目が覚めて時計を見ると、まだ早朝の4時だった。 寝ている姿勢の軸がずれているような気がして、背中を少し動かした瞬間、左腕に鈍い痛みが走った。 ああ、寝違えたか、と思ったが、体のコアはまだ半分眠っていて、痛みへの関心は薄かった。

あと2時間もすれば会社に行く準備をしなければならない。 そう思った途端、体中の筋肉と毛穴が一斉に収縮するのを感じた。 当然、髪の毛もその巻き添えを食うわけで、たまったものじゃない。 せっかく前回美容院に行ったとき、「髪の質が良くなってきましたね」と褒められたばかりなのに。

思考はまだ半覚醒のままなのに、そこへストレスが割り込んでくるものだから、体が本格的に拒絶反応を起こし始めているのが分かる。

安全な方向を探して、細胞たちがそれぞれ勝手に逃げ回っているような感覚がある。 だが「安全な場所とはどこだ」と言わんばかりに、みんな好き勝手に動くので、体が変形しているようにすら感じる。 今、体温を測ったら熱があるかもしれない。

こんなことが年に数回ある。 最初はただ辛いだけだと思っていたが、最近は今を生き抜くための、次の体に向けた進化なのではないかと感じている。

つい最近まで二か月ほど在宅勤務を続けていて、エレガントなニットがよく似合っていた。 それなのに「明日からまたしばらく本社に来てくれ」と言われた途端、家にいたい、現状を変えたくないという気持ちが一気に押し寄せた。 本当に会社に行きたくなくて、心にはかなりの負荷がかかっていた。

朝起きてカーテンを開け、コーヒーを淹れ、洗濯をして、ストレッチをして、部屋の温度を整え、自炊をして、好きなソファに座る。 俺はこの空間で仕事がしたいんだ。

しかし、うちの会社は請負の仕事ばかりだから、上司に「来い」と言われれば行かざるを得ない。 今の心身のままでは会社に行く負荷が高すぎるから、細胞たちが化学変化を起こして、少しでも楽にしてやろうと、俺を会社用の体に作り変えてくれているのだろう。

子どもの頃から「手から電気を出したい」と漠然と思っている夢がある。

人間が状況に合わせて進化できるのなら、電気を出さざるを得ない状況を作れば、そのように体も変化していくものではないのか？と考え、大学院では生物物理科に進学した。 ただ、大学院時代に一度研究を試みて以来、科学は「他人に伝えるためのもの」だと感じてしまい、科学的に理解しようという関心は薄れてしまった。 自分だけが辿り着きたい、現実的であり抽象的な場所なのだから、学問にわざわざ落とし込む必要もない、とは思うものの、何かしらのアプローチは必要で、やはりこれは人生の課題だなと思うと同時に、右脳に、普段はしない喝みたいなものを入れた。

そんなことを考えているうちに、「改造が終わったよ」と細胞がシグナルを送ってきたので、布団から起き上がり、会社に行く準備をした。 これまでは自然とスーツを着ていたが、今日は自然と私服を選んだ。 改造されたことが視覚的に分かりやすくて良いねと思いながら、いつかたどり着く場所とはまったく関係のない寄り道として、会社へ向かう事にした。

I try to leave the country once a year or so, and I'm currently in the process of getting in my trip for 2026. I started this post on the plane, continued it on the Tren Maya, and finished it in warm and beautiful Valladolid, Mexico, far from the snow-covered and frozen city of Pittsburgh. As excited as I am to warm up a bit, that's not the primary reason I travel. A lot of my best story ideas have come from visiting new places and experiencing life in a different part of the world. It's also a solid way to break up my routine, which I know I could technically do in Pittsburgh, too, but I'm very bad at veering away from my usual habits when I'm going about my day-to-day life. Jumping on a plane and heading to Mexico or Guatemala—or even just riding the train to Buffalo or Baltimore—is a foolproof way to ease myself out of any ruts I've gotten into. 

I will say, the way that I approach these trips isn't quite the way I think most would go into a vacation. For one thing, I usually don't completely take off work—I tend to drop down to more of a part-time workload, but continuing to work lets me take the kind of longer trips that allow me really sink into a new place. Especially when there's train travel involved, like on this trip, I view those periods as little mini writing retreats, because that environment is one where I find it very easy to get ideas flowing: you're stuck in one place without much to distract you, but there's also something new to see every time you look out the window in need of inspiration. 

But I also think I approach travel with a different mindset than the average non-writer would, and that's something you can do regardless of your employment situation or how long you're traveling. When you look at things the right way, you can find stories and inspiration that you can use to fuel your creativity for long after you're back on familiar turf. This post is both advice for other writers and a reminder to myself as I get ready to see some new places on how to make the most of my time while I wander. 

So how do you travel like a writer? For me, it comes down to 5 main things.

Get off the well-traveled trails

This doesn't mean you have to skip the big attractions. In each town I’m visiting, I'll probably take the expected wander into the central plaza and stare at the Spanish cathedral, like one does in an old Latin American city. But while I’m on that walk, I'm also going to have my eyes open for strange things that snag them, side-streets that look intriguing, or off-the-wall museums that most folks might walk by.

There are two types of places I always seek out when I'm in a new city: cemeteries and public parks. They're not the kinds of attractions that most would add to their itinerary, but they reveal so much about the place you're visiting and are usually among my favorite finds. I also get around on foot when I can, giving me a chance to take in the streets that would blur by from a vehicle. This often leads to discovering street art, statues, and other neat finds that I never would have known were there otherwise. I've even stumbled across festivals or hidden museums a time or two when exploring whatever city I'm in. 

The value of these unexpected finds is two-fold. One, that sense of discovery makes it stick in your brain more because it has emotion attached to it, and for me at least that is exactly the germ I need to turn it into inspiration. It also means you have things to write about that not everyone who visits that city will have seen. That's especially good if you want to write nonfiction like travel writing, but also gives you unique details you can use in fiction and poetry that will make it sound uniquely you, not like something regurgitated off of every travel blog. 

Learn the history

I'm what I'll call a half-spontaneous traveler. I'll plan out the big strokes of the trip like transportation and where I'm staying well in advance. When it comes to the things I see, though, I'll usually have a list of places I want to visit, but I don't go so far as to write out an itinerary. I like to leave myself room for discovery and not go into a place with too many preconceived ideas about it. 

Which might all sound like the opposite of what I'm suggesting in the heading here, but what's glorious about traveling in the 21st century is that the vast majority of us constantly carry a device that can access a significant percentage of accumulated human knowledge. You don't necessarily need to research in advance to get insight into the history of a place, and for me looking it up on the spot, while I'm at the site, often helps me connect that history to what I'm seeing and makes it stick more firmly in my memory. 

The advice I gave above also applies here. Informational signs and plaques are like catnip for me. Any time I see one I’m drawn to it, even when I'm walking around Pittsburgh. You can learn a lot from historical markers, or the signs and explanations posted in museums. If you see a random plaque on the side of an unassuming building, don’t breeze by it—give it a read to see if the building you were about to ignore actually has a fascinating history that will end up being the germ of your next story.

There are a lot of reasons I give this recommendation. For one, it puts what you're seeing in context so you can more fully understand it, and at the same time gain a deeper understanding of the place that you're visiting, which will help you to make it come across as real to the reader. You might also find some stories hiding in that history, or learn about famous figures, historical events, or other things that you want to look into further and use for something you're writing. When you're conducting research—whether before, during, or after your trip—let yourself fall down rabbit holes and go off on tangents, following your interests wherever they lead you. The whole point of traveling is to discover new things, and those new things don't only have to be the ones you physically encounter. 

Carry a notebook

I usually don't journal. I've done it at points in the past, but I tend to drop the habit pretty quickly. That said, whenever I'm traveling, I always make a point of journaling every day. I even type up these journals into a digital file after I get back so that there's no risk of them being lost if the physical copy gets misplaced. I usually do this within a month of getting home, and even with that little time in between the trip and when I'm going back over them, I still inevitably come across a few things I'd already forgotten about. 

That's the main reason I always keep a journal when I'm traveling. There's such an influx of new knowledge and sensory input that my brain just doesn't have space to process all of it before I feed more in, and inevitably some of that info is going to get lost. Keeping a notebook handy and consistently recording what I see, cool things I learn, and other details about the experience I might forget helps me to capture and freeze the ideas so that they're still there when I'm ready to make use of them. 

I suppose the physical notebook isn't a requirement anymore—it's the way I prefer to do things, but you could also use an app on your phone or something similar. The key is to use something that you can have with you while you're actively out exploring, not just after you get back to your hotel room. That lets you capture things as you see and feel them, which is the best way to retain those tangible, distinctive details that will really make the moment come to life if you decide to make use of it in a creative work.

Engage your full senses and attention

When I was in college I had the privilege of studying in Florence for six weeks, and was able to take an in-depth art history class during the session. One point that the instructor would always make as she was leading us around the various museums and landmarks is to not only focus at eye level. She was always directing our eyes up at a frescoed ceiling or down at an interesting paving stone, and her lesson stuck with me well after that trip. Staying too focused on one view of things could mean you miss details that you would've found even cooler.

This same advice goes in a broader sense, too. I try to remind myself when I'm traveling to just stop every once in a while and take everything in—to listen to the sounds, smell the air, feel the energy of the place I'm inhabiting. It's easy when you're traveling to feel like you need to constantly be moving to see everything you want to see, but you don't want to fall into the trap of trying to see so much that you don't end up really seeing any of it. 

Make smart use of your camera

This is another place where my habits are I think a bit different than the typical vacationer's. I often don't take many pictures of the big landmarks. If I want to remember what El Castillo in Chichen Itza looks like, I can find plenty of pictures of it taken by much better photographers than I am. But the internet won't help me remember the funny graffiti somebody scrawled on one of the signs, or that one other tourist wearing the crazy hat—those are the kinds of details I'm most likely to get out the camera for. Again, this isn't saying you can't also take Instagram-worthy pictures of the big sites, but that's another great thing about the modern era of the phone camera. It's not like you have a limited number of shots. You can do both. 

I will add onto this that you don't just have to use your camera to capture sights. If you come across a particularly interesting sign and want to make sure you remember all the details, snap a photo of it. These days, lots of museums and historical sites will use QR codes on the signs to lead you to places where you can learn more info, which can be a shortcut to following that “learn the history” tip I gave above. You can also use the other recording features to get snippets of music or street noise, or capture video to capture the energy of a crowd or the stillness of an empty park.

Of course, you don't want to go overboard. Even the best phone has a finite amount of storage and you don't want to be so intent on documenting everything that you forget to actually experience it. But when you come across one of those things that you want to make sure you remember, take full advantage of the tools at your disposal to give yourself memory triggers you can activate once you get home. 

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Every time you type a message on your smartphone, your keyboard learns a little more about you. It notices your favourite words, your common misspellings, the names of people you text most often. For years, this intimate knowledge was hoovered up and shipped to distant servers, where tech giants analysed your linguistic fingerprints alongside billions of others. Then, around 2017, something changed. Google began training its Gboard keyboard using a technique called federated learning, promising that your typing data would never leave your device. The raw text of your most private messages, they assured users, would stay exactly where it belonged: on your phone.

It sounds like a privacy advocate's dream. But beneath this reassuring narrative lies a more complicated reality, one where mathematical guarantees collide with practical vulnerabilities, where corporate interests shape the definition of “privacy,” and where the gap between what users understand and what actually happens grows wider by the day. As AI systems increasingly rely on techniques like federated learning and differential privacy to protect sensitive information, a fundamental question emerges: are these technical solutions genuine shields against surveillance, or are they elaborate mechanisms that create new attack surfaces whilst giving companies plausible deniability?

The Machinery of Privacy Preservation

To understand whether federated learning and differential privacy actually work, you first need to understand what they are and how they operate. These are not simple concepts, and that complexity itself becomes part of the problem.

