Designing for Model Swaps
When LLM providers ship breaking changes, a poor architecture turns a config tweak into a 400-line PR. This post introduces seam-driven design - five narrow interfaces (provider, prompt, tools, config, observability) each independently swappable - illustrated with a working FastAPI + LangChain reference app, a practical checklist, and two hands-on drills readers can run immediate
https://garybake.com/seams1.html
When LLM providers ship breaking changes, a poor architecture turns a config tweak into a 400-line PR. This post introduces seam-driven design - five narrow interfaces (provider, prompt, tools, config, observability) each independently swappable - illustrated with a working FastAPI + LangChain reference app, a practical checklist, and two hands-on drills readers can run immediate
https://garybake.com/seams1.html
Programming, Machine learning and Electronics chit chat
Designing for Model Swaps
Part of the Change Seams series - architecture patterns for LLM applications that survive the real world.
Validating data with pointblank in python
One of the most common tasks of any organization is reviewing data to ensure that it is accurate and does not contain errors. Commonly, this is done by producing graphs or summary information like a median or mean and confirming that it looks reasonable. Pointblank is a newer tool that allows you to really dig into a dataset and task assumptions in a robust and reproducible manner.
https://www.markpitblado.me/blog/validating-data-with-pointblank-in-python
One of the most common tasks of any organization is reviewing data to ensure that it is accurate and does not contain errors. Commonly, this is done by producing graphs or summary information like a median or mean and confirming that it looks reasonable. Pointblank is a newer tool that allows you to really dig into a dataset and task assumptions in a robust and reproducible manner.
https://www.markpitblado.me/blog/validating-data-with-pointblank-in-python
Mark Pitblado
Validating data with pointblank in python
One of the most common tasks of any organization is reviewing data to ensure that it is accurate and does not contain errors. Commonly, this is done by producing graphs or summary information like a median or mean and confirming that it looks reasonable.…
You’ve Been Underusing Dataclasses (These Tricks Are Wild)
The video demonstrates seven powerful, lesser-known techniques using Python dataclasses, including automatic class registration, lightweight validation, cached derived values, and context manager patterns. It also shows practical uses such as CLI generation and explains advanced features like InitVar, highlighting how dataclasses can enable cleaner and more expressive Python designs.
https://www.youtube.com/watch?v=Y9_h7ehjhO4
The video demonstrates seven powerful, lesser-known techniques using Python dataclasses, including automatic class registration, lightweight validation, cached derived values, and context manager patterns. It also shows practical uses such as CLI generation and explains advanced features like InitVar, highlighting how dataclasses can enable cleaner and more expressive Python designs.
https://www.youtube.com/watch?v=Y9_h7ehjhO4
YouTube
You’ve Been Underusing Dataclasses (These Tricks Are Wild)
🧱 Build software that lasts. Join the Software Design Mastery waiting list → https://arjan.codes/mastery.
In this video, I show you 7 powerful and lesser-known things you can do with dataclasses in Python. From automatic class registration and lightweight…
In this video, I show you 7 powerful and lesser-known things you can do with dataclasses in Python. From automatic class registration and lightweight…
100 days 100 iot project with Micropython
https://github.com/kritishmohapatra/100_Days_100_IoT_Projects
https://github.com/kritishmohapatra/100_Days_100_IoT_Projects
GitHub
GitHub - kritishmohapatra/100_Days_100_IoT_Projects: A 100-day challenge exploring IoT and embedded systems using ESP32, ESP8266…
A 100-day challenge exploring IoT and embedded systems using ESP32, ESP8266, and Raspberry Pi Pico with MicroPython. Each day covers a new sensor or module with complete code, circuit diagram, and ...
🤯2
What Python's asyncio primitives get wrong about shared state
https://www.inngest.com/blog/no-lost-updates-python-asyncio
https://www.inngest.com/blog/no-lost-updates-python-asyncio
Inngest
What Python's asyncio primitives get wrong about shared state - Inngest Blog
We tried Event, Condition, and Queue. Each one gets closer but still breaks under real concurrency. Here's the observable pattern that finally works.
