📅 Today’s Picks

mem0: Give Your AI Memory Without Building a Vector DB

Problem

When you build an AI app using the OpenAI or Anthropic API, every conversation starts from scratch with no built-in memory between sessions.

Adding memory yourself with a vector database like ChromaDB requires writing custom extraction, deduplication, and scoping logic on top of the storage layer.

Solution

mem0 handles all of that in a single function call. Just pass in conversations and retrieve relevant memories when needed.

Key features:

• Automatic fact extraction from raw conversations via memory.add()

• Cross-session retrieval with memory.search() in any future conversation

• Automatic conflict resolution when user preferences change over time

🧪 Run code

Browser-Use: Automate Any Browser Task with Plain English

Problem

Most data collection tasks go beyond simple extraction. You need to log in, apply filters, navigate pagination, and then gather results.

Selenium can handle navigation, but it requires maintaining CSS selectors that can easily break when a site changes.

Solution

Browser Use simplifies the entire process. Describe what you want, and it navigates, clicks, types, and extracts automatically.

Key features:

• Natural language task descriptions

• Works with GPT-4, Claude, Gemini, and local models via Ollama

• Structured output with Pydantic models

📚 Latest Deep Dives

How to Test GitHub Actions Locally with act

Debugging GitHub Actions is painfully slow. Every YAML change requires a commit, a push, and a 2-5 minute wait just to find out you missed a colon.

This article introduces act, a CLI tool that runs GitHub Actions workflows locally in Docker containers.

You’ll set up an ML testing pipeline and learn to pass secrets, run specific jobs, and validate workflows in seconds.

📖 View Full Article

☕️ Weekly Finds

open-webui [LLM] - A self-hosted AI platform with built-in RAG, model builder, and support for Ollama and OpenAI-compatible APIs

ragflow [RAG] - An open-source RAG engine with deep document understanding for unstructured data in any format

vllm [MLOps] - A high-throughput, memory-efficient inference and serving engine for LLMs with multi-GPU support

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• Tool Selector - Discover 70+ Python tools for AI and data science

• Production Ready Data Science - A practical book for taking projects from prototype to production

🤝 COLLABORATION

Give Your AI Agent Live Web Access with Bright Data MCP

With basic search APIs, agents often miss critical context from sources like social platforms, forums, news, and answer engines. That leads to incomplete or outdated responses.

Bright Data’s MCP server unifies all web data access into one interface your AI agent can use directly.

With Bright Data MCP, your AI agent can access:

• Search engines (Google, Bing, more)

• Social media (Twitter/X, Reddit, Instagram, TikTok)

• Web archives (historical web data, years deep)

• Answer engines (ChatGPT, Perplexity, Gemini)

All through one connection.

🔗 Try Bright Data MCP

📅 Today’s Picks

latexify_py: Turn Python Functions into LaTeX with One Decorator

Problem

Non-programmers cannot easily read Python logic. However, manually converting it to LaTeX is slow and quickly becomes outdated as the code changes.

Solution

latexify_py solves this with a single decorator, generating LaTeX directly from your function so the math stays readable and always in sync with the code.

Key capabilities:

• Three decorators for different outputs: expressions, full equations, or pseudocode

• Displays rendered LaTeX directly in Jupyter cells

• Functions still work normally when called

📖 View Full Article | 🧪 Run code

act: Run GitHub Actions Locally with Docker

Problem

GitHub Actions has no local execution mode. You can’t test a step, inspect an environment variable, or reproduce a runner-specific failure on your own machine.

Each change requires a commit and a wait for the cloud runner. A small mistake like a missing secret means starting the loop again.

Solution

With act, you can execute workflows locally using Docker. Failures surface immediately, making it easier to iterate and commit only when the workflow passes.

📖 View Full Article

☕️ Weekly Finds

json_repair [LLM] - A Python module to repair invalid JSON, especially from LLM outputs, with schema validation support

pyrsistent [Python Utilities] - Persistent, immutable, and functional data structures for Python

prek [Code Quality] - A faster, Rust-based reimagining of pre-commit with monorepo support and parallel hook execution

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• Tool Selector - Discover 70+ Python tools for AI and data science

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🤝 COLLABORATION

Give Your AI Agent Live Web Access with Bright Data MCP

With basic search APIs, agents often miss critical context from sources like social platforms, forums, news, and answer engines. That leads to incomplete or outdated responses.

Bright Data’s MCP server unifies all web data access into one interface your AI agent can use directly.

With Bright Data MCP, your AI agent can access:

• Search engines (Google, Bing, more)

• Social media (Twitter/X, Reddit, Instagram, TikTok)

• Web archives (historical web data, years deep)

• Answer engines (ChatGPT, Perplexity, Gemini)

All through one connection.

