---
title: "Python"
description: "252 posts about Python, summarised, each linking to the original."
---

# Python
> 252 posts about Python, summarised, each linking to the original.

## Articles

### [The State of Django 2026: Boring is so back](https://yomu.fyi/post/the-state-of-django-2026-boring-is-so-back.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Will Vincent
- Published: Aug 28, 2026
- Type: Explainer

The fifth annual Django Developers Survey, produced by Django Software Foundation and PyCharm, draws on responses from nearly 3,500 developers in more than 40 countries. It reports that Django’s core remains stable, with PostgreSQL used by 76–79% of respondents across five years, templates around 80%, and 43% already on Django 6.0. At the same time, AI is routine but fragmented: 58% of AI users work with it daily, while 56% use it only for chat and advice and just 27% use autonomous multi-step tasks. The survey also describes rapid consolidation around uv and Ruff, growing adoption of type hints, and a testing and CI/CD base that can verify agent-generated changes, although 19% write no automated tests. Django supports both server-rendered and API/frontend patterns, while its stable foundation lets developers change surrounding tools and workflows without replacing the framework.


### [Security Incident Affecting JetBrains Cadence](https://yomu.fyi/post/security-incident-affecting-jetbrains-cadence.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Daniel Gallo
- Published: Aug 28, 2026
- Type: Incident / postmortem

JetBrains reports a security incident affecting Cadence, its hosted service that integrates with PyCharm through an optional plugin and runs projects on cloud compute resources. Cadence used TeamCity to orchestrate workloads, and attackers exploited the environment through critical vulnerability CVE-2026-63077. The attackers accessed personal data, compromised a 2024 server backup and Cadence-related AWS IAM credentials, and accessed files in JetBrains AWS S3 buckets; the scope remains under investigation. JetBrains discovered the exploitation on August 23, took the server offline on August 24, invalidated plugin access tokens, and urges users to rotate secrets, investigate connected systems, review repositories, and treat executions and synchronized files as untrusted.


### [Building hermetic notebook images for Open Data Hub and Red Hat OpenShift AI](https://yomu.fyi/post/building-hermetic-notebook-images-for-open-data-hub-and-red-hat-opensh.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Vath Sok
- Published: Aug 28, 2026
- Type: Problem & solution

Open Data Hub and Red Hat OpenShift AI adopted hermetic container builds to eliminate network failures, satisfy Conforma compliance, and ensure reproducible notebook environments. Instead of pulling packages during image creation, dependencies are pinned in lockfiles, prefetched into a local cache using tools like Cachi2, and installed with the network disabled. The workflow relies on a unified Containerfile capable of running locally, within GitHub Actions, and on Konflux via Tekton tasks. To pass audit checks and avoid generic tarball downloads, the team packaged awkward binaries like ripgrep and Pandoc into multi-arch Python wheels while patching Node postinstall scripts that fetch files at install time. Upstream CentOS and downstream Red Hat Enterprise Linux dependencies are maintained in distinct lock trees to prevent subtle packaging mismatches.


### [How to Confirm Phone Number Ownership with Lookup Identity Match in Python](https://yomu.fyi/post/how-to-confirm-phone-number-ownership-with-lookup-identity-match-in-py.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Kelley Robinson, Dylan Frankcom
- Published: Aug 28, 2026
- Type: Tutorial

The tutorial shows how to use Twilio Lookup Identity Match in Python to confirm that signup information is associated with a phone number and reduce signup fraud. It builds a Flask application that accepts an E.164-formatted number, first name, and last name, then calls the Lookup API with fields="identity\_match" and the supplied identity attributes. The response exposes statuses including exact\_match, high\_partial\_match, no\_match, and no\_data\_available; the example accepts a result only when the number is valid and both names are exact or high partial matches. The implementation covers project setup with Flask, twilio, and python-dotenv, environment-based credentials, an intl-tel-input form, and frontend handling for success and failure messages. It also notes that Identity Match can use combinations of name, address, national ID, or date of birth, and points to other Lookup packages such as Line Type Intelligence and SIM swap detection.