Federated learning, first formally introduced by Google researchers in 2016, fundamentally reimagines how machine learning models are trained. In the traditional approach, organisations collect vast quantities of data from users, centralise it on their servers, and train AI models on this aggregated dataset. Federated learning inverts this process. Instead of bringing data to the model, it brings the model to the data.

The process works through a carefully orchestrated dance between a central server and millions of edge devices, typically smartphones. The server distributes an initial model to participating devices. Each device trains that model using only its local data, perhaps the messages you have typed, the photos you have taken, or the websites you have visited. Crucially, the raw data never leaves your device. Instead, each device sends back only the model updates, the mathematical adjustments to weights and parameters that represent what the model learned from your data. The central server aggregates these updates from thousands or millions of devices, incorporates them into a new global model, and distributes this improved version back to the devices. The cycle repeats until the model converges.

The technical details matter here. Google's implementation in Gboard uses the FederatedAveraging algorithm, with between 100 and 500 client updates required to close each round of training. On average, each client processes approximately 400 example sentences during a single training epoch. The federated system converges after about 3000 training rounds, during which 600 million sentences are processed by 1.5 million client devices.

Differential privacy adds another layer of protection. Developed by computer scientists including Cynthia Dwork of Harvard University, who received the National Medal of Science in January 2025 for her pioneering contributions to the field, differential privacy provides a mathematically rigorous guarantee about information leakage. The core idea is deceptively simple: if you add carefully calibrated noise to data or computations, you can ensure that the output reveals almost nothing about any individual in the dataset.

The formal guarantee states that an algorithm is differentially private if its output looks nearly identical whether or not any single individual's data is included in the computation. This is measured by a parameter called epsilon, which quantifies the privacy loss. A smaller epsilon means stronger privacy but typically comes at the cost of utility, since more noise obscures more signal.

The noise injection typically follows one of several mechanisms. The Laplace mechanism adds noise calibrated to the sensitivity of the computation. The Gaussian mechanism uses a different probability distribution, factoring in both sensitivity and privacy parameters. Each approach has trade-offs in terms of accuracy, privacy strength, and computational efficiency.

When combined, federated learning and differential privacy create what appears to be a formidable privacy fortress. Your data stays on your device. The model updates sent to the server are aggregated with millions of others. Additional noise is injected to obscure individual contributions. In theory, even if someone intercepted everything being transmitted, they would learn nothing meaningful about you.

In practice, however, the picture is considerably more complicated.

When Privacy Promises Meet Attack Vectors

The security research community has spent years probing federated learning systems for weaknesses, and they have found plenty. One of the most troubling discoveries involves gradient inversion attacks, which demonstrate that model updates themselves can leak significant information about the underlying training data.

A gradient, in machine learning terms, is the mathematical direction and magnitude by which model parameters should be adjusted based on training data. Researchers have shown that by analysing these gradients, attackers can reconstruct substantial portions of the original training data. A 2025 systematic review published in Frontiers in Computer Science documented how gradient-guided diffusion models can now achieve “visually perfect recovery of images up to 512x512 pixels” from gradient information alone.

The evolution of these attacks has been rapid. Early gradient inversion techniques required significant computational resources and produced only approximate reconstructions. Modern approaches using fine-tuned generative models reduce mean squared error by an order of magnitude compared to classical methods, whilst simultaneously achieving inference speeds a million times faster and demonstrating robustness to gradient noise.

The implications are stark. Even though federated learning never transmits raw data, the gradients it does transmit can serve as a detailed map back to that data. A team of researchers demonstrated this vulnerability specifically in the context of Google's Gboard, publishing their findings in a paper pointedly titled “Two Models are Better than One: Federated Learning is Not Private for Google GBoard Next Word Prediction.” Their work showed that the word order and actual sentences typed by users could be reconstructed with high fidelity from the model updates alone.

Beyond gradient leakage, federated learning systems face threats from malicious participants. In Byzantine attacks, compromised devices send deliberately corrupted model updates designed to poison the global model. Research published by Fang et al. at NDSS in 2025 demonstrated that optimised model poisoning attacks can cause “1.5x to 60x higher reductions in the accuracy of FL models compared to previously discovered poisoning attacks.” This suggests that existing defences against malicious participants are far weaker than previously assumed.

Model inversion attacks present another concern. These techniques attempt to reverse-engineer sensitive information about training data by querying a trained model. A February 2025 paper on arXiv introduced “federated unlearning inversion attacks,” which exploit the model differences before and after data deletion to expose features and labels of supposedly forgotten data. As regulations like the GDPR establish a “right to be forgotten,” the very mechanisms designed to delete user data may create new vulnerabilities.

Differential privacy, for its part, is not immune to attack either. Research has shown that DP-SGD, the standard technique for adding differential privacy to deep learning, cannot prevent certain classes of model inversion attacks. A study by Zhang et al. demonstrated that their generative model inversion attack in face recognition settings could succeed even when the target model was trained with differential privacy guarantees.

The Census Bureau's Cautionary Tale

Perhaps the most instructive real-world example of differential privacy's limitations comes from the US Census Bureau's adoption of the technique for the 2020 census. This was differential privacy's biggest test, applied to data that would determine congressional representation and the allocation of hundreds of billions of dollars in federal funds.

The results were controversial. Research published in PMC in 2024 found that “the total population counts are generally preserved by the differential privacy algorithm. However, when we turn to population subgroups, this accuracy depreciates considerably.” The same study documented that the technique “introduces disproportionate discrepancies for rural and non-white populations,” with “significant changes in estimated mortality rates” occurring for less populous areas.

For demographers and social scientists, the trade-offs proved troubling. A Gates Open Research study quantified the impact: when run on historical census data with a privacy budget of 1.0, the differential privacy system produced errors “similar to that of a simple random sample of 50% of the US population.” In other words, protecting privacy came at the cost of effectively throwing away half the data. With a privacy budget of 4.0, the error rate decreased to approximate that of a 90 percent sample, but privacy guarantees correspondingly weakened.

The Census Bureau faced criticism from data users who argued that local governments could no longer distinguish between actual errors in their data and noise introduced by the privacy algorithm. The structural inaccuracy preserved state-level totals whilst “intentionally distorting characteristic data at each sub-level.”

This case illuminates a fundamental tension in differential privacy: the privacy-utility trade-off is not merely technical but political. Decisions about how much accuracy to sacrifice for privacy, and whose data bears the greatest distortion, are ultimately value judgements that mathematics alone cannot resolve.

Corporate Privacy, Corporate Interests

When technology companies tout their use of federated learning and differential privacy, it is worth asking what problems these techniques actually solve, and for whom.

Google's deployment of federated learning in Gboard offers a revealing case study. The company has trained and deployed more than twenty language models for Gboard using differential privacy, achieving what they describe as “meaningfully formal DP guarantees” with privacy parameters (rho-zCDP) ranging from 0.2 to 2. This sounds impressive, but the privacy parameters alone do not tell the full story.

Google applies the DP-Follow-the-Regularized-Leader algorithm specifically because it achieves formal differential privacy guarantees without requiring uniform sampling of client devices, a practical constraint in mobile deployments. The company reports that keyboard prediction accuracy improved by 24 percent through federated learning, demonstrating tangible benefits from the approach.

Yet Google still learns aggregate patterns from billions of users. The company still improves its products using that collective intelligence. Federated learning changes the mechanism of data collection but not necessarily the fundamental relationship between users and platforms. As one Google research publication frankly acknowledged, “improvements to this technology will benefit all users, although users are only willing to contribute if their privacy is ensured.”

The tension becomes even starker when examining Meta, whose platforms represent some of the largest potential deployments of privacy-preserving techniques. A 2025 analysis in Springer Nature noted that “approximately 98% of Meta's revenue derives from targeted advertising, a model that depends heavily on the collection and analysis of personal data.” This business model “creates a strong incentive to push users to sacrifice privacy, raising ethical concerns.”

Privacy-preserving techniques can serve corporate interests in ways that do not necessarily align with user protection. They enable companies to continue extracting value from user data whilst reducing legal and reputational risks. They provide technical compliance with regulations like the GDPR without fundamentally changing surveillance-based business models.

Apple presents an interesting contrast. The company has integrated differential privacy across its ecosystem since iOS 10 in 2016, using it for features ranging from identifying popular emojis to detecting domains that cause high memory usage in Safari. In iOS 17, Apple applied differential privacy to learn about popular photo locations without identifying individual users. With iOS 18.5, the company extended these techniques to train certain Apple Intelligence features, starting with Genmoji.

Apple's implementation deploys local differential privacy, meaning data is randomised before leaving the device, so Apple's servers never receive raw user information. Users can opt out entirely through Settings, and privacy reports are visible in device settings, providing a degree of transparency unusual in the industry.

Apple's approach differs from Google's in that the company does not derive the majority of its revenue from advertising. Yet even here, questions arise about transparency and user understanding. The technical documentation is dense, the privacy parameters are not prominently disclosed, and the average user has no practical way to verify the claimed protections.

The Understanding Gap

The gap between technical privacy guarantees and user comprehension represents perhaps the most significant challenge facing these technologies. Differential privacy's mathematical rigour means nothing if users cannot meaningfully consent to, or even understand, what they are agreeing to.

Research on the so-called “privacy paradox” consistently finds a disconnect between stated privacy concerns and actual behaviour. A study analysing Alipay users found “no relationship between respondents' self-stated privacy concerns and their number of data-sharing authorizations.” Rather than indicating irrational behaviour, the researchers argued this reflects the complexity of privacy decisions in context.

A 2024 Deloitte survey found that less than half of consumers, 47 percent, trust online services to protect their data. Yet a separate survey by HERE Technologies found that more than two-thirds of consumers expressed willingness to share location data, with 79 percent reporting they would allow navigation services to access their data. A study of more than 10,000 respondents across 10 countries found 53 percent expressing concern about digital data sharing, even as 70 percent indicated growing willingness to share location data when benefits were clear.

This is not necessarily a paradox so much as an acknowledgment that privacy decisions involve trade-offs that differ by context, by benefit received, and by trust in the collecting entity. But federated learning and differential privacy make these trade-offs harder to evaluate, not easier. When a system claims to be “differentially private with epsilon equals 4,” what does that actually mean for the user? When federated learning promises that “your data never leaves your device,” does that account for the information that gradients can leak?

The French data protection authority CNIL has recommended federated learning as a “data protection measure from the outset,” but also acknowledged the need for “explainability and traceability measures regarding the outputs of the system.” The challenge is that these systems are inherently difficult to explain. Their privacy guarantees are statistical, not absolute. They protect populations, not necessarily individuals. They reduce risk without eliminating it.

Healthcare: High Stakes, Conflicting Pressures

Nowhere are the tensions surrounding privacy-preserving AI more acute than in healthcare, where the potential benefits are enormous and the sensitivity of data is extreme.

NVIDIA's Clara federated learning platform exemplifies both the promise and the complexity. Clara enables hospitals to collaboratively train AI models without sharing patient data. Healthcare institutions including the American College of Radiology, Massachusetts General Hospital and Brigham and Women's Hospital's Center for Clinical Data Science, and UCLA Health have partnered with NVIDIA on federated learning initiatives.

In the United Kingdom, NVIDIA partnered with King's College London and the AI company Owkin to create a federated learning platform for the National Health Service, initially connecting four of London's premier teaching hospitals. The Owkin Connect platform uses blockchain technology to capture and trace all data used for model training, providing an audit trail that traditional centralised approaches cannot match.

During the COVID-19 pandemic, NVIDIA coordinated a federated learning study involving twenty hospitals globally to train models predicting clinical outcomes in symptomatic patients. The study demonstrated that federated models could outperform models trained on any single institution's data alone, suggesting that the technique enables collaboration that would otherwise be impossible due to privacy constraints.

In the pharmaceutical industry, the MELLODDY project brought together ten pharmaceutical companies in Europe to apply federated learning to drug discovery. The consortium pools the largest existing chemical compound library, more than ten million molecules and one billion assays, whilst ensuring that highly valuable proprietary data never leaves each company's control. The project runs on the open-source Substra framework and employs distributed ledger technology for full traceability.

