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Grass management tools supplier
Hi Info,
This is Lee from China.
Our company is a turf care machine factory from China.
And we have over 20 years experience in the turf care area.
Over 10 years experience doing aftersale service for TORO, John Deere, Turfco, etc in China.
And then start to produce turf care machines over 10 years.
Now, we produce
Verti-Drain,
Turf Brush,
Verti-Cutter,
Turf Stripper,
Top Dresser,
Turf Harvester,
Sod Cutter,
Turf Tractor,
etc.
I write this e-mail to you to look for the opportunity to work with you in the future.
Best regards!
by "Suore Stedile" <suorestedile596@gmail.com> - 07:17 - 1 Jul 2025 -
High - Quality Woodworking Tools for Your Furniture Manufacturing Needs
Dear Sales02,I hope this email finds you well. I'm Xavier from Koocut Cutting Technology (Sichuan) Co., Ltd., a leading factory specializing in precision cutting tools with years of in - depth experience and advanced technology in the industry.We've noticed your remarkable achievements in the furniture manufacturing field. Our woodworking product series, such as TCT woodworking circular saw blades, PCD woodworking circular saw blades, and CNC woodworking routing cutters, are highly compatible with your business requirements. These products are made from top - notch raw materials sourced from renowned international brands like ThyssenKrupp steel plates from Germany and CERATIZIT carbide from Luxembourg.Our intelligent factory, built in line with China's Industry 4.0 standards, is equipped with advanced German production equipment and an intelligent manufacturing system. This enables us to provide customized services, meeting your unique needs precisely. Moreover, our efficient production process ensures reliable delivery times.Our products have been successfully sold to 126 countries and regions worldwide, and we've established long - term partnerships with numerous well - known enterprises. We believe our products and services can significantly enhance the efficiency and quality of your production.I'm looking forward to discussing the cooperation details with you. If you have any questions, please feel free to contact me.Best regards,Contact Information:
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Email: xavier@koocut.comKoocut Cutting Technology (Sichuan) Co., Ltd.
by "sales02" <sales02@koocut-tools.com> - 06:44 - 1 Jul 2025 -
How Spotify Uses GenAI and ML to Annotate a Hundred Million Tracks
How Spotify Uses GenAI and ML to Annotate a Hundred Million Tracks
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Disclaimer: The details in this post have been derived from the articles shared online by the Spotify Engineering Team. All credit for the technical details goes to the Spotify Engineering Team. The links to the original articles and sources are present in the references section at the end of the post. We’ve attempted to analyze the details and provide our input about them. If you find any inaccuracies or omissions, please leave a comment, and we will do our best to fix them.
Spotify applies machine learning across its catalog to support key features.
One set of models assigns tracks and albums to the correct artist pages, handling cases where metadata is missing, inconsistent, or duplicated. Another set analyzes podcasts to detect platform policy violations. These models review audio, video, and metadata to flag restricted content before it reaches listeners.
All of these activities depend on large volumes of high-quality annotations. These annotations act as the ground truth for model training and evaluation. Without them, model accuracy drops, feedback loops fail, and feature development slows down. As the number of use cases increased, the existing annotation workflows at Spotify became a bottleneck. Each team built isolated tools, managed their reviewers, and shipped data through manual processes that didn’t scale or integrate with machine learning pipelines.
The problem was structural. Annotation was treated as an isolated task instead of a core part of the machine learning workflow. There was no shared tooling, no centralized workforce model, and no infrastructure to automate annotation at scale.
This article explains how Spotify addressed these challenges by building an annotation platform designed to scale with its machine learning needs. It covers:
How was human expertise organized into a structured and scalable workflow?
What tools were built to support complex annotation tasks across different data types?
How was the infrastructure designed to integrate annotation directly into ML pipelines?
The trade-offs involved in balancing quality, cost, and speed.