### [Differential Privacy for Hugging Face Trainers – Without Rewriting Your Training Loop](https://yomu.fyi/post/differential-privacy-for-hugging-face-trainers-without-rewriting-your.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Katie Fraser
- Published: Aug 27, 2026
- Type: Announcement

Training large language models on sensitive data risks memorization and membership inference attacks, yet standard high-level transformer tools lack native differential privacy capabilities. JetBrains Research created and open-sourced DPTrainer, a library that integrates PyTorch's Opacus framework with the Hugging Face Trainer ecosystem. The library provides differential privacy through differentially private stochastic gradient descent without requiring manual training loops or source code modifications. DPTrainer extends base training arguments with a PrivacyArguments dataclass, managing per-sample gradient calculation, gradient clipping, noise injection, and Poisson-sampled data loading. A dynamic patching mechanism injects differential privacy into specialized subclasses like DPOTrainer and SFTTrainer, while built-in privacy accountants track the cumulative privacy budget across training steps and checkpoints.


### [How to Approve Real Users and Block Fake Accounts at Sign Up with Lookup and Verify in Python](https://yomu.fyi/post/how-to-approve-real-users-and-block-fake-accounts-at-sign-up-with-look.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Kelley Robinson, Dylan Frankcom
- Published: Aug 27, 2026
- Type: Tutorial

This Python tutorial builds a signup flow that screens phone numbers for fake accounts while aiming to keep legitimate onboarding smooth. It combines Twilio Lookup’s Line Type Intelligence, optional Line Status, and Identity Match checks before using Twilio Verify to send an OTP and confirm possession of the number. The provided Flask example collects a phone number and first and last names, rejects landlines, nonfixed VoIP, toll-free numbers, and pagers, and accepts Identity Match only when both name fields are exact\_match or high\_partial\_match. The post shows how to configure the project, run it locally, inspect outcomes such as LINE\_TYPE\_BLOCKED or identity-match failure, and consider pricing because the four API calls have different costs.


### [How Much Code Do Developers Really Let Agents Write?](https://yomu.fyi/post/how-much-code-do-developers-really-let-agents-write.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Mikhail Bogdanov
- Published: Aug 26, 2026
- Type: Benchmark

JetBrains surveyed over 15,000 professional developers globally in 2026 to measure how much production code is generated by AI agents versus manual authoring. On average, respondents report that agents fully write approximately 47% of their code, while 38% involves AI assistance and 27% is written manually. Adoption varies across ecosystems, with Go, JavaScript, and TypeScript developers reporting the highest agentic code generation at 54% to 55%, while C and C++ developers retain the highest manual share. Senior developers lead adoption over juniors, with about a quarter delegating more than 80% of code generation to agents. Cluster analysis categorizes the developer population into agentic coders (31%), AI-assisted coders (47%), and manual coders (23%).


### [OpenTelemetry Comes to IntelliJ IDEA, GoLand, PyCharm, and WebStorm](https://yomu.fyi/post/opentelemetry-comes-to-intellij-idea-goland-pycharm-and-webstorm.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Egor Klimov
- Published: Aug 26, 2026
- Type: Announcement

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.


### [GRPO fine-tuning on Red Hat OpenShift AI: Reinforcement learning from verifiable rewards with Training Hub](https://yomu.fyi/post/grpo-fine-tuning-on-red-hat-openshift-ai-reinforcement-learning-from-v.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Fiona Waters
- Published: Aug 26, 2026
- Type: Tutorial

Supervised fine-tuning for structured reasoning tasks like tool calling often demands expensive, labor-intensive dataset curation. Group Relative Policy Optimization (GRPO) resolves this by using programmatic reward functions to score multiple candidate responses without requiring human labels or a dedicated reward model. On Red Hat OpenShift AI, developers can implement GRPO using Training Hub, Kubeflow Trainer, and the Kubeflow SDK to fine-tune models like Qwen3-4B. The setup utilizes vLLM for candidate generation alongside LoRA weight updates, sharing storage via ReadWriteMany persistent volume claims. In a practical tool-calling test, five training iterations raised task accuracy from 33% to 67%, teaching the model to emit parseable function invocations rather than fabricated conversational text.


### [Build a Video Chat Application with Python, JavaScript and Twilio Programmable Video](https://yomu.fyi/post/build-a-video-chat-application-with-python-javascript-and-twilio-progr.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Miguel Grinberg
- Published: Aug 26, 2026
- Type: Tutorial

This tutorial builds a browser-based video chat application using Python, Flask, vanilla JavaScript, HTML, CSS, and Twilio Programmable Video. It adds text chat through Twilio Conversations and screen sharing, allowing participation on modern desktop and mobile browsers without installed software. The implementation covers Twilio account and API-key setup, environment variables, a Python virtual environment, client-side event handlers, and ngrok, which exposes the local Flask server through an HTTPS public URL for testing on other devices. The refreshed project targets Flask 3.x, twilio-python 9.x, Twilio Video JS 2.35.x, and Twilio Conversations classic JS 2.x, and the completed flow supports live calls, screen sharing, and text chat. Participants must use unique names because Twilio identifies them by token identity.