These initiatives demonstrate genuine value. Healthcare AI trained on diverse populations across multiple institutions is likely to generalise better than AI trained on data from a single hospital serving a particular demographic. Federated learning makes such collaboration possible in contexts where data sharing would be legally prohibited or practically impossible.

But the same vulnerabilities that plague federated learning elsewhere apply here too, perhaps with higher stakes. Gradient inversion attacks could potentially reconstruct medical images. Model poisoning by a malicious hospital could corrupt a shared diagnostic tool. The privacy-utility trade-off means that stronger privacy guarantees may come at the cost of clinical accuracy.

Regulation Catches Up, Slowly

The regulatory landscape is evolving to address these concerns, though the pace of change struggles to keep up with technological development.

In the European Union, the AI Act took full effect on 2 August 2025, establishing transparency obligations for general-purpose AI systems. In November 2025, the European Commission published the Digital Omnibus proposal, streamlining the relationship between the Data Act, GDPR, and AI Act. The proposal includes clarification that organisations “may rely on legitimate interests to process personal data for AI-related purposes, provided they fully comply with all existing GDPR safeguards.”

In the United States, NIST finalised guidelines for evaluating differential privacy guarantees in March 2025, fulfilling an assignment from President Biden's Executive Order on Safe, Secure, and Trustworthy AI from October 2023. The guidelines provide a framework for assessing privacy claims but acknowledge the complexity of translating mathematical parameters into practical privacy assurances.

The market is responding to these regulatory pressures. The global privacy-enhancing technologies market reached 3.12 billion US dollars in 2024, projected to grow to 12.09 billion dollars by 2030. The federated learning platforms market, valued at 150 million dollars in 2023, is forecast to reach 2.3 billion dollars by 2032, reflecting a compound annual growth rate of 35.4 percent. The average cost of a data breach reached 4.88 million dollars in 2024, and industry analysts estimate that 75 percent of the world's population now lives under modern privacy regulations.

This growth suggests that corporations see privacy-preserving techniques as essential infrastructure for the AI age, driven as much by regulatory compliance and reputational concerns as by genuine commitment to user protection.

The Security Arms Race

The relationship between privacy-preserving techniques and the attacks against them resembles an arms race, with each advance prompting countermeasures that prompt new attacks in turn.

Defensive techniques have evolved significantly. Secure aggregation protocols encrypt model updates so that the central server only learns the aggregate, not individual contributions. Homomorphic encryption allows computation on encrypted data, theoretically enabling model training without ever decrypting sensitive information. Byzantine-robust aggregation algorithms attempt to detect and exclude malicious model updates.

Each defence has limitations. Secure aggregation protects against honest-but-curious servers but does not prevent sophisticated attacks like Scale-MIA, which researchers demonstrated can reconstruct training data even from securely aggregated updates. Homomorphic encryption imposes significant computational overhead and is not yet practical for large-scale deployments. Byzantine-robust algorithms, as the research by Fang et al. demonstrated, are more vulnerable to optimised attacks than previously believed.

The research community continues to develop new defences. A 2025 study proposed “shadow defense against gradient inversion attack,” using decoy gradients to obscure genuine updates. LSTM-based approaches attempt to detect malicious updates by analysing patterns across communication rounds. The FedMP algorithm combines multiple defensive techniques into a “multi-pronged defence” against Byzantine attacks.

But attackers are also advancing. Gradient-guided diffusion models achieve reconstruction quality that would have seemed impossible a few years ago. Adaptive attack strategies that vary the number of malicious clients per round prove more effective and harder to detect. The boundary between secure and insecure keeps shifting.

This dynamic suggests that privacy-preserving AI should not be understood as a solved problem but as an ongoing negotiation between attackers and defenders, with no permanent resolution in sight.

What Users Actually Want

Amid all the technical complexity, it is worth returning to the fundamental question: what do users actually want from privacy protection, and can federated learning and differential privacy deliver it?

Research suggests that user expectations are contextual and nuanced. People are more willing to share data with well-known, trusted entities than with unknown ones. They want personalised services but also want protection from misuse. They care more about some types of data than others, and their concerns vary by situation.

Privacy-preserving techniques address some of these concerns better than others. They reduce the risk of data breaches by not centralising sensitive information. They provide mathematical frameworks for limiting what can be inferred about individuals. They enable beneficial applications, such as medical AI or improved keyboard prediction, that might otherwise be impossible due to privacy constraints.

But they do not address the fundamental power imbalance between individuals and the organisations that deploy these systems. They do not give users meaningful control over how models trained on their data are used. They do not make privacy trade-offs transparent or negotiable. They replace visible data collection with invisible model training, which may reduce certain risks whilst obscuring others.

The privacy paradox literature suggests that many users make rational calculations based on perceived benefits and risks. But federated learning and differential privacy make those calculations harder, not easier. The average user cannot evaluate whether epsilon equals 2 provides adequate protection for their threat model. They cannot assess whether gradient inversion attacks pose a realistic risk in their context. They must simply trust that the deploying organisation has made these decisions competently and in good faith.

The Question That Matters

Will you feel safe sharing personal data as AI systems adopt federated learning and differential privacy? The honest answer is: it depends on what you mean by “safe.”

These techniques genuinely reduce certain privacy risks. They make centralised data breaches less catastrophic by keeping data distributed. They provide formal guarantees that limit what can be inferred about individuals, at least in theory. They enable beneficial applications that would otherwise founder on privacy concerns.

But they also create new vulnerabilities that researchers are only beginning to understand. Gradient inversion attacks can reconstruct sensitive data from model updates. Malicious participants can poison shared models. The privacy-utility trade-off means that stronger guarantees come at the cost of usefulness, a cost that often falls disproportionately on already marginalised populations.

Corporate incentives shape how these technologies are deployed. Companies that profit from data collection have reasons to adopt privacy-preserving techniques that maintain their business models whilst satisfying regulators and reassuring users. This is not necessarily malicious, but it is also not the same as prioritising user privacy above all else.

The gap between technical guarantees and user understanding remains vast. Few users can meaningfully evaluate privacy claims couched in mathematical parameters and threat models. The complexity of these systems may actually reduce accountability by making it harder to identify when privacy has been violated.

Perhaps most importantly, these techniques do not fundamentally change the relationship between individuals and the organisations that train AI on their data. They are tools that can be used for better or worse, depending on who deploys them and why. They are not a solution to the privacy problem so much as a new set of trade-offs to navigate.

The question is not whether federated learning and differential privacy make you safer, because the answer is nuanced and contextual. The question is whether you trust the organisations deploying these techniques to make appropriate decisions on your behalf, whether you believe the oversight mechanisms are adequate, and whether you accept the trade-offs inherent in the technology.

For some users, in some contexts, the answer will be yes. The ability to contribute to medical AI research without sharing raw health records, or to improve keyboard prediction without uploading every message, represents genuine progress. For others, the answer will remain no, because no amount of mathematical sophistication can substitute for genuine control over one's own data.

Privacy-preserving AI is neither panacea nor theatre. It is a set of tools with real benefits and real limitations, deployed by organisations with mixed motivations, in a regulatory environment that is still evolving. The honest assessment is that these techniques make some attacks harder and enable some attacks we have not yet fully understood. They reduce some risks whilst obscuring others. They represent progress, but not a destination.

As these technologies continue to develop, the most important thing users can do is maintain healthy scepticism, demand transparency about the specific techniques and parameters being used, and recognise that privacy in the age of AI requires ongoing vigilance rather than passive trust in technical solutions. The machines may be learning to protect your privacy, but whether they succeed depends on far more than the mathematics.

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References and Sources

1. Google Research. “Federated Learning for Mobile Keyboard Prediction.” (2019). https://research.google/pubs/federated-learning-for-mobile-keyboard-prediction-2/

2. Google Research. “Federated Learning of Gboard Language Models with Differential Privacy.” arXiv:2305.18465 (2023). https://arxiv.org/abs/2305.18465

3. Dwork, Cynthia. “Differential Privacy.” Springer Nature, 2006. https://link.springer.com/chapter/10.1007/11787006_1

4. Harvard Gazette. “Pioneer of modern data privacy Cynthia Dwork wins National Medal of Science.” January 2025. https://news.harvard.edu/gazette/story/newsplus/pioneer-of-modern-data-privacy-cynthia-dwork-wins-national-medal-of-science/

5. NIST. “Guidelines for Evaluating Differential Privacy Guarantees.” NIST Special Publication 800-226, March 2025. https://www.nist.gov/publications/guidelines-evaluating-differential-privacy-guarantees

6. Frontiers in Computer Science. “Deep federated learning: a systematic review of methods, applications, and challenges.” 2025. https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1617597/full

7. arXiv. “Two Models are Better than One: Federated Learning Is Not Private For Google GBoard Next Word Prediction.” arXiv:2210.16947 (2022). https://arxiv.org/abs/2210.16947

8. NDSS Symposium. “Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning.” 2025. https://www.ndss-symposium.org/ndss-paper/manipulating-the-byzantine-optimizing-model-poisoning-attacks-and-defenses-for-federated-learning/

9. arXiv. “Model Inversion Attack against Federated Unlearning.” arXiv:2502.14558 (2025). https://arxiv.org/abs/2502.14558

10. NDSS Symposium. “Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning.” 2025. https://www.ndss-symposium.org/wp-content/uploads/2025-644-paper.pdf

11. PMC. “The 2020 US Census Differential Privacy Method Introduces Disproportionate Discrepancies for Rural and Non-White Populations.” 2024. https://pmc.ncbi.nlm.nih.gov/articles/PMC11105149/

12. Gates Open Research. “Differential privacy in the 2020 US census: what will it do? Quantifying the accuracy/privacy tradeoff.” https://gatesopenresearch.org/articles/3-1722

13. Springer Nature. “Meta's privacy practices on Facebook: compliance, integrity, and a framework for excellence.” Discover Artificial Intelligence, 2025. https://link.springer.com/article/10.1007/s44163-025-00388-5

14. Apple Machine Learning Research. “Learning with Privacy at Scale.” https://machinelearning.apple.com/research/learning-with-privacy-at-scale

15. Apple Machine Learning Research. “Learning Iconic Scenes with Differential Privacy.” https://machinelearning.apple.com/research/scenes-differential-privacy

16. Apple Machine Learning Research. “Understanding Aggregate Trends for Apple Intelligence Using Differential Privacy.” https://machinelearning.apple.com/research/differential-privacy-aggregate-trends

17. Deloitte Insights. “Consumer data privacy paradox.” https://www2.deloitte.com/us/en/insights/industry/technology/consumer-data-privacy-paradox.html

18. NVIDIA Blog. “NVIDIA Clara Federated Learning to Deliver AI to Hospitals While Protecting Patient Data.” https://blogs.nvidia.com/blog/clara-federated-learning/

19. Owkin. “Federated learning in healthcare: the future of collaborative clinical and biomedical research.” https://www.owkin.com/blogs-case-studies/federated-learning-in-healthcare-the-future-of-collaborative-clinical-and-biomedical-research

20. EUR-Lex. “European Commission Digital Omnibus Proposal.” COM(2025) 835 final, November 2025. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52025DC0835

21. CNIL. “AI system development: CNIL's recommendations to comply with the GDPR.” https://www.cnil.fr/en/ai-system-development-cnils-recommendations-to-comply-gdpr

22. 360iResearch. “Privacy-Preserving Machine Learning Market Size 2025-2030.” https://www.360iresearch.com/library/intelligence/privacy-preserving-machine-learning

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

Lately, I've been thinking a lot about how to unite two parts of my life that are seemingly disparate, opposite, irreconcilable, etc.

One is the fact that I've been working on a single endeavor, Write.as, for 11 years now — longer than I've worked on any single thing in my life. The other fact I'm trying to square is that these days, I spend a lot of time on things that have absolutely nothing to do with this, whether art, writing, photography, organizing meetups, or just grabbing a long lunch or taking the day (or three) off.

In a conversation with a friend last weekend, while I was in Brussels for FOSDEM, it finally clicked for me.

