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

A developer released E-- (English--), a programming language written in canonical English that compiles deterministically to Python, separating LLM-based code generation from runtime execution to ensure reproducibility and debuggability. The tool is available under Apache License 2.0 and can be installed via PyPI.

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