🔗 Try Bright Data MCP

📅 Today’s Picks

Chronos: Forecast Any Time Series Without Training a Model

Problem

Traditional forecasting requires domain-specific data, feature engineering, and multiple rounds of model tuning.

Solution

Chronos is a family of pretrained time series forecasting models from Amazon Science that deliver zero-shot predictions out of the box.

Simply load a pretrained model and generate forecasts on any time series data, with no fine-tuning required.

If zero-shot accuracy isn’t enough, you can fine-tune on your data with AutoGluon in a few lines.

🧪 Run code

altimate-code: The Missing AI Layer for Data Engineering Teams

Problem

General AI tools can write SQL and catch obvious mistakes. But they cannot systematically detect anti-patterns, trace lineage, or keep warehouse costs under control.

That gap can lead to inefficient queries, broken dependencies, and hidden compliance risks building up over time.

Solution

I recently tried altimate-code, an open-source agent with 100+ tools purpose-built for data engineers, and built a demo repo to test it.

From a single prompt, it generated a full dbt project with staging, intermediate, and mart layers, added automated tests, and built an interactive dashboard.

What makes it different:

• 100+ tools that analyze SQL through structural parsing, not text guessing

• Works across your stack including Snowflake, BigQuery, Databricks, DuckDB, and more

• Model-agnostic. Compatible with OpenAI, Anthropic, Gemini, Ollama, and others

🧪 Demo repo

☕️ Weekly Finds

timesfm [Machine Learning] - Pretrained time series foundation model by Google Research for zero-shot forecasting

darts [Machine Learning] - A Python library for user-friendly forecasting and anomaly detection on time series

orbit [Machine Learning] - A Python package for Bayesian time series forecasting with probabilistic models under the hood

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📅 Today’s Picks

Polars scan_csv: Merge CSVs with Different Schemas in One Call

Problem

Polars’ scan_csv lets you load multiple CSV files lazily, reading data only when needed.

But before v1.39.0, every file had to share the same columns, or you’d get a SchemaError.

Solution

Polars v1.39.0 introduces missing_columns="insert" in scan_csv, allowing you to combine multiple files in one call while null-filling any missing columns.

📖 View Full Article | 🧪 Run code

Build Professional Python Packages with UV --package

Problem

Python packages turn your code into reusable modules you can share across projects.

But building them requires complex setup with setuptools, managing build systems, and understanding distribution mechanics.

Solution

UV, a fast Python package installer and resolver, reduces the entire process to 2 simple commands:

• uv init --package sets up your package structure instantly

• uv build and uv publish to create and distribute to PyPI

📖 Dive deeper in Production Ready Data Science

📚 Latest Deep Dives

uv vs pixi: Which Python Environment Manager Should You Use for Data Science?

uv installs Python packages from PyPI, but it doesn’t support compiled C/C++ libraries.

The typical workaround is to install system libraries separately using an OS package manager, then manually align versions with your Python dependencies. Since these system dependencies aren’t captured in project files, reproducing the environment across machines can be unreliable.

pixi solves this by managing both Python packages from PyPI and compiled system libraries from conda-forge in a single tool.

Quick comparison:

• uv: fast, reliable lockfiles, Python-only

• conda: system libraries supported, but slower and no lockfiles

• pixi: fast, unified, with system libraries, lockfiles, and a built-in task runner

In this article, I compare uv and pixi on a real ML project so you can see how they perform in practice.

📖 View Full Article

☕️ Weekly Finds

datachain [Data Processing] - Process and curate unstructured data from cloud storages using local ML models and Python

label-studio [Data Processing] - Open source data labeling and annotation tool with standardized output format for ML workflows

qsv [Command Line] - Blazingly fast CSV command-line toolkit for slicing, dicing, and analyzing tabular data

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• Tool Selector - Discover 70+ Python tools for AI and data science

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📅 Today’s Picks

vLLM: Serve More LLM Users on the Same GPU

Problem

Most frameworks reserve GPU memory for the longest possible response, even when the actual output is short.

With multiple concurrent users, this quickly fills the GPU with reserved memory that is mostly never used.

Solution

vLLM’s PagedAttention allocates memory in small blocks as the response grows, instead of reserving everything up front.

This keeps memory usage efficient and allows the GPU to serve more requests.

Handle Messy Data with RapidFuzz Fuzzy Matching

Problem

Traditional regex approaches require hours of preprocessing but still break with common data variations like missing spaces, typos, or inconsistent formatting.

Solution

RapidFuzz eliminates data cleaning overhead with intelligent fuzzy matching.