### [Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers](https://yomu.fyi/post/training-and-finetuning-multi-vector-embedding-models-with-sentence-tr.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Tom Aarsen
- Published: Aug 26, 2026
- Type: Tutorial

Sentence Transformers v6.0 adds MultiVectorEncoder, a fourth model type for ColBERT-style late-interaction retrieval, alongside a training approach for finetuning existing models or building them from base transformers. Multi-vector models retain one small vector per token and score query-document pairs with MaxSim, where each query token selects its best document-token match and the scores are summed. The guide covers models, datasets, loss functions, training arguments, evaluators, trainers, callbacks, and multi-dataset training, with examples using pip install -U "sentence-transformers\[train\]". On a medical retrieval evaluation, the finetuned multi-vector-encoder/mLateOn-medical model, trained for 14.5 hours on a single RTX 3090, outperformed every general-purpose retrieval model tested across dense, sparse, lexical, and multi-vector systems.


### [Project Lighthouse — Part 3: Introducing project-lighthouse-anonymize](https://yomu.fyi/post/project-lighthouse-part-3-introducing-project-lighthouse-anonymize.md)
- Company: [Airbnb](https://yomu.fyi/company/airbnb.md)
- Author: Adam Bloomston
- Published: Aug 25, 2026

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.


### [Wire It, Run It, Deploy It: AI Workflows in Gradio](https://yomu.fyi/post/wire-it-run-it-deploy-it-ai-workflows-in-gradio.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: yuvraj sharma, Abubakar Abid
- Published: Aug 25, 2026
- Type: Tutorial

AI applications are presented as pipelines that chain image, text, audio, and dataset operations, with intermediate values otherwise investigated through print-debugging. gr.Workflow turns these pipelines into graphs of typed nodes, served as a drag-and-drop canvas where each node runs independently and its result remains visible. Operators can be Python functions, Hugging Face Inference Providers models, Gradio Spaces, Hub dataset rows, or GPU-backed functions using ZeroGPU, while fan-out connections run multiple analyses or generations in parallel. The same workflow also exposes labeled outputs as REST endpoints callable through the Gradio client or curl, and can be deployed to Hugging Face Spaces with one command.


### [Run, debug, and scale Databricks workloads from your local IDE](https://yomu.fyi/post/run-debug-and-scale-databricks-workloads-from-your-local-ide.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Tanishq Maheshwari, Matt Jones
- Published: Aug 24, 2026
- Type: Announcement

Databricks introduces updated IDE and CLI integrations that connect VS Code, Cursor, or a terminal directly to Databricks compute, workspace, and Unity Catalog. Existing Databricks Connect supported local Spark development, but remote non-Spark workloads and dependency synchronization remained pain points. The new SSH tunnel supports interactive execution and debugging of Python and SQL workloads on Serverless, AI Runtime, and dedicated clusters, while keeping files and dependencies aligned with Databricks Runtime and the workspace. CLI flags can select serverless, GPU-backed AI Runtime, or a dedicated cluster, and can start the tunnel in VS Code or Cursor; additional options configure base environments and usage policies, while the IDE can browse Unity Catalog assets.


### [Run LoRA fine-tuning on Red Hat OpenShift AI with Ray](https://yomu.fyi/post/run-lora-fine-tuning-on-red-hat-openshift-ai-with-ray.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Fiona Waters
- Published: Aug 24, 2026
- Type: Tutorial

Red Hat OpenShift AI 3.5 integrates Ray cluster runtime images with preinstalled Training Hub packages to support distributed fine-tuning algorithms including LoRA, SFT, OSFT, and GRPO. Using the CodeFlare software development kit within a Jupyter workbench, developers can provision an elastic single-GPU Ray cluster attached to shared ReadWriteMany persistent storage containing model weights and training datasets. The tutorial demonstrates fine-tuning the Qwen2.5-1.5B-Instruct model on the public sql-create-context dataset to produce direct, bare SQL output without explanatory conversational text. After training completes, the workbench loads and merges the resulting LoRA adapter checkpoints with the base model to evaluate output format correctness against sample database schema questions. The RayCluster automatically tears down resources once the job concludes, while the shared pipeline architecture allows switching between supported fine-tuning algorithms by modifying dataset inputs and configuration parameters.


