Bytes
Early screens that show who can actually build
Real code. Fast signal. Cut the noise before you ever get on a call.
4.9
4.9
5
How Bytes work
See a sample task and its test file
Each Byte gives candidates a clear README and a public test file. You can see exactly what the task asks for and how the code gets checked. No surprises, no hidden steps.
Starter code included
Tests are always visible
Tasks are focused and practical
Library breadth
800+
Tasks and Bytes across screening, take-home, and live follow-up.
Screening languages
12+
Language-specific runners and test files already wired in.
Candidate flow
README
Brief first, tests next, then code that has to hold up.
Languages in the Bytes library
Python
JavaScript
TypeScript
Ruby
Java
C
C++
C#
+4 more
Language runners available
Example Bytes
Signal weave encoder
Monitoring / platform tooling
npm run test
README brief
A monitoring console stores outgoing alert messages in a compact columnar format called a signal weave.
Normalize the message by lowercasing and removing non-alphanumeric input
Build a near-square grid where columns stay close to rows
Read the grid column by column and preserve trailing spaces when the last row is short
Public test preview
The candidate can read this file and work backwards from the expectations.
signal-weave.test.ts
signal-weave.test.ts
it("build signal rows creates a near square grid", () => {
expect(buildSignalRows("Never vex thine heart with idle woes")).toEqual([
"neverv",
"exthin",
"eheart",
"withid",
"lewoes",
]);
});
it("weave signal pads a short final row", () => {
expect(weaveSignal("Chill out.")).toEqual("clu hlt io ");
});What this screens for
Grid sizing on imperfect squares
Padding behavior on short final rows
Readable helper functions instead of one opaque transform
Live follow-up
See AI use with judgment
Bytes helps you spot who is strong early. In the live follow-up, you can see how finalists work with AI once the task gets collaborative, which suggestions they trust, and where they slow down to verify before shipping.
Prompt quality and critical evaluation
See whether the candidate gives the model enough context, asks for checks, and pushes back on weak output.
Task ownership under pressure
Watch whether they use AI as leverage or hand over the whole task once the pressure rises.
Code evidence when available
Bring code, terminal, and debugging evidence into the review instead of guessing from the outside.
AI Governance
AI judgment after the shortlist
Bytes gets you to the right shortlist. The live session shows how finalists actually work with AI once the task has moving parts.
Sample
79
/ 100
Critical Evaluation
8.2
Requests validation, tradeoffs, tests, and sanity checks instead of blind acceptance.
Prompt Quality
8.0
Specific asks with relevant repo context, constraints, and acceptance criteria.
Task Ownership
7.8
Keeps the engineer in charge by using AI for bounded steps rather than whole-task delegation.
Iterative Troubleshooting
7.6
Follows up, narrows scope, and builds on previous output when the first answer is not enough.
Measured in AI-enabled CodePair follow-up sessions
Strong candidates keep ownership: they frame the task clearly, verify results, and know when to trust the model versus when to step in.
Architecture ownership
Debugging independence
Integration quality
The communication between hiring managers, recruiters and candidates has been incredibly improved since we started using CodeSubmit. There is no 'back and forth' anymore and the technical assessment is running smoothly!
Virginie Raucoules
P&C Manager @ KONUX