Key benefits:

• Automatic handling of typos, spacing, and case variations

• Production-ready C++ performance for large datasets

• Full spectrum of fuzzy algorithms in one library

📖 View Full Article | 🧪 Run code

☕️ Weekly Finds

TextAttack [NLP] - Python framework for adversarial attacks, data augmentation, and model training in NLP

stanza [NLP] - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of 60+ languages

GLiNER2 [NLP] - Unified schema-based framework for entity extraction, text classification, and relation extraction in one pass

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• Tool Selector - Discover 70+ Python tools for AI and data science

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📅 Today’s Picks

Scrapling: Adaptive Web Scraping in Python

Problem

Traditional scraping with BeautifulSoup uses hardcoded CSS selectors to find elements on a page.

If the site updates its layout, those selectors no longer match and the scraper ends up returning empty data.

Solution

Instead of relying only on selectors, Scrapling records how elements appear during the initial scrape.

If the site is redesigned later, it can use that stored structure to find the same elements again.

Ibis: One Python API for 25+ Database Backends

Problem

Many data workflows begin with pandas for quick experimentation, while production pipelines might run on databases like PostgreSQL or BigQuery.

Moving from prototype to production usually means rewriting the same transformation logic in SQL. That translation takes time and can easily introduce errors.

Solution

Ibis solves this by letting you define transformations once in Python and compiling them into native SQL for 25+ backends automatically.

📖 View Full Article

☕️ Weekly Finds

Kronos [Machine Learning] - A decoder-only foundation model pre-trained on K-line sequences for financial market forecasting

pixi [Environment Management] - Fast, cross-platform package manager built on the Conda ecosystem, written in Rust

MinerU [OCR/PDF Processing] - One-stop tool for converting PDFs, webpages, and e-books into machine-readable markdown and JSON

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📅 Today’s Picks

Marker: Smart PDF Extraction with Hybrid LLM Mode

Problem

Standard OCR pipelines often miss inline math, split tables across pages, and lose the relationships between form fields.

Sending the full document to an LLM can improve accuracy, but it’s slow and expensive at scale.

Solution

Marker‘s hybrid mode takes a more targeted approach:

• Its deep learning pipeline handles the bulk of conversion

• Then an LLM steps in only for the hard parts: table merging, LaTeX formatting, and form extraction

Marker supports OpenAI, Gemini, Claude, Ollama, and Azure out of the box.

📖 View Full Article

Qdrant: Fast Vector Search in Rust with a Python API

Problem

Building a semantic search typically starts with storing vectors in Python lists and computing cosine similarity manually.

But brute-force comparison scales linearly with your dataset, making every query slower as your data grows.

Solution

Qdrant is a vector search engine built in Rust that indexes your vectors for fast retrieval.

Key features:

• In-memory mode for local prototyping with no server setup

• Seamlessly scale to millions of vectors in production with the same Python API

• Built-in support for cosine, dot product, and Euclidean distance

• Sub-second query times even for millions of vectors

🧪 Run code

📚 Latest Deep Dives

PDF Table Extraction: Docling vs Marker vs LlamaParse Compared

Extracting tables from PDFs can be surprisingly difficult. A table that looks neatly structured in a PDF is actually saved as text placed at specific coordinates on the page. This makes it difficult to preserve the original layout when extracting the table.

This article will introduce three Python tools that attempt to solve this problem: Docling, Marker, and LlamaParse.

📖 View Full Article

☕️ Weekly Finds

Dify [LLM] - Open-source LLM app development platform with AI workflow, RAG pipeline, and agent capabilities

PageIndex [RAG] - Document index for vectorless, reasoning-based RAG

MCP Server Chart [Data Visualization] - A visualization MCP server for generating 25+ visual charts using AntV

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pandas 3.0: 5-10x Faster String Operations with PyArrow

Problem

Traditionally, pandas stores strings as object dtype, where each string is a separate Python object scattered across memory.

This makes string operations slow and the dtype ambiguous, since both pure string columns and mixed-type columns show up as object.

Solution

pandas 3.0 introduces a dedicated str dtype backed by PyArrow, which stores strings in contiguous memory blocks instead of individual Python objects.

Key benefits:

• 5-10x faster string operations because data is stored contiguously

• 50% lower memory by eliminating Python object overhead

• Clear distinction between string and mixed-type columns

📖 View Full Article | 🧪 Run code

Build Self-Documenting Regex with Pregex

Problem

Regex patterns like [a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,} are difficult to read and intimidating.

Team members without regex expertise might struggle to understand and modify these validation patterns.

Solution

Team members without regex expertise might struggle to understand and modify these validation patterns.

Pregex transforms regex into readable Python code using descriptive components.

Key benefits:

• Code that explains its intent without comments

• Easy modification without regex expertise

• Composable patterns for complex validation

• Export to regex format when needed

📖 View Full Article | 🧪 Run code

📚 Latest Deep Dives

PDF Table Extraction: Docling vs Marker vs LlamaParse Compared

PDF files do not store tables as structured data. Instead, they position text at specific coordinates on the page. Table extraction tools must reconstruct the structure by determining which values belong in which rows and columns.

