AI coding assistant
Paste your code or describe what you are building and get engineering work you can use: an architecture with the tradeoffs spelled out, a code review that catches what matters before you merge, a systematic path through a stubborn bug, a test strategy, a plan to pay down tech debt. Zeplik reasons from the code and stack you share, not a generic template, and it is honest about the line: this is guidance and draft code to read critically, test, and security-check, not tested production software.
Who it is for
- Engineers who want a sharp second opinion on design and code
- Solo builders and small teams without a senior reviewer on call
- Developers debugging, testing, or refactoring an unfamiliar codebase
- Teams standardizing architecture, review, and delivery practices
How it works
Paste the code or describe the problem
Drop in the code, the error, the schema, or a description of the system and constraints. Nothing is assumed. The work is built only from what you share, and the more of your real stack and requirements you give, the more the answer fits your case.
Ask for the engineering work you need
Say it plainly, for example "review this before I merge" or "design the data model for this feature." Zeplik routes to the right engineering skill without you naming it, and you can steer language, framework, scale, and the tradeoffs you care about.
Read critically, test, and ship
You get a design, review, or draft with the reasoning shown. Read it critically against your stack, run it through your own tests and security review, and iterate in the same conversation before you ship.
What you can ask
Real asks in plain language, and the concrete deliverable you get back. Open the assistant to try your own; nothing runs until you send it.
Design the architecture for the system I will describe.
A proposed architecture from your requirements: the components and how they talk, the data model, the key tradeoffs and why, and the failure modes to plan for, so you can decide with eyes open.
Review this code and tell me what to fix before I merge.
A focused code review of what you paste: correctness bugs, edge cases, security and performance concerns, and clarity issues, prioritized, with concrete suggested changes rather than vague notes.
Help me debug this error systematically.
A structured path from the stack trace or symptom you paste: the likely causes ranked, what to check to confirm each, and the fix once the real cause is found, instead of guess-and-check.
Plan a test strategy for this feature.
A test plan for the feature you describe: what to cover at unit, integration, and end-to-end level, the edge cases that matter, and where to invest versus skip, so coverage is deliberate.
This function is a mess. Simplify it without changing behavior.
A cleaner version of the code you paste with behavior preserved, the reasoning for each change, and the risky spots called out, so you can refactor with confidence.
Give me a deployment checklist for shipping this to production.
A deploy checklist tuned to what you are shipping: the pre-flight checks, rollout and rollback plan, monitoring to watch, and the things teams forget, so the release is boring.
What it can do
The assistant routes to the right skill for you. Each one below is a focused, ready-to-run workflow; follow a link to see exactly what it does.
Architecture and design
Get the shape right before you write the code.
- System designDesign a system end to end: components, data flow, and tradeoffs.
- Backend architectureStructure services, boundaries, and state for a backend.
- Architecture decisionsWeigh options and record the reasoning behind a decision.
- API designDesign clear, consistent, evolvable APIs.
- Data modelingModel your data and schema for correctness and scale.
Code quality and review
Catch what matters before it merges, and keep the codebase clean.
- Code reviewA rigorous review: correctness, security, performance, clarity.
- Refactor and simplifyMake code cleaner and simpler without changing behavior.
- Error handlingRobust error handling and failure-mode patterns.
- Tech debtIdentify, prioritize, and plan paying down technical debt.
- Verify before doneCheck work is actually complete and correct before you call it done.
Debugging, testing, and performance
Find the real cause, prove it works, and make it fast.
- Systematic debuggingWork a bug down to root cause instead of guessing.
- Debugging strategiesTactics for the hard, intermittent, and heisenbugs.
- Test-driven developmentDrive design and correctness with tests first.
- Testing strategyDecide what to test and at which level for real coverage.
- PerformanceFind and fix the bottleneck with evidence, not guesses.
Ship and operate
Get it out the door and keep it running.
- Feature deliveryChain design, build, test, and ship into one workflow.
- Deploy checklistA pre-flight, rollout, and rollback plan for a release.
- Cloud and DevOpsCI/CD, infrastructure, and operations patterns.
