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Grade Coach is a complete AI teacher toolkit built on a shared substrate: every paper you grade compounds into per-student concept-mastery maps. Tools include rubric-locked paper grading, printable worksheet creation, personalized practice, parent reports, password-gated student views, personalized grading (one answer key per student), and class deep-dive analytics. Powered by Google Gemini 3.1 and gpt-image-2.
The core mechanic: lock your rubric on the first paper, grade the rest of the class against the same locked interpretation. No drift between paper 1 and paper 30. The teacher is always the final check.
Private by default. Student work never trains any AI, never gets shared with other teachers or schools. Built by an ex-teacher with 10+ years in international classrooms (RMIT, Scotch AGS, ISB, HCMC University of Science).
Free tier: 10 credits/month with a free account, no card required. Pro: $15/mo for 500 credits. Power: $30/mo for 1,200 credits. Top-up: $10 = 300 credits that never expire.
GitHub
Grade CoachGrade Coach's answer:
Grade Coach is in early-customer phase (post-launch, pre-scale). Tested by teachers at institutions where the founder has taught or partnered:
Grade Coach's answer:
Frontend - React 18 + TypeScript + Vite - Tailwind CSS v4 - Zustand (state)
Backend - Netlify Functions - Google Gemini 3.1 Pro - OpenAI gpt-image-2
Database & auth - Supabase Postgres
Payments & ops - Stripe
Grade Coach's answer:
Enzo, the founder, spent more than a decade teaching in international classrooms โ RMIT, Scotch AGS, ISB, HCMC University of Science, plus partnerships with Hanyang University, Shandong UST, and Teach For America. The grading pile was killing his weekends.
He tried ChatGPT for nearly a full term before building anything. It worked โ for a few papers. Then it drifted. Same essay scored differently on Tuesday vs Friday. Handwriting recognition was unreliable. The model would invent rubric criteria the teacher hadn't asked for.
The realization: general chatbots aren't built for grading consistency across a class set. A tool that locks the rubric, applies it the same way every time, and lets the teacher stay the final authority โ that's what was missing.
Grade Coach started as a personal tool to grade his own classes. Then friends asked for access. Then schools. Now it's a complete teacher toolkit built on a shared substrate: every paper graded compounds into per-student concept-mastery maps that power worksheet generation, parent reports, and class analytics.
The founder still teaches the product the way he wished someone had taught him.
Grade Coach's answer:
K-12 teachers grading rubric-based assignments, with a wedge focus on:
Secondary audience: university instructors grading short-answer or essay work where rubric-lock matters more than question-grouping.
Grade Coach's answer:
Three reasons, in order:
The teacher is always the final check. Every AI score and comment is shown to you for review and edit before anything is final. Other tools auto-submit; Grade Coach treats AI as a first pass, not the last word.
Private by default. Student work never trains any AI, never gets shared between teachers or schools. We don't sell student data. The privacy line is locked: student work stays between you and your students.
Consistency across the class. Where other graders treat each paper as a fresh AI prompt (and drift between them), Grade Coach locks the rubric on the first paper and uses that locked interpretation across the whole stack. The same essay would get the same grade if you re-ran it tomorrow.
The product is also honest about what it isn't. No AI detection (skeptical that it works reliably). No auto-submission. No chatbot pretending. Just rubric-locked grading that gives you your evening back.
Grade Coach's answer:
The locked-rubric guarantee is the core mechanic. When you upload your first paper, Grade Coach locks the AI's understanding of your rubric, then uses that exact same locked interpretation for every paper in the class set. Paper 1 and paper 30 are scored against identical criteria โ no model drift, no inconsistency.
A few other things that set it apart:
Based on our record, GitHub seems to be more popular. It has been mentiond 2466 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
// ==UserScript== // @name GitHub -> Obsidian Task // @namespace obsidian // @version 1.0 // @match https://github.com/*/*/issues/* // @match https://github.com/*/*/pull/* // @grant GM_setClipboard // ==/UserScript== (function () { 'use strict'; function getTitle() { return document.querySelector("bdi")?.textContent.trim(); } function copyTask() { ... - Source: dev.to / 1 day ago
Import requests From bs4 import BeautifulSoup From datetime import datetime Def fetch_github_trending(): url = "https://github.com/trending?since=daily" response = requests.get(url) soup = BeautifulSoup(response.text, 'html.parser') repos = [] for article in soup.select('article.Box-row'): repo_link = article.select_one('h2 a')['href'] stars_today =... - Source: dev.to / 2 days ago
Git clone https://github.com//.git /opt/app Cd /opt/app Docker build -t app . Docker run -d --name app --restart unless-stopped -p 8080:8080 app. - Source: dev.to / 5 days ago
The core of the ecosystem is the official open-source server hosted on GitHub. It is written in TypeScript and implements the full MCP specification. - Source: dev.to / 10 days ago
This is why the gate needs a trace it can trust, and why AgentLens is the other half of this workflow. agent-eval scores and gates the output; AgentLens captures the trace of how the agent got there โ every model call and tool step, the resolved inputs (not the templated ones), the raw outputs. That trace is exactly the unforgeable, agent-didn't-author substrate that Tier 1+2 need to score against. Without it,... - Source: dev.to / 11 days ago
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