He put it plainly: some projects you do for a long time — for him, ten years was about right — and others are simply for pleasure; a long night of great conversation, a good meal with friends, etc. The former makes up the purpose in life that keeps you grounded, and the latter is life for its own sake — and nothing more.

Maybe it sounds simple, but somehow I’d never heard it put this way before. At least in the US, the options in the tech industry seem to be: either take a steady paycheck, or launch a startup, raise some money, and grind until you hopefully make that sweet payday for you and your investors.

All are valid, of course. But there are fewer words spilled about what I’m doing with Write.as: building a small software company that sustains itself for several decades. Yet, “Where is the hustle? Why aren’t you working 12 hours a day, 6 days a week?,” I hear the grindmaxxers and hustlebros cry from across the tubes of the internet.

I’ve never worried about these hardcore 10X-er brogrammers before, but I have started to feel odd in this industry building something where its only goal is to… just last a long time. And obviously keep the people that use it happy.

The thing about doing one thing for a long time…

…is that you have do one thing for a long time. And if you’re the type of person who does that in the first place, you probably have more interests in life. And, well, you might get tired of doing that one original thing for so long. You feel yourself change; life changes from under you, and days pass by, expecting you to adapt right along with them.

It’s also easy to say “I’m going to do this forever” on day one — it’s all so new and exciting! there’s so much to do! It’s less exciting to say this after 11 years, after all the battles fought, won, and lost (even if it was never really that bad).

Enough time, and other things start to look appealing, like taking a long walk instead of responding to emails, or taking up a bartending job because you don’t have to worry about AI bots in real life (well— debatable), or starting a new meetup for writers (like I did last month), or making zines and writing poetry and taking pictures of street trash and so on.

So this is the general limbo I’ve found myself in regarding Write.as, really for the last three or four years (at least). Besides the business, over the years there was plenty that took a toll on me — relationships that came and went, the pandemic, the death of my brother and my dog. And there was all the good, too: moving to New York turned out to be all I wanted and far more; I’ve been able to travel and speak about the things I make all over the world; I’ve found myself in communities of creative people and builders and connectors all around me, all around the world. Some days I’ve wallowed in an unmotivated limbo, tired of running this marathon, and on others I’m proud of what I’ve built, and I know how lucky I am to have made it this far.

Sometimes it takes a good conversation over a pint in Brussels to remember that.

All of this to say…

By now, I know the perils of writing about how enthused I am about some new perspective I have on life and Write.as, both so intertwined, before having anything to show for it. So this time I’m just getting to work, and if you’re writing here, you’ll simply notice the progress. Keep up with the big updates on our big Blog (@blog@write.as), smaller updates on our Changelog (@updates@write.as), and everywhere else you can find us on the social web:

• @write_as@writing.exchange
• @write.as on ATproto / bluesky

Lastly, in case you missed it, we’re celebrating 11 years on the web with a sale on our 5-year Pro plan through February 16th, to help support our small bootstrapped business for the next five years and beyond.

Thanks to all who make this space such a great corner of the web — and all who bear with me through my own internal ups and downs :)

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Thoughts? Drop me a note @matt@writing.exchange, or on Remark.as.

CeCe makes Love

#nsfw #CeCe

That Thanksgiving night in our dorm room felt like the culmination of everything we'd built—years of friendship twisted into something deeper, more electric, amid the quiet hum of the city outside. The remnants of our makeshift meal lay scattered on the floor, forgotten as CeCe's confession hung in the air, her nervous tremors vibrating through our naked embrace. I pulled back slightly, cupping her face in my hands, my thumbs brushing away the stray tears that had gathered in her eyes. She was so beautiful like this, her caramel skin flushed, those full and captivating breasts rising and falling with her shaky breaths, her thick curves a testament to the fearless woman she'd become. But beneath it all, I saw her vulnerabilities—the way her mind fixated on routines, on the sensory overload that porn provided as her anchor in a world that often felt too chaotic.

“CeCe,” I whispered, my voice thick with emotion, “I've wanted this for so long. But we'll go slow, okay? Whatever feels right for you.” She nodded, her gaze intense, almost laser-focused in that way she had when something captivated her completely, like solving a complex equation or diving into one of her endless porn binges. I knew her brain worked differently—craving patterns, repetition, the reliable rush of stimulation that helped her navigate the unpredictability of emotions and touch. As someone who processed the world more straightforwardly, I admired it, even if it sometimes left me chasing to keep up. Gently, I leaned in, our lips meeting in a tentative kiss that quickly deepened, her mouth soft and eager against mine, tasting of cranberry and the salt of her earlier nerves.

We shifted on the bed, our bodies aligning naturally, skin to skin. I trailed my fingers down her arms, teasing the sensitive undersides, then along her sides, feeling the subtle shiver that ran through her—a response to the newness, the intimacy beyond her solo rituals. “Remember that first time I showed you porn?” I murmured against her neck, nipping lightly at the spot where her pulse raced. “I thought I was just helping you loosen up. But it changed everything—for both of us.” She moaned softly, her hands exploring my back, her touch methodical, almost exploratory, as if mapping every inch. “It did,” she agreed, her voice breathy. “It's my everything now. The way those Black women own their bodies in those videos... it's what I need to feel alive.” I confessed then, my lips brushing her ear, “I'm addicted too, CeCe. Hunting for those videos for you over the summer? It pulled me in. I can't stop thinking about it—about you. I love porn too.”

The admission hung between us, binding us closer. But CeCe's eyes flicked to the door, then the window—both already cracked open as per her ritual, the cool night air whispering in with distant city sounds. “Leave them open,” she said, her voice laced with that familiar thrill. “I need to feel... seen. The risk, the exposure—it's part of me.” My heart raced at the idea—the door ajar, anyone could wander by in the empty hall; the window framing us for anyone glancing up from the street below. It heightened everything, a rush of adrenaline that made my core ache. I'd never been with a woman before, but with her, it felt instinctive, right.

I really thought I was straight until I met CeCe. But right now, I don't have labels for any of this. I just have an undeniable emotional bond with someone stronger and more capable than they know. We kissed again, deeper, our tongues dancing as I guided her back against the pillows, my hands caressing her breasts, thumbs circling her hardening nipples until she arched into me.

CeCe's curiosity shone through as she pushed me gently onto my back, her eyes wide with fascination. “I want to try... tasting you,” she said, almost analytically, like testing a hypothesis. She lowered herself between my legs, her breath warm against my thighs, and I gasped as her tongue flicked out tentatively, then more confidently, lapping at my folds with focused precision. She explored me methodically—long, slow licks alternating with gentle sucks on my clit—her obsession with repetition turning it into a rhythmic bliss that had me writhing. “God, CeCe, that feels incredible,” I moaned, threading my fingers through her hair, the open door and window amplifying every sound, every sensation, as if the world might hear us. The risk made it hotter, my body thrumming with the danger of exposure.

I returned the favor, eager despite my inexperience, kissing down her body—her neck, her breasts, sucking each nipple until she whimpered—then lower, to the heat between her thick thighs. Her pussy was already slick from her chronic habits, glistening in the low light, and I savored her musky taste as I licked her slowly, circling her clit with my tongue while my fingers teased her entrance. CeCe bucked against my mouth, her moans echoing softly, one hand reaching for her phone to queue up a video—Black women entangled in passionate, exposed encounters, their bodies moving in ways that mirrored her deepest fixations.

We watched porn together for what felt like hours, edging each other mercilessly: my fingers plunging into her wetness, stroking her inner walls while she rubbed my clit in steady circles, building us both to the brink without tipping over. The porn played on mute at first, then low volume, its visuals fueling her, but I whispered consents and check-ins—”Is this okay? Tell me if it's too much”—honoring her need for control amid the sensory storm. It was healing, in a way—reclaiming the addiction that had isolated her, turning it into something shared, intimate. We have masturbated together a few times. Sure. But it was about the screen and our pleasure. Never us touching like this. This was special.

Finally, as the tension coiled unbearably, CeCe handed me the dildo from her drawer—a smooth, curved silicone toy, realistic in its girth. “Please, Tasha... I'm ready,” she said, her voice trembling with a mix of fear and desire. I coated it with lube, positioning myself between her legs, the open window letting in a breeze that pebbled our skin. Slowly, sensually, I pressed the tip against her virgin entrance, watching her face for any sign of discomfort. “Close your eyes and relax. Breathe with me,” I murmured, easing it in inch by inch, her tight walls yielding like warm velvet around the intrusion, a soft gasp escaping her lips as I filled her gently, rhythmically. It was exquisite—the way she stretched, her pussy clenching around the toy as I thrust shallowly at first, then deeper, my free hand rubbing her clit in tandem. She rocked against me, her hands gripping the sheets, the risk of our exposed position sending shivers through us both.

We built to a crescendo, the porn fading into the background as our connection took over. CeCe's first partnered orgasm crashed over her not from the screen, but from my touch—the dildo buried deep, my mouth on her breast, sucking hard as she cried out, her body convulsing in waves of pleasure that left her trembling and spent. I followed soon after, her fingers still working me expertly, the release washing away years of unspoken longing.

In the afterglow, we lay tangled on the bed, the door and window still open, a soft breeze cooling our sweat-slicked skin. I held her close, reflecting on the beauty of it all—how her obsessions, her differences, had led us here, healing wounds neither of us had fully acknowledged. CeCe nestled peacefully against my breasts, tearfully happy, her sobs turning to contented sighs as she traced lazy patterns on my skin.

“I love you, Tasha,” she confessed softly, her voice thick with emotion. “You have been my rock when no one else was there for me. My classmates think I'm weird, my mom thinks I'm a failure, I'm a socially awkward mess and you stand by me. I don't know how it'll work long-term, or if we should make it 'official'... but I wanted you to know. I'll always be there for you, even if I have to wear clothes.”

In Summary: * This quiet Sunday is winding down. It finds me listening to relaxing music, ready to load my nighttime meds onto a little plate, and happy with my decision to avoid tonight's Superbowl, its annoying halftime show, and the alternative patriotic halftime show. The time I would have wasted on those things will be much better spent focusing on my night prayers.

Prayers, etc.: * I have a daily prayer regimen I try to follow throughout the day from early morning, as soon as I roll out of bed, until head hits pillow at night. Details of that regimen are linked to my link tree, which is linked to my profile page here.

Health Metrics: * bw= 229.06 lbs. * bp= 157/92 (65)

Exercise: * morning stretches, balance exercises, kegel pelvic floor exercises, half squats, calf raises, wall push-ups

Diet: * 07:00 – 1 six inch submarine sandwich, 2 cookies * 11:55 – pork chops, noodles, baked beans, whole kernel corn * 15:50 – liver and onions * 17:25 – 1 fresh apple

Activities, Chores, etc.: * 06:30 – bank accounts activity monitored * 06:50 – read, pray, follow news reports from various sources, surf the socials * 10:00 – listen to the Pre-1955 Mass Propers for Sexagesima Sunday, Feb. 08, 2026 * 10:40 – listen to KAHL Radio * 12:30 – listening to The Home for IU Women's Basketball for the pregame show ahead of the call of this afternoon's game between the Hoosiers and Purdue's Boilermakers. * 14:55 – And the IU Women Win, final score 74 to 59. * 15:00 – now watching PGA Tour Golf, final-round play from the WM Phoenix Open. * 17:20 – listen to KAHL Radio

Chess: * 16:25 – have moved in all pending CC games

⚠️ SPOILER WARNING: MAJOR SPOILERS

In the desolate wasteland of New Vegas, three survivors and their loyal dog embark on a perilous journey through a post-apocalyptic world where every step could be their last.

My Rating: ⭐⭐⭐⭐ (4/5 stars)

Episodes: 8 | Aired: 12-16-2025

This season answered a lot of questions and introduced a few new ones. But I actually liked this season better than the first season. I think they did the flashbacks much better this season, and this is thoroughly engaging and enjoyable entertainment. The one negative I would have is that this is not the first TV show or movie that has tried to technologize the 50s after World War 2 and the 60s. I am always astonished at the technology they built but other things that seem like simpler items that they miss, and I say why doesn't the technology they have built work here or there. Oh, and I didn't miss the few references to modern day issues and Trump. I find it amazing how politics of today is meshing into TV shows. Some do it well (like here) and some don't (like the recent Superman of 2025). But one thing on the real life side is the old axiom, history seems to always repeat itself. Looking forward to Season 3.