The problem becomes even harder when tables include multi-level headers, merged cells, or complex layouts.

To explore this problem, I experimented with three tools designed for PDF table extraction: LlamaParse, Marker, and Docling.

This article shows the results of those experiments.

📖 View Full Article

☕️ Weekly Finds

Lance [Data Processing] - Modern columnar data format for ML with 100x faster random access than Parquet

Mathesar [Dashboard] - Spreadsheet-like interface for PostgreSQL that lets anyone view, edit, and query data

dotenvx [DevOps] - A better dotenv with encryption, multiple environments, and cross-platform support

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act: Run GitHub Actions Locally with Docker

Problem

GitHub Actions has no local execution mode. You can’t test a step, inspect an environment variable, or reproduce a runner-specific failure on your own machine.

Each change requires a commit and a wait for the cloud runner. A small mistake like a missing secret means starting the loop again.

Solution

With act, you can execute workflows locally using Docker. Failures surface immediately, making it easier to iterate and commit only when the workflow passes.

ScrapeGraphAI: Research Multiple Sites with One Prompt

Problem

With BeautifulSoup, every site needs its own selectors, and you need to manually combine the results into a unified format.

When any site redesigns its layout, those selectors break and you are back to fixing code.

Solution

ScrapeGraphAI‘s SearchGraph fixes this by replacing selectors with a natural language prompt.

Here’s what it handles:

• Automatic web search for relevant pages

• AI-powered scraping that adapts to any layout

• Structured output with source URLs for verification

• Works with any LLM provider (OpenAI, Ollama, etc.)

📖 View Full Article

🎓 Latest Interactive Course

Python Data Modeling with Dataclasses and Pydantic

Choosing between dict, NamedTuple, dataclass, and Pydantic comes down to how much safety you need. In this free interactive course, you’ll learn when to use each:

• Dictionary: Flexible, but no built-in field checks. Typos and missing keys only show up at runtime.

• NamedTuple: Immutable with fixed fields, helping catch mistakes early.

• dataclass: Mutable data containers with defaults and optional validation logic.

• Pydantic: Strong type validation, automatic coercion, and detailed error reporting.

All exercises run directly in your browser. No installation required.

☕️ Weekly Finds

agent-browser [Agents] - Headless browser automation CLI for AI agents, built on Playwright

pyscn [Code Quality] - Intelligent Python code quality analyzer with dead code detection and complexity analysis

pyupgrade [Code Quality] - Automatically upgrade Python syntax to newer versions of the language

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Evaluate LLM Apps in One Line with PydanticAI

Problem

Testing LLM apps means validating multiple factors at once: is the answer correct, properly structured, fast enough, and natural sounding?

Rewriting this logic for every project is inefficient and error-prone.

Solution

pydantic-ai includes pydantic-evals, which provides these capabilities out of the box. Simply choose the evaluators you need and add them to your evaluation suite.

Built-in evaluators:

• Deterministic: validate that outputs are correct, properly typed, and fast enough

• LLM-as-judge: have another LLM grade qualities like helpfulness or tone

• Report-level: generate classification metrics across all cases automatically

🧪 Run code

SQLFluff: Auto-Fix Messy SQL with One Command

Problem

Consistent SQL style matters. It improves readability, speeds up code reviews, and makes bugs easier to identify.

Manual reviews can catch formatting issues, but they’re time-consuming and often inconsistent.

Solution

SQLFluff solves this with automated linting and formatting across 30+ SQL dialects. It identifies violations, applies consistent standards, and auto-corrects many problems.

SQLFluff also supports the following templates:

• Jinja

• SQL placeholders (e.g. SQLAlchemy parameters)

• Python format strings

• dbt (requires plugin)

🧪 Run code

🎓 Latest Interactive Course

Python Data Modeling with Dataclasses and Pydantic

Choosing between dict, NamedTuple, dataclass, and Pydantic comes down to how much safety you need. In this free interactive course, you’ll learn when to use each:

• Dictionary: Flexible, but no built-in field checks. Typos and missing keys only show up at runtime.

• NamedTuple: Immutable with fixed fields, helping catch mistakes early.

• dataclass: Mutable data containers with defaults and optional validation logic.

• Pydantic: Strong type validation, automatic coercion, and detailed error reporting.

All exercises run directly in your browser. No installation required.

☕️ Weekly Finds

spec-kit [Dev Tools] - Toolkit for Spec-Driven Development that helps define specs, generate plans and tasks, and implement code with AI coding tools

ty [Code Quality] - Extremely fast Python type checker and language server written in Rust, by the creators of uv and Ruff

nbQA [Code Quality] - Run ruff, isort, pyupgrade, mypy, pylint, flake8, and more on Jupyter Notebooks

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Narwhals: One Decorator for pandas, Polars, and DuckDB

Problem

Writing a DataFrame function that supports multiple libraries usually means maintaining separate versions of the same logic for each one.