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Moving from Manual Workflow to Scalable Annotation
The starting point was a straightforward machine learning (ML) classification task. The team needed annotations to evaluate model predictions and improve training quality, so they built a minimal pipeline to collect them.
They began by sampling model outputs and serving them to human annotators through simple scripts. Each annotation was reviewed, captured, and passed back into the system. The annotated data was then integrated directly into model training and evaluation workflows. There was no full-fledged platform yet, but just a focused attempt to connect annotations to something real and measurable.
Even with this basic setup, the results were significant:
The annotation corpus grew by a factor of ten.
Annotator throughput tripled compared to previous manual efforts.
This early success wasn’t just about volume. It showed that when annotation is directly tied into the model lifecycle, feedback loops become more useful and productivity improves. The outcome was enough to justify further investment.
From here, the focus shifted from running isolated tasks to building a dedicated platform that could generalize the workflow and support many ML use cases in parallel.
Platform Architecture
The overall platform architecture consists of three pillars. See the diagram below for reference:
Let’s look at each pillar in more detail.
1 - Scaling Human Expertise
To support large-scale annotation work, Spotify first focused on organizing its human annotation resources. Instead of treating annotators as a generic pool, the team defined clear roles with distinct responsibilities and escalation paths.
The annotation workforce was structured into three levels:
Core annotators handled the initial pass across most annotation cases. These were domain experts trained to apply consistent standards across large datasets.
Quality analysts acted as the escalation layer for complex or ambiguous cases. These were top-level reviewers who ensured that edge cases were resolved with precision, especially when annotations involved subjectivity or context-specific judgment.
Project managers worked across teams to coordinate annotation efforts. They connected ML engineers and product stakeholders with the annotation workforce, maintained training materials, gathered feedback, and adjusted data collection strategies as projects evolved.
In parallel with the human effort, Spotify also developed a configurable system powered by large language models.
This system operates in conjunction with human annotators and is designed to generate high-quality labels for cases that follow predictable patterns. It is not a full replacement but a complement that handles clear-cut examples, allowing humans to focus on harder problems.
See the diagram below:
This hybrid model significantly increased annotation throughput. By assigning the right cases to the right annotator (human or machine), Spotify was able to expand its dataset coverage at a lower cost and with higher consistency.
2 - Building Annotation Tooling for Complex Tasks
As annotation needs grew beyond simple classification, Spotify expanded its tooling to support a wide range of complex, multimodal tasks.
Early projects focused on basic question-answer formats, but new use cases required more flexible and interactive workflows. These included:
Annotating specific segments in audio and video streams.
Handling natural language processing tasks involving context-sensitive labeling.
Supporting multi-label classification where multiple attributes must be tagged per item.
To support these varied requirements, the team invested in several core areas of tooling:
Custom interfaces were built to allow fast setup of new annotation tasks. This made it possible to launch new projects without writing custom code for each use case.
Back-office systems managed user access, assigned annotation tasks across multiple experts, and tracked the status of each project. This was critical for scaling to dozens of projects running simultaneously.
Project dashboards were introduced to give teams real-time visibility into key metrics such as:
Completion rates for each task.
Total annotation volume.
Per-annotator productivity.
See the diagram below for annotating tooling capabilities:
In cases where annotation tasks involved subjective interpretation or fine-grained distinctions, such as identifying background music layered into a podcast, different experts could produce conflicting results. To handle this, the system computed an agreement score across annotators. Items with low agreement were automatically escalated to the quality analysts for resolution.
This structure allowed multiple annotation projects to run in parallel with consistent oversight, predictable output quality, and tight feedback loops between engineers, annotators, and reviewers. It turned what was once a manual process into a managed and observable workflow.
3 - Foundational Infrastructure and Integration
To support annotation at Spotify scale, the platform infrastructure was designed to be flexible and tool-agnostic. No single tool can serve all annotation needs, so the team focused on building the right abstractions. These abstractions make it possible to integrate a variety of tools depending on the task, while keeping the core system consistent and maintainable.