- DockerContainerize and optimize images and local dev.
- Advanced GitBranching, rebasing, and recovery when Git gets hairy.
Stacks, docs, and AI engineering
Framework-specific help, clear docs, and building on LLMs.
- Next.jsIdiomatic Next.js patterns for app and API code.
- Python engineeringProduction-grade Python structure, typing, and tooling.
- Technical docsWrite docs engineers actually read and trust.
- READMEsA README that gets someone productive fast.
- Prompt engineeringDesign reliable prompts for LLM features.
- RAGBuild retrieval-augmented generation that actually grounds.
- MCP serversBuild Model Context Protocol servers and tools.
What a general chatbot gets wrong on coding, and what is different here
A raw model is genuinely useful for code, so the honest pitch is not that it is dangerous, it is that it is confidently generic. It writes code that looks right for a stack it is guessing at, invents an API that does not exist, and calls a bug fixed without proving it. Zeplik reasons from the code and constraints you share and treats its output as a draft to test, not a finished answer.
The failure. A general model answers for a generic stack, confidently calling a library method or config that does not exist in your version.
How Zeplik grounds it. Zeplik works from the code, framework, and versions you share, and when it is unsure it says so and tells you what to check, rather than inventing an API to fill the gap.
The failure. Asked to fix a bug, a general model declares it solved without reproducing it or considering the edge cases.
How Zeplik grounds it. The debugging skills push to a confirmed root cause, and a verify-before-done step checks the work is actually complete, so a fix comes with the reasoning and the tests to prove it, not just confidence.
The failure. A general model hands you code with no note about what it did not handle, and you find the security or scaling gap in production.
How Zeplik grounds it. Reviews and designs call out the tradeoffs, the failure modes, and what was left out, and every result reminds you to run it through your own tests and security review before it ships.
What it does not do
Knowing the boundary is part of using it responsibly. This is where a person, not the assistant, has to own the call.
- Draft code, not tested software. Everything is a draft to read critically and test. It has not run your test suite or your security scanners, so verify behavior in your environment before you ship.
- It reasons from what you share. It sees the code and context you paste, not your whole repo. Designs and reviews are only as accurate as the stack, versions, and constraints you give it.
- Confirm versions and APIs. Library APIs and versions move fast. Check any method, flag, or config it suggests against your actual dependency versions and the current docs.
- Security is still your job. It flags obvious issues, but it is not a security audit. Run security-critical code past the security assistant and a human reviewer before it ships.
This is engineering guidance and draft code for your review, not tested, production-ready software. It reflects general best practice, not your exact stack, versions, or constraints, so read it critically, run it against your own tests and security review, and confirm it behaves correctly in your environment before you ship.
Frequently asked questions
- Is the AI coding assistant free?
- Zeplik is free to start and every new account includes credits. Engineering skills run like any other Zeplik chat, so you spend only normal usage credits, with the cost shown before you send.
- Can it see my whole repository?
- Not yet. It reasons from the code and context you paste in the conversation, not your full repo. Repo and codebase ingestion is a documented next step. For now, share the relevant files and describe the surrounding system.
- Is the code it writes production-ready?
- Treat it as a well-reasoned draft, not tested software. It has not run against your tests, environment, or security scanners. Read it critically, test it, and review security-critical changes before shipping.
- Which languages and frameworks does it handle?
- It covers general software engineering plus specific stacks like Next.js, Python, Docker, cloud and DevOps, and LLM engineering (prompting, RAG, MCP). Tell it your language, framework, and versions so the answer fits your case.
- What do I need to give it?
- Paste the code, error, or schema, or describe the system and constraints. The more of your real stack, versions, and requirements you share, the more the design or review fits your situation instead of a generic one.
- Can it help with security review?
- It flags obvious issues during code review, and Zeplik has a dedicated AI security assistant for deeper vulnerability review, threat modeling, and hardening. Use both, and keep a human reviewer on security-critical changes.
More on Zeplik
Design or review your next change
Paste your code or describe what you are building and get an architecture, a review, or a debugging path you can use. Free to start, and honest that you own the testing and the ship.