If changes are needed, they need to be applied to every version.

Solution

With Narwhals‘ @narwhalify decorator, you write the logic once using a unified API.

The function then works with whatever DataFrame type is passed in and returns the same type, reducing friction when switching tools.

How is this different from Ibis? Ibis is built for data scientists switching between SQL backends. Narwhals is built for library authors who need their code to work with any DataFrame type.

📖 View Full Article | 🧪 Run code

Docling: Turn DOCX Reviewer Feedback into Structured Data

Problem

Pulling comments from Word files turns informal feedback into data you can analyze, manage, and act on in code.

Traditionally, this requires parsing raw XML and manually mapping each comment back to its referenced text.

Solution

Docling v2.71.0 simplifies this process. Converted documents now attach a comments field to every text item, making reviewer annotations accessible without manual XML handling.

This opens up workflows that were previously too tedious to automate:

• Flag unresolved comments before merging document versions

• Build dashboards tracking reviewer feedback across teams

• Feed comment data into LLMs for sentiment analysis or summarization

📖 View Full Article

📚 Latest Deep Dives

Portable DataFrames in Python: When to Use Ibis, Narwhals, or Fugue - Write your DataFrame logic once and run it on any backend. Compare Ibis, Narwhals, and Fugue to find the right portability strategy for your Python workflow.

☕️ Weekly Finds

pdfGPT [LLM] - Chat with the contents of your PDF files using GPT capabilities and semantic search with sentence embeddings

SandDance [Data Viz] - Microsoft Research data visualization tool that maps every data row to a visual mark for interactive exploration

trafilatura [Web Scraping] - Python package and CLI for web crawling, scraping, and text extraction with output as CSV, JSON, HTML, or XML

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Quarkdown: Build LaTeX-Quality Docs with Just Markdown

Problem

LaTeX produces beautiful academic papers, but its verbose syntax and nested environments make even simple layouts painful to write.

Solution

Quarkdown generates the same professional page output using clean Markdown syntax you already know.

Key features:

• Write once, export as paged documents, presentation slides, or websites

• Define reusable functions with conditionals and loops inside your documents

• Embed Mermaid diagrams and charts without external tools

• Live preview in VS Code as you type

Ibis: One Python API for 22+ Database Backends

Problem

Running queries across multiple databases often means rewriting the same logic for each backend’s SQL dialect. A query that works in DuckDB may require syntax changes for PostgreSQL, and another rewrite for BigQuery.

Solution

Ibis removes that friction by compiling Python expressions into each backend’s native SQL. Swap the connection, and the same code runs across 22+ databases.

Key features:

• Write once, run on DuckDB, PostgreSQL, BigQuery, Snowflake, and 18+ more

• Lazy execution that builds and optimizes the query plan before sending it to the database

• Intuitive chaining syntax similar to Polars

📖 View Full Article

📚 Latest Deep Dives

Portable DataFrames in Python: When to Use Ibis, Narwhals, or Fugue - Write your DataFrame logic once and run it on any backend. Compare Ibis, Narwhals, and Fugue to find the right portability strategy for your Python workflow.

☕️ Weekly Finds

graphiti [LLM] - Build real-time, temporally-aware knowledge graphs for AI agents with automatic entity and relationship extraction

doris [SQL] - High-performance MPP analytics database with MySQL compatibility that handles real-time ingestion and sub-second queries at scale

smallpond [Data Processing] - Lightweight distributed data processing framework by DeepSeek that scales DuckDB to PB-scale datasets using Ray

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  ---

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Loguru: From Print Statements to Production Logging in One Line

Problem

Data scientists often rely on print statements to monitor data processing pipelines during development.

But print provides no timestamps or severity levels.

Python’s built-in logging fixes that, but demands boilerplate: handlers, formatters, and log-level configuration just to get started.

Solution

Loguru replaces both with a single import: one line gives you structured, colored logging with no setup required.

Key features:

• Modern {} formatting that matches Python f-string syntax

• One-line file logging with automatic rotation and retention

• Readable tracebacks that show variable values at each stack level

• Custom sinks to route logs to Slack, email, or databases

📖 View Full Article | 🧪 Run code

Python 3.14: Type-Safe String Interpolation with t-strings

Problem

Building SQL queries with f-strings directly embeds user input into the query string, allowing attackers to inject malicious SQL commands.

Parameterized queries are secure but require you to maintain query templates and value lists separately.

Solution

Python 3.14 introduces template string literals (t-strings). Instead of returning strings, they return Template objects that safely expose interpolated values.

This lets you validate and sanitize interpolated values before building the final query.

