Algorithmic rewrites,
not just micro-opts.
ACENLY doesn't add caching or swap libraries. It finds a fundamentally better algorithm — O(n²) to O(n) — then proves it works on your actual data.
Download preview →Early access · Interface preview available · All platforms planned
Example optimization — deduplicate_users()
def deduplicate_users(users): seen = [] result = [] for user in users: if user['id'] not in seen: seen.append(user['id']) result.append(user) return result # list.append + "in" scan = O(n²) # 395 ms at n=10,000
def deduplicate_users(users): seen = {} result = [] for user in users: uid = user['id'] if uid not in seen: seen[uid] = True result.append(user) return result # dict lookup = O(1) per item = O(n) # 4.98 ms at n=10,000 ✓ identical output
Dashboard, results viewer, live output — everything runs on your machine.
def find_top_k_frequent(words, k):
counts = Counter(words)
sorted_words = sorted(counts, key=lambda w: (-counts[w], w))
return sorted_words[:k]
AST Analysis
ACENLY parses your function's abstract syntax tree, identifies the complexity class, and maps data structures being used.
Evolution Engine
Generates a population of candidate rewrites using an LLM. Tests them against each other in tournament rounds — survivors breed the next generation.
Oracle Coordinator
An LLM coordinator observes what strategies are working and directs the engine toward promising directions — no random searching.
Correctness Check
Every candidate is run against your test cases. Outputs must be identical. If they're not, the candidate is discarded — speed without correctness is worthless.
Benchmark Proof
The winning function is benchmarked head-to-head against the original using the same statistical rigor as the benchmark tool. The speedup you see is real.
Clean Output
You get back pure Python. By default, rewrites use the standard library only — no new dependencies. Dependency policy is configurable if your project allows more.
Works best on CPU-bound functions with deterministic output. Not suited for I/O-heavy code.