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Tutorial posts on Yomu, summarised newest first.
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Tutorial posts
Device Authorization Flow for a Rust CLI and an Axum API
Command-line tools lack browser redirect capabilities, preventing standard OAuth login flows. To resolve this limitation, the OAuth 2.0 Device Authorization Flow issues a temporary device code, presents a verification URL to the user, and waits for authentication in an external browser. A Rust workspace demonstrates this end-to-end pattern across a CLI, an Axum web API, and a shared authentication library. The shared library relies on jsonwebtoken and reqwest to fetch JSON Web Key Sets and validate token signatures, issuers, and audiences against typed claims. Axum secures protected routes through custom FromRequestParts extractors that decode Bearer headers, enabling verified CLI access.
Developing Nemotron 3.5 Lightning NVFP4 with QAD Using NVIDIA Model Optimizer
NVIDIA’s post presents a workflow for developing an NVFP4 checkpoint of the Nemotron 3.5 Lightning 30B-A3B model, targeting lower memory use and higher inference throughput without sacrificing accuracy. The method starts with post-training quantization (PTQ), producing a W4A16 student from the BF16 teacher, then applies quantization-aware distillation (QAD), where simulated quantization and KL-divergence loss align student logits with the frozen teacher. For the selected four_over_six recipe, 32K calibration performed best, and the recipe using W4A16 Mamba linears offered the best reported trade-off between accuracy degradation and inference performance; the checkpoint is 22 GB versus 66 GB and delivers up to 4x faster throughput. The workflow includes Model Optimizer examples and launchers for Megatron-LM and Megatron-Bridge, covering calibration, distillation, evaluation, and export.
A/B test models in production
A/B testing model changes in production by sending fixed shares of live endpoint traffic to one control and up to 20 variants, allowing teams to compare user-facing outcomes rather than relying only on shadow-traffic checks. The endpoint router keeps variants out of the base traffic split, resamples requests that reach the control, and treats experiment percentages as absolute shares independent of replica counts or autoscaling. A 95/5 experiment can be updated to 80/20 and then 50/50 by resending the complete member list; an etag rejects stale concurrent updates. Measurement joins per-deployment platform metrics with product signals using the deployment ID in response metadata. To finish, a winning variant is promoted through a blue-green rollout before deleting the experiment, while deletion alone returns traffic to the control and can unwind a losing test.
Using AI_Functions in Your Data Warehouse: Top Use Cases
Databricks AI Functions bring model inference into SQL queries, addressing the need to combine warehouse-resident structured data with unstructured reviews, tickets, PDFs, and transcripts without exporting rows to a separate service. The post presents six Lakehouse use cases: document parsing and extraction, sentiment classification, translation, ticket routing, sales-call extraction, and generative drafting with ai_query. Task-specific functions such as ai_parse_document, ai_extract, ai_classify, and ai_translate handle specialized work, while ai_query sends prompts to an accessible Databricks-hosted Foundation Model serving endpoint. Unity Catalog permissions, existing pipelines, and system.billing.usage keep governance and billing within Databricks, while SQL execution manages planning, parallelization, and retries. Production guidance includes tagging jobs, sampling at least 10,000 rows, choosing models deliberately, requesting structured output, and versioning prompts.
Record, train, and deploy from one place with Strands Agents, LeRobot, and Hugging Face Storage Buckets
This walkthrough presents a streaming data loop for Strands Robots that records robot demonstrations, stores them in Hugging Face Storage Buckets, trains from the growing dataset, and deploys a resulting policy back to hardware. It uses the LeRobot on-disk format throughout: Robot("so100") records through DatasetRecorder, sync_dataset_to_bucket writes changed bytes to a mutable bucket, and stream_dataset reads the dataset from the Hub without a full local download while decoding camera video on the fly. The agent can decide which episodes to keep and when to retrain, while the same Robot object records and streams data; deployment changes the robot mode keyword from simulation to real. The default simulation path runs with a mock policy that produces a valid dataset but not a useful one, while hardware, bucket, and training paths require additional dependencies.
Track generative AI costs with Amazon Bedrock inference profiles
When multiple departments share a single foundation model in Amazon Bedrock under one IAM role, usage aggregates into one billing line item, complicating department-level chargebacks and budget tracking. This issue can be resolved by deploying Amazon Bedrock application inference profiles wrapped around the shared foundation model and tagging each profile for cost allocation. The application layer determines the user department during authentication and passes the corresponding inference profile ARN as the model identifier. Once the designated cost allocation tag is activated in the AWS Billing and Cost Management console, usage separates into per-department line items in AWS Cost Explorer. This method incurs standard per-token model rates without extra charges for cost attribution.
