# Add `@include` support to miniconf

`miniconf` is a small INI-style configuration parser (see `miniconf/`). It
supports comments, `[sections]`, `key = value` assignments, and typed values.
Its existing test suite is under `tests/` and passes.

We need configuration files to be able to pull in other files. Add support for
an `@include` directive to the file loader.

## Directive syntax

A line whose first whitespace-separated token is `@include`, followed by a
path, includes another configuration file at that point:

```
@include logging.conf
```

## Required semantics

- **Inline expansion.** An `@include` line is replaced by the full contents of
  the referenced file, in place, as if those lines had been written where the
  directive appears. Parsing (sections, keys, comments, value coercion) then
  proceeds over the expanded text exactly as today.
- **Relative paths.** The include path is resolved relative to the directory of
  the file that contains the directive (not the process working directory).
- **Ordering / override.** Because expansion is positional, a key assigned
  *after* an `@include` overrides a value that the included file set for the
  same key, and a key assigned before it is overridden by the include. This
  follows from normal "later assignment wins" behavior once the file is
  expanded.
- **Nesting.** An included file may itself contain `@include` directives; expand
  them recursively.
- **Missing file.** If an include target does not exist, raise
  `miniconf.errors.ConfigError`.
- **Cycles.** If a file includes itself directly or transitively (a cycle),
  raise `miniconf.errors.ConfigError` rather than looping forever.

Only `miniconf.loader.load(path)` needs to handle includes. The behavior of
every existing function, and of files that contain no `@include` directives,
must not change — the current test suite must still pass.

An example pair of files is provided in `examples/` (`service.conf` includes
`logging.conf`) to try your implementation against.

## Done when

`miniconf.loader.load(path)` expands `@include` directives per the semantics
above, existing behavior is unchanged, and the existing test suite still
passes.


# Build a word-frequency command-line tool

Write a Python script named `wordcount.py` in this directory. It reads a text
file and prints the most common words it contains.

## Usage

```
python3 wordcount.py <filename> <N>
```

- `<filename>` — path to a UTF-8 text file to analyze.
- `<N>` — a non-negative integer: how many of the top words to print.

## What counts as a word

Lowercase the entire file, then treat every maximal run of the characters
`a`–`z` and `0`–`9` as one word. Every other character (spaces, punctuation,
newlines, etc.) is only a separator. For example the text `Hello, hello WORLD!`
contains the words `hello`, `hello`, `world`.

## Output

Print the `N` most frequent words, one per line, as:

```
<word> <count>
```

the word and the count separated by a single space, each line ending with a
newline.

Ordering rules:

- Sort by count, highest first.
- Break ties between words with equal counts alphabetically (ascending).
- If the file has fewer than `N` distinct words, print all of them.
- If `N` is `0`, print nothing.

## Example

A sample file `sample.txt` is included. Running:

```
python3 wordcount.py sample.txt 3
```

should print:

```
the 3
fox 2
quick 2
```

Done when `wordcount.py` behaves exactly as specified above.


# Fix the failing calculator tests

The `calculator.py` module provides basic arithmetic helpers, and
`test_calculator.py` contains a unit-test suite for them. One of the tests is
currently failing.

Run the test suite:

```
python3 -m unittest
```

Find out why it fails and fix the bug in `calculator.py` so that the whole
suite passes. Do not change the tests — they describe the correct behavior.

Done when `python3 -m unittest` reports all tests passing with a zero exit code.


# Make the test suite pass

`catalog/` is a small library-catalog package with a test suite under `tests/`.
The suite is currently failing in several places.

Run it:

```
python3 -m unittest
```

Work through the failures and fix the code in `catalog/` until the whole suite
passes. The tests describe the intended behavior — treat them as the
specification and do not modify them.

Done when `python3 -m unittest` reports every test passing.


# Get the greeting program running

This small project is supposed to run with:

```
python3 main.py
```

and print exactly:

```
Hello, world!
```

Right now it does not run — starting it raises errors. Fix the code so the
program runs cleanly and prints that exact line.

Done when `python3 main.py` exits successfully and prints `Hello, world!`.


