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Python
13 posts about Python. Every summary links to the original.
OpenTelemetry Comes to IntelliJ IDEA, GoLand, PyCharm, and WebStorm
With the 2026.2 release, JetBrains expanded its OpenTelemetry plugin from Rider to IntelliJ IDEA, GoLand, PyCharm, and WebStorm. The tool captures logs, metrics, traces, and service maps locally without requiring an external observability backend. Developers can search structured log records, plot metric values over time, inspect distributed spans, and verify communication paths across databases and message queues during local runs. To ingest telemetry, the plugin automatically configures OpenTelemetry Protocol environment variables for supported run configurations and terminal sessions, or accepts forwarded data from existing collectors. Additionally, experimental Model Context Protocol support allows AI coding agents to query gathered logs, spans, and service topology.
Egor KlimovAirbnb ·
Project Lighthouse — Part 3: Introducing project-lighthouse-anonymize
Airbnb has open-sourced project-lighthouse-anonymize, a Python library powering their privacy-preserving anonymization used to measure user experience disparities. The release is accompanied by technical papers detailing Core Mondrian, an extended partition-based anonymization algorithm, alongside a framework for measuring anonymized data quality. The library enforces k-anonymity and p-sensitive k-anonymity while offering built-in data quality metrics.
Adam BloomstonPyCharm for AI-assisted Django Workflows
According to the 2026 Django Developers Survey, ninety percent of respondents incorporate artificial intelligence into their weekly or daily workflows. While agents generate code rapidly, developers remain accountable for understanding, evaluating, and shipping changes within their applications. PyCharm addresses these demands by integrating support for external agents, local models via Ollama and LM Studio, and customizable agent skills across codebases. The IDE maintains version-specific framework assistance, including auto-completion for Django 6.0 template partials, alongside architectural inspection tools and visual diffs. Furthermore, developers can trace application components, inspect API endpoints, execute HTTP requests, and query database migrations directly through built-in tooling.
Will VincentGrab ·
Agent platform (Part 1): How we help Grab build and run AI agents at scale
Grab scaled its internal AI tooling by transforming the infrastructure behind a single technical support bot into a standardized agent framework named LLM-Kit. The original Go-based bot revealed critical bottlenecks, including the lack of automated evaluation, painful provider migrations, fragmented observability, and excessive production scaffolding overhead. To address these frictions, engineers developed a FastAPI template pre-wired with LangGraph ReAct agents, remote Model Context Protocol servers, and built-in ROUGE, BLEU, and LLM-as-judge evaluation suites. The framework centralizes model access through an OpenAI-compatible GrabGPT Gateway and unifies observability across services, outbound calls, and agent steps using OpenTelemetry. This extracted scaffolding reduced day-one production setup time from two weeks to roughly an hour while supporting hundreds of production agents.
Kendrick TanMeta ·
10 Years of Meta’s Commitment to Python
Meta has marked ten consecutive years as a sponsor of the Python Software Foundation (PSF) to ensure the language remains robust and sustainable. Python represents the most widely used programming language across Meta's infrastructure, powering applications like Instagram and Threads as well as internal AI research. Beyond financial backing, Meta engineers actively contribute core language features, author Python Enhancement Proposals (PEPs), and build open-source tools such as the Pyrefly type checker. Company sponsorship directly funds the PSF's Developer-in-Residence program, core infrastructure, and security enhancements for the Python Package Index (PyPI). Continued support of the PSF functions as a strategic investment in maintaining the stability of the broader open-source ecosystem.
Grab ·
DispatchGym: Grab’s reinforcement learning research framework
Applying reinforcement learning to dispatch systems is often hindered when the chosen control levers exert weak influence over reward functions. To streamline research, Grab built DispatchGym, a framework that connects reinforcement learning algorithms to a dispatch process simulation via the Gymnasium API. The simulation emphasizes directional accuracy over absolute precision, allowing researchers to evaluate relative metric shifts across supply and demand scenarios. Built in modular Python and accelerated with Numba, the system allows data scientists to test code locally and launch distributed Spark executions with a single command-line call. The framework has been used to evaluate various contextual bandit models and action sampling strategies for tuning dispatch hyperparameters.
Tan Sien YiGrab ·
Introducing the SOP-driven LLM agent frameworks
Standard operating procedure (SOP) driven Large Language Model agent frameworks address common generative AI challenges such as hallucinations, non-standard output formats, and branching navigation errors. Workflows are represented as hierarchical trees where nodes encapsulate actions or decision points that can be created using a visual editor and annotated with explicit external function calls. Execution relies on a tripartite architecture consisting of a Depth-First Search planner module with backtracking, an adaptive worker agent that limits API exposure and compresses context, and a multilingual user agent. Supporting tools include a Graph Retrieval-Augmented Generation pipeline, a plugin system integrating Python and SQL, and a state stack for pausing workflows during human intervention. In production deployments for fraud and account takeover investigations, the framework automated up to 87% of cases while cutting handling times substantially.
