Report · marked 25 Aug 2026
labmlai/annotated_deep_learning_paper_implementations
An agent will struggle here, mostly for fixable reasons.
Python · 67,353 stars · 2,171 files · branch master
52
/100
C+
Instructions
3/27Whether the repo tells an agent how to behave before it starts guessing. Losing marks on agent instruction file and instruction quality.Setup
9/14Whether an agent can install the project and get it running without a human. Losing marks on deterministic install and pinned runtime version.Verification loop
11/25Whether an agent can check its own work. This is the category that most decides whether agent output is trustworthy. Losing marks on tests exist and continuous integration.Context economy
17/20Whether the repo fits in a context window, or fights it. Losing marks on repository weight.Navigability
10/10How quickly anything new can orient itself. Full marks.
Higher is better. Accent marks what is costing you most.
Fix these first
01
Agent instruction file
Add an AGENTS.md at the root: what the project is, how to install, how to run, how to test, and the two or three conventions a newcomer always gets wrong.
02
Instruction quality
Instructions earn their keep by naming exact commands. 'Run the tests with `pnpm test`' beats three paragraphs of philosophy.
03
Deterministic install
Commit the lockfile your package manager generates. Without it, an agent's install and your install are different builds, and 'works on my machine' becomes unfalsifiable.
The full marking
Every deduction below names the file or setting it came from.
Instructions 3/27 F
Whether the repo tells an agent how to behave before it starts guessing.
✗Agent instruction file
0/12
None found. Every agent that opens this repo starts from zero.
Add an AGENTS.md at the root: what the project is, how to install, how to run, how to test, and the two or three conventions a newcomer always gets wrong.
Looked for AGENTS.md, CLAUDE.md, .github/copilot-instructions.md, .cursorrules
✗Instruction quality
0/12
Nothing to judge, since there are no instructions.
Instructions earn their keep by naming exact commands. 'Run the tests with `pnpm test`' beats three paragraphs of philosophy.
✓README as an entry point
3/3
README has a clear getting-started section.
Setup 9/14 B
Whether an agent can install the project and get it running without a human.
✗Deterministic install
3/6
Only requirements.txt, which pins loosely at best.
Commit the lockfile your package manager generates. Without it, an agent's install and your install are different builds, and 'works on my machine' becomes unfalsifiable.
✓Discoverable commands
6/6
Commands are declared where an agent will look for them.
Makefile
–Environment config
n/a
Not applicable — this reads as a library, with no environment to configure.
✗Pinned runtime version
0/2
No pinned runtime version.
Add a .nvmrc, .python-version, or equivalent so the agent's toolchain matches yours.
–Reproducible environment
n/a
Not applicable — a library doesn't need a container to be worked on.
Verification loop 11/25 C
Whether an agent can check its own work. This is the category that most decides whether agent output is trustworthy.
✗Tests exist
4/8
5 test files against 231 source files.
Coverage is thin enough that an agent can break real behavior and still see green.
e.g. docs/ja/optimizers/performance_test.html, docs/optimizers/performance_test.html, docs/si/optimizers/performance_test.html
✓Test command is discoverable
7/7
An agent can find and run `make test`.
✗Continuous integration
0/4
No CI config. Nothing outside a developer's laptop enforces correctness.
A CI workflow doubles as machine-readable documentation: it's the one file that states, unambiguously, the exact commands that must succeed.
✗Lint and format rules
0/3
No linter or formatter config. Style is tribal knowledge, so agent output will drift from yours.
A formatter config is the cheapest way to stop reviewing whitespace in agent diffs. It moves style from opinion to a command.
✗Static type checking
0/3
No static type checking.
A type checker gives an agent an error message instead of a runtime surprise. It's the second-fastest feedback loop after the compiler.
Context economy 17/20 A
Whether the repo fits in a context window, or fights it.
✓No committed build output
6/6
No generated directories committed.
✓.gitignore hygiene
3/3
.gitignore covers 22 patterns.
✓File sizes fit in context
6/6
No source file is large enough to crowd out a context window.
✗Repository weight
2/5
About 156MB checked out. Large enough that cloning and searching are both slow.
Large binaries and vendored trees slow every operation an agent performs. Git LFS or a separate assets repo keeps the working tree navigable.
Here is your AGENTS.md
Drafted from what is actually in this repository: the install
command from your lockfile, the commands you already declare, your real directory layout. Anything
marked TODO needs a person. Save it at the root as AGENTS.md.
# AGENTS.md 🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠 ## Setup ``` pip install -r requirements.txt ``` ## Commands ``` make clean make build # produce a build make check-content make check # lint and type check make upload make install ``` ## Layout - `labml_nn/` 222 source files - `docs/` 4 source files - `utils/` 4 source files ## Conventions - Tests live alongside the code they cover, following `docs/ja/optimizers/performance_test.html`. - TODO: add the two or three conventions a newcomer always gets wrong here. ## Gotchas - No lockfile is committed, so an install here may not match what CI produced. --- Drafted by agentreadme.com from what is in this repository. Everything marked TODO needs a human. Check it in as AGENTS.md at the root.
Open the raw markdown
or
curl -o AGENTS.md agentreadme.com/draft/labmlai/annotated_deep_learning_paper_implementations.md
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