Netuoso/qwepus-dataset

Hand-written engineering conversations. Real problems, real constraints, code that builds.

Model · What's inside · Format · What's next

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About

This is the first release of the dataset behind Qwepus.

It's small on purpose. Every conversation was written by hand, one at a time. Each one starts with a problem someone would actually bring to a senior engineer: a queue that drops messages under load, a flash sale that oversells, a regex that takes down production. The answer does what a good engineer does. It finds what's really broken, explains why the obvious fix won't work, writes the code, and shows how to check it.

No templates. No twenty variations of the same task. No "write a function that reverses a string".

The code compiles. All of it.

This is the rule we care about most. Before a conversation gets in, every code block is pulled out and run through the real toolchain: gcc and g++, rustc, go vet, tsc, node, pyflakes, a PostgreSQL parser for SQL, clang for eBPF, bash -n for scripts. If it doesn't build, it doesn't ship.

The only exceptions are a few short snippets quoted in the middle of an explanation (tagged ,ignore) and Unreal Engine code, which needs the engine's headers to build.

One more thing: the answers don't fake results. There are no made-up benchmark tables and no "all 12 tests passed" logs. Tests and benchmarks come as code plus the command to run them, and the answer explains what each test checks. We want the model to hand you a way to verify, not to claim it already did.

What's inside

Conversations	96
Format	ChatML in JSONL: system, user, assistant
Average answer	~28k characters
Size	~2.9 MB
Language	English

12 conversations per language: C · C++ · Rust · Go · Python · TypeScript · SQL (PostgreSQL) · HTML + JS (Three.js)

Area	For example
Systems	lock-free queues, memory ordering, SIMD, custom allocators, unsafe Rust and FFI, C++20 coroutines
OS and runtimes	io_uring, epoll, eBPF, mmap and the page cache, Go GC tuning, the CPython C API
Reliability	data races, graceful shutdown, cancellation, retries and circuit breakers
Databases	isolation anomalies, query plans, WAL and LSM trees, online migrations, partitioning
Distributed systems	Raft, CRDTs, hybrid logical clocks, consistent hashing, rate limiting, the outbox pattern
Algorithms	segment trees, max flow, robust geometry, Aho–Corasick, numerical stability
Compilers	Pratt parsing, bytecode VMs, regex engines, SSA and SCCP, type-level TypeScript
Games and graphics	UE5 GAS, ECS, rollback netcode, interactive Three.js scenes
Security	constant-time code, JWT pitfalls, seccomp and Landlock sandboxing, row-level security
Reverse engineering	decompiler output to source, binary format recovery, paper pseudocode to code

Format

One conversation per line:

{"messages": [
  {"role": "system", "content": "You are Qwepus, an elite software architect and systems engineer. ..."},
  {"role": "user", "content": "I'm writing an audio engine in Rust. The real-time thread ..."},
  {"role": "assistant", "content": "## Model\n\n...\n\n```rust\n..."}
]}

All conversations share one system prompt. Answers are Markdown with fenced code blocks, and source files are named with title="...".

from datasets import load_dataset

ds = load_dataset("Netuoso/qwepus-dataset", split="train")
print(ds[0]["messages"][1]["content"][:500])

Keep in mind

• 96 conversations is not a lot. The dataset teaches how to approach a problem, not every problem there is.
• Answers are long, some over 100k characters. Train with a long context or they'll get cut off.
• Compiling isn't the same as being correct. Every answer comes with tests, but tests can be wrong too.

What's next

This is v1, and it's far from finished. We'll keep working on it for a long time: more conversations, new areas, harder problems, and another pass over what's already here. Expect it to grow and change from version to version.

Found a mistake in an answer? Open a discussion. That helps more than you'd think.