chore(deps): bump mypy from 1.20.2 to 2.1.0

[dependabot]

Bumps mypy from 1.20.2 to 2.1.0.

Changelog

Sourced from mypy's changelog.

Mypy Release Notes

Next Release

Mypy 2.1

We’ve just uploaded mypy 2.1.0 to the Python Package Index (PyPI). Mypy is a static type checker for Python. This release includes new features, performance improvements and bug fixes. You can install it as follows:

python3 -m pip install -U mypy

You can read the full documentation for this release on Read the Docs.

librt.vecs: Fast Growable Array Type for Mypyc

The new librt.vecs module provides an efficient growable array type vec that is optimized for mypyc use. It provides fast, packed arrays with integer and floating point value types, which can be several times faster than list, and tens of times faster than array.array in code compiled using mypyc. It also supports nested vec objects and non-value-type items, such as vec[vec[str]].

Refer to the documentation for the details.

Contributed by Jukka Lehtosalo.

librt.random: Fast Pseudo-Random Number Generation

The new librt.random module provides fast pseudo-random number generation that is optimized for code compiled using mypyc. It can be 3x to 10x faster than the stdlib random module in compiled code.

Refer to the documentation for the details.

Contributed by Jukka Lehtosalo (PR 21433).

Mypyc Improvements

• Make compilation order with multiple files consistent (Piotr Sawicki, PR 21419)
• Fix crash on accessing StopAsyncIteration (Piotr Sawicki, PR 21406)
• Fix incremental compilation with separate flag (Vaggelis Danias, PR 21299)

Fixes to Crashes

• Fix crash on partial type with --allow-redefinition and global declaration (Jukka Lehtosalo, PR 21428)
• Fix broken awaitable generator patching (Ivan Levkivskyi, PR 21435)

Changes to Messages

... (truncated)

Commits

• c1c336d Remove +dev from version
• 74df14b Add changelog for mypy 2.1 (#21464)
• 022d9bc Revert "TypeForm: Enable by default (#21262)"
• 8826288 [mypyc] Document librt.random (#21463)
• 3f4067b Bump librt version to 0.11.0 (#21458)
• 2b1eb58 [mypyc] Enable incremental self-compilation (#21369)
• 8152f4a Respect file config comments for stale modules (#21444)
• 116d60b Fix nondeterminism from nonassociativity of overload joins (#21455)
• 6c4af8e Fix function call message change for small number of args (#21432)
• 4b8fdca [mypyc] Add librt.random module (#21433)
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[github-actions]

NeuralMind self-benchmark

Status: PASS — floor 4×, measured 5.9×.

Phase 1 — Reduction on committed fixture

• Average reduction: 5.9×
• Top-k retrieval hit rate: 71.7%
• Naive baseline: 47,360 tokens (all fixture files concatenated)
• NeuralMind total: 8,149 tokens across 10 queries
• Estimated monthly savings @ 100 queries/day on Claude 3.5 Sonnet: ~$35.30

#	Query	Shape	Naive	NeuralMind	Ratio	Hit
1	auth-flow	cross-file	4,736	815	5.8×	33.3%
2	api-endpoints	focused	4,736	809	5.9×	100.0%
3	billing-flow	cross-file	4,736	846	5.6×	33.3%
4	user-storage	cross-file	4,736	672	7.0×	50.0%
5	jwt-verify	focused	4,736	681	7.0×	100.0%
6	stripe-webhook	focused	4,736	838	5.7×	100.0%
7	create-user	cross-file	4,736	794	6.0×	50.0%
8	refund	focused	4,736	827	5.7×	100.0%
9	db-choice	identity	4,736	899	5.3×	100.0%
10	invoice-send	cross-file	4,736	968	4.9×	50.0%

Phase 2 — Learning uplift

• Memory events logged: 20
• Learned patterns: 20
• Reduction ratio after neuralmind learn: 5.9× (Δ +0.00× vs. cold)
• Top-k hit rate after learning: 71.7% (Δ +0.0 points vs. cold)

Note: uplift numbers on a 500-line fixture are intentionally modest — the point is to
verify the learning mechanism persists and applies. On real production repos the lift
is larger; this test only catches regressions in persistence.

Assumptions

• Baseline: every .py file in tests/fixtures/sample_project/ concatenated.
• Tokenizer: tiktoken GPT-4o encoding (per-model breakdown in multi_model.json if generated).
• Pricing: Claude 3.5 Sonnet input @ $3.0/MTok.
• Regression floor: 4× — well below NeuralMind's typical 40–70× on real repos.

Per-model token reduction

Model	Tokenizer	Naive	NeuralMind	Ratio	Source
GPT-4o / GPT-4o-mini	tiktoken o200k_base	4,739	927	5.1×	measured
GPT-4 / GPT-3.5-turbo	tiktoken cl100k_base	4,710	918	5.1×	measured
Claude 3.5 Sonnet	estimated: GPT-4o × 1.08 — install anthropic for an exact count	5,118	1,001	5.1×	estimated
Llama 3 (70B)	estimated: GPT-4o × 1.22 — Llama tokenizer requires model weights; estimate based on published vocab ratios	5,781	1,130	5.1×	estimated

Rows marked measured use the provider's real tokenizer. Rows marked
estimated apply a published vocab-size correction to the GPT-4o count —
honest approximations, not hardcoded claims.

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Automated by .github/workflows/ci-benchmark.yml — regenerate locally with python -m tests.benchmark.run.