🧪 Run code

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mistune [Python Utilities] - Fast Python Markdown parser with custom renderers and plugins that converts Markdown to HTML with minimal overhead

pyparsing [Python Utilities] - Python library for creating readable PEG parsers that handles whitespace, quoted strings, and comments without regex complexity

fastlite [SQL] - Lightweight SQLite wrapper by Jeremy Howard that adds Pythonic syntax, dataclass support, and diagram visualization for interactive use

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• Tool Selector - Discover 70+ Python tools for AI and data science

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📅 Today’s Picks

MLflow: Track Every LLM API Call with 1 Line of Code

Problem

Most teams building LLM apps don’t set up tracking because they see API calls as simple request-response operations.

But every API call costs money, and without token-level visibility, you can’t tell where your budget is going until it’s already spent.

Solution

MLflow‘s autolog traces every OpenAI API call with just one line of code, so you always know what was sent, what came back, and how many tokens it used.

Key capabilities:

• Track token usage per call to identify which requests consume the most

• View full prompt and response content for every call

• Measure latency per call to find which requests are slowing down your app

• Works with OpenAI, Anthropic, LangChain, LlamaIndex, and DSPy

Rembg: Remove Image Backgrounds in 2 Lines of Python

Problem

Removing backgrounds from images typically requires Photoshop, online tools, or AI assistants like ChatGPT.

But these options come with subscription costs, upload limits, or privacy concerns with your images on external servers.

Solution

Rembg uses AI models to remove backgrounds locally with just 2 lines of Python.

It’s also open source and compatible with common Python imaging libraries.

🧪 Run code

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☕️ Weekly Finds

awesome-claude-skills [AI Tools] - Curated list of Claude Skills, resources, and tools for customizing Claude AI workflows with community-contributed templates and integrations

zvec [Vector Database] - In-process vector database built on Alibaba’s Proxima engine that searches billions of vectors in milliseconds with zero server setup

langflow [AI Agents] - Visual platform for building and deploying AI-powered agents and workflows with drag-and-drop interface, multi-agent orchestration, and MCP server support

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• Tool Selector - Discover 70+ Python tools for AI and data science

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Granite Vision: Convert Chart Images to CSV with Transformers

Problem

Chart data often contains valuable insights, but extracting numbers from these charts manually is time-consuming and tedious.

Solution

IBM’s Granite Vision 3.3 2B converts chart images directly into structured CSV data using Hugging Face Transformers.

Here’s how to extract structured data from any chart image in three steps.

1. Load the Model

Load the chart-to-CSV model from HuggingFace using the transformers library.

from transformers import AutoProcessor, AutoModelForVision2Seq
from huggingface_hub import hf_hub_download
from PIL import Image
import torch

model_path = "ibm-granite/granite-vision-3.3-2b-chart2csv-preview"
device = "cuda" if torch.cuda.is_available() else "cpu"

processor = AutoProcessor.from_pretrained(model_path)
model = AutoModelForVision2Seq.from_pretrained(model_path).to(device)

2. Prepare Your Chart

Define the chart image and task instruction in a conversation format.

# Load a chart image
img_path = hf_hub_download(
    repo_id=model_path, filename="example.jpg"
)
img = Image.open(img_path)

# Use the chart-to-CSV prompt
conversation = [
    {"role": "user", "content": [
        {"type": "image", "url": img_path},
        {"type": "text", "text": "Parse the chart in the image to CSV format."}
    ]}
]

3. Generate CSV Output

Apply the chat template, generate tokens, and decode back to CSV text.

inputs = processor.apply_chat_template(
    conversation,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt"
).to(device)

output = model.generate(**inputs, max_new_tokens=500)
csv_output = processor.decode(output[0], skip_special_tokens=True)
print(csv_output)

Output:

State,2017,2018
NJ,4.6,4.1
CT,4.7,4.1
DE,4.5,3.8
NY,4.7,4.1
PA,4.9,4.3

PydanticAI: Control OpenAI Data Retention with openai_store

Problem

By default, OpenAI may retain your API request data for internal review and model improvement. For healthcare, finance, and legal applications, this default creates compliance risks you can’t afford.

Solution

PydanticAI v1.52.0 introduces the openai_store setting to explicitly disable data retention in one line.

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☕️ Weekly Finds

msgvault [Data Management] - Archive a lifetime of email and chat locally with full Gmail backup, search, DuckDB-powered analytics, an interactive TUI, and an MCP server for querying messages with AI

monty [Developer Tools] - Minimal, secure Python interpreter written in Rust designed for use by AI agents, providing sandboxed code execution with safety guarantees

baserow [No-Code Platform] - Open-source no-code platform for building databases, applications, automations, and AI agents with enterprise-grade security and self-hosted deployment options

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📅 Today’s Picks

Write Readable Multi-Condition Logic with Polars when-then-otherwise

Problem

pandas requires np.where() for simple conditions which breaks method chaining and becomes nested and hard to read for multiple conditions.

