Do you know what really affects the cost and quality of jewelry? 💎 Two pieces may look almost identical, but differences in materials, stones, polishing, plating, and workmanship can lead to very different prices and quality. So how do we make sure the quality stays consistent when working with different factories? It all starts with the right samples and clear quality standards. 👆 Got more questions about CJ jewelry sourcing? Let us know below! #cjdropshipping# #dropshipping# #ecommerce# #jewelry# #jewelrybusiness# #jewelrysourcing# #productsourcing# #qualitycontrol# #ecommercetips# #onlineseller# #ecommerceseller#

Great blog from Kevin Lu at @vllm_project about quality control at the inference engine. Unfortunately, @AnushElangovan has not provided enough stable AMD QA clusters to vLLM, which leads to an order of magnitude worse software quality on AMD versus CUDA. Multiple AMD CI vLLM fleet-wide outages happen every month.

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# Practical and Useful Patterns for OpenAI Agent SDK 🌍 How you orchestrate multiple agents determines the quality and reliability of your entire system. Master LLM-driven and code-driven patterns to build the right architecture for each use case. 📌 Title: Agent orchestration 🔗 URL: 🧩 Overview Multi-agent orchestration comes in two flavors: LLM-driven (the model dynamically decides routing via handoffs) and code-driven (Python code explicitly controls the flow). Combined with the Manager pattern (`as_tool`) and handoff-based delegation, these patterns let you build anything from open-ended research assistants to deterministic content pipelines. 🛠 Usage For LLM-driven orchestration, define `Agent(name="researcher", handoffs=[web_search_agent, code_exec_agent])` and let the model dynamically choose. For code-driven pipelines, define `researcher`, `outliner`, `writer`, and `critic` agents, then in `blog_pipeline(topic)` call `await input=...)` followed by `await input= + [...])` and `await input=...)` sequentially. The generate-evaluate loop uses `for i in range(3)` to have the `critic` review, breaking if `"no issues"` is found, otherwise sending feedback back to `writer`. For parallel execution, use `asyncio.gather(*[ input=data) for a in agents])` to run sentiment, topic, and summary analyses concurrently. The Manager pattern uses `Agent(name="manager", tools=[ to call child agents as tools while retaining control. 🏗 Practical Patterns **LLM-Driven — Open-Ended Tasks** Equip a research agent with web search, file search, code execution, and specialist handoffs. The LLM dynamically selects the best approach based on context. Ideal for "investigate this technology" type queries where you can't predetermine the flow. **Code-Driven — Deterministic Pipelines** When steps are known upfront (research, outline, write, critique, improve), define the pipeline explicitly in Python. Each step's output can be programmatically validated and branched, giving you much tighter quality control. **Generate-Evaluate Loop** Use a `while`/`for` loop to repeatedly generate and evaluate until quality criteria are met. A critic agent reviews output until it passes — achieving quality that single-shot generation can't match. Always set a maximum iteration count. **Parallel Execution with asyncio.gather** Run independent tasks (sentiment analysis, topic extraction, summarization) concurrently with `asyncio.gather`. Dramatically reduces latency compared to sequential execution. **Manager Pattern (as_tool) vs Handoff** `as_tool` keeps the Manager in control — it calls child agents as tools and gets results back to synthesize. Use this when you need to combine outputs from multiple specialists. Handoffs transfer control entirely — use this when the specialist should handle the rest of the conversation independently. 💡 Use Cases 🔬 Research investigation (LLM-driven with dynamic web/paper/code analysis selection) 📝 Content creation pipeline (code-driven: research, write, review, improve) ⚡ Parallel data analysis (asyncio.gather for sentiment/topic/summary) 👔 Manager pattern (synthesize analysis team results into a unified report) ⚠️ Caveats - LLM-driven patterns are flexible but may produce unnecessary handoffs or tool calls due to model judgment errors. Use code-driven control for critical flows. - Always set a maximum iteration count on generate-evaluate loops. Unbounded loops cause runaway token costs. - When using `asyncio.gather` for parallel execution, consider `return_exceptions=True` so one task's failure doesn't cancel others. - Child agents called via `as_tool` return results as text to the Manager. Specify `output_type` if you need structured data. ✨ Combine LLM-driven and code-driven orchestration to build the optimal multi-agent architecture for each use case! #OpenAIAgentSDK# #AIAgent#

I think a downstream consequence of increased commitment to safety and alignment is that RL envs companies get screwed, or at least held to a much higher standard and thus their unit economics changes. The evidence is mixed on this early but it seems likely that impossible to poor quality envs lead to misalignment and poor behaviour in the models (as they try to do anything to get reward). So why would the labs risk this quality control with anything but big in-house efforts moving forward

# Learning Palantir Foundry 🚀 Bring complex logic that no-code can't reach into your data platform, along with full software-engineering quality control. That's what Code Repositories delivers. 📌 Title and Feature URL Title: Code Repositories (Python Transforms) URL: 📝 Overview Code Repositories is a web-based integrated development environment (IDE) for creating and collaborating on production-ready code within Foundry. It provides a friendly UI over the underlying Git repositories, so teams can work without command-line access. With platform-specific features, you can apply software development practices directly to data engineering. 🔧 How It Works Version control and collaboration are at its core. - Common Git tasks (branching, committing, release tagging) execute through the web UI - Pull requests drive code review, with "highly configurable" permissions that support quality assurance such as mandatory reviews - IntelliSense, linting, error checking, and contextual help dialogs are available across all repository types - Transforms repositories let you author data transformation logic in Python, Java, or SQL with preview and debugging - Functions repositories natively integrate the Ontology and run low-latency business logic in TypeScript or Python 🛠 Practical Usage - Use PySpark to implement billion-row entity resolution and complex business rules in code - Require PR reviews so a second reviewer and CI checks must pass before merge - Add unit tests to guard transform logic against regressions - In Functions repositories, leverage Ontology-data-type autocomplete to write logic safely - Bring machine learning workflows into the platform via model development repositories 🎯 Use Cases - Implementing complex reconciliation and business rules in PySpark that Pipeline Builder can't express - Structurally eliminating "regressions from editing production directly" through mandatory reviews and branch-based workflows - Implementing derived KPIs and validation logic as Functions reused across apps - Managing ML model training and inference code under governance ⚠️ Caveats - The docs note that Japanese translations are machine-generated and unverified, so localized content may have accuracy limitations - Each repository type (Transforms/Functions/Model) supports different languages and purposes, so pick the one that fits your goal - Being a pro-code environment, the quality benefits only materialize if your organization establishes review, CI, and test practices #PalantirFoundry# #DataEngineering#

Just Salad founder and CEO Nick Kenner details how the fast-casual chain is handling food prep amid the ongoing cyclospora outbreak. "We are crazy about quality control," Kenner tells Yahoo Finance.

🏴󠁧󠁢󠁥󠁮󠁧󠁿 England 🆚 🇭🇷 Croatia Will England’s attacking talent and depth prove too much, or can Croatia’s experience and midfield quality control the game and deliver a statement win? #England# #Croatia# #ENGvCRO#