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The Sperm Donation Is Free, but There’s a Catch

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An expensive and convoluted health-care system has led some couples to look for unregulated genetic material on Facebook.

Tonya Russell

Sperm swimming toward a Facebook logo

Adam Maida / The Atlantic

Madison Hess and her partner are looking for sperm donors and hoping that the third time’s a charm. Hess has already tried clinical insemination twice with sperm from a cryobank, a method that her private insurance fully covered. On both occasions, she didn’t get pregnant. “Since neither time worked, and because I want to cut costs, I’m trying an at-home method,” she told me via email. By this, Hess means that she’s using Facebook groups with names such as Sperm Donation USA that are dedicated to matching hopeful mothers with individuals willing to donate their genetic material for free or next to nothing.

These informal pages purport to cut out the fees associated with pricey fertility centers. But the pages have also provided a way for sperm recipients and donors to circumvent the medical and ethical standards established by licensed clinics, if they wish.

A typical sperm seeker starts by posting their photo, usually with their partner, as well as a brief biography, their location, and their preferred insemination methods. Whether they are gainfully employed—so that donors know they can likely afford a child—is also considered pertinent. Donors, for their part, usually post their own baby photo or one of their biological children. Like online dating, the matchmaking kicks off with a direct message from either party expressing interest, before an offline get-to-know-you.

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Couples seeking donors may also post a list of preferences, such as eye color, height (most want a donor taller than 5 foot 10), and education level. In many cases, the physical preferences are meant to match a partner who is not contributing DNA. In others, couples’ desires are simply aesthetic and can cause arguments in the comment section: for instance, a single white woman asking for a Black donor, or a darker-skinned Black couple asking for a white or biracial donor.

Nicole Bergen, a researcher from the University of Ottawa, in Canada, has studied men who donate via these groups. She told me over email that no two donors are the same. “Some men were selective about who they would donate to; others would give to most anyone,” she said. “We heard a range of rendezvous stories, from meeting at a medical office after hours, to sending sperm via courier.”

These groups fill a gap in a convoluted health-care system, Bergen told me. “I think these groups arose from discontent with institutionalized fertility options, which have become increasingly expensive and are regulated to an extent that deters some people,” she said. “Given the growing mistrust of the health system and societal divisiveness that has occurred since the COVID-19 pandemic, I would guess that the demand for these groups will not dissipate soon.”

The birth rate in the United States was already declining, but the pandemic has brought its own baby bust. In part, that’s because the cost of raising babies in America is exorbitant—a 2017 report found that after birth, middle-income married parents with two children would typically spend $12,350 to $13,900 on each child annually, mostly on housing, food, and child care or education. For LGBTQ partners and couples with fertility issues, the costs of even getting pregnant are high. Although health insurance eases some of the burdens for people who have it, only 15 states require insurers to cover fertility testing and treatment, according to the National Conference of State Legislatures. A 2020 Kaiser Family Foundation report found that just eight state Medicaid programs specified that they covered diagnostic testing. Only New York’s Medicaid program covers ovulation medications; the program doesn’t include artificial insemination.

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As a result, families with fertility issues sometimes face a heart-wrenching decision: Give up on having a baby or patch together finances, perhaps through crowdfunding on GoFundMe, or grants and scholarships specifically designed to cover these costs.

For couples who choose to pay out of pocket for fertility treatments, the sperm vials alone could cost about $1,000. The cost can increase by thousands of dollars when factoring in doctor visits, medication to help produce an egg, testing, and supplies. This is for one attempt to conceive using basic treatments. For a more extensive procedure, such as in vitro fertilization, the expenses are higher: One cycle can cost $12,000 to $17,000.

For women who believe they have a healthy uterus and ovaries and who perhaps lack health insurance, a Facebook group may seem like a sound alternative. Magean Garay, 26, and her wife have found clinical costs disheartening. “We wanted to start our family, and the processes of insemination and adoption are so long, costly, cold, and harsh,” Garay told me. “Facebook has been a way to get what we need in a safe way to … create our family.”

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Yet a free sperm donor comes with biological risks. For example, at most fertility centers, donors undergo genetic screenings for disorders such as cystic fibrosis, which a baby has a 25 percent chance of developing if both parents are carriers, Meghan Smith, a doctor at Nashville Fertility Center, told me.

Smith also explained that the odds of conception are better through clinics, because they offer monitoring, testing, and sperm analysis, tools that can help pinpoint when someone is ovulating and whether a donor or would-be father has sperm-motility issues. Clinics also help negotiate refunds or replacements for nonviable donor sperm.

