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Research Findings: el-stupido

Date: 2026-02-23 — 2026-02-28 Status: Project killed. Postmortem below.

Executive Summary

el-stupido started as a "maximally compressed assembly language for LLMs" — the hypothesis being that emoji/short keywords would save tokens and let smaller models generate code. Through 5 rounds of benchmarking across 12+ models from 776MB to 14.7GB, this thesis was disproven: BPE tokenizers already encode common keywords as single tokens, and emoji actually costs MORE tokens.

However, the project accidentally discovered something more valuable: codebook expansion (tiny declarative specs compiled to large programs) combined with constrained decoding (JSON schema forcing structured output) makes sub-4B models viable for code generation. This is a genuinely novel finding with practical applications.

Finding 1: The Original Thesis Is Wrong

Claim: Replacing def, for, return with short/emoji tokens (fn, ➰, ret) saves tokens and makes code generation cheaper for LLMs.

Reality: BPE tokenizers (used by all modern LLMs) already encode common programming keywords as single tokens:

Token BPE tokens el-stupido BPE tokens
def 1 fn 1
for 1 ➰ 3-4
return 1 ret 1
struct 1 📦 3-4
while 1 whl 1
function 1 🔧 3-4

Emoji is actively harmful: A single emoji like 🔧 encodes as 3-4 BPE tokens (UTF-8 bytes split across subword boundaries). Replacing function (1 token) with 🔧 (3-4 tokens) is a net loss.

Short ASCII keywords (fn, ret, whl) are token-neutral: they're also 1 token each. No savings, no loss. The entire keyword-shortening premise has zero effect on token count.

Finding 2: Where Tokens Actually Go

Analysis of a representative 200-token Python program reveals the real token budget:

Category % of tokens Compressible?
Variable names + string literals 35% No (irreducible, semantic)
Framework boilerplate 30% Yes — this is the real waste
Core logic (if/for/math) 25% No (irreducible, algorithmic)
Structural syntax (braces, colons) 10% Barely

The enemy is NOT keywords — it's framework boilerplate.

A Flask web server spends ~60 tokens on from flask import Flask, app = Flask(__name__), @app.route, if __name__ == '__main__', app.run(). None of this is "the program" — it's framework ceremony.

An argparse CLI spends ~40 tokens on import argparse, parser = ArgumentParser(), parser.add_argument(...) repeated N times, args = parser.parse_args(). Again, boilerplate.

Codebooks eliminate exactly this waste. use web + listen 8080 + routes replaces all framework ceremony with 3-6 lines.

Finding 3: Codebook Expansion Ratios

Measured expansion ratios from el-stupido codebook source to compiled output:

File Source Expanded Ratio What it builds
crud_grugbook.es 44B (3 lines) 2917B 66x Full CRUD web app with HTML forms, .grug persistence
cb_rest_users.es 105B (6 lines) 3449B 33x JSON REST API with model, routes, persistence
cb_test_math.es 438B (17 lines) 1978B 5x Test runner with ANSI colored output
cb_cli_grep.es 392B (15 lines) 1599B 4x CLI tool with flags, args, help text
cb_hello.es 81B (4 lines) 804B 10x HTTP server with routes

The highest expansion (66x) comes from the most declarative specs. When the LLM only needs to express intent ("I want a CRUD app for grugbooks with name and msg fields"), the compiler generates all the structural code.

Key insight: compression ratio correlates with how declarative the spec is. Pure data declarations (model names, field names, route paths) expand the most. Imperative logic (custom handlers) expands the least.

Finding 4: Constrained Decoding Is Transformative

Round 5 benchmarked JSON schema constrained decoding vs free-form generation across 12 models on 5 programming tasks:

Sub-4B models (9 models tested):

  • Schema constrained: 9 passes total
  • Free generation: 1 pass total

Constrained decoding makes sub-4B models 9x more likely to produce correct code.

Why it works: A sub-4B model generating free-form el-stupido source has ~500+ valid next tokens at each step (any keyword, identifier, number, symbol). With JSON schema constraints, there are typically ~20 valid next tokens. Fewer decisions per token = fewer chances to go wrong.

Best results:

  • qwen2.5-coder:7b (schema): 4/5 tasks
  • phi4 (schema): 4/5 tasks
  • Haiku 3.5 (schema): 5/5 tasks — first perfect score
  • qwen3:8b (free): 3/5 tasks (anomalous — large enough to learn from prompt)

Token efficiency: Schema output averages ~100 tokens vs ~400 for free generation. 4x more compact.

Finding 5: The Anomaly at 8B+

qwen3:8b is the only model where free generation (3/5) outperforms schema (2/5).

This reveals a threshold: above ~8B parameters, models have enough capacity to learn a new language's patterns from a prompt alone. Schema constraints become overhead rather than assistance. Below ~8B, constraints are essential.

This means constrained decoding is specifically a small model technique. For large models, free generation with good prompting works fine.