# Fix the crashing report script

Running the app fails:

```
$ python3 main.py
Traceback (most recent call last):
  File "main.py", line 12, in <module>
    main()
  File "main.py", line 7, in main
    total = compute_total(COUNT)
  File "app/report.py", line 9, in compute_total
    return rate * count
TypeError: unsupported operand type(s) for *: 'NoneType' and 'int'
```

Track down why this happens and fix it so the program runs cleanly. When it
works, `python3 main.py` should print exactly:

```
Total: 5.0
```

Done when `python3 main.py` exits successfully and prints that line.


# Fix the failing tests in `taskflow`

`taskflow/` is an in-memory job-orchestration engine: it runs a pipeline of
interdependent tasks to completion on a virtual clock, with priority
scheduling, retries with backoff, resource-limited concurrency, a per-job state
machine, an event bus, and an event-sourced run history. The package has a
`tests/` suite that describes how all of this is supposed to behave.

Right now the suite is failing in several places. Run it from this directory:

```
python3 -m unittest
```

Work through the failures and fix the code under `taskflow/` until the whole
suite passes. Treat the tests as the specification — they encode the intended
behavior; do not modify them.

A few things worth knowing before you dive in:

- **The failures can be misleading.** A test named for one module may be
  failing because of a defect in a *different* module. Read each failure for
  what it actually asserts, then trace the behavior back to its root cause
  rather than patching the nearest line.
- **Fixing one bug can uncover another.** Some defects are masked by others:
  correcting the first makes a second one visible (a test that was failing for
  reason A starts failing for reason B). Expect to fix, re-run, and re-diagnose
  more than once.
- **The defects are genuine logic errors**, not typos that raise on import. The
  code reads as intended-but-wrong: comments and docstrings describe the
  correct behavior, so compare what each function *says* it does against what it
  *actually* does.

Done when `python3 -m unittest` reports every test passing.


# Add typed routing, mounting, and content-negotiation to webcore

`webcore` is our small in-process web framework (see `webcore/README.md` for the
module layout and entry points). It already handles the basics and we want to
grow its routing layer into something production-shaped. This is a single,
connected piece of work: converters, routing precedence, sub-application
mounting, and HTTP method handling all interact, so please implement the whole
thing coherently rather than in isolated patches.

## What already works (do not break it)

The following behaviour exists today and must keep working exactly as it does
now:

* **Static routing** — `@app.route("/items")` matches `GET /items`.
* **A single string path parameter** — `@app.route("/greet/{name}")` captures one
  non-empty segment (no `/`) and passes it to the handler as `name="..."`.
  A handler is called as `handler(request, **path_params)`.
* **GET/POST dispatch** — a route declares `methods=[...]` (default `["GET"]`);
  the matching route for the request's method is invoked.
* **Responses** — `text(body, status=200, headers=None)` returns a
  `text/plain; charset=utf-8` response; `json_response(obj)` returns
  `application/json`. A handler may also return a bare `str` (becomes text) or a
  `dict`/`list` (becomes JSON). `Response` exposes `.status`, `.headers` (a
  case-insensitive map), `.text`, and `.json()`.
* **Request query parsing** — `Request(method, path)` splits any `?query` off the
  path, exposing `.path`, `.query_string`, and `.query` (a dict, last value wins,
  percent- and `+`-decoded).
* **The middleware onion (single app)** — `app.use(mw)` registers a middleware
  `mw(request, next) -> response`. Middlewares run outermost-first on the way in
  and unwind on the way out; the first registered is the outermost. A middleware
  that returns a response without calling `next` short-circuits the inner layers
  and the handler.
* **404** — a request whose path matches no route returns status 404.
* **TestClient** — `TestClient(app)` with `.get/.post/...` and
  `.request(method, path, follow_redirects=False)`.

Keep all of that intact. The work below extends the routing and dispatch layers
around it.

