JDFit VS Tempreon

JDFit's answer

I'm Matt, a cybersecurity practitioner and Army veteran. JDFit started with a pain I know firsthand: looking at a job posting and trying to work out whether I'm actually a good fit. The tools that help tend to cost $40 or more a month, and most give you a score without the reasons. I also know how hard it is for students and veterans to line up a resume with a job description, so JDFit gives an honest read at a price people can pay. It's built by SecurityMinded Solutions, the team behind DeliverTrust and SecurityScout.

Tempreon's answer:

Tempreon started with a simple observation: AI models keep changing, but the thing that makes them useful to you — your context, your preferences, your judgment — gets rebuilt from scratch inside every tool, and lost every time you move.

We built the layer that fixes that: person-owned memory served over the open Model Context Protocol, so it works across assistants instead of belonging to one. Along the way we open-sourced the pieces that are useful to everyone regardless of whether they use Tempreon — like memhaul, our MIT-licensed CLI for turning ChatGPT and Claude data exports into files you own.

The through-line is custody: the model is temporary, your memory shouldn't be.

JDFit's answer

Most tools in this space hand you a single match score or a keyword list, and many cost $40 or more a month. JDFit checks your fit line by line: each requirement in the posting is marked a match, a partial match or a gap, next to the line of your resume it matched. It works from a Chrome extension right on LinkedIn and ATS (e.g. Greenhouse and Ashby) postings, and costs $11/month after the free trial, with 35% off for students and 55% off for military, veterans and military spouses.

Tempreon's answer:

Most alternatives in this space are memory infrastructure for developers building their own AI apps. If you're the person using several AI tools every day, that's not your problem — your problem is re-explaining yourself to each of them and losing everything when you switch.

• Tempreon solves that one: one memory, every assistant, no re-onboarding.
• The model landscape changes every few months — a memory layer that belongs to you is the thing that shouldn't.
• No lock-in by design: plain-text exports, open-source export tooling, portable formats.

The choice is really about who the memory is for. Ours is for you.

JDFit's answer

JDFit goes requirement by requirement instead of counting keywords. Each requirement in the posting gets a verdict, met, partially met or a gap, with the line from your resume that backs it up, and it separates what a job requires from what it only prefers. It shows you the gaps, and you make the changes in your own words.

Tempreon's answer:

Tempreon is built for the person, not the app. Most memory products are developer APIs for adding memory to a single product; Tempreon is a memory layer you own that travels with you across every AI tool you use — Claude, ChatGPT, Cursor, anything MCP-capable.

• It learns, it doesn't just store. How you work, what you decide, how you like things done — refined over time, not filed away.
• One memory, every assistant. Captured once in one tool, available in all of them. No re-explaining yourself.
• Custody is structural, not marketing. Your data exports anytime, the formats are portable, and our export tooling (memhaul) is open source. We monetize the service, never the custody.

JDFit's answer

People applying for jobs in the US who want an honest read on whether they fit a role before they apply: people moving up to their next role, career changers, students and new graduates, and veterans translating military experience into what a civilian job asks for.

Tempreon's answer:

Individuals who live in AI tools all day: operators, consultants, founders, sales professionals, and knowledge workers who use more than one assistant and are tired of being a stranger to each of them.

If you've ever pasted the same context into Claude and ChatGPT in the same week — you're the audience.

JDFit's answer

React and TypeScript for the web app and site, a Chrome (Manifest V3) extension, Python on Google Cloud, Firebase Authentication, Stripe for billing, and TypeSafe's Jev model for the requirement-by-requirement judgments.

Tempreon's answer:

• Model Context Protocol (MCP) over streamable HTTP — the core of it. This is what makes Tempreon work in any compliant client rather than one walled garden.
• OAuth 2.1 with dynamic client registration and PKCE for authorization.
• TypeScript and Postgres under the hood.

The protocol choice is the product decision: build on the open standard, and your memory works everywhere the standard does.