Search Results for "shipping"

Covalent is a Pythonic workflow tool for computational scientists, AI/ML software engineers, and anyone who needs to run experiments on limited or expensive computing resources including quantum computers, HPC clusters, GPU arrays, and cloud services. Covalent enables a researcher to run computation tasks on an advanced hardware platform – such as a quantum computer or serverless HPC cluster – using a single line of code. Covalent overcomes computational and operational challenges inherent...

...The tool analyzes an image to infer the intended grid size, then refines and quantizes the artwork so pixels snap into consistent cells and the final result looks crisp and intentional. This makes it useful for game developers, sprite artists, and hobbyists who want to use AI-assisted ideation without shipping “almost pixel art” assets. It is designed to be easy to slot into an existing pipeline, whether you are batch-processing images or cleaning up a few key assets for a project.

Anthropic’s courses repository is a growing collection of self-paced learning materials that teach practical AI skills using Claude and the Anthropic API. It’s organized as a sequence of hands-on courses—starting with API fundamentals and prompt engineering—so learners build capability step by step rather than in isolation. Each course mixes short readings with runnable notebooks and exercises, guiding you through concepts like model parameters, streaming, multimodal prompts, structured...

...It is free software (both as in free beer and as in free speech), open to suggestions and external contributions. The project aims at providing people with an easily setup, easily maintained and full-featured mail server while not shipping proprietary software nor unrelated features often found in popular groupware. Security, enforced TLS, DANE, MTA-STS, Letsencrypt!, outgoing DKIM, anti-virus scanner, Snuffleupagus, block malicious attachments. Antispam, auto-learn, greylisting, DMARC and SPF, anti-spoofing. Freedom, all FOSS components, no tracker included.

...The platform supports a range of large language models and integrates seamlessly with different databases, vector stores, and APIs. Phidata offers pre-configured templates to accelerate development and deployment, allowing users to quickly go from building agents to shipping them into production. It includes features like real-time monitoring, agent evaluations, and performance optimization tools, ensuring the reliability and scalability of AI solutions. Phidata also allows developers to bring their own cloud infrastructure, offering flexibility for custom setups. The platform provides robust support for enterprises, including security features, agent guardrails, and automated DevOps for smoother deployment processes.

...A single command builds your app, bundles dependencies, generates infrastructure templates, and deploys to your account with sensible defaults. The framework includes local development tools so you can iterate on HTTP routes and inspect logs before shipping to the cloud. It also manages per-stage configuration, environment variables, IAM permissions scaffolding, and packaging optimizations to keep cold starts small. While it’s intentionally lightweight, it supports common production needs like CORS, custom authorizers, binary responses, and fine-grained IAM so you can grow from prototype to production without rewriting your stack.

...The library abstracts the raw SOAP or REST endpoints exposed by Correios, providing Pythonic methods to perform common tasks like tracking a package by its code or computing shipping cost/lead time between postal codes. It handles serialization and mapping of API responses into Python objects so developers don’t manually parse raw XML or JSON. With this tool, developers building Brazilian market e-commerce or logistics solutions can integrate postal services smoothly. Because it is open source, improvements can be contributed to support new endpoints, changes in the postal service API, or additional features like caching or async requests.

...Scaffolding a project takes seconds and it gives you authentication, user accounts, and the docs and tests to support them. Just add your own resources to the api and start shipping. Start off with full test coverage, continuous integration, and continuous deployment. Complete Django Rest Framework integration. Auto deployment to Heroku included. Also since we’re using containers we can easily deploy anywhere. Always current dependencies and security updates enforced by pyup.io. A slim but robust foundation – just enough to maximize your productivity, nothing more. ...

A simple cursor-based step by step install program enveloping the basic Archlinux installation process. It is used with Archlinux installation ISO image, in x86_64 live system only. The asif-pkg.tar shipping with packages does not require a network connection. The asif.tar needs a network connection, and please remember to configure your /etc/pacman.d/mirrorlist. f2fs and MVNe support is added. I will update the UI to make it selectable later. Sorry I got no spare time for now. 0.53 update packages for 2017.08.01 Previous updates: Boot-loader on GTP-UEFI. ...

Stop shipping unsafe AI output. Local-first verification, safety monitoring, and guardrails for any LLM 100% Local | No Telemetry | Privacy-First | 500 free calls/day Links Product Page: subodhkc.com/products/llmverify npm: npmjs.com/package/llmverify GitHub: github.com/subodhkc/llmverify-npm Python Package (PyPI): github.com/subodhkc/llmverify Author: Subodh KC — AI governance, compliance, and security leader Feature One-liner What it catches Verify output await verify(aiResponse) Hallucinations, harmful content, quality issues Block injections isInputSafe(userMessage) Prompt injection, jailbreaks, malicious input Redact PII redactPII(text) Emails, phones, SSNs, credit cards, API keys Fix broken JSON repairJSON(broken) Missing brackets, trailing commas, unquoted keys Monitor health monitorLLM(client) Latency spikes, token drift, behavioral changes Sentinel tests sentinel.quick(client, model) LLM regressions b