Mastering Edge AI on Raspberry Pi with LiteRT and Gemma
Running edge AI on a Raspberry Pi 5 is presented as a way to build autonomous, low-latency systems that operate offline, with Reachy Mini serving as the demonstration. The setup combines LiteRT, LiteRT-LM, Gemma models, and LiteRT’s WebGPU (Vulkan) backend via ML Drift, splitting workloads between the CPU and VideoCore VII GPU. On CPU, Gemma 4 E2B reportedly reaches 99 tokens/sec prefill, 9 tokens/sec decode, and 1,432 MB peak memory; its tokenizer supports about 27.3 characters/sec in the voice demo. The pipeline runs YOLO object detection on the GPU, Moonshine speech recognition, Gemma reasoning, and text-to-speech on the CPU, while LiteRT CLI covers conversion, quantization, benchmarking, inference, and model execution from the LiteRT Hugging Face Community.
Using the GitHub Copilot SDK for Java
The GitHub Copilot SDK for Java offers a framework-agnostic client library to orchestrate AI agent sessions and tool execution directly from server-side Java code. Unlike framework-dependent alternatives, the SDK supports direct model providers such as OpenAI, Azure, and Anthropic through custom endpoint configurations without requiring a Copilot subscription. Developers can register tools declaratively using the experimental @CopilotTool annotation processor or dynamically through inline lambda definitions with ToolDefinition.from. When integrated into a Jakarta EE 11 application on Open Liberty, agent workflows run on container-managed virtual threads that propagate CDI and transaction contexts during blocking calls like sendAndWait. Real-time event subscriptions capture model execution steps and tool invocations to stream updates over WebSockets without exhausting platform threads.
14 Social Media Holiday to Celebrate This September
The September social media calendar features 14 holidays and events that brands can use to plan content, connect with audiences, and encourage engagement. The list spans World Letter Writing Day, International Day of Charity, Labor Day, International Literacy Day, National Video Games Day, International Equal Pay Day, and International Podcast Day, among others. Each entry pairs its date and hashtag with a suggested angle, such as handwritten letters, employee recognition, literacy infographics, gaming contests, equal-pay advocacy, or sustainable travel destinations. The calendar also includes September 11’s National Day of Service and Remembrance, Hobbit Day, the First Day of Fall, and World Tourism Day, with ideas ranging from commemoration and themed visuals to seasonal recommendations and education. It closes by directing readers to a 2026 social media holiday calendar for further content-planning ideas.
How to Show Your Logo in Apple Mail
Displaying a company logo in Apple Mail can improve inbox recognition, trust, and visual distinctiveness, while cited studies associate logos with higher brand recall, open rates, purchase likelihood, and email confidence. The article presents Apple Branded Mail, a proprietary format shipped in iOS 18.2 in 2024, as a free alternative to inconsistent provider settings and the paid certificates and trademarks sometimes needed for broad BIMI coverage. Setup begins with a DMARC policy of p=quarantine or p=reject, then uses Apple Business Connect to add a brand, upload a PNG, HEIF, or JPEG logo at least 1024 × 1024 pixels, select domains, and verify identity. Verification can take up to seven business days; root-domain branding covers unconfigured subdomains, while brands needing broader client coverage are directed to BIMI.
A beginner’s guide to Uber vouchers for riders
Uber connects riders with drivers through a mobile application, while Uber Vouchers lets transit agencies, universities, and colleges subsidize eligible rides. A voucher added to a rider’s account generally covers trip costs up to a stated amount; any excess is charged to the default payment method, while tips remain optional and are charged separately. The guide explains how to download the app, create an account with matching contact information, link payment, track and verify the arriving vehicle, complete a trip, rate the driver, and access in-app Help. It also covers requesting rides by text-capable phone through 833-USE-UBER (833-873-8237) from 4am to 10pm ET, service-animal access, lost items, and the in-app Safety Toolkit.
Model routing with Google Cloud API Gateway
Google Cloud API Gateway adds model routing in Public Preview for AI applications that need to direct traffic among models without hardcoded endpoints or open-source proxies. The serverless ingress layer accepts OpenAI-compatible requests and uses routing rules in an OpenAPI 3.x specification, where x-google-api-management defines backends and routers map virtual model names to targets such as Gemini, Claude, or OpenAI OSS-GPT. After deployment, clients call a stable endpoint; the Gateway transcodes the request to the backend’s native schema, adds an Agent Platform authentication token, and forwards it, separating client authentication from provider credentials. The guide also notes that every backend in one router must share a host, and shows standalone routing alongside possible Agent Gateway integration for security governance.
How to Run Isolated Tenant Kubernetes Clusters on Shared GPU Infrastructure
Organizations sharing a single Kubernetes cluster across multiple teams frequently encounter conflicting custom resource definitions, overlapping role-based access control, and difficulties budgeting GPU capacity. To address these operational conflicts without provisioning separate hardware, administrators can deploy vCluster alongside KAI Scheduler on a shared host cluster. vCluster supplies each team with a dedicated, virtualized control plane featuring its own API server and permissions, while KAI Scheduler dynamically time-slices GPU resources through hierarchical queues. In a demonstration using an NVIDIA L40S GPU on MicroK8s, three separate tenant clusters successfully scheduled workloads with fractional allocations of 0.33 GPU each. The combination achieves tenant autonomy and visibility isolation while consolidating AI workloads on shared physical infrastructure.