The foundation includes:
Generic APIs and data models that support multiple types of annotation tools. This allows teams to choose the right interface for each task without being locked into a specific implementation.
Interoperable interfaces that let engineers swap tools, layer them together, or use specialized tools for audio, video, text, or metadata tasks without rewriting pipelines.
See the diagram below:
Integration was built across two levels of ML development:
For early-stage and experimental work, lightweight command-line tools and simple UIs were created. These allow teams to run ad hoc annotation projects with minimal overhead.
For production-grade workflows, the platform connects directly to Spotify’s internal batch orchestration systems and workflow infrastructure. This supports large-scale, long-running annotation jobs that need reliability, tracking, and throughput guarantees.
The result is a system that supports both fast iteration in research environments and stable operation in production pipelines.
Engineers can move between these modes without changing how they define tasks or access results. The infrastructure sits behind the tooling, but it is what allows the annotation platform to scale efficiently across diverse use cases.
Impact on Annotation Velocity
The shift from manual, fragmented workflows to a unified annotation platform resulted in a sharp increase in annotation throughput.
Internal metrics showed a sustained acceleration in annotation volume over time, driven by both improved tooling and more efficient workforce coordination. See the figure below that shows the rate of annotations over time.
Source: Spotify Engineering Blog This increase in velocity directly reduced the time required to develop and iterate on machine learning models. Teams were able to move faster across several dimensions:
Training cycles became shorter, as labeled data could be collected, reviewed, and integrated into models with fewer handoffs and delays.
Ground-truth quality improved, thanks to structured escalation paths, agreement scoring, and the ability to resolve edge cases consistently.
GenAI experimentation became more efficient since the platform supported high-volume annotation needs with less setup overhead and more reliable output.
As a result, ML teams could test hypotheses, refine models, and ship features faster. The annotation platform became a core enabler for iterative, data-driven development at scale.
Conclusion
Spotify’s annotation platform is built on a clear principle: scaling machine learning requires more than just more data or larger models.
It depends on structured, high-quality annotations delivered through systems that are efficient, adaptable, and integrated into the full model development lifecycle. Relying entirely on human labor can create bottlenecks. On the other hand, full automation without oversight can lead to quality drift. Real leverage comes from combining both, with humans providing context and judgment and automation handling volume and repeatability.
By moving from isolated workflows to a unified platform, Spotify turned annotation into a shared capability rather than a one-time cost. The implementation of standardized roles, modular tools, and consistent infrastructure allowed ML teams to build and iterate faster without rebuilding pipelines from scratch.
This approach supports fast experimentation and scaling across a wide range of use cases.
References:
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by "ByteByteGo" <bytebytego@substack.com> - 11:36 - 1 Jul 2025 -
quartz tube factory
Hello Info
We supply quartz tubes/rod/plate/flange with high quality and low price, with more than 10 years manufacturing experience!
Any questions, contact me freely.
Thank you!
Best regards
by "Zimmerle Steagall" <zimmerlesteagall749@gmail.com> - 05:18 - 1 Jul 2025 -
Excelling as a leader: How to evolve
Email 5 of 7
How to evolve
In the previous session, we looked at the potential impact of gen AI and how leaders can leverage the technology to outcompete in an era of perpetual change. In this fifth email, we’ll explore how to evolve as a leader—from aspirations through actualization to passing the torch. Here’s where to start.
Hitting your marks is enough to succeed as an average leader. But if you’re reading this, we’re guessing you don’t want to be just an average leader. Excellent leadership—the kind that takes you to a C-suite office—takes hard work, perseverance, and the kind of difficult self-reflection that leads to personal growth.
“Yes, CEO is an extraordinary role with a lot of responsibility,” says McKinsey senior partner Carolyn Dewar. “But it’s a role that you can learn, and it’s a role that you can experience and live into as opposed to just being a secret club.”