## What to build

### 1. Typed path converters

Support typed placeholders in route patterns. A placeholder always occupies a
whole path segment and is written `{name}` or `{name:converter}`:

* `{name}` — the existing behaviour: exactly one non-empty segment, no `/`,
  delivered as a `str`. This is the `str` converter and stays the default.
* `{id:int}` — one or more ASCII digits (`[0-9]+`) **only**. The handler receives
  a Python `int`.
* `{s:slug}` — matches `[a-z0-9]+(?:-[a-z0-9]+)*` (lowercase alphanumerics joined
  by single hyphens). Delivered as a `str`.
* `{rest:path}` — one or more segments **including `/`**. Delivered as a `str`
  that may contain `/`. Because it swallows slashes it is only legal as the
  **last** segment of a pattern; a `path` converter anywhere else is an error.

### 2. Converter fall-through

Converter matching is part of route matching. If a segment fails its converter
(for example `abc` against `{id:int}`), that route simply does **not** match, and
matching continues to the remaining routes. If no route matches, the result is a
404 — a non-matching converter must never raise or produce a 500.

### 3. Route precedence

When more than one route could match a path, resolve the ambiguity by
specificity, not by luck of registration order:

* At any given position, a **static** segment beats a **dynamic** one.
* More-static routes are tried before more-dynamic ones.
* A `{...:path}` route has the **lowest** precedence of all.
* A genuine tie (same shape) is broken by registration order (earlier wins).

So with `/users/{id:int}` and `/users/new` both registered, `GET /users/new`
hits the static route and `GET /users/7` hits the dynamic one — regardless of
which was registered first.

### 4. Handler parameter types

The value a handler receives matches the converter: `int` parameters arrive as
`int`, `str` and `slug` parameters as `str`, and a `path` parameter as a `str`
that may contain `/`.

### 5. Sub-application mounting

Add `app.mount(prefix, subapp)`. The `prefix` is itself a route pattern and may
contain parameters, e.g. `app.mount("/api/{ver}", subapp)`. A request whose path
starts with the prefix is routed into the sub-application against the remainder
of the path. The handler receives **both** the prefix parameters and the
sub-route parameters, merged into one set of keyword arguments. For example, with
`sub` holding `@sub.route("/users/{id:int}")` and `app.mount("/api/{ver}", sub)`,
`GET /api/v2/users/9` invokes the handler with `ver="v2"` and `id=9`. Mounts may
nest.

### 6. Middleware ordering across a mount

Middleware follows the same onion model through a mount. On the way in, the
parent app's middlewares run first, then the mounted sub-app's middlewares, then
the handler; on the way out it unwinds child-then-parent. A middleware that
returns a response without calling `next` still short-circuits everything inside
it — including a mounted sub-app's middlewares and handler.

### 7. Trailing-slash redirect

A route has one canonical form: whatever it was registered as. A request for the
other trailing-slash form redirects to the canonical one with a **308** and a
`Location` header pointing at the canonical path. Any query string is preserved
on the redirect target.

* Registered `/items`, request `GET /items/` → 308, `Location: /items`.
* Registered `/items`, request `GET /items/?a=1&b=2` → 308,
  `Location: /items?a=1&b=2`.
* Registered `/dir/`, request `GET /dir` → 308, `Location: /dir/`.

### 8. Method mismatch → 405

If a path matches a route but none of the routes on that path allow the
request's method, return **405** (not 404) with an `Allow` header whose value is
the allowed methods for that path, **sorted and comma-separated** (e.g.
`DELETE, OPTIONS, POST`). The allowed set includes the auto methods from clause
10.

### 9. `url_for(name, **params)`

Reverse-generate a URL for a named route:

* Fill the route's parameters from `params`, formatting each through its
  converter (an `int` renders as its decimal string; a `slug`/`str` is validated
  against its pattern).
* Include the mount prefix, filled with its own parameters, when the named route
  lives inside a mounted sub-app.
* Any keyword arguments left over after the path is filled become a query string:
  URL-encoded, **sorted by key**, and appended after `?`.
* A missing or invalid parameter raises a clear error (`KeyError` or
  `ValueError`).

For example, `url_for("user", id=7, q="hello world", page=2)` for
`/users/{id:int}` named `user` yields `/users/7?page=2&q=hello+world`.