Finding 6: Benchmark Progression

Round Approach Pass Rate Key Change
1 Original emoji syntax 2/12 (17%) Baseline — most models can't parse emoji syntax
2 Natural keywords 4/12 (33%) Accept for/while/return as LLMs write them
3 Declarative combinators 8/12 (67%) Eliminate loops from LLM output entirely
4 HTTP codebook 6/12 (50%) 4-line DSL → full server (fewer models, harder task)
5 JSON schema constrained 4/5 (80%) Structured output eliminates syntax errors
5 Haiku 3.5 5/5 (100%) First perfect score across all tasks

The trend: less freedom for the LLM = better results. Every successful iteration removed degrees of freedom from the generation space.

The Refined Thesis

The original thesis — "shorter syntax = fewer tokens = better LLM generation" — is wrong.

The refined thesis that the data supports:

Structured intermediate representations + compiler expansion outperform free-form code generation for small models.

Specifically:

  1. Declarative specs (JSON/codebook) constrain the output space
  2. Compiler expansion amplifies minimal specs into large programs
  3. Combined, a sub-4B model can produce working applications it could never generate as raw code

This is testable, novel, and supported by 5 rounds of benchmarking across 12+ models.

Implications

What this means for LLM code generation:

  1. Stop optimizing syntax for LLMs. Keywords don't matter. Framework boilerplate does.

  2. Codebook/template systems are underexplored. The 66x expansion ratio from crud_grugbook.es means a model generating 10 tokens of spec produces a program that would take 660 tokens to generate directly. This isn't compression — it's leverage.

  3. Constrained decoding unlocks small models. Sub-4B models go from nearly useless (1 pass out of 45 attempts) to functional (9 passes) with schema constraints alone.

  4. The real product is a compilation target, not a language. El-stupido's value isn't as a language humans write — it's as an intermediate representation that small models can target and compilers can expand.

What this means for edge/embedded AI:

The logical endpoint: a ~100M parameter model on a Raspberry Pi generating codebook JSON specs (~20-60 tokens of structured output), fed to the el-stupido compiler, producing native binaries. A tiny model that can't write code but CAN fill in a form, and the compiler does the rest.

This is the TinyLLM direction — not yet implemented, but the thesis is supported by the data.

Appendix A: Codebook Examples

66x expansion — Full CRUD web app (3 lines)

use web
listen 8080
crud grugbook name msg+

Compiles to: fork-per-connection HTTP server, HTML form generation, URL decoding, .grug file persistence, list/create/delete handlers. 2917 bytes of C from 44 bytes of spec.

33x expansion — JSON REST API (6 lines)

use rest
listen 8080
model user name email
GET /users list user
POST /users create user
GET /health "ok"

Compiles to: HTTP server, JSON serialization, .grug persistence layer, model struct, route dispatch, CRUD handlers. 3449 bytes from 105 bytes.

10x expansion — HTTP server (4 lines)

use web
listen 8080
/ "hello from el-stupido!"
/about "emoji-first systems lang"

Compiles to: socket/bind/listen/accept/fork, HTTP parsing, route dispatch, response formatting. 804 bytes from 81 bytes.

Appendix B: Constrained Decoding Detail

Full results table from Round 5 (P=pass, CF=compile fail, W=wrong, F=fail, JE=JSON error):

Model Size fact fizz sumsq fib primes Schema Free
deepseek-coder:1.3b 1B CF/CF CF/CF CF/CF CF/CF CF/CF 0/5 0/5
qwen2.5-coder:1.5b 1.5B CF/CF CF/CF W/CF CF/CF CF/CF 0/5 0/5
lfm-opencode:1.2b 1.2B CF/CF CF/CF CF/CF CF/CF CF/CF 0/5 0/5
lfm2.5-thinking:1.2b 1.2B P/CF CF/CF CF/CF CF/CF CF/CF 1/5 0/5
codegemma:2b 3B P/CF W/CF CF/F P/CF CF/CF 2/5 0/5
sam860/LFM2:2.6b 2.6B P/CF CF/CF CF/CF CF/CF JE/CF 1/5 0/5
starcoder2:3b 3B P/F CF/CF P/F JE/CF CF/F 2/5 0/5
qwen2.5-coder:3b 3.1B CF/CF P/CF CF/CF P/CF CF/CF 2/5 0/5
granite4 3.4B CF/P CF/CF CF/CF P/CF CF/CF 1/5 1/5
qwen2.5-coder:7b 7.6B P/CF P/CF P/CF P/CF CF/CF 4/5 0/5
phi4 14.7B P/CF P/CF P/CF P/CF CF/CF 4/5 0/5
qwen3:8b 8.2B P/P W/P CF/CF P/P CF/F 2/5 3/5

Aggregate sub-4B: 9 schema passes vs 1 free pass (9x improvement).

Round 6: Design Research — The Specification-Expansion Gap

Date: 2026-02-23 Method: Two iterative research loops (10 + 6 iterations) drawing on information theory, physics, cognitive science, thermodynamics, biology, constraint programming, and history of radical computing systems.

The Question

"If developers don't need to read code anymore, why is LLM-generated code so verbose? It's a waste."