Kimi K3: the complete developer guide
Kimi K3 is presented as Moonshot AI’s 2.8-trillion-parameter open-weights model for long-horizon coding, end-to-end knowledge work, and deep reasoning, served through Together AI’s OpenAI-compatible API. Its architecture combines Kimi Delta Attention (KDA), Attention Residuals, and Stable LatentMoE, which activates 16 of 896 experts per token—roughly 2%—and supports a 1M-token context. The implementation guide shows Python calls for configurable reasoning effort, reasoning-free instant mode, streaming, multi-image vision, strict JSON-schema output, and function tools, while noting automatic prefix caching. It also covers production-oriented details such as generous max_tokens for reasoning-heavy structured output, a 100 MB request-body limit for vision, tool-call history handling, and deployment from US infrastructure.
Using Uber: your guide to Pace's RAP Program
Pace’s Ride Access Program (RAP) is a supplementary service for ADA paratransit riders in its six-county region, using transportation network companies such as Uber to expand transportation options through ride vouchers. RAP riders can use subsidies for up to eight Uber trips daily, pay a $3.25 co-pay, and receive up to $26.75 per trip, while covering costs above the $30 total limit; trips must start and end within the Pace ADA service area. The guide explains enrollment-related account setup, including matching the Uber account’s contact details to the RAP profile, verifying identity, and recognizing the in-app voucher through a strikethrough market price and subsidized initial charge. It also documents a no-smartphone booking option at 833-USE-UBER, support channels, optional tipping, service-animal access, lost-item recovery, and in-app safety tools.
Using Uber: your guide to NJ TRANSIT’s Access Link Riders’ Choice Pilot 2.0
NJ TRANSIT’s Access Link Riders’ Choice (ALRC) Pilot 2.0 gives eligible Access Link customers another option for eligible trips through participating transportation network companies, including Uber. Access Link subsidizes up to 10 Uber trips per month at up to $20 per trip, provided each ride begins and ends within an eligible service area and occurs between 6 a.m. and 9 p.m., seven days a week. After enrollment, riders match their Uber account information to their ALRC profile, accept the voucher from Uber, verify it in the Uber Wallet, and confirm its application before requesting a ride. The guide explains app and phone booking, voucher opt-out, payment, safety checks, support, tipping, service animals, lost items, and incident reporting.
Enable on-demand expertise with Agent Skills in Genkit Go
Agent Skills in Genkit Go address the prompt-context problem caused by loading every procedure, guide, and document into an agent’s persistent context. The tutorial defines skills as discoverable knowledge packs organized around a SKILL.md file, whose frontmatter is exposed first and whose instructions, references, assets, and scripts are loaded through progressive disclosure when needed. It integrates the Skills middleware with Genkit’s wrapper architecture, scans SkillPaths for metadata, matches descriptions, and invokes the use_skill tool before injecting the selected skill into active context; Go examples use ai.WithUse during Generate or GenerateText. Recipe and image-restoration examples show skills activating for matching requests, while the post notes that the restoration skill may require several attempts because model behavior is non-deterministic.
How to Self-Host a Validated AI Coding Assistant with NVIDIA NeMo Guardrails
Deploying AI coding assistants in regulated or sovereign environments presents risks around data privacy, hallucinated dependencies, and auditability. This tutorial outlines a self-hosted architecture deploying StarCoder2-7B as an NVIDIA NIM endpoint served entirely from local GPUs. NVIDIA NeMo Guardrails sits as an OpenAI-compatible proxy between developer IDEs and the model to intercept and reject requests for restricted or human-only code paths. Downstream CI gates catch package hallucinations and verify changes, while commit trailers and a Prometheus and Grafana metrics loop monitor defect escape rates. Because the model remains distinct from the external policy and validation layers, teams can incrementally adopt components or upgrade models without rearchitecting the pipeline.
Configuring Dedicated Model Inference
Dedicated Model Inference is organized around three parts: an endpoint, deployments that run a model revision with replicas, and immutable configs that define how it runs. A deployment binds one model revision to one config and an autoscaling policy, while the endpoint provides a stable API-facing name and traffic split. Routing uses each deployment’s weight multiplied by its ready-replica count, so equal weights produce a 25%/75% split when deployments have one and three ready replicas; unavailable replicas contribute no capacity. The configuration workflow uses certified model-and-config profiles, selectable by accelerator, GPU count, optimization target, and topology, while new config revisions preserve tested rollback targets. This capacity-aware design makes scaling, rollouts, A/B tests, shadow traffic, and zero-downtime changes possible, but raw API or SDK deployments must be added to the traffic split before they receive requests.
Build a Flask App with Okta for Secure OIDC Login and Authorized API Calls
Flask web applications require dedicated mechanisms to authenticate users securely and authorize access to downstream services. Developers can address this by integrating Okta as an OpenID Connect identity provider using the Authlib library. Authlib registers Okta as a remote provider and automates the Authorization Code flow using Proof Key for Code Exchange with SHA-256 code challenge verification. After logging in through Okta and completing the callback route, the Flask web application uses the acquired OAuth 2.0 access token to query a protected backend resource server. This architecture ensures that user credentials remain managed by Okta while client requests to backend API endpoints carry valid, scoped authorization tokens.
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