Comparing Python packages for A/B test analysis (with code examples)
https://e10v.me/python-packages-for-ab-test-analysis/
https://e10v.me/python-packages-for-ab-test-analysis/
Evgeny Ivanov
Comparing Python packages for A/B test analysis: tea-tasting, Pingouin, statsmodels, and SciPy
A practical comparison of tea-tasting, Pingouin, statsmodels, and SciPy for A/B test analysis, with code examples.
City2Graph
Transform geospatial relations into graphs for Graph Neural Networks and network analysis.
https://github.com/c2g-dev/city2graph
Transform geospatial relations into graphs for Graph Neural Networks and network analysis.
https://github.com/c2g-dev/city2graph
GitHub
GitHub - c2g-dev/city2graph: Transform geospatial relations into graphs for Graph Neural Networks and network analysis
Transform geospatial relations into graphs for Graph Neural Networks and network analysis - c2g-dev/city2graph
databricks-solutions / ai-dev-kit
Databricks Toolkit for Coding Agents provided by Field Engineering
https://github.com/databricks-solutions/ai-dev-kit
Databricks Toolkit for Coding Agents provided by Field Engineering
https://github.com/databricks-solutions/ai-dev-kit
GitHub
GitHub - databricks-solutions/ai-dev-kit: Databricks Toolkit for Coding Agents provided by Field Engineering
Databricks Toolkit for Coding Agents provided by Field Engineering - databricks-solutions/ai-dev-kit
Custom Data Structures in E-Graphs
The post explores how Egglog’s e-graph framework can be extended with custom container data structures to represent algebraic structures (like multisets) more efficiently and reduce expression blow-up during rewriting. It shows that by embedding structural invariants into the data representation and using higher-order functions, you can express powerful rewrite rules without explosive gr...
https://uwplse.org/2026/02/24/egglog-containers.html
The post explores how Egglog’s e-graph framework can be extended with custom container data structures to represent algebraic structures (like multisets) more efficiently and reduce expression blow-up during rewriting. It shows that by embedding structural invariants into the data representation and using higher-order functions, you can express powerful rewrite rules without explosive gr...
https://uwplse.org/2026/02/24/egglog-containers.html
Deprecate confusing APIs like “os.path.commonprefix()”
The article argues that confusing APIs like Python’s os.path.commonprefix() should be deprecated because their misleading naming and placement cause developers to misuse them and introduce security vulnerabilities. It shows how decades of confusion around this function led to real path-traversal bugs and concludes that unsafe or misleading APIs should be replaced by clearer alternatives ...
https://sethmlarson.dev/deprecate-confusing-apis-like-os-path-commonprefix
The article argues that confusing APIs like Python’s os.path.commonprefix() should be deprecated because their misleading naming and placement cause developers to misuse them and introduce security vulnerabilities. It shows how decades of confusion around this function led to real path-traversal bugs and concludes that unsafe or misleading APIs should be replaced by clearer alternatives ...
https://sethmlarson.dev/deprecate-confusing-apis-like-os-path-commonprefix
sethmlarson.dev
Deprecate confusing APIs like “os.path.commonprefix()”
The os.path.commonprefix() function has been an API in the Python
standard library for at least 35 years (since February 1991)
and in that time has been confusing users and creating security
issues...
standard library for at least 35 years (since February 1991)
and in that time has been confusing users and creating security
issues...
❤1
code-graph-rag
Code-Graph-RAG builds knowledge graphs from multi-language codebases and enables AI-powered querying and editing. It uses Tree-sitter to parse ASTs across 11 languages, stores structural data in Memgraph, and translates natural language questions into Cypher graph queries. Unlike vector-based RAG, it preserves exact code relationships: call chains, inheritance hierarchies, and module dep...
https://github.com/vitali87/code-graph-rag
Code-Graph-RAG builds knowledge graphs from multi-language codebases and enables AI-powered querying and editing. It uses Tree-sitter to parse ASTs across 11 languages, stores structural data in Memgraph, and translates natural language questions into Cypher graph queries. Unlike vector-based RAG, it preserves exact code relationships: call chains, inheritance hierarchies, and module dep...