In the Facebook groups, members trade advice: Donors and recipients answer questions about ovulation tests and what day to inseminate, and even recommend their favorite kit, syringe, or cup. The advice may not be medically vetted—if someone is having a difficult time conceiving, members might recommend Mucinex to increase their odds. (According to Smith, some people believe that because the medicine thins secretions, it will thin cervical mucus. “There isn’t high-quality data to suggest that this improves pregnancy rates at all,” she said. “There is little harm in taking it when trying, although I would not rely on it as a method to treat infertility.”)

But the groups seem to run on an honor system. Members might have to take one another at their word when it comes to STD testing and psychological evaluations, common steps at clinics. Yet people have posted warnings claiming that some donors have faked their STD-test results. The group administrators provide guidelines, but enforcing hard rules can be impossible. One group, USA Sperm Donation, encourages donors and recipients to make sure both parties are 18 or older, and to make sure that a third party knows they’ll be meeting up with a stranger to inseminate. Kyle Gordy, who runs the separate Sperm Donation USA, says that although the group cannot directly enforce guidelines, the community is largely self-regulated: If a donor doesn’t have reliable genetic and STD testing, for instance, they’ll have trouble matching with a recipient. “You don’t need to enforce anything, because it’s already happening on its own,” he says.

There’s another elephant in the room: What would compel someone to want to donate their sperm for free or at very little cost, when cryobanks compensate donors?

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Some group members say that certain men will donate only if it’s through intercourse. “I’ve had encounters where people tell me, ‘I’m sorry, I can’t do artificial insemination. I only do natural insemination, but you are really pretty and I’m sure you’ll find a donor,’” Garay said. “How in the world is that okay? They fight back, saying they are giving you a child, so the least we can do is make them ‘comfortable.’”

Some members are apparently motivated by the desire to create a legacy. In Bergen’s research, which draws from interviews with six online informal sperm donors, “all men wanted to make a long-term and lasting contribution to society, and for most, this seemed to emanate from a sense of altruism,” she told me. “There were also men who seemed attracted to the idea of having as many biological offspring as possible, to create a kind of clan amongst the various families.”

Bergen noted that the small sample of men she interviewed had one thing in common: “None of the men that we spoke to for our study were in fulfilling relationships themselves.” She believes that even though some had children from past relationships, they felt that donating sperm would help their lineage survive. Some men also hoped that their children would seek them out once they were adults, making them what Bergen calls an “estranged patriarch.”

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“There are good people who truly want to be of service in the world, but I would err on the side of caution” when meeting possible donors through informal channels, says Cleopatra Kamperveen, a University of Southern California professor in social work, psychology, and gerontology who runs the Fertility & Pregnancy Institute—a program she founded to assist potential parents in preparing for childbirth. “Having a baby with someone—under any circumstances—is a serious and lifelong proposition. Even when using a sperm donor, you are connected to that person for the rest of your life through the child.”

One donor, a 29-year-old sociologist from Austin, Texas, is a new member of Sperm Donation USA. He is bisexual and in a relationship with a woman, and says that he wants to give back to the LGBTQ community, because many couples face discrimination when looking for donors. He’s currently communicating with four couples, and if all goes well, he may donate to each of them. (He declined to be identified out of concern for professional repercussions.)

But he is selective about who gets his DNA. When white women seek him out, he questions how they intend to raise a Black child in America. He explains, “‘I’m a Black man, and you may one day have a Black child.’ I ask how they’ll prepare them for being Black in this country. Many don’t know how to answer that.”

He’s also wary because he’s seen posts about recipients getting into a financial bind and returning to their donor for child support. For that reason, many donors say that they strongly prefer to work with women who are in relationships, and, if recipients are single, they prefer college-educated women who are steadily employed. The sociologist once had a near miss. “I was vetting an LGBTQ couple for over two weeks, and all of a sudden, I got a message calling off the arrangement because they’d broken up,” he said. “What would have happened if she was already pregnant?” He plans to protect himself from the possibility of future litigation by drawing up contracts.

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Parental rights and obligations for sperm donors vary greatly by state. But according to Cathy Lively, an attorney focused on family law and bioethics, informal contracts made by individuals on Facebook groups seem less likely to be upheld in court than contracts offered by clinics. Individuals may use legally unenforceable language in their agreements or may not be familiar with the relevant laws in their jurisdiction.

For those considering seeking a donor via a Facebook group, Smith recommended that they instead check their insurance policy for any fertility benefits. Then, she said, at least consult with a doctor for estimated costs and treatments. “Doctors will often try to work to help get you cost-effective treatment,” she said.