The best leaders know that learning leads to evolution, at all stages of the leadership journey. Here are five steps to get started.This email contains information about McKinsey’s research, insights, services, or events. By opening our emails or clicking on links, you agree to our use of cookies and web tracking technology. For more information on how we use and protect your information, please review our privacy policy.
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by "McKinsey Publishing Guides" <publishing@email.mckinsey.com> - 03:09 - 1 Jul 2025 -
High - Quality Hydraulic Products to Enhance Your Machinery Performance
Dear Info,
I hope this email finds you well.
Hy-waloil are a R&D professional hydraulic products supplier dedicated to providing high - quality hydraulic solutions. We understand that in your industry, the performance and reliability of machinery are crucial, and hydraulic components play a vital role in this regard.
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If you are interested in our products, please feel free to contact us. We are looking forward to establish a long - term business partnership.Best regards,
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Whatsapp/Wechat: +8618012085789
Email: shining@hywal.cn
by "Halama Pedeare" <halamapedeare@gmail.com> - 01:34 - 1 Jul 2025 -
How are GLP-1s fueling demand for medical aesthetics?
On McKinsey Perspectives
Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:24 - 1 Jul 2025 -
Give your production line a new lease of life: Discover our pressing technology!
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by "HyPresssales07" <HyPresssales07@hydraulcpress.com> - 02:57 - 30 Jun 2025 -
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by "William Ethan" <william.globalestimations@gmail.com> - 10:07 - 30 Jun 2025 -
Broadband Performance Iteration of Steel Wire Rope Vibration Isolators
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by "iris_2" <iris_2@hopriocn.com> - 06:39 - 30 Jun 2025 -
Quality Engine Parts That Drive Success
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Looking forward to collaborating with you!
Warm regards,
Yu Qiuhua
Xinnongkai Auto Part
by "sale20" <sales20@aufeary.com> - 04:46 - 30 Jun 2025 -
Helping employees get healthier: A leader’s guide
Leading Off
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Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of Leading Off. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
Making investments in employee health can be critical to boosting an organization’s productivity and success. McKinsey research shows that improving employee health and well-being could generate $3.7 trillion to $11.7 trillion in global economic value. As many workers continue to struggle with stress and burnout, it’s important for leaders to take a holistic view of employee health that encompasses physical, mental, social, and spiritual well-being. This week, we look at the current state of workforce health and how organizations can help employees thrive.
Today’s workers need help to get healthier. Among 30,000 global employees surveyed by the McKinsey Health Institute (MHI), only 57 percent reported good holistic health, while another 20 percent reported burnout symptoms. With health outcomes varying across industries and demographics, McKinsey’s Barbara Jeffery, Brooke Weddle, Jacqueline Brassey, and Shail Thaker say leaders will need to tailor their efforts to address the health challenges among their people and the workplace factors that contribute to them. They identify six steps for organizations to get started. These include understanding employees’ baseline health status, piloting evidence-based interventions, and tracking three to five metrics to measure success. “Today, many organizations do not see or cannot measure the benefits of their current investments in employee health,” the authors say. “They also don’t allocate resources in the most effective way—often, the issue is not how much is being invested but the type of investment being made.”
That’s up to how much global economic value can be realized by 2050 if the public, private, and social sectors invest in scaling mental health interventions, according to an MHI study. McKinsey Partners Erica Coe and Kana Enomoto and their coauthors note that globally, mental health conditions receive 2 percent of domestic government healthcare funding, which creates an annual funding gap of $200 billion to $350 billion. The study indicates that mental health conditions cause more years of poor health around the world than the combined impact of the four leading non-mental-health diseases: cardiovascular diseases, chronic respiratory diseases, diabetes and kidney diseases, and cancers.