The apply() alternative is slow and also breaks the DataFrame workflow.

Solution

Polars provides when().then().otherwise() chains that integrate naturally with method chaining.

With pandas, nested np.where() calls stack up for each additional condition, creating deeply nested expressions. Polars replaces this with readable chains where each condition appears sequentially.

Key benefits:

• Natural flow with method chaining

• Each condition stands on its own line

• No nested function calls

• Maintains data transformation workflow

The pattern scales cleanly from two conditions to ten without sacrificing readability.

📖 View Full Article | 🧪 Run code

Extract Text from Any Document Format with Docling

Problem

Have you ever needed to pull text from PDFs, Word files, slide decks, or images for a project? Writing a different parser for each format is slow and error-prone.

Solution

Docling‘s DocumentConverter takes care of that by detecting the file type and applying the right parsing method for PDF, DOCX, PPTX, HTML, and images.

Other features of Docling:

• AI-powered image descriptions for searchable diagrams

• Export to pandas DataFrames, JSON, or Markdown

• Structure-preserving output optimized for RAG pipelines

• Built-in chunking strategies for vector databases

• Parallel processing handles large document batches efficiently

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☕️ Weekly Finds

lm-evaluation-harness [Machine Learning] - Unified framework for testing and evaluating generative language models across a wide range of benchmarks and tasks with support for local models and custom metrics

PyMC [Probabilistic Programming] - Probabilistic programming library for Python that allows users to build Bayesian models with a simple Python API and fit them using state-of-the-art methods

Quarkdown [Documentation] - Modern Markdown typesetting system with powerful extensions for creating books, articles, and presentations. Supports function calls, custom functions, and outputs HTML, PDF, and slides

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📅 Today’s Picks

Narwhals: One Function for pandas, Polars, and DuckDB

Problem

Teams today use multiple DataFrame libraries side by side. Each backend has its own syntax, so your utility functions end up full of if/elif chains checking types.

This makes even small logic changes expensive, since every backend implementation must be updated.

Solution

Narwhals removes this complexity by providing a unified DataFrame API.

How it works:

• Wrap any DataFrame with nw.from_native() (pandas, Polars, DuckDB, PySpark, PyArrow)

• Write transformations once using Polars-style operations

• Convert back to the original type with nw.to_native()

• Zero extra dependencies. Each backend keeps its native performance

📖 View Full Article | 🧪 Run code | ⭐ View GitHub

uv: Switch Python Versions Without Rebuilding Environments

Problem

Switching Python versions typically requires recreating virtual environments and reinstalling all dependencies from scratch.

This workflow wastes time and can introduce version conflicts when dependencies need to be resolved again.

Solution

UV allows seamless Python version upgrades with uv python pin while preserving existing dependencies.

The process is simple:

• Pin the version with uv python pin 3.x

• Sync dependencies with uv sync

• All cached packages are preserved automatically

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☕️ Weekly Finds

Airbyte [Data Engineering] - Data integration platform with 600+ connectors for ETL/ELT pipelines from APIs, databases, and files to warehouses and lakes

act [DevOps] - Run GitHub Actions locally for fast feedback without commit/push cycles, using Docker containers

Dash [AI Agents] - Self-learning text-to-SQL agent that grounds answers in six layers of context and improves automatically from failures

📚 Latest Deep Dives

5 Python Tools for Structured LLM Outputs: A Practical Comparison - Compare 5 Python tools for structured LLM outputs. Learn when to use Instructor, PydanticAI, LangChain, Outlines, or Guidance for JSON extraction.

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📅 Today’s Picks

Build AI Agent Memory with Graphiti Knowledge Graphs

Problem

Traditional RAG pipelines rely on batch processing and static document summaries. When data changes, you re-embed, re-index, and wait.

That delay means your agent is always working with stale information, unable to track how facts evolve over time.

Solution

Graphiti is an open-source Python framework that builds knowledge graphs with real-time, incremental updates. This lets you add new information at any time without reprocessing your entire dataset.

Key features:

• Track when facts happened and when they were recorded, so you always know what’s current

• Search by meaning, keywords, or relationships in one query

• Get the most relevant results for a specific person, company, or entity

• Works with Neo4j, FalkorDB, and Kuzu as the graph backend

⭐ View GitHub

Polars sink_csv: Stream Million-Row Exports Without Memory Spikes

Problem

Writing large DataFrames to CSV is memory-intensive because the entire dataset is serialized in memory before being written to disk.

Solution

Polars’ streaming CSV sink avoids this by writing data in chunks rather than all at once.

