Bullets written from what you actually did
Describe what you actually did in your own words, even if it is rough or unfinished. AI turns it into clear, compelling resume bullet points while keeping your achievements accurate and never inventing experience.
The blank bullet is where resumes die. People know what they did and cannot make it sound like anything, so they write "responsible for managing the team" and move on. What we generate is not better prose for its own sake. It is the same information in the structure that survives a six-second scan: result first, mechanism second, scale attached.
The constraint that matters is what it will not do. It will not add a percentage you did not mention, invent a team size, or upgrade your title. If a bullet needs a number to be credible and you have not given one, it asks you for it. An impressive resume you cannot defend in an interview is worse than an honest one.
In short
- Keeps your numbers; never invents new ones
- Three to five options per generation, not one
- Rewrites for tone, length and seniority
- Version snapshot taken before any bulk edit
How it works
- 01
You describe, in any state
"I fixed the checkout thing that kept timing out, took it from like 2 seconds to under a second, took me about a month" is a perfectly good input.
- 02
It returns three to five options
Different emphases (one leading on the metric, one on the scope, one on the mechanism), so you are choosing rather than accepting.
- 03
You edit, then it fits
Rewrite any line shorter, longer, more senior, or less technical. Length control matters more than people expect: a bullet that wraps to a third line costs you a bullet elsewhere.
What it writes besides bullets
A professional summary from the rest of the document, so it actually reflects the resume underneath it rather than the aspirations of whoever wrote the template. Skills suggestions for a target title, drawn from what your experience already implies. Cover letters that open on something specific from the posting. Resignation letters, for the part of the process nobody wants to draft.
It also translates a whole document into another language while leaving the layout, dates and structure untouched, which is the difference between applying abroad and rebuilding your resume from scratch.
Why generation is metered and scoring is not
Every generation is a model call with a real cost, so it draws on a monthly credit allowance: one credit for a set of bullets, three for a cover letter, five to tailor a whole document. Scoring, editing, exporting and switching templates cost nothing, on every plan.
The free plan includes ten credits, which is enough to write a summary, generate bullets for two roles and see whether the output is worth paying for. That is the intended use.
Questions
AI writing questions
Will an employer know it was written with AI?
They will know if it reads like nobody was there: no specifics, no numbers, the same three adjectives everyone uses. Written from your real work with your real figures, it reads like a resume with a good editor, which is what it is. Edit the output; that is what the field is for.
Which model is behind it?
Llama 3.3, running on Cloudflare's network rather than a third-party API, so your text is not handed to another vendor to get a bullet back. The reply is constrained by the document's own schema during decoding and validated against it afterwards, so it arrives as structure rather than prose we have to parse.
Can it make things up if I ask it to?
It will not add metrics or claims you have not provided. If you type a false number into the input yourself, it will use it. The honesty of the input is yours to own.
Related
An ATS score that tells you what to change
Around sixty rules across six categories, run locally on every keystroke. Free on every plan, because you should never hesitate to check your own document.
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