#review #tv #streaming

There are passages of Scripture you read, and they settle into the heart gently, like a soft rain. Then there are passages you encounter that don’t simply rest on you—they reshape you. They confront you. They invite you into something higher, deeper, and more demanding than you expected when you first cracked open the page. Luke 6 is one of those chapters. It is a mountain of teaching disguised in the plain tone of a gospel narrative, but beneath every line, something volcanic rumbles. Jesus isn’t merely telling stories or offering gentle encouragement; He is reintroducing humanity to the original blueprint of the Kingdom of God, overturning the assumptions of religious culture, reconstructing what spiritual maturity looks like, and pulling His listeners out of the shadow-world of shallow observation into the bright clarity of transformative obedience. And the more I’ve sat with this passage, the more I’ve returned to it like someone who knows they missed something the first five times, and the sixth time feels like the first time all over again.

The imagery of Luke 6 doesn’t unfold politely. It breaks down the door of the soul. It starts with the tension of Sabbath disputes, moves into the calling of the twelve, and suddenly Jesus descends from the mountain and begins teaching people who came to hear Him and be healed. Something about that movement already tells us that what happens here isn’t meant to be confined to the upper heights, the private and hidden moments with God alone—it’s meant to descend. It’s meant to enter the valleys where people are aching, confused, uncertain, and spiritually hungry. The mountain is where understanding begins; the plain is where understanding becomes responsibility. Luke notes that Jesus came down and stood on a level place—not above the people, but with them—delivering words that would either put fire in their bones or send them running. It’s a remarkable picture of the God who goes up to pray but always comes down to transform.

As I read Luke 6, I feel the slow unwinding of every casual assumption about the Christian life. We’ve become accustomed to the idea that faith is personal, private, and mostly inward. But Jesus speaks here as if faith is supposed to echo. As if faith is supposed to be loud—not in volume but in consequence. He begins with blessings and woes that invert the logic of society. Blessed are you who are poor. Blessed are you who hunger. Blessed are you who weep. Blessed are you when people exclude you. Four blessings that would never make a modern motivational poster. And then He reverses the pattern with four woes, reserved for those who have already claimed all the comfort, affirmation, and satisfaction the world can offer. Jesus is pointing out that the Kingdom trades in a different economy than the one we’re used to. What looks like failure to the world often looks like preparation to God; what looks like success to the world often looks like sedation to Heaven.

There’s something in this opening section of Luke 6 that makes me pause every time: Jesus isn’t calling pain virtuous; He’s calling surrender powerful. Poverty, hunger, and sorrow in themselves have no holiness. But when they strip away the false security that blinds people from their need for God, they create the very hunger that Heaven answers. It is the posture, not the pain, that carries the blessing. And likewise, it is the posture, not the pleasure, that carries the woe. It’s entirely possible to have abundance and remain spiritually sharp—but too often, comfort numbs the soul until it no longer hears the whisper of God. Jesus is calling His listeners to recognize the subtle danger of satisfaction and the surprising opportunity of brokenness. When your life feels upside-down, the Kingdom might actually be right-side up.

But the real turning point—the seismic shift of Luke 6—happens when Jesus launches into the command most Christians admire from afar but rarely approach up close: the call to love your enemies. In a world where retaliation made sense, where revenge had cultural logic, and where forgiveness was considered admirable only as long as it wasn’t too costly, Jesus detonates the entire framework. Love your enemies. Do good to those who hate you. Bless those who curse you. Pray for those who mistreat you. And suddenly we’re not dealing with simple moral teachings anymore—we’re dealing with a revolution of the heart.

Whenever I meditate on this section, I feel a tug inside me, a reminder that the Kingdom isn’t about behaving better—it’s about becoming different. Anyone can restrain anger for a moment. Anyone can smile politely at someone they dislike. Anyone can maintain a civil tone while the mind rehearses arguments and wounds. But loving your enemies requires something supernatural. It requires letting God into the places you’ve guarded, the hurts you’ve replayed, the memories you’ve nurtured, the small secret corners of bitterness you’ve considered justified. Jesus isn’t telling people to become pushovers; He's telling them to become conduits. Love your enemies isn’t a command to be weak—it’s a command to be so spiritually transformed that retaliation loses its appeal. True strength isn’t in the clenched fist but in the open hand.

Jesus goes deeper still. If someone strikes you on one cheek, offer the other. If someone takes your cloak, don’t withhold your tunic. Give to everyone who asks. Do to others as you would have them do to you. This is the Kingdom ethic. Not fairness. Not reciprocity. Not social negotiation. Radical generosity. Radical mercy. Radical love. But only someone who has been deeply healed can live this way. This is why so few do. Because the flesh resists what the Spirit invites. The ego resists what surrender gives. The world resists what Heaven commands. And Jesus says plainly: if you love only those who love you, what credit is that to you? Even sinners do that. The point is unmistakable—Christian maturity isn’t revealed by how you treat your friends; it’s revealed by how you treat your opposers.

I remember reading this passage years ago and feeling quietly defensive. I wanted to treat Jesus’ words like poetry—beautiful, lofty, symbolic. But Jesus wasn’t speaking in metaphor here. He was laying out a blueprint for a kind of humanity that doesn’t exist naturally. Everything He describes in Luke 6 requires transformation from within. You can’t grit your way into loving your enemies. You can’t force your heart to bless people who wound you. You can’t fabricate compassion for people who despise you. But you can open yourself to a God who changes the interior motives that drive your exterior responses. You can allow God to perform heart surgery on your war instincts. You can allow the Holy Spirit to remove the unconscious contracts you’ve made with resentment. Jesus is calling His followers to a maturity powered by Heaven.

Then comes the part of Luke 6 that always stops me cold: Be merciful, just as your Father is merciful. Mercy is the currency of Heaven. It’s not a gesture or a mood. It’s a posture, a way of seeing, a way of responding. It requires humility. It requires surrender. It requires acknowledging the mercy you’ve received and letting it flow outward like a river that refuses to run dry. We tend to give mercy in teaspoons, but God pours it in oceans. Jesus says plainly: do not judge, and you will not be judged. Do not condemn, and you will not be condemned. Forgive, and you will be forgiven. This isn’t a threat—it’s a spiritual principle. The measure you use becomes the measure that shapes you. When you judge harshly, your heart becomes harsh. When you condemn readily, your spirit grows brittle. When you hold grudges, your soul starts to calcify. But when you extend mercy, something inside softens. Something heals. Something grows. Something awakens.

And then the teaching that echoes through centuries: Give, and it will be given to you. A good measure, pressed down, shaken together, running over. Jesus isn’t talking about a cosmic transaction where generosity becomes a vending machine—you put something in, God spits something out. He’s describing a life where generosity becomes a way of being, and in that way of being, you discover that God has been generous toward you all along. Giving enlarges the soul. Withholding shrinks it. The measure you use—toward people, toward God, toward yourself—shapes the life you live. When you live with open hands, abundance flows through you, not because you manipulated Heaven but because you aligned with it.

Then Jesus shifts into a series of images that expose the inner contradictions we all carry. Can the blind lead the blind? Won’t they both fall into a pit? Why do you look at the speck in your brother’s eye while ignoring the log in your own? These aren’t gentle metaphors. They are targeted diagnoses. Jesus is calling out the human tendency to become experts in the flaws of others while remaining novices in self-awareness. It is far easier to critique than confess. It is far easier to point out what someone else should fix than to sit with God long enough to let Him fix something in us. But Jesus is cutting off that escape route. First take the log out of your own eye, then you will see clearly to remove the speck from your brother’s eye. Notice the nuance: Jesus isn’t saying we should never correct others—He’s saying correction is a ministry reserved for the healed, not the reactive.

The passage then transitions to the imagery of trees and fruit—another moment where Jesus shows that real transformation is never cosmetic. A good tree cannot produce bad fruit, and a bad tree cannot produce good fruit. In other words, behavior flows from being. Actions flow from essence. You don't fix fruit; you tend the roots. This is why superficial Christianity collapses under pressure. Anyone can behave for a season, but only those whose hearts have been shaped by God can live consistently in love, mercy, and integrity. Jesus is calling His followers not to better performance but to deeper belonging—to let His life become the root system that produces the fruit of the Spirit.

Then Jesus lands the entire chapter with one of the most sobering warnings in all of Scripture: Why do you call me “Lord, Lord” and do not do what I say? Two houses. Two builders. One storm. And suddenly everything hidden becomes revealed. One house stands because it was built on the rock of obedience. The other collapses because it was built on the sand of admiration without action. Jesus isn’t impressed by people who like His teachings—He’s moved by people who embody them. A storm will always reveal what your foundation is made of. And the storms come to everyone. Being a believer doesn’t spare you from storms; it anchors you through them.

Luke 6 is demanding. It is confrontational. It is liberating. It is deeply uncomfortable. And it is beautifully healing. If the Sermon on the Mount is the constitution of the Kingdom, the Sermon on the Plain is its manifesto. It strips away the veneers of religious performance and demands a heart that beats with the heartbeat of Heaven. And at the center of it all is a simple invitation: become who you were always meant to be by becoming more like the One who stood at that level place, surrounded by broken people, speaking words that could either rebuild them or expose them.

Luke 6 is where the words of Jesus begin to circle the heart like a hawk riding thermal winds, tightening the arc, coming in closer and closer until the soul realizes it has nowhere left to run. You can admire Luke 6 from a distance, but the closer you read, the more it becomes a mirror. It stops being a chapter and becomes an encounter. It stops being information and becomes a confrontation. And eventually, it stops being a teaching and becomes a choice.

I’ve always loved that Jesus doesn’t let His listeners walk away with the illusion that spiritual maturity is the result of learning alone. You can learn every verse in the chapter and still not live a single line of it. This is why the foundation metaphor at the end is not accidental—it is purposeful and piercing. Jesus is saying plainly: admiration is not obedience. Affection is not allegiance. Agreement is not transformation. Calling Him Lord without living His teachings is like building a house with no foundation at all and expecting it to stand when the storm finally arrives. But storms do not lie. Storms reveal. Storms report the truth.

Before we arrive at that final image, though, we have to linger over the middle terrain of Luke 6—the region where Jesus exposes motives, reveals the cost of discipleship, and names the quiet realities that shape the Christian journey long before behavior ever changes on the outside. This is where the Kingdom stops being a philosophy and becomes a way of being. This is where the human heart, once tightly wound in self-protection and self-preservation, begins to surrender its scaffolding and allow the winds of Heaven to reconstruct its architecture.

Something profound happens when Jesus speaks about mercy, judgment, forgiveness, and generosity. These aren’t isolated qualities; they form a spiritual ecosystem. They shape each other. They interdepend. Mercy purifies judgment. Forgiveness heals memory. Generosity dissolves fear. And judgment, when misused, poisons all three. Jesus is describing a life where the heart is open—not unguarded in a reckless sense, but uncluttered in a spiritual sense. A heart that releases more than it retains. A heart free from the hoarding of emotional debts. A heart that is no longer negotiating the terms on which it will love. That kind of heart is the soil where the Kingdom grows.

We live in a world that trains people to filter every experience through self-protection. What will this cost me? What advantage does this give me? How do I avoid being used? How do I maintain control? But Jesus is describing a life that no longer organizes itself around fear. Fear is the architect of walls. Love is the architect of bridges. Fear isolates. Love integrates. Fear withholds. Love pours. Fear keeps score. Love erases the chalkboard before the tally is even complete. The radical life Jesus describes requires the dismantling of fear, not the modification of behavior. You cannot tweak your way into mercy. You cannot adjust your way into forgiveness. You must surrender your way into it.

