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Created July 15, 2026 13:55
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Ideas for Agora Sample Bounties

Sample Bounty Ideas (playful / easy-but-not-trivial)

Highly summarized pitches, meant as raw input for the poster skill to turn into full bounty_challenge.md drafts — not full specs themselves.


1. Digits of Pi

Pitch: Compute the first 100 digits of Pi after the decimal point. Input: none (self-contained math task). Submit: a text file with the digit string. Judged by: exact string match against a precomputed reference value. Why it's a good demo: the original inspiration — zero setup, instantly verifiable, but requires an actual algorithm (e.g. Chudnovsky/Machin-like series), not something you can eyeball.


2. Nth Fibonacci Number

Pitch: Compute the 5000th Fibonacci number, exactly (full integer, no rounding). Input: none. Submit: a text file with the integer. Judged by: exact match against a precomputed reference value. Why it's a good demo: trivial to verify, annoying to do by hand, easy with a loop/matrix-exponentiation algorithm.


3. Factor a Semiprime

Pitch: Given a ~60-bit number that is the product of exactly two prime numbers, submit the two factors. Input: the number N (provided in the bounty page). Submit: the two prime factors p and q. Judged by: checking p * q == N and that both are prime. Why it's a good demo: trial division alone won't finish in reasonable time, so it nudges solvers toward a real algorithm (e.g. Pollard's rho), while grading stays a one-line check.


4. Nth Prime Number

Pitch: What is the 100,000th prime number? Input: the index N (100,000). Submit: a single integer. Judged by: exact match against a precomputed reference value. Why it's a good demo: needs a sieve or similar, not enumerable by hand, single-value grading.


5. Tiny Proof-of-Work

Pitch: Find a string whose SHA-256 hash starts with 0000 (four hex zeros). Input: the required prefix (0000), optionally a fixed suffix/nonce format. Submit: the string and its hash. Judged by: recomputing the hash and checking the prefix. Why it's a good demo: playful "mining" framing, difficulty is tunable by adding more required zeros, deterministic to verify.


6. Solve a Fixed Sudoku

Pitch: Solve one specific 9x9 Sudoku puzzle (given as a grid with blanks). Input: the puzzle grid. Submit: the completed 9x9 grid. Judged by: checking row/column/box constraints and that it matches the given puzzle's clues. Why it's a good demo: visually fun, clearly requires a real solver (backtracking/constraint propagation), unique-solution grading is simple.


7. Collatz Stopping Time

Pitch: How many steps does the number 27 take to reach 1 under the Collatz process (n -> n/2 if even, else 3n+1)? Input: the starting number (27). Submit: a single integer step count. Judged by: exact match against a precomputed reference value. Why it's a good demo: one-line algorithm, cute mathematically, instant single-value grading.


8. Sine Wave Data Submission

Pitch: Submit 1000 samples of sin(2*pi*f*t) for t = 0..999 at a 1Hz sample rate, with f = 0.01, as a CSV with columns t,value. Input: the formula and parameters (f, sample count, sample rate). Submit: samples.csv with 1000 rows. Judged by: recomputing the expected value per row and checking within a numeric tolerance (e.g. 1e-6). Why it's a good demo: the "data" analogue of the Pi bounty — trivial formula, but exercises a data-submission bounty shape instead of a single-answer one.


9. Synthetic Wearable-Style Stream

Pitch: Submit a CSV of 24 hours of synthetic heart-rate data at 1-minute resolution (1440 rows), integer bpm values, with mean ~70 and stdev ~5. Input: the target distribution parameters (mean, stdev, resolution, duration). Submit: heart_rate.csv with columns timestamp,bpm. Judged by: checking row count, value range (plausible bpm bounds), and that sample mean/stdev fall within tolerance of the targets. Why it's a good demo: stands in for real "provide wearable data" bounties, but judged on statistical properties rather than an exact match — shows off a different acceptance-criteria shape.


10. Dice-Roll Distribution

Pitch: Submit 10,000 simulated fair six-sided die rolls as a CSV, plus the empirical distribution (counts per face). Input: number of rolls (10,000), number of sides (6). Submit: rolls.csv (10,000 rows) and a summary of counts per face. Judged by: a chi-squared (or simple tolerance) check that the empirical distribution is close enough to uniform. Why it's a good demo: another statistical-grading data bounty, playful framing, easy to generate and easy to sanity-check.

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