In fact, almost every expert I spoke with advised caution, if not outright avoidance, of unregulated sperm-donation groups. Yet, after joining numerous groups and having countless sketchy conversations—for example, with men who had no profile photo—the 31-year-old Hess is finalizing plans with a donor she met through Sperm Donation USA for artificial insemination. “I automatically feel warm towards this person who’s been very kind throughout,” she said. “I feel like I can trust him so far.”

Meanwhile, Haliee Weidensall, 21, has finally made her match. She says that she and her partner took their time choosing someone, and the support of the Facebook groups helped her get past her nerves. “It took a month of studying the page before I found a donor. Once we connected with one, we talked for an entire month before we decided to move forward with the process,” she told me.

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Weidensall determined herself when she was ovulating and when to meet her donor in person for artificial insemination. She and her partner also paid him a few hundred dollars. Weidensall said she would definitely go through the process again. “I had the best experience, and the page has been amazing and helpful,” she told me. She has advice for anyone interested in finding a donor in one of the groups: “When you connect with one, make sure you get to know all about them and their history before making your decision, because once the decision is done, it’s done.” In Weidensall’s case, the donor left it up to the couple to decide whether they’d want him to be involved in the baby’s life, and though no contracts were signed, she feels certain that she won’t have any issues.

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Meet the Developers – Linux Kernel Team (David Vernet)

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Credit: Larry Ewing (lewing@isc.tamu.edu) and The GIMP for the original design of Tux the penguin.

Intro

For today’s interview, we have David Vernet, a core systems engineer on the Kernel team at Meta. He works on the BPF (Berkeley Packet Filter) and the Linux kernel scheduler. This series highlights Meta Software Engineers who contribute to the Linux kernel. The Meta Linux Kernel team works with the broader Linux community to add new features to the kernel and makes sure that the kernel works well in Meta production data centers. Engineers on the team work with peers in the industry to make the kernel better for Meta’s workloads and to make Linux better for everyone.

Tell us about yourself.

I’m a systems engineer who’s spent a good chunk of his career in the kernel space, and some time in the user-space as well working on a microkernel. Right now, I’m focusing most of my time on BPF and the Linux kernel scheduler.

I started my career as a web developer after getting a degree in math. After going to grad school, I realized that I was happiest when hacking on low-level systems and figuring out how computers work.

As a kernel developer at Meta, what does your typical day look like?

I’m not a maintainer of any subsystems in the kernel, so my typical day is filled with almost exclusively coding and engineering. That being said, participating in the upstream Linux kernel community is one of the coolest parts of being on the kernel team, so I still spend some time reading over upstream discussions. A typical day goes something like this:

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  1. Read over some of the discussions taking place on various upstream lists, such as BPF and mm. I usually spend about 30-60 minutes or so per day on this, though it depends on the day.

  2. Hack on the project that I’m working on. Lately, that’s adding a user-space ringbuffer map type to BPF.

  3. Work on drafting an article for lwn.net.

What have you been excited about or incredibly proud of lately?

I recently submitted a patch-set to enable a new map type in BPF. This allows user-space to publish messages to BPF programs in the kernel over the ringbuffer. This map type is exciting because it sets the stage to enable frameworks for user-space to drive logic in BPF programs in a performant way.

Is there something especially exciting about being a kernel developer at a company like Meta?

The Meta kernel team has a strong upstream-first culture. Bug fixes that we find in our Meta kernel, and features that we’d like to add, are almost always first submitted to the upstream kernel, and then they are backported to our internal kernel.

Do you have a favorite part of the kernel dev life cycle?

I enjoy architecting and designing APIs. Kernel code can never crash and needs to be able to run forever. I find it gratifying to architect systems in the kernel that make it easy to reason about correctness and robustness and provide intuitive APIs that make it easy for other parts of the kernel to use your code.

I also enjoy iterating with the upstream community. It’s great that your patches have a whole community of people looking at them to help you find bugs in your code and suggest improvements that you may never have considered on your own. A lot of people find this process to be cumbersome, but I find that it’s a small price to pay for what you get out of it.

Tell us a bit about the topic you presented at the Linux Plumbers Conference this year.

We presented the live patch feature in the Linux kernel, describing how we have utilized it at Meta and how our hyper-scale has shown some unique challenges with the feature.

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What are some of the misconceptions about kernel or open source software development that you have encountered in your career?

The biggest misconception is that it’s an exclusive, invite-only club to contribute to the Linux kernel. You certainly must understand operating systems to be an effective contributor and be ready to receive constructive criticism when there is scope for improvement in your code. Still, the community always welcomes people who come in with an open mind and want to contribute.

What resources are helpful in getting started in kernel development?