There is a reason Jesus tells us not to judge. He isn’t saying we should never evaluate situations or discern right from wrong. He is saying we must stop forming verdicts on people’s worth. Judgment is a spirit, a posture, a habit of the heart that assigns value based on visible behavior while ignoring invisible battles. Judgment is the arrogance of assuming knowledge we do not have. Judgment is the assumption that someone else's worst moment reveals their truest identity. And Jesus has no tolerance for this, because judgment is the counterfeit of discernment. Discernment seeks truth with humility. Judgment declares truth with pride. Discernment loves people enough to see their potential. Judgment punishes people for not yet being what they could be. Jesus tells us plainly to let it go. Release the instinct to categorize. Release the instinct to accuse. Release the instinct to evaluate someone’s worthiness of love, respect, compassion, or patience. Judgment shrinks people. Mercy restores them.

Forgiveness, too, is woven throughout Luke 6 like an unspoken thread. Jesus doesn’t use the word every time, but it’s present beneath every command. Turning the other cheek requires forgiveness. Loving your enemies requires forgiveness. Giving to those who take from you requires forgiveness. Blessing those who curse you requires forgiveness. Forgiveness is the current running beneath the surface, powering everything Jesus is teaching. And yet, forgiveness is one of the most misunderstood spiritual practices in the Christian life.

Forgiveness is not saying the wound didn’t matter—it mattered deeply. It is not saying the person was right—they were not. It is not saying you will trust them again—trust is earned, forgiveness is given. Forgiveness is the release of the right to become the judge, jury, and executioner of someone else’s soul. It is the choice to place the weight of the offense into God’s hands instead of your own. It is the refusal to carry the poison others handed you. Forgiveness is not weakness. It is power. It is strength. It is authority over the narrative of your own story. Forgiveness is what frees the future from the grip of the past.

When Jesus says, give and it will be given to you, He is showing that life flows out of whatever space we open. When we open the space of giving, blessing flows. When we open the space of forgiveness, healing flows. When we open the space of mercy, restoration flows. The measure we use doesn't just determine how much we receive—it determines how much we are transformed. A small measure creates a small life. A large measure creates a large life. God will never be stingy with those who refuse to be stingy with others. Not financially. Not emotionally. Not spiritually. Not relationally.

This brings us to the blind guiding the blind. Jesus isn’t offering a cynical view of humanity; He’s issuing a warning about leadership and influence. Every one of us is leading someone, whether we realize it or not. Our children. Our friends. Our coworkers. Our audience. Our readers. Our listeners. And the sobering truth is that you cannot lead someone into clarity if you are walking in fog yourself. You cannot lead someone into freedom if you refuse to confront your own chains. You cannot lead someone into healing if you are committed to pretending you were never wounded. Jesus is calling His followers to leadership that begins with self-awareness. A disciple is not above his teacher, but when he is fully trained, he will be like his teacher. Transformation doesn’t happen by proximity alone—it happens by surrender.

Then we reach the speck and the log. Everyone knows this image. Even people far from Christianity quote it. But few understand the power of it. Jesus isn’t merely telling us to avoid hypocrisy. He’s showing us that spiritual sight is sharpened by humility. When you address your own brokenness first, you become gentle in the way you help others address theirs. When God has dealt with your log, you stop using someone else’s speck as leverage. When you’ve been humbled by your own need for grace, you stop using truth as a weapon and start using it as medicine. Jesus is telling us that correction requires clarity, and clarity comes from a heart purified by self-honesty.

Now the tree and the fruit. Jesus is pressing deeper into the origin of behavior. He is refusing to let us judge ourselves or others by appearances. If the fruit is rotten, the problem is not the fruit—it is the root. You can wash fruit. You can polish it. You can rearrange it. But if the tree is sick, nothing changes. Jesus is calling for transformation at the root level: your beliefs, your motives, your interior world, the unspoken narratives you live from. The Christian life is not a behavior modification program—it is a character transformation journey. Behavior is the echo of the heart. Fruit is the biography of the root system. If the heart belongs to God, the life begins to reflect it—not perfectly, not instantly, but inevitably.

This leads us to the question Jesus asks that pierces deeper than any theological debate, deeper than any doctrinal argument, deeper than any intellectual exercise: Why do you call me Lord, Lord, and do not do what I say? There is no hiding behind symbolism here. There is no escaping into metaphor. Jesus is asking a question that echoes across centuries and lands in our laps with full weight. If He is Lord, then our lives should look like something. Not flawless. Not sterile. Not artificially perfect. But surrendered. Responsive. Evolving. Alive.

This is why Jesus ends with the parable of the two foundations. Two builders. Both hear the words of Jesus. Both build houses. Both face the storm. But only one survives. Not because he had better luck or better weather or better circumstances, but because he dug down deep and laid the foundation on rock. The difference between superficial faith and solid faith is depth. The superficial believer builds quickly. The deep believer digs. The superficial believer responds emotionally. The deep believer responds obediently. The superficial believer admires Jesus. The deep believer imitates Him. The superficial believer listens. The deep believer acts.

And when the storm comes—and it always comes—the truth of your foundation is revealed. Jesus isn't promising storm-free living. He’s promising storm-proof living. Storms expose the strength or weakness of whatever we’ve built our lives upon. Storms tell the truth we didn’t want to admit. Storms become teachers, revealing whether we built our faith on emotions, reputation, habits, knowledge, or the actual teachings of Jesus.

Luke 6 reminds us that the Christian life is not a negotiation. It is not an arrangement. It is not a selective acceptance of Jesus’ teachings. It is a full-hearted surrender to a Kingdom that turns the world’s values inside out. It is the slow, consistent shaping of your character into the likeness of Christ. It is the surrender of retaliation. The embrace of mercy. The practice of forgiveness. The generosity of spirit. The courage to self-examine. The humility to learn. The hunger to grow. The willingness to be changed.

And the beauty of all of this is that Jesus never calls us into a life He hasn’t already lived Himself. He loved His enemies. He blessed those who cursed Him. He forgave those who nailed Him to the cross. He offered mercy to those who did not deserve it. He walked without judgment yet with perfect clarity. He bore good fruit because His roots were anchored in the Father. He built His life on obedience. He lived the very sermon He preached.

Luke 6 is an invitation to join Him on that path. Not in perfection, but in direction. Not in flawless execution, but in faithful intention. Not in performance, but in transformation. It is a call to become the kind of person who doesn’t just know the teachings of Jesus but embodies them in the quiet places where no applause can be heard. It is a call to live a life that is so saturated with mercy that people taste the Kingdom in your presence without knowing why. It is a call to build something deep, something solid, something eternal, something storm-proof.

And when you embrace Luke 6, not as a chapter but as a lifestyle, everything changes. Relationships change. Reactions change. Priorities change. Desires change. The way you see people changes. The way you see yourself changes. The way you see God changes. This chapter, when allowed to soak into the soul, doesn’t produce nicer people—it produces transformed people. People whose lives look like a lived sermon. People whose steps echo the footsteps of Jesus. People whose character has been shaped by Heaven.

This, ultimately, is the legacy of Luke 6: a new humanity emerging in those willing to surrender everything for the sake of the Kingdom. People who love extravagantly, forgive fiercely, give freely, judge slowly, and build deeply. People who have dug into the rock until they found the foundation that cannot be shaken. People who have chosen the path that leads not to applause but to transformation. People who have discovered that the greatest spiritual victories are won in the interior rooms of the heart long before they ever show up in behavior.

Luke 6 is not just a chapter. It is a doorway. And once you walk through it, the air on the other side tastes different. The light is different. The priorities are different. The journey itself becomes different. You become different.

This article is meant to be lived, not admired. And my hope is that every word becomes a gentle push toward the life that Jesus describes—a life that reflects Heaven’s values in an earthly world, a life that extends mercy where none is expected, a life that forgives where others would retaliate, a life that gives where others withhold, a life anchored so deeply in obedience that no storm can shake it loose.

And as you carry Luke 6 with you, may you find your roots deepening, your foundations strengthening, your character evolving, and your faith expanding. May you become a living picture of mercy, love, clarity, generosity, humility, and courageous obedience. May you become the kind of person who hears the words of Jesus and does them, not out of fear, but out of love. Not out of obligation, but out of identity. Not out of duty, but out of transformation.

The Kingdom is calling. Luke 6 is the map. And your life—your actual lived life—is the ground where this teaching becomes real. Step into it with your whole heart. The world needs people whose foundations are built on rock. The world needs people who choose mercy over judgment. The world needs people who refuse to retaliate and choose love that costs something. The world needs people shaped by Luke 6. The world needs you.

Your friend, Douglas Vandergraph

Watch Douglas Vandergraph’s inspiring faith-based videos on YouTube: https://www.youtube.com/@douglasvandergraph

Support the ministry by buying Douglas a coffee: https://www.buymeacoffee.com/douglasvandergraph

There’s a strange beauty in the idea that if you don’t believe in God, you should pray that God believes in you. It sounds almost like a paradox, almost like a philosophical knot tied too tightly to pull apart, yet when you sit with it—really sit with it—we discover that it’s not a knot at all. It’s a doorway. A doorway into the quiet, overlooked truth that long before belief ever rises in us, God’s belief has already risen over us. Long before we whisper His name with sincerity or clarity, He has spoken ours with love and certainty. This entire thought—this reversal of expectation—feels like an invitation to step outside the way we’ve been trained to see faith, doubt, and divine connection, and instead walk into the raw and tender place where God meets people exactly where they are, not where they’re “supposed” to be. Talk to enough people who’ve lived through spiritual droughts, confusion, heartbreaks, and intellectual wrestling matches with the universe itself, and you’ll notice a simple pattern: almost nobody doubts God because they want to. They doubt because of wounds. They doubt because of mismatches between expectation and experience. They doubt because life hit them harder than they ever expected and religion didn’t prepare them for what real pain feels like. They doubt because the image of God they were taught did not survive contact with the world they live in. They doubt not out of rebellion, but out of exhaustion. And exhaustion doesn’t need a lecture—it needs a place to rest. That’s where this seemingly inverted sentence becomes a soft landing spot for the soul: if you don’t believe in God, pray that God believes in you. Because even the skeptic, the wounded, the bewildered, and the distant can ask one thing: “If there’s Someone out there, let them not give up on me.” That fragile, almost trembling desire reveals more about the human heart than any argument ever could.

I’ve always felt that faith isn’t born at the front door of certainty—it’s born in the side-alley moments. The quiet crises. The moments of internal contradiction when a person silently whispers to themselves, “I don’t know anymore.” But uncertainty is not the enemy of faith. Indifference is. And there’s a world of difference between someone who says, “I don’t care,” and someone who says, “I don’t know.” When a person says, “I don’t know,” there’s still a reaching happening beneath the surface. It might be small, barely visible, almost fragile, but it’s there. And I believe God honors the smallest reach. If a whisper is all you have left, Heaven listens like it’s thunder. If the only prayer you can muster is, “If You’re real, find me,” God treats that like a door swinging wide. If the heart says, “If You believe in me, show me,” then the God of all creation bends low enough to meet that heart where it stands. And all of this matters because there are people walking around today feeling like they’re not allowed to be honest with God. As if doubt disqualifies. As if questions insult Him. As if struggle means distance. But the truth is far more compassionate. God’s belief in you is not based on your belief in Him. His belief in you is anchored in His nature, not your performance. He doesn’t need your certainty to be committed to you. He doesn’t need your perfection to walk beside you. He doesn’t need your theological clarity to wrap His arms around your life. If anything, He steps closest when clarity is the hardest to find.