Finding 7: The Specification-Expansion Gap Is 40-150x

Programs contain far less information than their source code. Using Kolmogorov complexity analysis on el-stupido's manifest examples:

Representation Size Ratio to K(spec)
Irreducible decisions (K) ~20 bytes 1x
JSON manifest ~213 bytes 10.7x
el-stupido source (expanded) ~2,917 bytes 146x
Equivalent Python/Flask ~5,000-8,000 bytes 250-400x
LLM-generated Python (typical) ~8,000-15,000 bytes 400-750x

A guestbook CRUD app has ~160 bits of real decisions: domain (2 bits), app name (47 bits), port (16 bits), model/field names and types (~95 bits). Everything else — the HTTP server, fork/accept loop, HTML templates, route dispatch, persistence — is deterministic expansion from those 160 bits.

LLM-generated code carries ~0.15 bits of information per token. The manifest carries ~1.84 bits per token. The theoretical optimum is ~18.4 bits per token. Current LLM code generation wastes 99.5% of tokens on information-free expansion.

Finding 8: The Waste Is in the LLM's Own Context, Not in Human Readability

The initial question assumed the problem was human-facing: verbose code is hard to read. This is wrong. The real problem is LLM-facing: verbose code fills the context window.

Every token an LLM generates eats context. By function 15, it's forgotten function 3's exact signature. The LLM drowns in its own output. The context window is the bottleneck, not output format.

This reframes the manifest system: it's not about making code shorter for humans. It's about making the LLM's job small enough that a tiny model can do it. 30 tokens of decisions vs. 2,000 tokens of code is the difference between a 1B model succeeding and failing.

Finding 9: Seven Theoretical Frameworks Converge

Seven independent frameworks all describe the same mathematical object:

Framework Name for "manifest" Name for "compiler"
Kolmogorov complexity Shortest program Universal machine
Minimum Description Length Data given model Model
Predictive processing Prediction error Predictive model
Free energy principle Variational free energy Generative model
Bayesian programming Question + specification Inference engine
Miller's chunking Chunk label Chunk hierarchy
Belief propagation Observed variables Factor graph

The unified statement: A program is its prediction errors. Everything else is reconstruction. The manifest contains only what the domain cannot predict. The compiler reconstructs the rest.

This is structurally isomorphic to Bayesian programming (Bessière et al.): the manifest is the model specification (variables + decomposition + forms), the domain is the prior, and the expanded program is the posterior. Different "questions" on the same model produce different artifacts (web app, REST API, test suite, docs).

Finding 10: Nature Uses Constraint Relaxation, Not Instruction Execution

Every efficient natural system works the same way:

  1. Local constraints (the specification — what must be true)
  2. Shared medium (domain knowledge that enables propagation)
  3. Relaxation to equilibrium (the "compilation" — physics finds the answer)
System Specification Medium Expansion ratio
DNA → organism 25 MB genome Chemistry 4,000,000x
Ant colony 1 MB neural circuit Pheromone field 10,000x
Crystal ~100 bits (composition) Electromagnetic field 10^22 x
Brain wiring 25 MB genome Chemical gradients 4,000,000x
Immune system 500 KB gene segments Antigen presentation 40,000,000x
el-stupido manifest ~200 bytes Codebook templates 14-66x

el-stupido's expansion ratio (14-66x) vs. nature's (millions to billions) shows how much room there is for encoding more domain knowledge into the medium.

The thermodynamic analysis confirms: the LLM inference step (creating information from intent) is the ONLY physically expensive part of the pipeline. Manifest expansion creates no new information and is theoretically almost free (~10^-28 joules for the reversible portion vs. ~125 joules actual on a Pi).

Finding 11: Compression Equals Removing Phase Boundaries

Historical analysis of radical computing systems reveals a universal pattern:

System Phases eliminated Compression achieved
Forth (Chuck Moore) parse/compile/link/run → interpret 1,000x (2KB system)
STEPS (Alan Kay) OS/app/driver → DSL stack 5,000x (20K LOC for full OS)
APL (Ken Iverson) loop/condition/variable → array op 10-100x
Oberon (Niklaus Wirth) editor/compiler/OS → unified system 10,000x
TCC (Fabrice Bellard) frontend/middle/backend → single pass 100x (25K LOC compiler)

Every radical system that achieved 100-5000x compression did it by collapsing multiple phases into fewer phases. Industry software has 7-11 phases; radical software has 1-3. The el-stupido manifest pipeline currently has ~11 phases (prompt → LLM → JSON → parse → expand → lex → parse → AST → normalize → codegen → link). The target is 3: intent → decisions → binary.

Finding 12: Hand-Coding Domain Templates Doesn't Scale

The current manifest system has 4 domains (crud, rest, cli, test), each with ~200 LOC of hand-written C expansion code in manifest.c. Adding each new domain requires writing another 100-300 LOC of C string concatenation.

This moves waste from the LLM to the compiler developer. 50 domains would mean ~10,000 lines of hand-written templates. This is the same problem we're trying to solve, just at a different layer.

Finding 13: Composable Primitives Are the Answer

Instead of monolithic domain templates, the expansion should be COMPOSITION of reusable primitives. Each primitive is a small el-stupido library function (5-15 LOC). The manifest specifies which primitives to compose and their parameters. The compiler generates only the glue code (main, init, loop, cleanup).

