Log Reader
A log file analyzer app idea: find the signal in a wall of logs. Weekend build with Next.js, Web Workers and Claude API, for developers without a full monitoring setup.
Why now
Logs had a busy month: Parseable launched on Show HN on Oct 6, and a Dev.to post on Oct 12 found a single console.log costing $40,000 a year. Another on Oct 29 showed a log file could hijack an AI, so treat logs as untrusted input.
- Oct 29Ghost in the Shell: Can a Log File Hijack an AI? (opens in a new tab)Dev.to
- Oct 12A single console.log was costing us $40,000 a year. I found it by accident. (opens in a new tab)Dev.to
- Oct 6Show HN: Parseable, an open observability datalake, handles 100M time-series/min (90 points, 25 comments) (opens in a new tab)Hacker News
Checked at the source on Nov 2, 2026. Points, comments, and stars are from that day. GitHub counts are keyword matches, so treat them as a rough guide.
The spec
Drop in a log file to see errors grouped by pattern, when each one began, how often it occurs, and a plain guess about the likely cause.
Build time
WeekendBasic logic / CRUD · Level 2 of 5Audience
Developers without a full monitoring setup.
Stack
- Next.js
- Web Workers
- Claude API
Fun factor
Forty thousand log lines turn out to be three actual problems.
Core features
- Upload or paste logs
- Group similar errors
- A timeline of when they happened
- Runs in the browser when possible
AI features
- Explains an error group in plain words
- Suggests what to check next
Stretch goals
- Remove secrets before upload
- Compare two log files
- Live tail from a command-line tool
First version
Upload a log file and see error lines grouped and sorted by count.
Build plan
4 milestones, in the order you would build them. Saved ideas get this plan with checkboxes and notes.
The build prompt
Paste this into ChatGPT, Claude, Codex, or Cursor, then build the first version before anything else.
# Build Log Reader You are a senior full-stack engineer pairing with me on a side project. Build a working first version of the product described below. Read the whole brief before writing any code, then follow "How to work" at the end. ## The idea **Log Reader.** Drop in a log file to see errors grouped by pattern, when each one began, how often it occurs, and a plain guess about the likely cause. **Why now:** Logs had a busy month: Parseable launched on Show HN on Oct 6, and a Dev.to post on Oct 12 found a single console.log costing $40,000 a year. Another on Oct 29 showed a log file could hijack an AI, so treat logs as untrusted input. **Who it is for:** Developers without a full monitoring setup **What makes it fun:** Forty thousand log lines turn out to be three actual problems. ## Scope - **Build time:** Weekend. Size every decision to ship a working first version inside this window. - **Technical ambition:** Level 2 of 5, "Basic logic / CRUD". A few screens over a simple data model with create, read, update, and delete. One lightweight persistence layer, no heavy infrastructure. - **First version only.** Ship the core features below end to end. Stretch goals are listed so you can design around them, not build them. ## Core features (each must work end to end in the first version) 1. Upload or paste logs 2. Group similar errors 3. A timeline of when they happened 4. Runs in the browser when possible ## AI features - Explains an error group in plain words - Suggests what to check next How to build them: - Put every model call behind one server-side module with a typed interface, so the provider can change without touching the UI. - Read API keys from environment variables only, and document each one in `.env.example`. - Write prompts as versioned constants with clear instructions and an example of the expected output; parse and validate responses before using them. - Handle timeouts, rate limits, and malformed output with a graceful fallback the user can understand. Stream responses where a wait would exceed a couple of seconds. - Never let a feature depend on the model being perfect. Show the result, let the user edit or retry. ## Stretch goals (later, not in the first version) - Remove secrets before upload - Compare two log files - Live tail from a command-line tool Leave clean seams for these (a module boundary, a nullable column, a feature flag), but do not implement them unless I ask. ## Tech stack Use exactly: Next.js, Web Workers, Claude. Stay on current stable versions, follow each tool's official conventions, and add a library only when the standard library or the chosen stack cannot do the job cleanly. If part of this stack is a poor fit for a specific requirement, say so and propose the smallest change. ## Owner's notes Written by me, the project owner. Treat these as requirements and preferences that override the generated material above when they conflict. The first version should be exactly this, and no bigger: Upload a log file and see error lines grouped and sorted by count. ## Build plan (with saved progress) 0 of 4 steps complete. Resume at step 1: Parse logs. Before you start it, check what is already implemented so you do not repeat finished work. 1. **Parse logs** [not started] Handle large files without freezing. 2. **Group errors** [not started] Treat similar lines as one pattern. 3. **Timeline** [not started] When each pattern appears. 4. **Explain** [not started] A hint for the selected group. ## Definition of done for the first version - [ ] Upload or paste logs works end to end, with real data, not a placeholder. - [ ] Group similar errors works end to end, with real data, not a placeholder. - [ ] A timeline of when they happened works end to end, with real data, not a placeholder. - [ ] Runs in the browser when possible works end to end, with real data, not a placeholder. - [ ] Every screen handles loading, empty, and error states. - [ ] Typecheck, lint, and any tests pass, and the main user flow was exercised by hand. - [ ] A README explains setup, environment variables, and how to run it locally. ## Quality bar - Responsive from a phone to a desktop, keyboard-accessible, semantic HTML, visible focus states, labelled inputs, and sensible contrast. - Loading, empty, and error states for every screen that reads or writes data. Errors say what happened and what to do next. - Validate on the server, never trust the client, and keep secrets in environment variables with a committed `.env.example`. - Type-safe end to end. No `any` without a comment explaining why. - Small, readable modules named after what they do. No premature abstraction, no dead code, no TODOs left for later without a note. - Sensible defaults over settings. If a choice is not in this brief, pick the simplest option that keeps the product coherent and say what you chose. ## How to work 1. **Read, then restate.** Summarize the product in three sentences and list your assumptions. Ask only the questions that block implementation. For everything else, decide and note the decision. 2. **Plan before code.** List the files and modules you will create, in build order, mapped to the core features. If a build plan is included above, use it instead: resume at the first step not marked done and check what already exists before touching it. 3. **Set up.** If a project already exists, inspect its code, instructions, and conventions first and build on them. Otherwise scaffold the stack above with linting, formatting, a typecheck script, `.env.example`, and a README stub. 4. **Model the data first.** Define the types, schema, and any migrations before the UI, so the shape of the product is settled early. 5. **Build in vertical slices.** Finish one core feature end to end (data, logic, UI) before starting the next. Keep the app runnable after every slice and commit each slice with a message that says what changed and why. 6. **Then the interface.** Apply the quality bar above to each screen as it lands, not as a final pass. 7. **Verify.** Run typecheck, lint, and tests. Exercise the main user flow by hand. Check each item in the definition of done and each acceptance criterion. 8. **Report.** Tell me what works, what you tested and how, what is not done, every setup step I must complete myself, and the three most useful next steps. Never claim a service, test, or flow was verified when it was not. If you can edit and run the project, start implementing right after the short plan. If you cannot, give me concrete code and the exact commands to run for the next step. Rules: keep the product name and the scope as written, do not invent features, prefer boring and proven choices, and stop to ask me when a decision would be expensive to reverse.
How to use
- Copy the prompt above.
- Open ChatGPT, Claude, or your AI editor and paste it into a new conversation.
- Build milestone one, run it, then move to the next. Add stretch goals only after the first version works.
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FAQ
Frequently asked questions
Answers on what Log Reader is, how to build it, and where example ideas come from.
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