https://github.com/vitali87/code-graph-rag
GitHub
GitHub - vitali87/code-graph-rag: The ultimate RAG for your monorepo. Query, understand, and edit multi-language codebases with…
The ultimate RAG for your monorepo. Query, understand, and edit multi-language codebases with the power of AI and knowledge graphs - vitali87/code-graph-rag
❤1
Pytest parameter functions
The post shows how to make complex pytest.mark.parametrize data more readable and maintainable by wrapping each test case in small helper functions instead of raw tuples. It also demonstrates using pytest.param with custom IDs inside those helpers so long, messy parameter values become clear, concise test names that are easier to work with.
https://nedbatchelder.com/blog/202602/pytest_parameter_functions
The post shows how to make complex pytest.mark.parametrize data more readable and maintainable by wrapping each test case in small helper functions instead of raw tuples. It also demonstrates using pytest.param with custom IDs inside those helpers so long, messy parameter values become clear, concise test names that are easier to work with.
https://nedbatchelder.com/blog/202602/pytest_parameter_functions
Nedbatchelder
Pytest parameter functions
Pytest’s parametrize can be made even more powerful with your own helper functions to build test cases.
❤1
Invent your own comprehensions in Python
Python doesn't have tuple, frozenset, or Counter comprehensions, but you can invent your own by passing a generator expression to any iterable-accepting callable.
https://www.pythonmorsels.com/custom-comprehensions/
Python doesn't have tuple, frozenset, or Counter comprehensions, but you can invent your own by passing a generator expression to any iterable-accepting callable.
https://www.pythonmorsels.com/custom-comprehensions/
Pythonmorsels
Invent your own comprehensions in Python
Python doesn't have tuple, frozenset, or Counter comprehensions, but you can invent your own by passing a generator expression to any iterable-accepting callable.
GraphZero
High-Performance, Zero-Copy Graph Engine for Massive Datasets on Consumer Hardware.
https://github.com/KrishSingaria/graphzero
High-Performance, Zero-Copy Graph Engine for Massive Datasets on Consumer Hardware.
https://github.com/KrishSingaria/graphzero
GitHub
GitHub - KrishSingaria/graphzero: graphzero: High performance C++ backed python library for graphs
graphzero: High performance C++ backed python library for graphs - KrishSingaria/graphzero
D-MemFS
In-process virtual filesystem with hard quota for Python.
https://github.com/nightmarewalker/D-MemFS
In-process virtual filesystem with hard quota for Python.
https://github.com/nightmarewalker/D-MemFS
GitHub
GitHub - nightmarewalker/D-MemFS: In-process virtual filesystem with hard quota for Python
In-process virtual filesystem with hard quota for Python - nightmarewalker/D-MemFS
AutoResearchClaw
Fully autonomous & self-evolving research from idea to paper. Chat an Idea. Get a Paper.
https://github.com/aiming-lab/AutoResearchClaw
Fully autonomous & self-evolving research from idea to paper. Chat an Idea. Get a Paper.
https://github.com/aiming-lab/AutoResearchClaw
GitHub
GitHub - aiming-lab/AutoResearchClaw: Fully autonomous & self-evolving research from idea to paper. Chat an Idea. Get a Paper. 🦞
Fully autonomous & self-evolving research from idea to paper. Chat an Idea. Get a Paper. 🦞 - aiming-lab/AutoResearchClaw
ClawTeam
One Command Line: Full Automation - agents spawn swarms, delegate tasks, and deliver results.
https://github.com/HKUDS/ClawTeam
One Command Line: Full Automation - agents spawn swarms, delegate tasks, and deliver results.
https://github.com/HKUDS/ClawTeam
GitHub
GitHub - HKUDS/ClawTeam: "ClawTeam: Agent Swarm Intelligence" (One Command → Full Automation)
"ClawTeam: Agent Swarm Intelligence" (One Command → Full Automation) - HKUDS/ClawTeam