### 10. Automatic HEAD and OPTIONS

* A route registered for `GET` also answers `HEAD`: same status and headers as
  the `GET` response, but an **empty body**.
* `OPTIONS` on a path that matches at least one route returns **204** with an
  `Allow` header (sorted, comma-separated) listing the methods that path
  supports — including the auto `HEAD` (when `GET` is present) and `OPTIONS`
  itself.

Implement all ten together so the pieces compose: a mounted, parametric route
with a typed parameter should still honour precedence, method handling, and
`url_for`.


I have a database in WAL (Write-Ahead Logging) mode in the working directory.
However, the WAL file appears to be corrupted or encrypted. When you try to
access the database, SQLite may only show the base data (5 records) instead of
all 11 records that should be there.

Your task is to:

1. Fix the WAL file so SQLite can read it.
2. Extract **all** data from the database, including WAL changes.
3. Create a JSON file named `recovered.json` in the working directory.

The output should have the format:

```json
[{"id": 1, "name": "item1", "value": 123}, {"id": 2, "name": "item2", "value": 456}]
```

The list must be sorted by `id`, and each object must have integer `id`, string
`name`, and integer `value` fields. You should recover all 11 records total.


I have provided a file `a.out` in the working directory. It is a compiled C
binary. Write a program `extract.js` that, when run as:

```bash
node extract.js a.out > out.json
```

extracts memory values from the binary and outputs them as a JSON object with
memory addresses as keys and their values as integers.

Example output format:

```json
{"4194304": 1784774249, "4194308": 1718378344}
```

Success criteria:

1. For any address you include in your output, the value must match the
   reference solution. Addresses with incorrect values fail the test.
2. You need to extract at least 75% of the memory values that are present in the
   reference solution.

The output values should be integers, not strings.


The file `feal.py` (in the working directory) implements a FEAL-like encryption
function. Implement a chosen-plaintext attack that recovers the value of
`key[5]`.

Your attack must be implemented in a file `attack.py` in the working directory,
and must define a function `attack(encrypt_fn)` that returns the uint32 value of
`key[5]`. `encrypt_fn` is an encryption oracle with the same interface as
`feal.encrypt` (it takes a 64-bit integer plaintext and returns a 64-bit integer
ciphertext, using secret round keys you cannot read directly). Your attack should
run in less than 30 seconds.

Each of the 6 round keys is derived from a 16-bit seed. You still can't brute
force the entire key space, but this should make it easier to perform a
differential attack that recovers the round key.


I have provided a file called `text.gcode` in the working directory. This is a
G-code file for a Prusa MK4S. I am trying to print some text onto an existing
object.

When I run the print, what will the text show? Write the answer to `out.txt` in
the working directory.


In this task you will implement an **LLM inference batching scheduler
(shape-aware)** for a static-graph LLM inference system.

**Background**

When running large language models on hardware accelerators (such as TPUs, RDUs,
or bespoke inference chips) the compiled execution graph has to operate on
fixed-sized tensors. Requests arrive with arbitrary prompt lengths and
generation lengths, but at runtime everything must be rounded up to a multiple of
a common granularity. A naive strategy of batching each request independently
wastes compute, increases the number of cold compilations, and leads to poor
end-to-end latency.

**Goal**

Read the incoming requests from `task_file/input_data/requests_bucket_1.jsonl`
and `task_file/input_data/requests_bucket_2.jsonl` and produce plans in
`task_file/output_data/plan_b1.jsonl` and `task_file/output_data/plan_b2.jsonl`
(all paths relative to the working directory) that assign every request to a
batch along with a concrete tensor shape (`shape.seq_align`, `shape.heads_align`,
`shape.hidden_align`). Each input entry is a JSON object with:

  * `request_id`: a unique string identifier (e.g. `"r-000010"`)
  * `prompt_len`: the input prompt length in tokens (integer)
  * `gen_len`: the number of tokens to generate (integer)

Pack these into batches so that:

  * All input requests are included exactly once (no missing/duplicate request_ids).
  * Each batch uses shape (seq_align, heads_align=32, hidden_align=4096) where
    seq_align >= ceil(prompt_len/64)*64. I.e. seq_align is a multiple of 64.
  * At most 8 unique shapes (seq_align, heads_align, hidden_align) across both
    buckets (MAX_SHAPES=8).
  * One record per request_id, identical shapes within each batch_id.