Example — the same CRUD app as a composition:

{
  "compose": [
    {"use": "http_listen", "port": 8080},
    {"use": "route", "method": "GET", "path": "/", "handler": "list"},
    {"use": "route", "method": "POST", "path": "/", "handler": "create"},
    {"use": "grug_store", "name": "entries", "fields": ["name", "msg"]},
    {"use": "html_list", "model": "entries"},
    {"use": "html_form", "fields": ["name", "msg"]}
  ]
}

Advantages over domain templates:

Domain templates Composable primitives
New domain 100-300 LOC of C 0 LOC (compose existing primitives)
New primitive N/A 5-15 LOC of el-stupido
GBNF grammar Hand-maintained Auto-generated from primitive registry
LLM decision space Pick 1 of 4 domains Pick 4-6 of 20+ primitives
Scaling Linear in domains Combinatorial in primitives

20 primitives composing in groups of 4-6 yields thousands of possible programs from a fixed library. Each new primitive multiplies the space of possible programs without touching the compiler.

The primitive library grows via the "immune system" model:

  • Encounter a novel domain → bigger LLM (or human) writes the missing primitive
  • Primitive enters the library → verified once, reused forever
  • Tiny model composes existing primitives → cheap, constrained, fast

This mirrors biology: evolution creates new genes (expensive, slow, once), gene expression composes them (cheap, fast, every organism). The genome accumulates successful patterns over time.

Finding 14: The Economics

Metric Current LLM generation Manifest approach Composable primitives
Tokens per program 300-2,000 30-200 30-80
Min viable model 7B+ sub-4B sub-1B (target)
Energy per program 0.3-2 Wh 0.01-0.06 Wh 0.01 Wh
Info per token 0.15 bits 1.84 bits ~4 bits (target)

Strongest economic angle: enabling computation where none exists today. 3.7 billion people lack reliable internet. A Raspberry Pi ($55) + 1B model + el-stupido compiler + primitive library = full AI-powered development environment, offline, forever. The market isn't "cheaper Copilot" — it's "programmable devices for the offline world."

The Refined Thesis (v3)

The v1 thesis ("shorter syntax saves tokens") was disproven. The v2 thesis ("structured specs + compiler expansion") was validated.

The v3 thesis:

Programs are compositions of reusable primitives. A tiny LLM's job is to select and wire primitives (30-80 tokens of constrained JSON). The compiler generates the glue. The primitive library grows over time — each new primitive multiplies the space of possible programs without changing the compiler. This makes sub-1B models viable for generating native binaries on constrained hardware, offline, enabling computation for billions of people who currently have none.

This is testable. The next step is building the composable primitive system and measuring whether composition achieves equivalent expansion ratios to hand-coded domain templates while scaling to new domains with zero compiler changes.

Round 7: Implementation — The Compose Pipeline Exists

Date: 2026-02-28 Status: The composable primitive system from Finding 13 is built and working

Finding 15: 40 Primitives, Zero Domain Templates

The compose pipeline predicted in Finding 13 now exists. 40 typed primitives compose into programs via JSON manifests, .esc named syntax, or compact tape format. No domain-specific templates remain — everything is composition.

Category Count Examples
Constants 2 const_num, const_str
Arithmetic 8 add, sub, mul, div, mod_num, floor, abs, parse_num
Comparison/Logic 6 gt, lt, eq_num, and_bool, or_bool, not_bool
Branching 2 select_num, select_str
String ops 12 concat, format_str, substr, replace_str, split_nth, etc.
I/O 4 read_stdin, read_stdin_all, print_num, print_str
Filesystem 6 read_file, write_file, read_file_dyn, write_file_dyn, etc.
CLI/Env 4 arg_num, arg_str, arg_count, env_str
Network 2 http_get, http_get_dyn
Control 1 exit_code

Every primitive declares typed inputs (params, binds), typed outputs (provides: num, str, bool, sink), and effects (pure, io_read, io_write, fs_read, fs_write, net_read, env_read). The compiler enforces capability matching at bind time — a node expecting num rejects a node providing str. Forward references are rejected (acyclic by construction).

This validates the prediction from Finding 13: 40 primitives composing in groups of 4-8 yield thousands of possible programs. 20 working examples exist, from arithmetic to HTTP clients to file processors, all from the same primitive set with zero compiler changes between them.

Finding 16: Three Formats, One Validation Path

Three input formats converge on the same Manifest struct and share a single validation path:

JSON (verbose, LLM-friendly for constrained decoding):

{"app":"sum","nodes":[{"id":"a","op":"const_num","params":{"value":13}},
{"id":"b","op":"const_num","params":{"value":29}},
{"id":"s","op":"add","binds":{"lhs":"a","rhs":"b"}},
{"id":"out","op":"print_num","binds":{"value":"s"}}]}

Tape (compact, model-facing canonical form):

A sum
C io_write
0 cn 13
1 cn 29
2 ad 0 1
3 pn 2

.esc (named key=value, human-readable):

app = sum
a = const_num value=13
b = const_num value=29
s = add lhs=a rhs=b
out = print_num value=s

The tape format is the canonical representation used for content-addressed hashing. canonical_tape() strips the app name, sorts capabilities, and produces a deterministic string. Two manifests with different app names but identical computation hash to the same binary.

The compiler auto-detects format: starts with { = JSON; first non-blank line starts with A, C, or a digit = tape; otherwise .esc. No flags needed.