That’s McKinsey Senior Partners Kristi Weaver and Stefan Rickert and their coauthors on how millennials’ and Gen Z’s preferences are reshaping the global wellness market. A survey of more than 9,000 consumers reveals that younger people are increasingly prioritizing wellness—and, compared with older generations, feel more strongly that the current market for wellness goods and services doesn’t fully suit their needs. The authors note that younger consumers report higher levels of stress, which may explain why they feel more dissatisfied with existing mental health solutions.
Investing in employee well-being isn’t just about reducing absenteeism related to poor health. Senior Partner and MHI Global Leader Lucy Pérez stresses that it’s just as important to avoid “presenteeism”—that is, when employees show up to work but are not at their full potential. “You have days when you just can’t give it your all. It may be for physical reasons, but increasingly, it can also be for reasons related to your mental health, your spiritual health, your social functioning,” she shares with McKinsey’s Brooke Weddle and Bryan Hancock in an episode of McKinsey Talks Talent. “It’s important to think holistically about what enables an employee to be at 100 percent full health.” Leaders can do so by ensuring that managers create a psychologically safe work environment for everyone and give employees clarity on how decisions are made. “We don’t necessarily have to throw lots of money at it; it is embedded in how we build the capabilities of our organizations,” Pérez says.
Since late 2023, demand has soared for weight loss drugs such as Ozempic, Wegovy, and other brands, putting some companies and employees at odds over insurance coverage for these medications. Yet the popularity of these GLP-1 drugs could lead to broader and vital improvements in metabolic health, according to Senior Partners and MHI Global Leaders Drew Ungerman and Hemant Ahlawat and their coauthors. “The rise of GLP-1s is shifting the discussion of obesity from an intractable issue to a treatable medical condition—a change that is inspiring investment and innovation across industries, including healthcare, medtech, food, and retail,” they say. Rather than reacting to obesity with weight management drugs and medical treatments, the authors observe that society can pursue greater prevention efforts such as advancing scientific understanding of metabolic health and making healthy food more accessible. They estimate that such efforts could lead to an estimated annual GDP gain of $5.65 trillion in 2050.
Lead by enhancing employee health.
— Edited by Eric Quiñones, senior editor, New Jersey
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by "McKinsey Leading Off" <publishing@email.mckinsey.com> - 04:44 - 30 Jun 2025 -
Managing yourself: How to build a team
Email 5 of 7
How to build a team
In the previous session, we uncovered practical ways you can better manage your time. In this email, we’ll dive into what it takes to build a winning team.
You can hire a group of highly skilled individuals, but if they don’t operate as a cohesive team, will they drive success? Effective teams develop better strategies, perform more consistently, and increase the confidence of stakeholders. But a team can’t be effective in a culture that lacks collaboration, trust, and inclusion—and that starts at the top.
To build your dream team, check out these four key actions, and improve microculture, foster integration, and adopt a “servant leader” mentality, which enhances both team performance and satisfaction, says director of research and economics Tera Allas.This email contains information about McKinsey’s research, insights, services, or events. By opening our emails or clicking on links, you agree to our use of cookies and web tracking technology. For more information on how we use and protect your information, please review our privacy policy.
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by "McKinsey Publishing Guides" <publishing@email.mckinsey.com> - 03:13 - 30 Jun 2025 -
High-Performance UAVs at Competitive Pricing - Seeking Global Partners
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by "Henry" <Henry@zhongkeuav.com> - 02:24 - 30 Jun 2025 -
Harnessing multimodal AI’s power
On McKinsey Perspectives
Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
•
Addressing risks. Multimodal AI faces the same risks and limitations, such as bias and data privacy concerns, as other gen AI applications. To mitigate these risks, companies can keep a human in the loop for more sensitive tasks. McKinsey Senior Partners Ida Kristensen and Lareina Yee and their coauthors suggest that organizations can develop a plan for implementing AI quickly and safely. Explore the McKinsey Explainer “What is multimodal AI?” to learn how organizations can use the technology.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:27 - 30 Jun 2025
