Production-Ready Approach With Labs That Actually Ship
Your lab manual becomes the mentor, and your rubric becomes the marker.
Assignment
Summarise the daily sales drop
A campus store drops a daily sales CSV into object storage.
The finance team wants one JSON summary per day: total revenue,
units sold, and the three best-selling items.
Write a function that runs when the file lands and writes the summary back.
Deliverables
- A handler triggered by the upload, not a script you run yourself
- Totals computed from the file, with rows that fail to parse skipped
- The summary written back to storage as JSON
- One test that proves a malformed row does not break the run
The checks on the right are published with the assignment, so nobody is guessing what the bar is.
Evaluation checks
0/4 ruled
Handler is triggered by the upload
30Totals match the source file
30Malformed rows are skipped, not fatal
25Summary written back as JSON
15Attainment so far
0 of 100 weight settled
0/100
The surface that runs every week, not once a year.
Your lab manual becomes the mentor and the marker
You bring the assignment you already set. We index the manual, the SDK docs and the reference repos into a knowledge graph the mentor agent draws on to answer students' questions well, and every submitted repo comes back scored against your rubric, not ours.
- Runs inside
- Your existing course
- Owned by
- The department
- Leaves behind
- Attainment evidence
How it runs
The manual becomes teachable and the bar gets written down before the session starts.
- 1
Your manual becomes a knowledge graph
We index the lab manual, the SDK documentation and the sample repos exactly as they are. No new wiki to write, no bespoke course content.
- 2
The graph becomes the teaching assistant
It answers students in their editor and the browser, grounded in your sources, so a cohort of 300 is not waiting on one demonstrator.
- 3
Your rubric becomes the checks
Each check carries a weight, where in the code the answer lives, what makes it a pass and what makes it a fail. Students see them, so the bar is stated rather than guessed at.
- Add a deliverable
Drafting. Nothing is visible to students yet.
Publish to cohortThe records are ready when you need them
Marks alone do not say what a student could and could not do, so someone ends up rebuilding that from memory months later. Here every check is recorded as the work is marked, so the record is written while it is still true.
One line per check
You get a result for each check you wrote, not one mark for the whole lab. It is clear which part a student got right and which they did not.
Every score points at the code
Each result names the line that earned it. Anyone reviewing the work later can open the repository and see for themselves.
Yours as a spreadsheet
One file per cohort with every submission, check and result. Download it, keep it, share it with whoever asks.
The roles hiring right now
These titles barely existed when most syllabi were written. What each one needs is teachable, but only where students integrate, direct agents and ship.
Forward-deployed engineer
Sits with the customer and makes it work on their stack.
- Read an unfamiliar SDK fast
- Integrate against someone else's API
- Ship under a customer's constraints
Built by
Labs set on a real SDK, judged on integration rather than output
Full-stack product engineer
Owns a feature from the database to the button.
- Wire services together end to end
- Make something that runs for someone else
- Decide the trade-offs alone
Built by
Challenges and hackathons: a repo that works, not a slide that claims it
AI integration engineer
Builds with agents, and catches them when they are wrong.
- Direct an agent to a useful result
- Review code they did not write
- Spot a confidently wrong answer
Built by
The mentor in the student's editor, plus a judge that catches confident nonsense
One course, one section, one semester
No curriculum committee, no migration, nothing to rip out. A pilot is small enough for one faculty member to run and honest enough to judge on the results.
You bring
- One lab course, one section, one semester
- The manual, SDK docs and reference repos you already publish
- Your rubric, as the checks the judge must rule on
We set up
- The knowledge graph and the Skill students install in one command
- Assignment deadlines on the scheduler, reminders included
- Faculty, HoD and coordinator access, each scoped to what they need
You leave with
- A scorecard per student, cited to specific lines of code
- One cohort workbook, exportable, with per-criterion attainment
- A ranked view of who is actually production-ready
University Connect
Start with one lab
Bring an assignment you already set this semester and we will show you what the evidence looks like coming back, on your own rubric.