Yesterday a new site called synthesize.sh popped up on r/PromptEngineering, and it’s basically LeetCode for people who direct AI agents instead of writing code by hand. If you’ve spent any time on LeetCode or HackerRank, you know the format: a problem shows up, a timer starts, and you grind until the tests go green. This site takes that exact format and flips who’s doing the typing.
Here’s the twist: you don’t solve the algorithm problem yourself. You write a prompt that tells an AI agent how to solve it, and the site grades three things, not just whether the code runs.
- Correctness: does the agent’s solution actually pass?
- Token cost: how efficient was your prompt (and the agent’s output)?
- Generation time: how fast did the whole thing run?
That’s the real shift here. Most “practice AI coding” sites just check if you can get a model to spit out working code, treating the prompt like a throwaway instruction you type once and forget. This one turns it into an efficiency game, which lines up with where hiring is actually heading. Interviews are drifting away from “invert this binary tree on a whiteboard” and toward “can you get a system to do the right thing, fast and cheap, without babysitting it line by line.” System design and AI-assisted coding are becoming the actual skill being tested, not raw syntax memorization. A developer who can write one crisp prompt that nails correctness in a fraction of the tokens is worth more on a team right now than one who can recite algorithm names but freezes when handed an agent.
What makes this interesting isn’t just the novelty, it’s the incentive structure. A vague, rambling prompt might still get you a correct answer, but it’ll cost you on tokens and time, and now you can actually see that cost instead of guessing at it. It turns prompt writing from a fuzzy art into something you can iterate on with real numbers in front of you, which is exactly the kind of feedback loop that made LeetCode useful for algorithms in the first place.
Mini-workflow if you want to try it:
- Go to synthesize.sh and grab a problem 🧩
- Write a prompt that directs the agent, don’t just paste the problem statement and hope for the best. Give it constraints, edge cases to watch for, and a sense of what a clean solution should look like
- Run it, check your correctness, token, and time scores side by side so you can see where the prompt is actually leaking efficiency
- Tweak the prompt to cut tokens without breaking correctness. Try trimming filler instructions, being more specific about the expected input format, or telling the agent exactly which approach to skip
- Repeat until you’ve got a tight, cheap, fast solve ⚡
Pro tip: treat token cost like code golf. Every extra sentence in your prompt is a line of code you didn’t need to write. The fastest agent isn’t always the cheapest one either, so before you start chasing a leaderboard number, decide whether you’re optimizing for speed, cost, or correctness, because those three don’t always move together. If you’re prepping for a role where latency matters, like a real-time tool or a customer-facing feature, lean into the time score. If you’re prepping for something budget-constrained, like a startup running lean on API spend, the token score is the one to obsess over. Match your practice to the job you’re actually prepping for instead of just chasing whichever number is easiest to move.
It’s also worth running the same problem through a couple of different framings just to see how much the prompt structure alone changes the score. Sometimes a numbered list of constraints beats a paragraph. Sometimes naming the algorithm explicitly saves the agent from wasting tokens exploring options. You won’t know which until you test it, and that’s the whole point of a site like this existing.
Heads up: it’s a solo-dev beta with a 10 runs/day cap, so expect rough edges. Some problems might be under-specified, scoring might drift as the builder tunes the token and time formulas, and you’ll probably hit a bug or two along the way. The builder is actively asking for bug reports and security findings, so if you break it, tell them. That kind of early feedback loop is usually how these tools get sharp fast, and getting in now means your bug reports actually shape what the site becomes.
Worth a look if you want to practice the skill that’s actually getting tested in interviews now: directing an agent well, not just typing code yourself 🚀
A place to practice coding with AI!
by u/hahayes9 in PromptEngineering