There is a lot of information out there that people have written on how to get integrated into the Linux kernel community. I wrote a blog post on how to get plugged into Linux kernel upstream mailing list discussions, and another on how to submit your first patch. There is also a video on writing and submitting your first Linux kernel patch from Greg Kroah-Hartman.

In terms of resources to learn about the kernel itself, there are many resources and books, such as:

Where can people find you and follow your work?

I have a blog where I talk about my experiences as a systems engineer: https://www.bytelab.codes/. I publish articles that range from topics that are totally newcomer friendly to more advanced topics that discuss kernel code in more detail. Feel free to check it out and let me know if there’s anything you’d like me to discuss.

To learn more about Meta Open Source, visit our open source site, subscribe to our YouTube channel, or follow us on Twitter, Facebook and LinkedIn.

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Get started with WhatsApp Business Platform in Minutes with Postman

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Our collaboration brings tools you already use to WhatsApp Business Platforms APIs

Postman is a best-in-class API platform used by 20M developers worldwide. Using Postman simplifies each step of the API lifecycle and streamlines collaboration.

Postman’s strong platform and broad adoption in the developer community made deciding to work with Postman to deliver a robust developer experience an easy decision for our WhatsApp Business Platform product team.

What Postman means for your WhatsApp projects

The benefits of this collaboration for developers are clear – you can easily leverage Postman’s platform with your Meta projects to onboard, collaborate, and contribute towards documentation and best practices as you build out your integrations.

Fast Onboarding

The WhatsApp team is able to offer, via Postman, an API collection that pre-fills environment variables and walks you through your initial test requests – helping developers dive right in to using the Cloud API. Our product managers show you how easy it is to get started with Postman in this session from Conversations:

Foster Collaboration

The public Postman workspace fosters collaboration – allowing environments, collections, and documentation augmentation to happen in one place.

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Enhance Documentation

Postman’s API documentation tools augment our own documentation and allows developers to contribute directly to the community’s shared knowledge, building a strong reference library for all developers and encouraging new, innovative use cases.

The Results

Working with Postman from the beginning helps create a developer-friendly experience for the WhatsApp Business Platform – allowing you to get started quickly, build community, and share knowledge.

Want to know more about our partnership with Postman? Check out their case study, follow along with the video above, or dive right into the Postman Workspace for the WhatsApp Business Platform.

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1. https://www.postman.com/state-of-api/

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Summer of open source: building more efficient AI with PyTorch

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Note: Special thanks to Less Wright, Partner Engineer, Meta AI, for review of and additional insights into the post.

This post on creating efficient artificial intelligence (AI) is the second in the “Summer of open source” series. This series aims to provide a handful of useful resources and learning content in areas where open source projects are creating impact across Meta and beyond. Follow along as we explore other areas where Meta Open Source is moving the industry forward by sharing innovative, scalable tools.

PyTorch: from foundational technology to foundation

Since its initial release in 2016, PyTorch has been widely used in the deep learning community, and its roots in research are now consistently expanding for use in production scenarios. In an exciting time for machine learning (ML) and artificial intelligence (AI), where novel methods and use cases for AI models continue to expand, PyTorch has reached the next chapter in its history as it moves to the newly established, independent PyTorch Foundation under the Linux Foundation umbrella. The foundation is made up of a diverse governing board including representatives from AMD, Amazon Web Services, Google Cloud, Microsoft Azure and Nvidia, and the board is intended to expand over time. The mission includes driving adoption of AI tooling through vendor-neutral projects and making open source tools, libraries and other components accessible to everyone. The move to the foundation will also enable PyTorch and its open source community to continue to accelerate the path from prototyping to production for AI and ML.

Streamlining AI processes with Meta open source

PyTorch is a great example of the power of open source. As one of the early open source deep learning frameworks, PyTorch has allowed people from across disciplines to experiment with deep learning and apply their work in wide-ranging fields. PyTorch supports everything from experiments in search applications to autonomous vehicle development to ground-penetrating radar, and these are only a few of its more recent applications. Pairing a versatile library of AI tools with the open source community unlocks the ability to quickly iterate on and adapt technology at scale for many different uses.

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As AI is being implemented more broadly, models are trending up in size to tackle more complex problems, but this also means that the resources needed to train these models have increased substantially. Fortunately, many folks in the developer community have recognized the need for models to use fewer resources—both from a practical and environmental standpoint. This post will explore why quantization and other types of model compression can be a catalyst for efficient AI.

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Establishing a baseline for using PyTorch

Most of this post explores some intermediate and advanced features of PyTorch. If you are a beginner that is looking to get started, or an expert that is currently using another library, it’s easiest to get started with some basics. Check out the beginner’s guide to PyTorch, which includes an introduction to a complete ML workflow using the Fashion MNIST dataset.