One of the great tragedies of spiritual culture is that people have been made to feel like faith requires flawless conviction. But think of every person in history who’s ever become anything meaningful in their walk with God—they all began in some version of confusion. They all carried questions. They all wrestled with doubts so real and so heavy they could barely lift their own heads. And yet God still moved in them. He still believed in them. He still breathed life into the places that felt hollow. If the greatest stories in Scripture were built on shaky beginnings, then why do we expect modern believers to start their journey perfectly stable? God has always done His best work in people who came to Him imperfect, unsure, unsteady, and halfway broken. Because belief isn’t a ladder—it’s a seed. And seeds don’t start strong. They start hidden. They start quiet. They start in darkness. They start in soil that doesn’t look like anything is happening at all. And yet, under that soil, life begins. Under that soil, roots take hold. Under that soil, growth starts its sacred, unseen work. Belief works the same way. It does not burst from the ground fully formed. It begins unseen. It begins inside. It begins in whispers like, “God, I don’t know You yet… but if You believe in me, help me believe in myself the way You do.”

There’s also this deep tenderness woven into that idea—that God believes in you. Just pause with that. Let it soak. The Creator believing in the created. The Eternal believing in the temporary. The One who has no beginning believing in the one still struggling to begin. He believes in your capacity to rise. He believes in your ability to heal. He believes in the parts of you you’ve written off. He believes in the version of you that you can’t quite see yet. He believes in your future while you’re still stuck in your past. He believes in your potential even if your history tries to shout otherwise. He believes in the arc of redemption written through every life that still has breath in it. God doesn’t just believe in you as you are—He believes in the you that’s becoming. And when you realize that, when you feel it not as a religious slogan but as a truth that reaches down into your bones, everything shifts. Suddenly you don’t walk like you’re abandoned—you walk like someone held. You don’t think like someone unwanted—you think like someone chosen. You don’t live like someone left behind—you live like someone God refuses to give up on.

There is a phenomenon that happens when people get hurt deeply enough: they don’t stop wanting God—they stop trusting the idea of being disappointed again. And this is where belief becomes complicated. So many people aren’t rejecting God Himself—they’re rejecting the pain attached to previous attempts at faith. They’re rejecting the versions of God handed to them by flawed voices. They’re rejecting the interpretations that hurt more than they healed. They’re rejecting the expectations that were too heavy to carry. And in that place, “I don’t believe in God” often means, “I can’t afford to be let down again.” That kind of declaration isn’t coldness—it’s self-protection. So imagine what happens when we offer them a new doorway: “If you don’t believe in God, then ask that He believes in you.” That’s not a challenge. It’s not an argument. It’s not a debate. It’s an open hand. A pathway for the weary. An invitation for those who’ve been bruised by life. A gentle whisper saying, “You don’t have to know everything. You don’t have to decide everything. You don’t have to resolve everything today. Just ask for one thing: that the One who made you hasn’t lost faith in who you can become.”

And the beauty of that ask is that it matches God’s heart perfectly. Because God has always been the God who believes before you do. Look through Scripture, through history, through the testimonies of countless lives changed—not one of them begins with someone who had it all together. They were uncertain, unqualified, unprepared, undone. God didn’t wait for them. He believed in them and then walked them forward. The fisherman who doubted himself. The woman who felt unworthy. The outcast who wondered if life held anything else. The leader who never asked to lead. The wanderer who had no direction. The broken who felt useless. They weren’t chosen because they believed—they grew because He believed. And the same story continues in our time. You don’t need perfect belief to start this journey. You need honesty. You need willingness. You need that slight leaning of the heart that says, “If You believe in me, then maybe I can take one more step.”

Think of how many people live every day feeling unseen. Feeling like their best efforts fall short. Feeling like nobody recognizes what they carry, what they fight through, what they survive. The thought that God believes in them becomes more than theology—it becomes oxygen. It becomes something that keeps them from sinking. It becomes a lifeline when they feel adrift. Because if God believes in you, then there must be something in you worth believing in. Something that hasn’t been ruined by your mistakes. Something unbroken by your past. Something untouched by the disappointments that shaped you. Something sacred. Something intentional. Something God still plans to use. And that realization alone can lift a person out of despair. It can lift them out of self-condemnation. It can lift them out of the belief that they are too far gone to matter.

When you tell someone, “Pray that God believes in you,” you’re telling them something deeply empowering: you’re saying that the relationship between God and the human soul doesn’t begin with your perfection—it begins with His persistence. His pursuit. His unwavering commitment to who you really are beneath the layers. You’re saying that God has already invested Himself in your life long before you ever learned how to look back at Him. You’re saying that faith is not a mountain you climb alone—it’s a journey where God walks toward you even as you stumble toward Him. You’re saying that the pressure to have every answer figured out is replaced with the invitation to simply be honest, open, and willing.

This idea frees people. It frees them from religious performances. It frees them from the fear that doubt separates them from God. It frees them from the lie that God is disappointed by their humanity. And in that freedom, faith grows more authentically than it ever could under pressure. Because faith that grows by force is fragile. Faith that grows by honesty is durable. And faith that grows from the realization that God believes in you before you believe in Him becomes almost unbreakable. It becomes rooted not in your own strength, but in His. Not in your consistency, but in His faithfulness. Not in your understanding, but in His insight into who you truly are.

This world is full of people who carry quiet battles nobody else knows about. Anxiety that keeps them awake at night. Guilt that eats at them in the morning. Fear that follows them like a shadow. Memories they wish they could erase. Pressure that makes them feel like they’re drowning from the inside out. These people often avoid faith conversations because they believe they’re already disqualified. They think God only wants the strong, the certain, the steady. But imagine the healing that begins when they hear: “Even if you don’t believe in God… He hasn’t stopped believing in you.” That statement alone can crack open a wall someone has held up for decades. Because suddenly, faith is no longer a competition. It’s no longer a requirement. It’s an invitation back to themselves. It’s a reminder that they are not alone in the fight to become whole.

And this is where the real transformation begins. When someone takes that first step—not a confident step, not a sophisticated step, not a doctrinally precise step—but a real step. A step like, “God, if You’re there, I need You to believe in me because I don’t know how to believe in myself.” That moment becomes sacred soil. Heaven meets people there. God bends low to that place. It’s the place where the divine and human heart breathe at the same rhythm. It’s where hope begins rebuilding its foundation. It’s where the seed of belief finally gets its chance to open. And once it opens, even slightly, even subtly, everything begins to change.

Because when belief begins to grow in the soil of honesty instead of pressure, it becomes a different kind of belief. It becomes humble. It becomes authentic. It becomes patient with itself. And most importantly, it becomes sustainable. People who try to force themselves into belief often end up exhausted, and exhaustion is not faith—it’s performance. But people who let belief grow from a place of being seen, understood, and believed in by God discover a faith that carries them instead of a faith they must constantly carry. It becomes something alive instead of something heavy. It becomes something they look forward to instead of something they're afraid they will fail at. Because when you know God already believes in you, your fear of disappointing Him begins to dissolve. You stop bracing for judgment and start opening yourself to transformation. You stop hiding from God and start letting Him into the rooms of your soul that you kept closed for years. You stop expecting perfection from yourself and start welcoming progress. This is the beginning of real faith, and it is holy in its simplicity.

There’s also another dimension to this: when God believes in you, He believes in the story He’s writing through you. People often think their story is defined by what they’ve done, but God defines your story by what He’s doing. People look at their failures and see endings; God looks at the same failures and sees setups. People see brokenness; God sees building material. People see disqualification; God sees invitation. And when you begin to understand that God isn’t writing you off, you begin to participate in the story He’s still writing. That’s when faith stops feeling like a distant concept and becomes an unfolding reality inside you. One day you wake up and realize you’re speaking differently, thinking differently, walking differently, loving differently. Not because someone told you to change, but because the God who believes in you is awakening the version of you He always knew was there.

And the beautiful thing is that God’s belief in you doesn’t just shape your inner world—it shapes how you move in the outer one. You start to walk with a quiet confidence. The kind that isn’t loud, but steady. The kind that doesn’t need to shout, but still shifts the atmosphere. When you know God believes in you, you approach challenges differently. You don’t treat them as signs you’re failing—you treat them as proof you’re growing. You don’t hide from responsibility—you rise to it. You don’t retreat in the face of adversity—you lean into purpose. Because a person who knows they are believed in becomes a person who is able to believe in what God is doing in them. This is why people of great spiritual depth don’t always start with great belief—but they always end with it. Their belief becomes the harvest of being believed in by a God who refuses to walk away from them.

And this understanding does something else—something powerful. It softens your judgment of others. When you know how patient God has been with your process, you begin to carry patience for the process of others. Suddenly you don’t look at doubters with frustration—you look at them with compassion. You don’t see skeptics as threats—you see them as people in pain. You don’t see wanderers as defiant—you see them as searching. You don’t see people struggling with faith as failures—you see them as future testimonies in progress. This is because once you truly experience a God who believes in you even when you don’t believe in Him, you begin to reflect that same belief toward those who are still struggling. You become a carrier of the same grace that carried you.

And perhaps this is one of the most transformative parts of the entire concept—that God’s belief in you becomes a model for how you treat the world around you. Instead of becoming someone who polices belief, you become someone who nurtures it. Instead of becoming someone who judges the uncertain, you become someone who walks with them. Instead of becoming someone who pressures people toward faith, you become someone who creates safe spaces where faith can grow naturally. You begin to see that belief is not a battlefield—it’s a journey. And journeys take time. They take patience. They take compassion. They take understanding. They take room to breathe. And when you carry God’s belief in you, you naturally create that room for others.

It also shifts the way you see yourself. You stop defining yourself by the worst things you’ve done. You stop defining yourself by the hardest seasons you’ve lived through. You stop defining yourself by the failures that once haunted you. Instead, you define yourself by the God who has never given up on you. And that shift changes the entire architecture of your identity. Suddenly your past isn’t your prison—it becomes the soil where your calling grows. Your regrets aren’t chains—they’re lessons. Your wounds aren’t disqualifiers—they’re testimonies waiting to be told. And when someone says, “If you don’t believe in God, pray that God believes in you,” what they’re really saying is, “Let God begin the work in you that you don’t yet know how to begin in yourself.”

And in time, faith will come. Not forced. Not rushed. Not pressured. But naturally. Quietly. Authentically. Faith will rise like morning light—gentle, gradual, revealing what has always been there but was hidden in the dark. One day you’ll look back and realize belief didn’t come the way you expected. It didn’t arrive with fireworks or arguments or sudden bursts of clarity. It arrived the way God often arrives—in the stillness, in the whisper, in the gentle stirring of a heart that finally realized it was safe to hope again. And that kind of faith is deep. It’s rooted. It’s unshakeable. Because it’s faith born from being loved, not faith born from being pressured.

If the world understood this, faith conversations would change. Instead of trying to force belief on people, we’d speak to the parts of them that long to be believed in. We’d talk to the hurt before we talked to the doubt. We’d talk to the longing before we talked to the theology. We’d talk to the heart before we talked to the doctrine. Because in the end, people aren’t looking for a God to argue with—they’re looking for a God who hasn’t abandoned them. They’re looking for a God who can handle their uncertainty. They’re looking for a God who doesn’t vanish when life gets hard. They’re looking for a God who believes they’re worth the effort of redemption. That’s the God who shows up when someone whispers that first hesitant prayer: “If You believe in me… help me believe again.”

So if you’re reading this today, and you’re wrestling with your own doubts, your own questions, your own fears, your own distance from God, let this be a soft place for your soul to land. You don’t have to pretend. You don’t have to perform. You don’t have to exaggerate your faith or hide your uncertainty. Just start with honesty. Start with the simple acknowledgment that your heart is still open enough to ask. And if you don’t know how to believe in God right now, then simply pray this: “God, I pray that Your belief in me becomes the anchor I can’t give myself.” That prayer is not small. It is not weak. It is not inadequate. It is sacred. It is powerful. And it is enough.

Because God’s belief in you has been steady from the start. He has never withdrawn it. He has never reconsidered it. He has never questioned whether you are worth the investment. His belief in you is not based on who you were, but on who He knows you can become. So take the pressure off yourself today. You are not behind. You are not failing. You are not forgotten. You are not disqualified. You are simply in process. And that process is holy.