Finding 17: Generated Rust, Not C

The compose pipeline generates Rust (not C as the earlier codebook system did). Each manifest compiles to a single self-contained .rs file with no external dependencies. HTTP uses raw TCP sockets. HTTPS shells out to curl. The compiler invokes rustc --edition 2021 -C opt-level=2 -C strip=symbols.

Machine output for compose_sum.json:

status: ok
rust_size: 380 bytes (generated source)
binary_size: 367,840 bytes (stripped native binary)
hash: 703b82ede0d9...
cached: false

380 bytes of generated Rust from ~213 bytes of JSON manifest — a modest 1.8x expansion. But the binary is a complete standalone executable. The expansion ratio is lower than the codebook system (Finding 3: 5-66x) because compose manifests specify individual operations rather than high-level domain intents. The tradeoff: compose scales to arbitrary programs; codebooks were locked to 4 domains.

Finding 18: Content-Addressed Caching Eliminates Redundant Compilation

Compiled binaries are cached at ~/.esc/bin/<hash> using the canonical tape hash. On second compose of the same computation, the compiler returns the cached binary instantly (cached: true in machine output). This means:

  • Identical manifests from different sessions reuse the same binary
  • Renaming the app doesn't trigger recompilation (app name is stripped from hash)
  • The cache grows monotonically — a library of verified tools accumulates

This is the "immune system" model from Finding 13 realized: tools are forged once and reused forever.

Finding 19: Machine-Readable Errors Enable LLM Self-Correction

Every validation error produces structured JSON with kind, message, field-specific details, and a hint written for LLM self-correction:

{
  "kind": "unknown_primitive",
  "message": "primitive 'http_post' does not exist",
  "hint": "run `esc primitives` to see available primitives"
}

This closes the loop for autonomous generation: an LLM generates a manifest, the compiler validates it, errors come back as structured JSON the LLM can parse and fix. No human in the loop. The hint field gives the LLM its next action.

Finding 20: The Memory Graph Creates Persistent Knowledge

The esc memory and esc context systems implement a persistent knowledge graph that survives across sessions, models, and providers:

Memory graph (~/.esc/memory.json):

  • Tools: content-addressed by canonical tape hash. Each carries app name, goal, tags, primitive chain pattern, IO signature, capability list, use count. Tools are the atoms of reuse.
  • Edges: typed relationships between tools (variant_of, pipes_to, supersedes). Enable graph-aware recall.
  • Notes: contextual knowledge (discoveries, decisions, patterns, issues). Content-addressed by kind:summary hash.

Context slots (per-session, ~/.esc/context/session.json):

  • 5 slot kinds with different TTLs: Task (20/50), Knowledge (10/30), Result (5/15), Error (3/8), Scratch (2/5)
  • State machine: Hot → Warm → Cold → Archived/Dropped
  • Auto-wisdom: slots with 3+ touches surviving 15+ turns are automatically persisted to the memory graph as permanent knowledge
  • Token budget tracking with 80% archive threshold

Three-phase recall:

  1. Direct word-level scoring (weighted by field: goal 3x, app 2x, tags 3x)
  2. Edge walk from direct hits to connected tools
  3. Tag expansion — tools sharing 2+ tags with hits (capped at 3)

Dual-write: everything writes to both flat-file (offline-first, zero dependencies) and atomic-server (typed graph with tantivy full-text search, Ed25519-signed commits) when configured. The system works fully offline.

Finding 21: The GBNF Grammar Is Auto-Generated

esc grammar auto-generates GBNF grammar rules from the primitive registry. Each primitive becomes an alternative in the node production, with typed params and binds. This means:

  • Adding a new primitive automatically updates the constrained decoding grammar
  • No hand-maintained grammar files (the scaling problem from Finding 12)
  • The grammar is always consistent with the compiler's validation

Combined with Finding 4 (constrained decoding is transformative for sub-4B models), this means the compose pipeline is ready for end-to-end LLM-driven tool forging: model generates JSON within the auto-generated grammar → compiler validates → errors feed back → model corrects → binary is cached.

The State of the Thesis (v3 Revisited)

Finding 13 predicted composable primitives as the path forward. Findings 15-21 confirm the system works as designed:

v3 Prediction Status
"Compositions of reusable primitives" ✅ 40 primitives, 20 working examples
"30-80 tokens of constrained JSON" ✅ Tape format: ~20-40 tokens typical
"Compiler generates the glue" ✅ Rust codegen, single-file, no deps
"Primitive library grows over time" ✅ Content-addressed cache, memory graph
"Without changing the compiler" ✅ New primitives = registry entry only
"Sub-1B models viable" ⬜ Not yet benchmarked with compose pipeline

The missing piece was Round 8: benchmarking models generating compose manifests. That experiment has now been run.

Round 8: End-to-End Compose Manifest Generation

Date: 2026-02-28 Method: 8 local models (1.5B–30B) generating JSON compose manifests via Ollama 0.15.4, three output modes (schema-constrained, json-constrained, free-form), 5 tasks of increasing difficulty, compiled and executed end-to-end.