### [Quantum-Augmented Applications: Integrating Quantum Subroutines into Classical Software Stacks](https://yomu.fyi/post/quantum-augmented-applications-integrating-quantum-subroutines-into-cl.md)
- Company: [Stack Overflow](https://yomu.fyi/company/stack-overflow.md)
- Author: Dr. Ahmad Mateen Ishanzai
- Published: Aug 20, 2026
- Type: Explainer

Quantum-augmented applications treat Quantum Processing Units (QPUs) as targeted coprocessors for NP-hard subroutines rather than standalone computing replacements. Near-term noisy intermediate-scale quantum systems handle tasks like combinatorial optimization, high-dimensional state sampling, and kernel mapping while classical stacks retain business logic and pipeline control. A hybrid runtime architecture routes parameterized ansatz circuits through middleware to the QPU, returning raw measurements for error mitigation and classical optimization. A Python implementation using Qiskit demonstrates this interaction by running a parameterized variational ansatz inside a classical COBYLA minimization loop. Operating this hybrid model requires navigating physical constraints including coherence limits, circuit transpilation latencies, and network bandwidth bottlenecks across the classical-QPU boundary.


### [PyCharm for AI-assisted Django Workflows](https://yomu.fyi/post/pycharm-for-ai-assisted-django-workflows.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Will Vincent
- Published: Aug 20, 2026
- Type: Announcement

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.


### [Developing NVIDIA Holoscan Applications with CLI, Skills, and AI Coding Agents](https://yomu.fyi/post/developing-nvidia-holoscan-applications-with-cli-skills-and-ai-coding.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Elizabeth Goodman
- Published: Aug 19, 2026
- Type: Tutorial

NVIDIA Holoscan is a platform for real-time AI applications at the edge, and HoloHub provides reference applications and components. This post describes an engineer-guided workflow in which a coding agent builds a separate Python endoscopic tool segmentation application from the existing MONAI model, sample video, and Holoscan patterns. The Holoscan CLI, invoked through ./holohub, exposes shared commands for scaffolding, running, modes, tests, and benchmarking, while development skills and documentation guide the agent through project-specific operations. The application connects video replay, preprocessing, TensorRT inference, segmentation postprocessing, telemetry, and HoloViz visualization, with visual, smoke, and benchmark modes; the post reports that the CLI-plus-skills-plus-docs/examples configuration produced a separate application in 40 minutes using 11M tokens, and concludes that this combination offered the lowest resource overhead among the compared configurations.


### [How AI Coding Agents Can Unlock Materials Simulation with NVIDIA ALCHEMI Toolkit](https://yomu.fyi/post/how-ai-coding-agents-can-unlock-materials-simulation-with-nvidia-alche.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Elizabeth Goodman
- Published: Aug 18, 2026
- Type: Benchmark

Atomistic simulation combines scientific judgment, compute-efficient implementation, and an accessible interface to the simulation stack, but the MLIP ecosystem lacks the mature interfaces available for classical force fields. NVIDIA ALCHEMI Toolkit addresses implementation with composable, PyTorch-native, GPU-accelerated building blocks and in-flight batching, while its agent skills and reference files supply API patterns to coding agents. The post benchmarks 45 generated pipelines across silicon equation of state, copper adsorption, and lithium self-diffusion, checking code features and running representative workloads on NVIDIA H200 GPUs. All 45 scripts used batched GPU execution, 38 completed screening, and all 15 production representatives completed; however, agents did not assess physical validity, and thermostat choices could suppress measured lithium diffusion by 3–5×, so independent validation against DFT, experiment, or another reference remains necessary.


### [Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers](https://yomu.fyi/post/multi-vector-late-interaction-embedding-models-with-sentence-transform.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Tom Aarsen, Antoine Chaffin, Raphael Sourty
- Published: Aug 18, 2026
- Type: Tutorial

Sentence Transformers v6.0 adds MultiVectorEncoder, bringing ColBERT-style late-interaction retrieval into the library’s existing API for dense, sparse, and reranker models. The encoder retains one projected, typically 128-dimensional vector per token instead of pooling a document into one vector, allowing documents to be encoded independently and indexed offline. At scoring time, MaxSim finds each query token’s highest dot-product match among document tokens and sums those maxima; contextual embeddings support both paraphrase matching and exact-token evidence. The guide covers checkpoint loading, encoding, scoring, semantic search, retrieval and reranking, indexing, token pooling, inference speed, evaluation, and retrieval from page images, audio, and video. It also quantifies the storage trade-off: 4,874 passages produced 608,414 token vectors and a 311.5 MB float32 index, while fast-plaid compression reduced that example to 92 MB.


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