Project
Check limits zero, one and two, a square composite and the upper bound, rejecting negative, fractional or Boolean limits before allocating the sieve.
AI coding workspace
Bounded prime sieves
SieveFern provides a Windows workspace for examining prime-generation code with AI. Open the project folder, connect a compatible model endpoint and review the proposed sieve against its allocation limit and small-number cases.
● ● ●SieveFern●
A concrete starting point · Bounded prime sieves ×
from math import isqrt
def primes_through(limit):
if type(limit) is not int or not 0 <= limit <= 1000000:
raise ValueError("limit.range")
sieve = bytearray([1]) * (limit + 1)
sieve[0] = 0
if limit >= 1:
sieve[1] = 0
for p in range(2, isqrt(limit) + 1):
if sieve[p]:
for multiple in range(p * p, limit + 1, p):
sieve[multiple] = 0
return [i for i, prime in enumerate(sieve) if prime]✳List all primes up to an inclusive integer limit using a sieve capped at 1000000.
✓ ChangesReview before applying ↗
Illustrative workspace
Review before applying
Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes.
Connection
Base URL, credentials and a reachable server.
A concrete starting point
from math import isqrt
def primes_through(limit):
if type(limit) is not int or not 0 <= limit <= 1000000:
raise ValueError("limit.range")
sieve = bytearray([1]) * (limit + 1)
sieve[0] = 0
if limit >= 1:
sieve[1] = 0
for p in range(2, isqrt(limit) + 1):
if sieve[p]:
for multiple in range(p * p, limit + 1, p):
sieve[multiple] = 0
return [i for i, prime in enumerate(sieve) if prime]Include the workload limit in the change request
A routine can produce the right answer and still accept more work than an application is prepared to handle. SieveFern provides a Windows AI coding workspace for examining that gap in project code. Describe the accepted workload, the rejected workload and the response callers should receive before asking the model to change an implementation.
For instance, bring a bounded prime-generation routine into the conversation. Ask the model to inspect where the input limit is checked relative to allocation and computation. Include small accepted inputs and a request beyond the stated limit, so the proposed patch must account for both useful results and controlled rejection.
Review any suggested increase to the limit as a separate decision. A changed constant may affect memory requirements, runtime and the interface that exposes the operation. Run project checks at the cases you intend to support, and measure resource use where it matters. Keep measured observations distinct from estimates offered in the conversation.
SieveFern lets you configure supported OpenAI-compatible or Anthropic model connections, including credentials when required. If an uncensored AI model is offered by your endpoint, its actual policies still govern responses. Available tools and context capacity also depend on the model and server; they do not determine a safe workload limit for your application.
Reviewing a resource-bounded feature
Where should an oversized request be rejected?
Inspect whether validation happens before the expensive allocation or operation. Ask the model to trace that ordering and the caller’s error handling. Test a request just beyond the accepted bound to confirm that rejection follows the intended path.
Is raising a size limit just a configuration change?
It may change the workload substantially. Review the implementation and measure relevant resource use in your environment before increasing it. Also check whether callers, timeouts or surrounding interfaces rely on the old maximum.
What makes a useful performance observation?
Record the tested input, runtime environment and measurement method with the result. Compare like conditions when assessing a proposal. A model estimate can help plan an experiment, but it is not a measurement of your implementation.
Can I assume resource checks run beside a local model?
No. A local model needs its own running server, while connected tools may execute elsewhere or contact remote services. Confirm the actual execution setup before interpreting memory or timing results as representative of your target machine.
SieveFern · Updated 2026-10-25