works in practice. You start by identifying the boundary of what needs to change. Is it an API endpoint? A CSS file? A configuration value? You define that boundary in a single JSON manifest file. Then you spin up a sandboxed container using a Docker image I wrote for this purpose. The container mounts only the files and dependencies relevant to your scope. No monorepo headaches. No installing 47 packages you do not need.
I have used this exact setup to ship A/B test logic on a Django project last October. The change was a single view function modification. Normally that would mean spinning up the full dev environment, running migrations, connecting to the staging database, and hoping nothing broke. With Making Hacks Simple, I wrote the manifest, mounted a mocked version of the database, and had a working test in about six minutes. The change got approved, merged, and deployed the same day. That is the difference this approach makes. Not dramatically faster because the work is shorter, but because the overhead around the work disappears.
How to Actually Use It
There is a GitHub repository where the full project lives. I do not host a traditional download page because the toolkit is designed to be cloned and run locally, not installed through a package manager that adds overhead. You can find it at github.com/alexchen-hacks/making-hacks-simple. The README is reasonably thorough but I will walk through the actual flow here because the README skips the part that trips people up. First, install Docker. That is non-negotiable. The sandboxing layer depends on it. Then clone the repository and run the setup script in the scripts directory. It installs the CLI tool and configures the default container profiles. After that, you create your first manifest. The syntax is intentionally minimal: {
Get the Full Details
"name": "my-first-change", "target": "api/user_profile_endpoint", "files": ["./src/views/user.py", "./config/settings.json"],
"mocks": ["database", "external_api_call"], "timeout_minutes": 10 }
That is it. You do not need a separate test configuration. You do not need to set up environment variables. The container injects mock responses for anything listed in the mocks array. Database queries return stubbed data. External API calls return preconfigured JSON payloads. You write your change, run the cli tool with the manifest path, and it spins up the container, mounts only those files, and gives you a terminal session inside the sandbox. I learned this the hard way. Early on I kept getting errors because I forgot to list the external API dependency in the mocks array. The container would hang waiting for a real response that never came, and the timeout would kill it before I could debug anything. I wasted two days on that before realizing the timeout was eating the debug time, not the error itself. Once I added that dependency to the mocks section with a fake 200 response, everything started working cleanly. It is a small thing but it took me longer to figure out than the entire rest of the setup combined.
Things the Documentation Does Not Tell You
The first counter-intuitive thing is that Making Hacks Simple is actually worse for large-scale refactors. I tried using it for a migration where we were changing the entire authentication flow across five services. It fell apart because the manifest system was not designed to track cross-service dependencies. The sandbox isolates too aggressively. For that kind of work, you are better off using a traditional local dev environment or Kubernetes minikube clusters with proper service mesh tooling. Making Hacks Simple excels at targeted changes under 200 lines of modified code. It breaks down when the change spans multiple unrelated subsystems. The second thing is that the mocking system assumes you can predict what your dependencies will return. In practice, you often cannot. During a recent payment integration test, the external gateway returned a completely different error structure than the documentation showed. The stubbed mock forced a 200 response, so my change passed inside the sandbox and failed in production immediately. The workaround was to add a passthrough option to the manifest that forwards matching requests to a real staging endpoint instead of returning stubbed data. That added about 30 seconds to the setup but saved me from deploying broken code. It is worth noting that this passthrough mode requires credentials configured in a separate secrets file, which is not obvious from the default setup.
When to Actually Use This
Use Making Hacks Simple when you have a single change that touches one or two files, you need to validate behavior before committing, and you are working in a team where spinning up full environments causes coordination delays. It cuts the iteration time from roughly 40 minutes of setup overhead down to about five. The container warming time is real, so batch your changes rather than opening and closing sandboxes constantly. Do not use it when you are debugging memory leaks in production, when your change requires real database state, or when you are part of a organization that already has a well-tuned local development environment. The isolation that makes this tool useful is also what makes it useless for problems that depend on environment complexity. That is not a flaw in the design. It is a boundary condition. The project is open source and maintained on GitHub. I check issues regularly and merge pull requests when they are reasonable. The license is MIT, which means you can modify it without worrying about compliance paperwork. If you decide to fork it, just be aware that the container base images are pinned to specific versions and updating them requires careful testing of the mock injection layer. I learned that one during a security patch cycle when an updated base image broke the volume mounting behavior for all existing manifests.