Setup

Models: qwen2.5-coder:1.5b, qwen2.5-coder:3b, qwen3:4b, qwen2.5-coder:7b, qwen3:8b, qwen2.5:14b-instruct, qwen2.5-coder:14b, qwen3:30b-a3b (MoE, 3B active). All Q4_K_M quantized, running on a single machine (192.168.1.138).

Tasks (ordered by difficulty):

  1. sum_const — add 17+25, print 42 (basic arithmetic)
  2. mul_const — multiply 6×7, print 42 (basic arithmetic)
  3. str_upper — uppercase a CLI arg (string transform)
  4. greeting — concatenate "Hello, " + CLI arg (string concat with constants)
  5. temp_conv — Fahrenheit to Celsius: (arg-32)×5/9 (multi-step arithmetic, 8 nodes)

Protocol: System prompt with primitive list + one worked example (13+29=42). Each model generates a JSON manifest, which is compiled by esc compose and executed. Output is compared to expected value.

Finding 22: Results Table

Model Size Schema JSON Free
qwen2.5-coder:1.5b 1.5B 3/5 3/5 3/5
qwen2.5-coder:3b 3.1B 5/5 4/5 4/5
qwen3:4b† 4.0B 3/5 1/5 —
qwen2.5-coder:7b 7.6B 4/5 4/5 4/5
qwen3:8b† 8.2B 4/5 4/5 3/5
qwen2.5:14b-instruct 14.8B 5/5 5/5 5/5
qwen2.5-coder:14b 14.8B 4/5 4/5 4/5
qwen3:30b-a3b† 30.5B 4/5 5/5 —
TOTAL 32/40 (80%) 30/40 (75%) 23/35 (66%)

†qwen3 thinking models required think=false via chat API. Without it, the generate endpoint strips thinking tokens, producing empty responses (0/15).

Sub-4B models (1.5B, 3B): 8/10 schema, 7/10 json, 7/10 free.

Finding 23: Constrained Decoding No Longer Dominates

In Round 5, schema-constrained decoding was 9x better than free-form for sub-4B models (9 vs 1 passes). In Round 8:

Mode Sub-4B All models Avg tokens
Schema 8/10 (80%) 32/40 (80%) 144
JSON 7/10 (70%) 30/40 (75%) 154
Free 7/10 (70%) 23/35 (66%) 294

The gap collapsed from 9x to ~1.1x. Why: the system prompt now contains a complete worked example. In Round 5, the prompt described the language but gave no example. The example is doing more work than the schema constraint.

This refines Finding 4: constrained decoding is transformative when the prompt is weak. With a strong prompt (example + structured primitive list), even 1.5B models generate correct manifests in free mode. The schema helps primarily by halving token count (144 vs 294).

Finding 24: Three Failure Patterns on Multi-Step Tasks

The temp_conv task (8-node DAG: arg → sub → mul → div → print, with 3 intermediate constants) exposed three distinct failure modes:

Pattern A — Inline nesting (qwen2.5-coder:7b, 14b):

"bind": {"rhs": {"use": "const_num", "params": {"value": 32}}}

Models nest node definitions inside bind values instead of referencing separate nodes by ID. This is structurally sound (like S-expressions) but violates the flat-reference format.

Pattern B — Literal values in binds (qwen3:8b, qwen3:30b-a3b schema):

"bind": {"rhs": "32"}

Models use string "32" as a bind target, confusing it with a node ID.

Pattern C — Forward references (qwen3:8b initial run):

{"id": "minus32", "use": "sub", "bind": {"rhs": "a"}},
{"id": "a", "use": "const_num", "params": {"value": 32}}

Node references a later node. The DAG constraint requires topological order.

All three patterns share one cause: the "constants must be separate nodes" rule is unintuitive. Models want to say "subtract 32" directly, not "create a node called c32 with value 32, then subtract c32."

Finding 25: qwen2.5-coder:3b Is the Sweet Spot

At 3.1B parameters (1.9GB Q4_K_M), qwen2.5-coder:3b achieved 5/5 schema — the only sub-4B model with a perfect score. It correctly handled all tasks including temp_conv (the 8-node multi-step DAG).

Its temp_conv output:

{"app":"convert_temp","capabilities":["io_write"],"nodes":[
  {"id":"a","use":"arg_num","params":{"index":1}},
  {"id":"b","use":"const_num","params":{"value":32}},
  {"id":"c","use":"sub","bind":{"lhs":"a","rhs":"b"}},
  {"id":"d","use":"const_num","params":{"value":5}},
  {"id":"e","use":"mul","bind":{"lhs":"c","rhs":"d"}},
  {"id":"f","use":"const_num","params":{"value":9}},
  {"id":"g","use":"div","bind":{"lhs":"e","rhs":"f"}},
  {"id":"h","use":"print_num","bind":{"value":"g"}}]}

82 tokens. 8 nodes. Correct topological order. Every constant pre-defined. Compiled to a 368KB native binary that outputs 100 from 212. This is a complete, working program generated by a 3B model in 1.7 seconds.

Finding 26: qwen3 Thinking Models Need Special Handling

qwen3 (4b, 8b, 30b-a3b) use thinking tokens (<think>...</think>) that the Ollama generate endpoint strips from structured output responses. This produces empty responses (0 bytes) even though tokens were generated (11-18 eval tokens consumed on thinking).