Here are some other resources that you might check out if you’re new to PyTorch:

  • PyTorch Community Stories: Learn how PyTorch is making an impact across different industries like agriculture, education, travel and others
  • PyTorch Beginner Series: Explore a video playlist of fundamental techniques including getting started with tensors, building models, training and inference in PyTorch.

Quantization: Applying time-tested techniques to AI

There are many pathways to making AI more efficient. Codesigning hardware and software to optimize for AI can be highly effective, but bespoke hardware-software solutions take considerable time and resources to develop. Creating faster and smaller architectures is another path to efficiency, but many of these architectures suffer from accuracy loss when compared to larger models, at least for the time being. A simpler approach is to find ways of reducing the resources that are needed to train and serve existing models. In PyTorch, one way to do that is through model compression using quantization.

Quantization is a mathematical technique that has been used to create lossy digital music files and convert analog signals to digital ones. By executing mathematical calculations with reduced precision, quantization allows for significantly higher performance on many hardware platforms. So why use quantization to make AI more efficient? Results show that in certain cases, using this relatively simple technique can result in dramatic speedups (2-4 times) for model inference.

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The parameters that make up a deep learning model are typically decimal numbers in floating point (FP) precision; each parameter requires either 16 bits or 32 bits of memory. When using quantization, numbers are often converted to INT4 or INT8, which occupy only 4 or 8 bits. This reduces how much memory models require. Additionally, chip manufacturers include special arithmetic that makes operations using integers faster than using decimals.

There are 3 methods of quantization that can be used for training models: dynamic, static and quantize-aware training (QAT). A brief overview of the benefits and weaknesses is described in the table below. To learn how to implement each of these in your AI workflows, read the Practical Quantization in PyTorch blog post.

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Quantization Method

Benefits

Weaknesses

Dynamic

  • Easy to use with only one API call
  • More robust to distribution drift resulting in slightly higher accuracy
  • Works well for long short-term memory (LSTM) and Transformer models

Additional overhead in every forward pass

Static (also known as PTQ)

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  • Faster inference than dynamic quantization by eliminating overhead

May need regular recalibration for distribution drift

Quantize-Aware Training (QAT)

  • Higher accuracy than static quantization
  • Faster inference than dynamic

High computational cost

Additional features for speeding up your AI workflow

Quantization isn’t the only way to make PyTorch-powered AI more efficient. Features are updated regularly, and below are a few other ways that PyTorch can improve AI workflows:

  • Inference mode: This mode can be used for writing PyTorch code if you’re only using the code for running inference. Inference mode changes some of the assumptions when working with tensors to speed up inference. By telling PyTorch that you won’t use tensors for certain applications later (in this case, autograd), it adjusts to make code run faster in these specific scenarios.

  • Low precision: Quantization works only at inference time, that is, after you have trained your model. For the training process itself, PyTorch uses AMP, or automatic mixed precision training, to find the best format based on which tensors are used (FP16, FP32 or BF16). Low-precision deep learning in PyTorch has several advantages. It can help lower the size of a model, reduce the memory that is required to train models and decrease the power that is needed to run models. To learn more, check out this tutorial for using AMP with CUDA-capable GPUs.

  • Channels last: When it comes to vision models, NHWC, otherwise known as channels-last, is a faster tensor memory format in PyTorch. Having data stored in the channels-last format accelerates operations in PyTorch. Formatting input tensors as channels-last reduces the overhead that is needed for conversion between different format types, resulting in faster inference.

  • Optimize for inference: This TorchScript prototype implements some generic optimizations that should speed up models in all environments, and it can also prepare models for inference with build-specific settings. Primary use cases include vision models on CPUs (and GPUs) at this point. Since this is a prototype, it’s possible that you may run into issues. Raise an issue that occurs on the PyTorch GitHub repository.

Unlocking new potential in PyTorch

Novel methods for accelerating AI workflows are regularly explored on the PyTorch blog. It’s a great place to keep up with techniques like the recent BetterTransformer, which increases speedup and throughput in Transformer models by up to 2 times for common execution scenarios. If you’re interested in learning how to implement specific features in PyTorch, the recipes page allows you to search by categories like model optimization, distributed training and interpretability. This post is only a sampling of how tools like PyTorch are moving open source and AI forward.

To stay up to date with the latest in Meta Open Source for artificial intelligence and machine learning, visit our open source site, subscribe to our YouTube channel, or follow us on Facebook, Twitter and LinkedIn.

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