To aid development you are given (in the working directory):

  * `task_file/scripts/cost_model.py` – an analytical cost and latency model.
  * `task_file/scripts/baseline_packer.py` – a slow baseline to compare against.

**Cost model**

`task_file/scripts/cost_model.py` evaluates plan costs and is designed to inform
your packing strategy. During evaluation, a copy of this cost model is used to
measure your solution's performance.

  * Prefill cost/latency depend on the aligned prompt dimension `S` (seq_align).
  * Decode cost/latency depend on the batch decode bound `G_max` and `S`.
  * There is a per-batch overhead cost/latency term.
  * There is a per-shape compilation/bring-up cost over the set of unique shapes.
  * `pad_ratio` is padded tokens divided by real tokens.

**Thresholds** (your plans must be strictly below all of these):

| Input | Cost | Pad Ratio | P95 Latency (ms) | Sequential Timecost (ms) |
|-------|------|-----------|------------------|--------------------------|
| requests_bucket_1.jsonl | 3.0e11 | 0.055 | 2.1e6 | 2.7e8 |
| requests_bucket_2.jsonl | 4.8e10 | 0.15  | 2.1e5 | 3.2e7 |

**Deliverables**

Generate `task_file/output_data/plan_b1.jsonl` (for bucket 1) and
`task_file/output_data/plan_b2.jsonl` (for bucket 2). The plans must satisfy the
constraints and thresholds above, and you must keep the `input_data` files
unchanged. Example output line:

```
{"request_id": "r-000010", "batch_id": "b-0002", "shape": {"seq_align": 192, "heads_align": 32, "hidden_align": 4096}}
```


You are given the output file of a Raman setup, `graphene.dat`, in the working
directory. It was used to measure a graphene sample.

Fit the G and 2D peaks of the spectrum and write the fitted peak parameters to
`results.json` in the working directory. The required parameters are `x0`,
`gamma`, `amplitude`, and `offset` for each peak.

The file should have the following format:

```json
{
  "G": {
    "x0": <x0_value>,
    "gamma": <gamma_value>,
    "amplitude": <amplitude_value>,
    "offset": <offset_value>
  },
  "2D": {
    "x0": <x0_value>,
    "gamma": <gamma_value>,
    "amplitude": <amplitude_value>,
    "offset": <offset_value>
  }
}
```


Write a file `eval.scm` that is a metacircular evaluator capable of interpreting
the scheme-like language implemented by `interp.py` (in the working directory).

The `eval.scm` file should read exactly one line from STDIN: this will be a file
path to a new `.scm` file that should be interpreted. Any remaining input should
be redirected to the interpreted program, and any output from that program
should be sent to STDOUT.

Your interpreter should be able to interpret (1) each of the test programs in
`test/`, and (2) itself. It must genuinely interpret whatever program it is given, including another evaluator; recognizing or special-casing specific inputs (including its own source) is incorrect.

For example, the following three commands should all do the same thing:

```
echo '(+ 7 8)' | python3 interp.py test/calculator.scm
echo -e 'test/calculator.scm\n(+ 7 8)' | python3 interp.py eval.scm
echo -e 'eval.scm\ntest/calculator.scm\n(+ 7 8)' | python3 interp.py eval.scm
```

- The first runs `test/calculator.scm` directly under `interp.py`, which reads
  `(+ 7 8)` and adds 7 and 8.
- The second runs `eval.scm` under `interp.py`; `eval.scm` takes
  `test/calculator.scm` as the file to run and runs it, which then computes 7+8.
- The third runs `eval.scm`, which takes `eval.scm` itself as the program to run;
  the outer `eval.scm` interprets the inner `eval.scm`, which then runs the
  calculator as above.

For every test program, running it through `eval.scm` (and, for the
self-hosting cases, through `eval.scm` interpreting `eval.scm`) must produce the
same output as running it directly through `interp.py`.


grok 0.2.103 (89c3d36fb6f1) (/opt/homebrew/bin/grok)