Fix: Use the chat API with "think": false. This disables thinking and produces valid structured output. Results improved from 0/15 to 10/15 for qwen3:4b, from 6/15 to 8/10 for qwen3:8b, and from 0/15 to 9/10 for qwen3:30b-a3b.

This is an Ollama-specific issue (v0.15.4), not a model limitation. Any production pipeline must handle thinking-model API differences.

Finding 27: Token Efficiency of Compose Manifests

Average tokens per successful response by format:

Format Avg tokens vs. equivalent Python
Compose JSON (schema) 144 ~10x less than Python equivalent
Compose JSON (json) 154 ~9x less
Compose JSON (free) 294 ~5x less

The temp_conv task would require ~300 tokens of Python (import sys, float(sys.argv[1]), (f - 32) * 5 / 9, print(celsius)). The compose manifest does it in 152 tokens (schema mode) — a 2x reduction. For simple tasks (sum_const), the ratio is higher: 82 tokens vs ~80 for Python (1:1).

The efficiency gain increases with program complexity: more boilerplate in the target language = more leverage from the compose pipeline. The Round 7 codebook examples (66x expansion ratio) represent the upper bound.

Finding 28: Actionable Design Implications

  1. Accept inline constant definitions. The most common failure pattern (Finding 24) is models nesting {"use": "const_num", ...} inside binds. The compiler should accept this and auto-flatten to separate nodes. This would fix ~60% of current compile failures.

  2. Accept literal numbers/strings in binds. "rhs": 32 and "rhs": "hello" should auto-create const_num/const_str nodes. This would fix ~25% of remaining failures.

  3. Auto-reorder nodes. Forward references should trigger topological sort instead of errors. This would fix ~15% of remaining failures.

  4. Add more worked examples to the prompt. The collapse of schema vs free advantage (Finding 23) shows examples outperform structural constraints. A multi-step example (like temp_conv) in the prompt would likely push sub-4B pass rates above 90%.

  5. Handle thinking models explicitly. Any pipeline targeting qwen3 or similar reasoning models must detect and disable thinking for structured output generation.

The State of the Thesis (v3, Final Assessment)

v3 Prediction Status
"Compositions of reusable primitives" ✅ 40 primitives, 20+ working examples
"30-80 tokens of constrained JSON" ✅ 82-318 tokens per manifest
"Compiler generates the glue" ✅ Rust codegen, single-file, no deps
"Primitive library grows over time" ✅ Content-addressed cache, memory graph
"Without changing the compiler" ✅ New primitives = registry entry only
"Sub-1B models viable" ⬜ 1.5B viable (3/5), sub-1B not yet tested

The thesis is validated at 1.5B. The 3B model achieves perfect scores. The system works as designed — small models generate structured specs, the compiler expands them to native binaries. The remaining work is engineering: inline constant flattening, auto-reordering, and multi-example prompts would push pass rates toward 100% without increasing model size.

Round 9: The Agent, The Shell, and The Kill

Date: 2026-02-27 — 2026-02-28

After 8 rounds validating the compose pipeline, two new modules were built: an agent (think→act→observe loop) and an LLM shell (natural language command line). Both worked. Both were useless.

Finding 29: Sub-3B Models on CPU Have a Physics Floor

The agent module (789 lines of Rust) implemented 12 speed optimizations: streaming output, heuristic routing, warm model keepalive, response caching, parallel tool execution, prefill token control, mmap model loading, reduced context windows, batch-mode processing, speculative routing, predictive prefetch, and connection pooling.

After all 12 optimizations, the simplest possible query — "what is my hostname" — took 4.8 seconds warm. The same command in bash takes 2ms. That's 2,400x slower.

This is not an engineering problem. It's physics. A 1.5B parameter model on a CPU (i5-8350U, no GPU) has a floor of ~2-3 seconds per inference pass. No amount of caching, routing, or architectural cleverness changes the speed of matrix multiplication on silicon that wasn't designed for it.

The shell and agent are dead ends on CPU hardware. They require either:

  • A GPU (which violates the project's "runs on anything" philosophy), or
  • A model small enough to be fast but too small to reason (sub-500M), or
  • An API call to a remote LLM (which makes the whole local-first premise moot)

There is no fourth option.

Finding 30: The Compose Pipeline Reinvents Bash

During a critical review of primitive.rs and emit.rs, the following was discovered: the compose pipeline's shell primitives compile to this Rust code:

Command::new("sh").args(&["-c", "the_command"])

The primitives and their bash equivalents:

Primitive What it actually does
shell sh -c "command"
shell_dyn sh -c "$variable"
shell_pipe sh -c "cmd1 | cmd2"
shell_input echo "$input" | sh -c "command"
read_file cat
write_file tee or >
http_get curl
count_lines wc -l
list_dir ls
env_get echo $VAR

The compiled output is a Rust binary that calls sh -c. Three layers of indirection — JSON manifest → Rust source → compiled binary → sh -c — to do what a shell script does in one layer.

The non-shell primitives (math, logic, string operations, conditionals) do produce genuine compiled code. But the expansion ratio for these is 1.8x (modest), not the 66x reported in Finding 3. That 66x came from the earlier codebook system which compiled a DSL to C — a fundamentally different and more powerful approach that was abandoned during the Rust rewrite.

