Salesforce introduces Slackforce Surfaces for AI-built dashboards, reports, and interactive work inside Slack

High-Frequency AI Prompts for Work, Learning, and Teaching guide

High-Frequency AI Prompts for Work, Learning, and Teaching

Practical AI Side Project Course guide

Practical AI Side Project Course

Skild AI S1 uses NVIDIA physical-AI infrastructure to learn unfamiliar long-horizon robot tasks from one video

NVIDIA described Skild AI's S1 robot foundation model on September 10. An operator records a task video and uses it as a prompt; S1 interprets the demonstrated intent, objects, and sequence, then maps them into actions for the robot without updating model weights or performing task-specific post-training. NVIDIA says S1 can handle unfamiliar tasks lasting up to ten minutes, including plant potting, pancake making, pour-over coffee, and kit assembly. In tests reported by Skild, the model succeeded at about 66 percent of steps on new multistep tasks versus 9 percent for a similar system. The companies use NVIDIA infrastructure for synthetic data, training, simulation, reinforcement learning, and deployment. These are vendor-reported demonstrations and tests, not evidence that every factory can operate without supervision; deployment still requires staged safety, reliability, and recovery validation.

A production release gate for Structured Outputs: validate the schema, refusals, incomplete responses, and semantics

Structured Outputs lets compatible models return data that adheres to a supported JSON Schema, providing a stronger contract than JSON mode, which guarantees valid JSON but not schema adherence. OpenAI documents two forms: function calling for connecting a model to application tools, and a structured response format for shaping user-facing output. In strict schemas, object fields must be listed as required and objects must set additionalProperties to false; an optional value can be represented with a union that includes null. Schema conformance covers the shape of a successful response, not factual accuracy, business rules, or a completed request. A production release gate should pin the model and schema version, test normal output, refusals, output-token truncation, content-filter incompleteness, and unsupported schema errors, then run independent semantic validation before any write or external action.

Inside OpenAI Habitat: scaling online storage past 70M requests per second and migrating from Python to Rust

OpenAI published an engineering account of Habitat on September 11. The company says its online storage platform now handles more than 70 million requests per second, supports products used by over one billion people each week across almost 40 regions, and serves more than 500 petabytes of data. Habitat began as a Python client library for GPTs in 2023, then became a standalone service so deployments, observability, access controls, audit logging, and platform improvements could be managed centrally. In the second quarter of 2026, two engineers used Codex and GPT-5.5 to rewrite the service in Rust. OpenAI reports that the Rust service now handles 95% of production requests and is six times more CPU-efficient and fifteen times more memory-efficient than the Python version. These are internal production measurements, so other teams should adopt the migration discipline rather than assume identical gains.

AWS benchmarks OpenAI models by cost per correct outcome instead of token price alone

AWS published an open-source benchmarking approach on September 11 for comparing GPT-5.6 Luna, Terra, and Sol on Amazon Bedrock with GPT-5.4 mini and nano on the OpenAI API. Instead of ranking models by dollars per million tokens, the harness measures cost per correct answer, cost per passing multi-turn research result, and cost per rubric-accepted professional deliverable. It uses one Responses API client and records timestamped result files, frozen grading prompts, and chart inputs. The post also documents important limits: sample sizes range from 48 to 198 items, Bedrock models ran with reasoning disabled, and API baselines used their defaults. AWS describes this as a practical configuration comparison rather than an estimate of intrinsic capability. Teams should rerun the method on 50 to 100 of their own tasks and include failures, retries, turns, latency, review, and rework in the decision.

Three Google-supported Android XR projects premiere at Venice with Gemini conversations and spatial filmmaking

Google highlighted three projects supported by its 100 ZEROS filmmaking initiative on September 11. The projects premiered during the 83rd Venice International Film Festival and are designed for Android XR. NEVATARS, a mixed-reality animated short directed by Andy Serkis, includes Gemini-powered conversations and interactive moments. Galápagos: The Last Eden is an immersive journey narrated by Margot Robbie that lets viewers interact with a Gemini-powered digital Charles Darwin. Sedona, a psychological-thriller teaser from Asylm Studios, uses Google's 2D-to-3D XR auto-spatialization technology for a YouTube experience. Google says the broader lineup uses conversational AI powered by Gemini and Vertex AI, spatial film, and stereoscopic depth while keeping human creativity at the center. The announcement presents creative experiments rather than evidence that AI tools replace writers, directors, performers, or audience testing.

NVIDIA and Palantir turn supply-chain allocation expertise into a governed Nemotron training loop

NVIDIA published a supply-chain case study with Palantir Foundry on September 10. The workflow combines a governed Ontology, cuOpt optimization, planner decisions and rationales, point-in-time backtesting, and post-training of Nemotron 3.5 Lightning for material allocation recommendations. NVIDIA reports that its post-trained 30B model reached 86.7% allocation-decision accuracy on the development benchmark, compared with 55.5% for Nemotron 3 Ultra and 17.5% for the base Lightning model. The company also says a human planner reviews recommendations and makes the final call, while accepted, edited, and overridden outcomes feed future governed retraining. This is an official case study and development benchmark for a bounded allocation task. It does not establish broader general intelligence or general superiority for the 30B model beyond the specialized data, task, and evaluation design.

Slackforce Surfaces: AI-Built Dashboards, Decks & Reports in Slack