Fujiao LiuGrab ·
LLM-assisted vector similarity search
Vector similarity search often struggles with conceptual nuances, negations, and complex constraints despite its efficiency in high-dimensional semantic retrieval. To address this limitation, a two-step retrieval pipeline combines Facebook AI Similarity Search (FAISS) using OpenAI's text-embedding-ada-002 embeddings with a large language model re-ranking step powered by GPT-4o. The initial FAISS search extracts a candidate shortlist of matches, which GPT-4o then re-ranks based on contextual relevance and query intent. Evaluations on structured datasets showed that while simple queries yielded comparable results across methods, the LLM-assisted approach effectively resolved negations and conceptual matching where raw vector search failed. Internal production trials on relational datasets exceeding 4,500 rows demonstrated similar relevance improvements, with additional query latency being the primary operational trade-off.
Md RiyadhGrab ·
Leveraging RAG-powered LLMs for analytical tasks
Data analysts at Grab faced an increasing volume of stakeholder data requests that required manually writing and executing repetitive SQL queries with minor parameter changes. To automate routine tasks like metric reporting and fraud investigations, the Integrity Analytics team paired an internal prompt-management platform, Spellvault, with Data-Arks, an in-house Python-based middleware that packages SQL queries and Python functions into APIs. A scheduler triggers automated report summarization, while an analytical bot named A* bot executes relevant investigation queries via RAG and returns summarized findings in Slack. Automated report generation saves an estimated three to four hours per report and shortens multi-query fraud investigations to minutes. Grab favored RAG over fine-tuning because it avoids continuous retraining costs, scales faster across diverse use cases, and surfaces current production data.
Edmund HongGrab ·
Grab Experiment Decision Engine - a Unified Toolkit for Experimentation
Grab's Experimentation platform tests thousands of experimental variants weekly, but analyses were largely handled manually using disparate tools unsuited to multi-sided marketplace nuances. To standardize workflows and eliminate reliability issues, Grab developed the GrabX Decision Engine, an internally open-sourced Python toolkit integrated with its Automated Experiment Analysis application. The framework includes a Trusted Advisor component that automates power analysis and sample size estimation while adjusting for clustered standard errors across metric data types. It also bundles a post-experiment statistical toolbox executing default and custom evaluations, ranging from non-parametric tests to regression models that control for confounders and fixed effects. This unified platform institutionalizes experimentation best practices across teams, streamlining collaboration and ensuring consistent statistical inference.
Ruike ZhangGrab ·
Securing GitOps pipelines
Grab's real-time data platform team transitioned from an Atlantis-driven Terraform workflow to an in-house GitOps platform called Khone to manage streaming infrastructure resources like Kafka topics and Flink pipelines. The earlier setup suffered from coarse-grained access controls, required manual merge request comments, and lacked flexible validation capabilities within native configuration files. Khone derives environment parameters directly from standardized directory paths and uses Python with the python-hcl2 library to inspect and validate resource definitions before executing Terraform stages in parallel. To prevent configuration tampering in merge requests, CI/CD pipeline definitions and execution scripts are isolated in a separate administrative repository and fetched during job runs using shallow Git clones.
Thang LeGrab ·
Automated Experiment Analysis - Making experimental analysis scalable
Manual ad-hoc analysis of online controlled experiments at Grab introduced operational inefficiencies, inconsistent quality control, and scalability barriers across teams. To resolve these issues, Grab extended its GrabX experimentation platform with an Automated Experiment Analysis system that standardises metrics and automates statistical evaluations. The architecture stores experiment configurations and metric definitions from Cosmos DB into Azure Data Lake as bronze datasets, uses Spark on Databricks via Azure Data Factory to process subjects into silver datasets, and applies an internal Python Decision Engine to generate final gold results. These gold datasets are stored in star-schema fact and dimension tables and presented directly in the GrabX interface using embedded Power BI visualisations. The automation eliminates repetitive data pipeline construction for analysts, ensures reproducible findings aligned with initial hypotheses, and accelerates product launch decisions.
Albert ChengGrab ·
Griffin, an Anti-fraud Risk Rule Engine Making Billions of Predictions Daily
Grab's Trust/Identity/Safety team built Griffin, an in-house anti-fraud risk rule engine designed to process billions of daily predictions across multiple business verticals. Initially, Grab managed fraud rules directly within backend service code, but escalating rule complexity, tight rule interdependencies, and translation gaps between data scientists and developers caused deployment delays and misfiring errors. To overcome the limitations and steep learning curves of third-party engines like Drools, the team separated the workflow into data orchestration and rule-based prediction. Griffin enables analysts and data scientists to author Python-based rules directly via a web portal and reload updated logic into memory without manual developer intervention. By eliminating I/O during rule evaluation and leveraging Gunicorn multi-processing, the engine handles over 100,000 queries per second at peak on six EC2 instances with single-prediction latencies under six milliseconds.
Muqi Li