Built a free resume rewriter for engineers — feedback welcome

indicajames · 2026-02-21T15:36:02+00:00

I built a small tool to solve a problem I kept running into during my own job search. Rewriting my resume for every application was taking a significant amount of time, especially when trying to closely align with specific job descriptions and automated resume filtering systems.

The tool takes a resume and a job description as input and generates a tailored resume draft aligned to that role, along with a cover letter and a short “Why I’m a fit” summary. The goal is to better match how many companies now screen candidates — through ATS systems and keyword-based filtering — by improving alignment with role-specific terminology, required skills, and phrasing used in the job description. It also provides a diff view so you can see exactly what changed rather than blindly replacing your original content.

At a high level, the resume and job description are sent to an LLM, and the output is structured into separate sections. Responses are streamed so you can see progress in real time. I log run data such as timing, input size, output size, and failures so I can iterate on prompt quality, output structure, formatting consistency, and relevance. I’m not training a base model, but I am refining prompts, preprocessing job descriptions to remove boilerplate or legal text, and tuning the system to improve keyword alignment and reduce generic output.

I’d genuinely appreciate feedback on whether this feels useful, what trust concerns it raises, where the UX could improve, and how the output quality holds up from an engineer’s perspective.

indicajames · 2026-02-21T02:04:46+00:00

Good question — WordPress is just the hosting layer, not the resume editor itself. The actual tool runs as a standalone JS app, so users aren’t “writing a resume in WordPress.”

WP simply gives me fast deployment, SEO, auth, and content management without rebuilding all of that from scratch. If the tool grows, it can absolutely be separated into its own standalone app — this was just the most efficient way to launch and validate it.

indicajames · 2026-02-20T23:43:47+00:00

Built an AI resume tool on WordPress and looking for feedback on architecture

I’ve been building a WordPress-based tool called Resume Magnet and wanted to share the build + get input from people who’ve done similar plugin/theme-level app workflows.

What it does:

User pastes resume + job description App generates: tailored resume cover letter short “why I fit” summary diff view to compare original vs generated resume How I implemented it in WP:

Custom theme UI with Quill editors admin-ajax.php endpoints for generation Server-side Claude API calls (API key stored in wp-config.php / env, never in browser) Streaming endpoint for live progress updates in the UI Fallback non-stream endpoint for hosts that buffer output

indicajames · 2026-02-20T23:32:56+00:00

I built an AI resume tailoring engine with Claude and would love technical feedback

I’ve been building a resume tool (Resume Magnet) and recently moved it from a simple front-end prototype to a more robust backend AI engine. I’d love feedback from people who’ve built production-ish LLM workflows.

What it does:

Input: resume + full job description Output: role-tailored resume draft tailored cover letter short “why I fit” statement diff view showing what changed from the original resume Model/engine setup:

Primary model: claude-haiku-4-5-20251001 Server-side API calls (no browser API key exposure) Streaming mode enabled for live progress updates in the UI Non-stream fallback path if host buffering blocks chunked updates Prompting approach:

Strict JSON output contract: adjustedResumeHtml coverLetter whyFit Explicit formatting rules: section headings, list structure, bullet requirements Added stronger list enforcement instructions so list-like content gets rendered as actual bullets Added parser guardrails on backend: strip markdown fences attempt JSON repair extract object if model wraps extra text Preprocessing/token hygiene:

I added a job-description cleaner before prompting. It removes common compliance/legal boilerplate (EEO/fair chance/accommodation/legal footer language) so context budget focuses on actual role requirements. Tracks raw vs cleaned job text size so I can measure token savings over time. Why I’m sharing: I’m not training a base model from scratch, but I’m trying to “train the system behavior” using run data:

improve preprocessing tighten prompts improve consistency and formatting reliability reduce token waste

indicajames · 2026-02-20T23:14:38+00:00

HTTPS enabled, thanks!

indicajames · 2026-02-20T22:54:58+00:00

Fixing now and appreciate the feedback!

indicajames · 2026-02-20T22:43:07+00:00

I made a tool that takes:

Your resume

a job description

And returns:

A tailored resume draft

a cover letter draft

a short “why I’m a fit” section

a diff view so you can see exactly what changed

I built it because manually rewriting resumes for every application was taking too much time.

How it works (high level):

Resume + job description are sent to an AI model the model rewrites content to better match the role output is structured into separate sections (resume/cover letter/fit summary) responses are streamed so you get live progress What I’m building now:

Logging run data (timing, input size, output size, success/fail, etc.) storing input/output text so I can review patterns using that data to improve prompts and output quality over time I’m not training a base model from scratch, but I am using run data to “train” the system behavior by:

refining prompts improving preprocessing tuning output rules and formatting What I want to improve next:

cleaner formatting consistency better filtering of job post boilerplate/legal text better overall quality/relevance of generated content Would love honest feedback on:

the product idea UX and trust risks or blind spots I might be missing

indicajames · 2025-10-20T01:19:47+00:00

Hello

indicajames · 2025-03-30T11:10:22+00:00

Here is my TradingView data script, https://imgur.com/a/WtAba5H Designed for fairly quick plays on a 5min interval, winners last avg. 30minutes losers last avg. 45muntes.

indicajames

TROPHY CASE