PythonHub

token-optimizer Find the ghost tokens. Fix them. Survive compaction. Avoid context quality decay. https://github.com/alexgreensh/token-optimizer

Behavior-Oriented Concurrency for Python https://microsoft.github.io/bocpy/

What’s the simplest way to distribute a Python app to normal users? https://www.reddit.com/r/learnpython/comments/1t7y5m7/whats_the_simplest_way_to_distribute_a_python_app/

lightning PyPI Compromise: A Bun-Based Credential Stealer in Python The post describes a PyPI supply-chain compromise in lightning 2.6.2/2.6.3, where importing the package silently downloads Bun and runs an obfuscated JavaScript credential stealer. It also says the payload steals GitHub, cloud, and other secrets, then uses any captured credentials to spread further and commit exfiltrated data back into victim repos. https://snyk.io/blog/lightning-pypi-compromise-bun-based-credential-stealer/

Full-Text Search with DuckDB The post shows how DuckDB’s full-text search extension can index a large email corpus and run BM25-ranked keyword search directly in SQL, without needing a separate search engine. It also walks through practical preprocessing and filtering steps, then demonstrates conjunctive queries that return only documents matching all search terms. https://peterdohertys.website/blog-posts/full-text-search-w-duckdb.html

How we rebuilt search ranking at Faire with deep learning From XGBoost to deep learning: a two-year rebuild of Faire’s ranking stack. https://craft.faire.com/how-we-rebuilt-search-ranking-at-faire-with-deep-learning-14f080679c83

Full Python GUI apps in the browser – no JavaScript, no server https://github.com/pthom/imgui_bundle

Rapid-MLX Run AI on your Mac. Faster than anything else. https://github.com/raullenchai/Rapid-MLX

Datanomy Datanomy is a terminal-based tool for inspecting and understanding data files. It provides an interactive view of your data's structure, metadata, and internal organization. https://github.com/raulcd/datanomy

Do you actually read the source code of libraries you install? https://www.reddit.com/r/Python/comments/1t7yfuw/do_you_actually_read_the_source_code_of_libraries/

Python Hub Weekly Digest for 2026-05-10 https://pythonhub.dev/digest/2026-05-10/

Databases Were Not Designed For This The post defines defensive databases as systems designed to protect data from buggy, noisy, or autonomous applications through safeguards such as idempotency, auditability, soft deletes, controlled writes, and strict permissions. As AI agents and distributed services generate more unpredictable traffic, data stores must actively preserve integrity rather than assuming every client behave... https://arpitbhayani.me/blogs/defensive-databases

Choosing a Python Logging Library in 2026 Compare Pythons standard logging module structlog and Loguru with real benchmarks OpenTelemetry integration paths and frameworkspecific guidance for Django FastAPI and Flask. https://www.dash0.com/guides/python-logging-libraries

Easily Stream LLM Responses with Django-Bolt and PydanticAI A guide showing how easy it is to start using django-bolt and PydanticAI agents together. https://www.caktusgroup.com/blog/2026/04/27/django-bolt-easy-pydanticai-streaming/

Single file Python CLIs when do you split, when do you keep it monolithic? https://www.reddit.com/r/Python/comments/1t0crw2/single_file_python_clis_when_do_you_split_when_do/

honker SQLite extension + bindings for Postgres NOTIFY/LISTEN semantics with durable queues, streams, pub/sub, and scheduler https://github.com/russellromney/honker

ml-intern An open-source ML engineer that reads papers, trains models, and ships ML models. https://github.com/huggingface/ml-intern

What's new in pip 26.1 pip 26.1 adds support for dependency cooldowns, experimental support for reading/installing from standard lockfiles (pylock.toml), fixes several long-standing limitations of the 2020 resolver, and drops support for Python 3.9. https://ichard26.github.io/blog/2026/04/whats-new-in-pip-26.1/

google-deepmind / gemma Gemma open-weight LLM library, from Google DeepMind https://github.com/google-deepmind/gemma

Don’t Use Boolean Flags in Python, Use Policies Instead The Policy Pattern replaces large conditional blocks by breaking rules into small, composable components that can be combined into flexible pipelines. This approach makes code easier to extend, test, and manage, especially when dealing with feature flags and configuration changes. https://www.youtube.com/watch?v=wYeDGkdMi3g