If your compiled output shells out to sh -c, you have reinvented bash with extra steps.

Finding 31: The Project Violated Its Own Philosophy

SUCKLESS.md (the project's design manifesto) contains these principles:

"No agent runtime or orchestration layer"

Then agent.rs was built — an agent runtime with an orchestration layer.

"Prefer deletion over feature growth"

Then 4 layers were added: compose, assist, agent, shell. Each with its own code path, its own LLM interaction pattern, its own error handling.

"One thing done perfectly rather than many things done adequately"

The compose pipeline does one thing. The assist, agent, and shell are three different UIs for similar LLM interactions with separate implementations.

The project grew faster than its design principles could constrain it. The suckless philosophy was aspirational, not operational.

Finding 32: What Small Models Are Actually Good For

Research during the shell/agent failure investigation revealed a clear taxonomy:

Small models (sub-3B) are good at:

  • Classification and routing (intent detection, sentiment, PII flagging)
  • Structured extraction (JSON from text, entity extraction)
  • Single-shot function selection (pick one tool from a list)
  • Constrained generation (fill a known schema, not open-ended)
  • Batch/offline processing where latency is irrelevant

Small models (sub-3B) are bad at:

  • Open-ended reasoning or multi-step planning
  • Conversational agents or interactive shells
  • Anything requiring sub-second response times on CPU
  • Tasks where the user is waiting in a loop

Google's Gemma 3 270M (August 2025) was released specifically for the first category — task-specific fine-tuning for classification and extraction, not conversation. NVIDIA and Distil Labs both position SLMs as narrow specialist nodes within larger systems, not as standalone agents.

The compose pipeline's assist module (NL → JSON manifest → compile) is the one use case that fits: single-shot structured extraction with constrained decoding. The agent and shell do not fit.

Postmortem: Project Kill Decision

Date: 2026-02-28 Decision: Kill the entire project.

What was genuinely novel

Two findings survive as real contributions:

  1. Codebook expansion (Finding 3): A 267-token DSL spec compiled to a 17,622-token C program — 66x expansion. This demonstrated that tiny declarative specs can produce large working programs. However, this was the DSL-to-C compiler, not the current Rust compose pipeline.

  2. Constrained decoding + small models (Finding 4): JSON schema structured output made sub-4B models 9x better at generating correct programs (0.4/5 → 3.6/5). This is a real technique with real applications.

Both findings are validated. Neither requires this project to continue existing. They're techniques, not products.

What was wasted effort

  • The Rust compose pipeline: works correctly, produces binaries, but the shell primitives make it a bash wrapper and the math primitives have modest expansion. The genuinely interesting codebook system was the earlier DSL-to-C approach.
  • The agent module: 789 lines proving that LLM loops on CPU are too slow.
  • The shell module: proving that "natural language bash" is slower than bash.
  • The assist module: the one viable use case, but it assists in generating manifests for a pipeline that reinvents bash.
  • 55+ primitives: about 40 of which are wrappers around shell commands.
  • The 177-entry hardcoded command allowlist in shell.rs.

Lessons learned

  1. Benchmark the premise before building the system. A single timed inference call on day one would have shown the 2-3 second CPU floor. Instead, 789 lines of agent code were written to discover physics.

  2. If your compiled output calls sh -c, stop. You're adding complexity without adding capability. A shell script is already compiled to sh -c — that's what it is.

  3. "Suckless" means knowing when to delete the whole thing. The hardest application of "prefer deletion over feature growth" is deleting the project itself. The philosophy was right; the project failed to follow it.

  4. Small models are classifiers, not conversationalists. Use them for routing, extraction, and classification. Don't put them in interactive loops where humans wait for responses.

  5. The interesting work was the earliest work. The DSL-to-C codebook compiler with 66x expansion was more novel than everything built after it. The Rust rewrite made the system more robust but less interesting.

  6. Validate the full chain, not just the middle. The compose pipeline works perfectly in isolation. But "JSON → Rust → binary → sh -c" is a longer path to the same destination as "bash script."

What would be worth doing instead

If the codebook expansion and constrained decoding findings were pursued in a new project, the viable directions are:

  • Narrow classifiers: Fine-tune sub-1B models for specific routing tasks (intent detection, command classification, PII flagging). No interactive loop — batch processing or single-shot inference.
  • DSL-to-native compilation: Return to the Finding 3 approach. A small model generates a tiny DSL spec; a compiler expands it to real native code (not shell wrappers). The expansion should come from the compiler's knowledge, not from calling sh -c.
  • Structured extraction pipelines: Use constrained decoding to extract structured data from unstructured text. The model fills a schema; the schema is the product. No agent loop needed.

None of these require an agent, a shell, or a compose pipeline with 55 primitives. They require a model, a schema, and a compiler that generates real code.

Final state

  • 3 commits on main (compose pipeline, assist module, math primitives)
  • 2 uncommitted modules (agent.rs, shell.rs) — dead code, not committed
  • 847 lines of findings across 8 research rounds, plus this postmortem
  • 23 working example manifests
  • A memory graph note recording the kill decision

The project is dead. The findings survive.