# Show HN: E– – a language you dial between English and Python

> Source: <https://github.com/frmoded/e-->
> Published: 2026-07-10 19:08:13+00:00

A programming language you write in plain, canonical English — and that compiles

deterministicallyto Python.

E-- ("English--") is English with the ambiguity removed: a closed grammar and a fixed vocabulary, with exactly one canonical phrasing per construct. It is meant to read and edit like English while still compiling to ordinary, reproducible Python.

LLM-generated code is fuzzy at runtime: non-reproducible, expensive per call, hard to debug. E-- separates the LLM's role from execution — LLM (optionally) writes canonical E-- at authoring time; a deterministic parser compiles the E-- to Python; runtime is pure. Best of both worlds: LLM creativity when you need it, deterministic behavior forever after.

Install from PyPI:

```
pip install e-minus-minus
```

Transpile a canonical E-- file to Python:

```
emm-transpile examples/describe.emm
```

That's it. No LLM required for canonical E-- with no `{{ }}`

slots. See
"Running E--" below for more, and "Resolving `{{ }}`

slots" for the LLM setup
when you use free-English input or value slots. The LLM path is optional and
lives behind the `[llm]`

extra: `pip install "e-minus-minus[llm]"`

.

Developing on E-- itself? Clone the repo and invoke the CLI as a module while
your working tree is on `PYTHONPATH`

:

```
PYTHONPATH=src python -m e_minus_minus.transpiler examples/describe.emm
```

E-- is licensed under **Apache License 2.0** (see [ LICENSE](/frmoded/e--/blob/main/LICENSE)) —
permissive, with an explicit patent grant, so it can be embedded in commercial
products freely.

Two clarifications:

**The license covers the E-- tooling.** The Python that E-- generates is yours — the output is not encumbered by this project's license.**The LLM is your own.** E--'s normalizer and`{{ }}`

resolution require a language model that you supply; that provider's terms are separate from this project.

Programmatic API:

``` python
from e_minus_minus import transpile

python_source = transpile(emm_source)
```

`transpile()`

is pure: no network, no side effects. Pass a `resolve_slot`

callable to handle `{{ ... }}`

slots (see [ docs/spec.md](/frmoded/e--/blob/main/docs/spec.md) and
the CLI implementation in

`src/transpiler.py`

for the injected-resolver
pattern).E-- is a two-stage pipeline, split so that the unreliable part and the deterministic part never mix:

``` php
Free English  --LLM (transpile-time)-->  Canonical E--  --plain parser-->  Python
```

**Normalizer (LLM, optional).** Turns free-form English into canonical E--. This is the only stage that deals with linguistic ambiguity.**Compiler (deterministic).** Turns canonical E-- into Python with an ordinary parser — no LLM, fully reproducible and debuggable.

**The LLM runs only at transpile time, never at runtime.** Generated Python is
always pure and self-contained. The LLM is never allowed to decide program
structure; it is used only to fill clearly-delimited value slots written as
`{{ ... }}`

, and those resolutions are cached so builds stay reproducible.

Canonical E--:

```
Set result to [[fibonacci]]( {{the first prime number greater than 5}} ).
Do [[print]](result).
```

compiles to:

```
result = fibonacci(7)
print(result)
```

Markers keep it unambiguous: `[[name]]`

is a function call, a bare word is a
variable, `"x"`

/`3`

are literals, `<1, 2, 3>`

is a list, and `{{ ... }}`

is an
English phrase the transpiler resolves once and bakes in.