Key benefits:

• Eliminate out-of-memory errors on large exports

• Write multi-million row DataFrames with minimal RAM

• Support for cloud storage destinations (S3, GCS, Azure)

Switch from write_csv to sink_csv on a lazy frame to enable streaming.

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☕️ Weekly Finds

Flowise [AI Agents] - Low-code platform for building AI agents and workflows visually with drag-and-drop components

cleanlab [Machine Learning] - Data-centric AI package that automatically detects data quality issues, label errors, and outliers in ML datasets

OpenBB [Finance] - Open-source financial data platform for analysts, quants, and AI agents with dozens of data integrations

📚 Latest Deep Dives

5 Python Tools for Structured LLM Outputs: A Practical Comparison - Compare 5 Python tools for structured LLM outputs. Learn when to use Instructor, PydanticAI, LangChain, Outlines, or Guidance for JSON extraction.

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MLflow: Built-in Scorers for LLM Evaluation Without Custom Logic

Problem

Evaluating LLM outputs for correctness, relevance, and guideline adherence requires writing custom evaluation logic for each criterion.

This creates repetitive boilerplate code and slows down your iteration cycle.

Solution

MLflow provides pre-built scorers for common evaluation patterns like correctness and guideline adherence.

Key benefits:

• Pre-built scorers for correctness, relevance, and safety checks

• Simple decorator syntax for custom metrics

• Automatic tracking of scores, rationales, and metadata in MLflow

• Combine multiple scorers in a single evaluation run

Integrate with existing MLflow workflows without additional setup.

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🔄 Worth Revisiting

Swap AI Prompts Instantly with MLflow Prompt Registry

Problem

Finding the right prompt often takes experimentation: tweaking wording, adjusting tone, testing different instructions.

But with prompts hardcoded in your codebase, each test requires a code change and redeployment.

Solution

MLflow Prompt Registry solves this with aliases. Your code references an alias like “production” instead of a version number, so you can swap versions without changing it.

Here’s how it works:

• Every prompt edit creates a new immutable version with a commit message

• Register prompts once, then assign aliases to specific versions

• Deploy to different environments by creating aliases like “staging” and “production”

• Track full version history with metadata and tags for each prompt

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CodeCut Premium bridges that gap with interactive courses that let you:

• Execute code directly in your browser

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I plan to add new courses regularly, with a focus on quality and depth. The catalog is still growing, and Founding Members get early access plus exclusive perks as it expands.

Founding Members receive lifetime $12/month pricing, full access to all courses, and early influence on future content.

Founding pricing ends March 31, 2026.

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☕️ Weekly Finds

zipline [Finance] - Pythonic algorithmic trading library with event-driven backtesting for building and testing trading strategies

outlines [LLM] - Structured text generation library that constrains LLM outputs to follow specific schemas, formats, and data types

responses [Testing] - Utility library for mocking out the Python Requests library in tests with simple decorators and context managers

📚 Latest Deep Dives

5 Python Tools for Structured LLM Outputs: A Practical Comparison - Compare 5 Python tools for structured LLM outputs. Learn when to use Instructor, PydanticAI, LangChain, Outlines, or Guidance for JSON extraction.

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What Data Engineers Really Think About Airflow (5.8K Surveyed)

Astronomer analyzed 5.8k+ responses from data engineers on how they are navigating Airflow today and the findings might surprise you.

You’ll learn:

• How early adopters are using Airflow 3 features in production

• Which teams are bringing AI into production and what’s holding others back

• 94% believe that Airflow is beneficial to their career

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📅 Today’s Picks

Build Structured LLM Outputs with Guidance Constraints

Problem

Tools like Instructor and PydanticAI validate outputs after generation. If validation fails, they send the error back to the LLM and retry.

Each retry means paying for tokens that didn’t produce usable output.

Solution

Guidance works differently. It constrains tokens during generation, so invalid outputs can’t be produced in the first place.

Key capabilities:

• Constrained outputs via regex patterns and selection functions

• Python control flow (if/else, loops) during generation

• JSON generation with Pydantic schema validation

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pandas 3.0: The End of SettingWithCopyWarning

Problem

When you filter a DataFrame and modify the result, you expect the original to stay unchanged.

But sometimes pandas modified your original data anyway, triggering the SettingWithCopyWarning.

Solution

pandas 3.0 fixes this. Filtering now always creates a separate copy, so modifying the result never affects your original data.

Upgrade to pandas 3.0 with “pip install -U pandas”.

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graphic-walker [Data Viz] - Open-source Tableau alternative for data scientists to analyze data and visualize patterns with drag-and-drop operations

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From CSS Selectors to Natural Language: Web Scraping with ScrapeGraphAI - Web scraping without selector maintenance. ScrapeGraphAI uses LLMs to extract data from any site using plain English prompts and Pydantic schemas.

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