E-- source files use the ** .emm** extension (English--). The deterministic
canonical-to-Python core is implemented; you can transpile and run

`.emm`

files
from the command line.Given this canonical E-- source at `examples/describe.emm`

:

```
Define [[describe]] taking n:
    If n is greater than 10:
        Give back "big".
    Give back "small".

For each n in <3, 42, 7>:
    Do [[print]]([[describe]](n)).
```

transpile it and print the generated Python to your screen:

```
python3 src/transpiler.py examples/describe.emm
```

prints:

``` python
def describe(n):
    if n > 10:
        return "big"
    return "small"
for n in [3, 42, 7]:
    print(describe(n))
```

Write the generated Python to a file instead of the screen:

```
python3 src/transpiler.py examples/describe.emm -o out.py
```

Transpile **and run** it, so you see the program's actual output:

```
python3 src/transpiler.py examples/describe.emm --run
```

prints:

```
small
big
small
```

See the generated Python **and** run it in one go with `--show`

(alias `-s`

):

```
python3 src/transpiler.py examples/describe.emm --run --show
```

prints the code and its output, separated by comment lines:

``` python
# --- generated Python ---
def describe(n):
    if n > 10:
        return "big"
    return "small"
for n in [3, 42, 7]:
    print(describe(n))
# --- output ---
small
big
small
```

The delimiters are Python comments, so the whole block stays copy-pasteable.
`--show`

on its own (without `--run`

) just prints the Python, like the default.

Notes:

- The
`.emm`

extension is the convention for E-- source files. `{{ ... }}`

LLM value slots**are** runnable — see "Resolving`{{ }}`

slots" below for the one-time setup. Files with no slots (like`examples/describe.emm`

) need no key and`--run`

works with no model.

A `{{ ... }}`

slot is an English phrase that the transpiler resolves to a Python
expression **once, at transpile time**, using an LLM — then caches the result so
later builds are offline and reproducible. Files with **no** `{{ }}`

slots need
no API key and no setup.

To run a slot example end to end:

```
# 1. create and activate a virtual env
python3 -m venv .venv && source .venv/bin/activate

# 2. install dependencies (the Anthropic SDK)
pip install -r requirements.txt

# 3. set your Anthropic API key
export ANTHROPIC_API_KEY="sk-ant-..."

# 4. transpile and run a slot example
python3 src/transpiler.py examples/primes.emm --show --run
```

`examples/primes.emm`

is minimal:

```
For each p in {{the first five prime numbers, as a Python list}}:
    Do [[print]](p).
```

which transpiles to:

```
for p in [2, 3, 5, 7, 11]:
    print(p)
```

and prints `2 3 5 7 11`

(one per line). The `{{ ... }}`

slot resolved once via
the LLM to the concrete list `[2, 3, 5, 7, 11]`

, was cached, and gets used for
every subsequent build with no API call.

The first run calls the model (Anthropic Haiku) to resolve each slot, writes the
resolved Python expression to ** .emm_cache.json**, and bakes it into the
output. Every later run is an

**offline cache hit**— no model call, identical result. The cache file maps the exact slot text to its resolved expression and is meant to be

**committed**, so resolved values stay diffable and reviewable.

Editing a slot's text is a cache miss and re-resolves; deleting the cache forces
full re-resolution. Files without `{{ }}`

slots (like `examples/describe.emm`

)
never touch the API.

You can also build a slot example inline without any file setup:

```
printf 'Set year to {{the current year, as an integer literal}}.\nDo [[print]](year).\n' > hello.emm
emm-transpile hello.emm --show --run
```

The slot at line 1 is at **expression** position (inside `Set year to ...`

), so
the LLM returns a single Python expression — e.g. `2026`

— and the compiler
splices it in:

```
year = 2026
print(year)
```

Prints `2026`

. Second run is offline (cache hit).

A `{{ ... }}`

slot can appear at a **statement** position, not just an
expression position — putting it on its own line, at a block's indentation,
delegates one or more Python statements to the LLM. Author writes the
surrounding structure; the LLM fills the region.

```
Define [[summarize]] taking numbers:
    {{ compute mean, median and count of numbers into mean_v median_v count_v }}
    Do [[print]](count_v).
    Do [[print]](mean_v).
    Give back mean_v.
```

At transpile time the statement slot resolves to real Python:

``` python
def summarize(numbers):
    from statistics import mean, median
    mean_v = mean(numbers)
    median_v = median(numbers)
    count_v = len(numbers)
    print(count_v)
    print(mean_v)
    return mean_v
```

**Trade-off**: `[[wikilinks]]`

or callable references inside a code-slot's
resolved Python are **opaque** to any downstream tool that inspects the E--
source. Author knowingly accepts DAG invisibility inside code-slot regions in
exchange for region-level delegation. Use expression slots when you need
graph visibility; use code slots when you're delegating structure the LLM
knows better than you do.

`examples/code_slot_example.emm`

is a runnable code-slot demo:

```
Define [[summarize]] taking numbers:
    {{ compute the mean, median and count of numbers into named variables mean_v median_v and count_v }}
    Do [[print]](count_v).
    Do [[print]](mean_v).
    Do [[print]](median_v).
    Give back mean_v.

Set data to <2, 3, 5, 7, 11, 13>.
Do [[summarize]](data).
```

Transpile and run it:

```
emm-transpile examples/code_slot_example.emm --show --run
```

The statement slot on line 2 resolves to real Python that binds `mean_v`

,
`median_v`

, `count_v`

— e.g. via `from statistics import mean, median`

plus
three assignments. Because it's a code slot, it can add the `import`

on its
own line, which a value slot cannot.

**Code slots let you delegate imports.** A value slot at expression position
can only emit a single Python expression, so getting the current year with a
value slot produces the awkward
`__import__('datetime').datetime.now().year`

. A code slot at statement
position sidesteps the constraint:

``` python
printf '{{ import datetime and set x to the current year }}\nDo [[print]](x).\n' > hello.emm
emm-transpile hello.emm --show --run
```

The LLM resolves the statement slot to:

``` python
import datetime
x = datetime.datetime.now().year
print(x)
```

Prints the current year. Same syntax as a value slot; position determines shape.

You don't have to write canonical E-- by hand. The transpiler's first phase
**normalizes** free-English E-- into canonical E-- with an LLM, then compiles
the canonical form to Python — one input, two outputs. An English source
(`examples/describe_en.en`

) reads like prose:

```
Define a function called describe that takes a number n. If n is greater than
ten, give back the string "big". Otherwise, give back the string "small".
Then, for each n in the list 3, 42 and 7, print describe of n.
```

Normalize it to canonical and run the result, saving the canonical form too:

```
python3 src/transpiler.py examples/describe_en.en --canonical-out out.em --run
```

`out.em`

holds the canonical E-- (equivalent to `examples/describe.emm`

) and the
program prints `small / big / small`

.

Two properties make this safe and cheap:

**The parser is the canonical-detector.** Whether a file "is already canonical" is decided by trying to parse it deterministically — no LLM, no heuristic. An**already-canonical file needs no API key**: normalization short-circuits before any model call. Only genuinely English input hits the model.** Fixed point + cache.**Feeding the canonical output (`out.em`

) back in parses as canonical, so Phase 1 does nothing and reproduces the same outputs. Normalizations are cached in a committed`.emm_norm_cache.json`

(keyed by source text), so re-running English input is an offline cache hit. Setup is the same as for slots:`pip install -r requirements.txt`

and`export ANTHROPIC_API_KEY=...`

.

Normalization and `{{ }}`

slot resolution are independent, separately cached LLM
touchpoints — a canonical file with all slots cached makes **zero** live calls.

Early design. The language is specified in [ docs/spec.md](/frmoded/e--/blob/main/docs/spec.md). The
deterministic canonical-to-Python core (lexer, parser, emitter) is implemented
with a runnable CLI — see "Running E--" above — and

`{{ }}`

slot resolution is
wired up (Anthropic Haiku + a committed cache; see "Resolving `{{ }}`

slots").
The LLM normalizer (free English → canonical) is wired up at whole-file
granularity (see "Writing in free English"); per-region normalization is the
next refinement.— the language specification (source of truth).`docs/spec.md`

/`docs/cowork-protocol.md`

— internal development workflow.`docs/cc-prompt-queue.md`