Let this be your reminder: if you don’t believe in God right now, it’s okay. It truly is. Just pray that God believes in you. And when you do, you’re not awakening something in Him—you’re awakening something in yourself. You’re stepping into a truth that has always been waiting for you. You’re allowing God’s belief in you to breathe where doubt had stolen your breath. You’re letting the One who formed you remind you why He formed you. And eventually, you’ll discover that belief isn’t something you achieved; it’s something you received. Something that grew quietly as you allowed God’s love to work in you.

And when that happens, when belief rises from being believed in, you’ll find a faith that’s not fragile—it’s alive. It’s resilient. It’s personal. It’s rooted in relationship rather than rules. And that faith will carry you farther than you ever imagined. So keep going. Keep whispering. Keep reaching. Even your smallest prayer is big in God’s hands. Even your weakest faith is precious to Him. Even your uncertainty is welcome in His presence. And even your doubts cannot stop His belief in you.

In time, you will look back and see that faith wasn’t something you built from the ground up—it was something God breathed into the deepest parts of you from the very beginning. And that breath is still in you. That purpose is still in you. That calling is still in you. And God’s belief in you is still the foundation under your feet.

You’re not lost. You’re becoming. And Heaven has never been more certain of you than it is right now.

Your friend, Douglas Vandergraph

Watch Douglas Vandergraph's inspiring faith-based videos on YouTubehttps://www.youtube.com/@douglasvandergraph

Support the ministry by buying Douglas a coffeehttps://www.buymeacoffee.com/douglasvandergraph

as sweet as possible as spontaneous as possible as sincere as possible as serene as possible as strong as possible as symbolic as possible as soothing as possible as soulful as possible

/feb26

A Message of Hope

Each of you is unique, unrepeatable, irreplaceable, incomparable, separate, and distinct. You have been given a body and a pyche which are sometimes similar in character type and/or traits to others, but beyond that your are a spirit person with a limited degree of freedom and a capacity to respond to life an its demands. There never was, there never is, there never will be an absolute twin, a clone, one who can replace you. You are a one of a kind and life is calling, inviting, and challenging you to become the authentic you by trancending yourself and at the same time forgetting yourself.

If you simply search for pleasure or power, you will experience something missing. You will at some moment feel empty, a void, a vacuum. You will wonder, “What's it all about?”

When the need for meaning finally occurs to you, you will beging to seach for meaning every day.

...

McKilopp, T. (1993) A MESSAGE OF HOPE, The International Forum of Logotherapy, p. 4

#LogoTherapy #FranklViktor #McKillopp #hope #UniquePerson #meaning

This fairly recent obsession with metrics in the workplace is driving companies insane.

A while back, I watched a video about all the ways hotels are trying to save money by, among other things, eliminating storage space, making the bathroom less private, removing desks, and pressuring guests to work at the bar, where they can spend more money. (By the way, that bartender? They're also the receptionist.) These changes are, of course, driven by metrics like “GSS” and “ITR,” whatever the f@*k those are.

Is there a kernel of truth to all of this? Sure. Aloft Hotels are cozy, and they seem to follow this playbook. I didn't mind staying in one when I was stuck in San Francisco for one night more than ten years ago. Would I want to stay in one of their rooms during a business trip or anything else lasting more than a couple of days? Hell no. I'd like a desk and somewhere to put clothes. (I know, I'm so needy. I travel with clothes.)

Metrics are fine, sometimes, when their use is limited and their shortcomings are genuinely appreciated. Taking them too seriously and letting them make the decisions, however, is a recipe for disaster. Hard questions demand more thoughtfulness than that. “GSS” and “ITR” are meaningful until they aren't, and nobody is going to find solace in those abbreviations when generations of potential customers steer clear of your business because they actually want something good.

Sadly, I don't think most businesses think that far ahead.

Show me the metric which proves that your business isn't incurring massive risk by ignoring common sense. Until then, I don't care about “the numbers.”

#Life #SoftwareDevelopment #Tech

Our Mothers — This Documentary is a Very Keen Look at Our Dear Mothers

Lydia Joly, middle, on her parents’ farm circa 1967 — son, Loran, left; sister, right; my great-grandmother, back row. When great-grandmother was not visiting, I would sometimes sleep in the bed she had slept in when at the farm… “The apple doesn’t fall far from the tree?”

A documentary on the “astounding impact” of a mother on others, was created by Michael DuBois, a few years ago …

“Becoming Home – full film”:

https://youtu.be/NtPbAuFMI0c?si=bcCTE2fZH3PVN7vy

“The documentary “Becoming Home” touched my heart, a few years ago. Make by filmmaker Michael DuBois, he chronicled the “first year after the death of his mother. He set out to discover why she had the astounding impact on others that she did…”

Michael lives on Cape Cod, as of when he created this documentary…

“Becoming Home” is his finished story. It is the story of his mother, and her grace through life. It is the story of his childhood. And it is the story of learning to move forward after those losses, without moving away from them. Directed by Michael F. DuBois Produced by Bert Mayer and Larissa Farrell Director of Photography Mark Kammel Original Music by Derek Hamilton Featuring Music by Sky Flying By and Pete Miller”

Denzel Washington has this to say, about mothers, also…

“The Power of a Mother’s Love | Denzel Washington’s Inspiring Speech on Gratitude and Respect”

My mother, Lydia Joly, age 87, war refugee from Piaski, Poland, with time in a relocation camp in northern Germany after World War I also — arrived Ellis Island 1950 — image by son Loran

Christmas card 2024 with Lydia’s self-made Gingerbread house

Lydia — my mother — was born in Lubelskie County, Poland.

We see her village, Piaski, here, with beautiful music…

“Piaski Lubelskie”:

https://youtu.be/XF04EznukOY?si=E2qJLDS5jNsJxzaI

No wonder she loves gardening and flowers…

Lydia, gardening, 2025, age 87

Caveat: this article contains a detailed examination of the state of open source/ weight AI technology that is accurate as of February 2026. Things move fast.

I don’t make a habit of writing about wonky AI takes on social media, for obvious reasons. However, a post from an AI startup founder (there are seemingly one or two out there at the moment) caught my attention.

His complaint was that he was spending $1,000 a week on API calls for his AI agents, realised the real bottleneck was infrastructure rather than intelligence, and dropped $10,000 on a Mac Studio with an M3 Ultra and 512GB of unified memory. His argument was essentially every model is smart enough, the ceiling is infrastructure, and the future belongs to whoever removes the constraints first.

It’s a beguiling pitch and it hit a nerve because the underlying frustration is accurate. Rate limits, per-token costs, and context window restrictions do shape how people build with these models, and the desire to break free of those constraints is understandable. But the argument collapses once you look at what local models can actually do today compared to what frontier APIs deliver, and why the gap between the two is likely to persist for the foreseeable future.

To understand why, you need to look at the current open-source model ecosystem in some detail, examine what’s actually happening on the frontier, and think carefully about the conditions that would need to hold for convergence to happen.

The open-source ecosystem in early 2026

The open-source model ecosystem has matured considerably over the past eighteen months, to the point where dismissing it as a toy would be genuinely unfair. The major families that matter right now are Meta’s Llama series, Alibaba’s Qwen line, and DeepSeek’s V3 and R1 models, with Mistral, Google’s Gemma, and Microsoft’s Phi occupying important niches for specific use cases.

DeepSeek’s R1 release in January 2025 was probably the single most consequential open-source event in the past two years. Built on a Mixture of Experts architecture with 671 billion total parameters but only 37 billion activated per forward pass, R1 achieved performance comparable to OpenAI’s o1 on reasoning benchmarks including GPQA, AIME, and Codeforces. What made it seismic was the claimed training cost: approximately $5.6 million, compared to the hundred-million-dollar-plus budgets associated with frontier models from the major Western labs. NVIDIA lost roughly $600 billion in market capitalisation in a single day when the implications sank in.

The Lawfare Institute’s analysis of DeepSeek’s achievement noted an important caveat that often gets lost in the retelling: the $5.6 million figure represents marginal training cost for the final R1 phase, and does not account for DeepSeek’s prior investment in the V3 base model, their GPU purchases (which some estimates put at 50,000 H100-class chips), or the human capital expended across years of development. The true all-in cost was substantially higher. But even with those qualifications, the efficiency gains were highly impressive, and they forced the entire industry to take algorithmic innovation as seriously as raw compute scaling.

Alibaba’s Qwen3 family, released in April 2025, pushed things further. The 235B-A22B variant uses a similar MoE approach, activating 22 billion parameters out of 235 billion, and it introduced hybrid reasoning modes that can switch between extended chain-of-thought and direct response depending on task complexity. The newer Qwen3-Coder-480B-A35B, released later in 2025, achieves 61.8% on the Aider Polyglot benchmark under full precision, which puts it in the same neighbourhood as Claude Sonnet 4 and GPT-4.1 for code generation specifically.

Meta’s Llama 4, released in early 2025, moved to natively multimodal MoE with the Scout and Maverick variants processing vision, video, and text in the same forward pass. Mistral continued to punch above its weight with the Large 3 release at 675 billion parameters, and their claim of delivering 92% of GPT-5.2’s performance at roughly 15% of the price represents the kind of value proposition that makes enterprise buyers think twice about their API contracts.

According to Menlo Ventures’ mid-2025 survey of over 150 technical leaders, open-source models now account for approximately 13% of production AI workloads, with the market increasingly structured around a durable equilibrium. Proprietary systems define the upper bound of reliability and performance for regulated or enterprise workloads, while open-source models offer cost efficiency, transparency, and customisation for specific use cases.

By any measure, this is a serious and capable ecosystem. The question is whether it’s capable enough to replace frontier APIs for agentic, high-reasoning work.

What happens when you run these models locally

The Mac Studio with an M3 Ultra and 512GB of unified memory is genuinely impressive hardware for local inference. Apple’s unified memory architecture means the GPU, CPU, and Neural Engine all share the same memory pool without the traditional separation between system RAM and VRAM, which makes it uniquely suited to running large models that would otherwise require expensive multi-GPU setups. Real-world benchmarks show the M3 Ultra achieving approximately 2,320 tokens per second on a Qwen3-30B 4-bit model, which is competitive with an NVIDIA RTX 3090 while consuming a fraction of the power.

But the performance picture changes dramatically as model size increases. Running the larger Qwen3-235B-A22B at Q5 quantisation on the M3 Ultra yields generation speeds of approximately 5.2 tokens per second, with first-token latency of around 3.8 seconds. At Q4KM quantisation, users on the MacRumors forums report around 30 tokens per second, which is usable for interactive work but a long way from the responsiveness of cloud APIs processing multiple parallel requests on clusters of H100s or B200s. And those numbers are for the quantised versions, which brings us to the core technical problem.

Quantisation is the process of reducing the numerical precision of a model’s weights, typically from 16-bit floating point down to 8-bit or 4-bit integers, in order to shrink the model enough to fit in available memory. The trade-off is information loss, and research published at EMNLP 2025 by Mekala et al. makes the extent of that loss uncomfortably clear. Their systematic evaluation across five quantisation methods and five models found that while 8-bit quantisation preserved accuracy with only about a 0.8% drop, 4-bit methods led to substantial losses, with performance degradation of up to 59% on tasks involving long-context inputs. The degradation worsened for non-English languages and varied dramatically between models and tasks, with Llama-3.1 70B experiencing a 32% performance drop on BNB-nf4 quantisation while Qwen-2.5 72B remained relatively robust under the same conditions.

Separate research from ACL 2025 introduces an even more concerning finding for the long-term trajectory of local models. As models become better trained on more data, they actually become more sensitive to quantisation degradation. The study’s scaling laws predict that quantisation-induced degradation will worsen as training datasets grow toward 100 trillion tokens, a milestone likely to be reached within the next few years. In practical terms, this means that the models most worth running locally are precisely the ones that lose the most from being compressed to fit.

When someone says they’re using a local model, they’re usually running a quantised version of an already-smaller model than the frontier labs deploy. The experience might feel good in interactive use, but the gap becomes apparent on exactly the tasks that matter most for production agentic work. Multi-step reasoning over long contexts, complex tool use orchestration, and domain-specific accuracy where “pretty good” is materially different from “correct.”

The post-training gap that open source can’t easily close

The most persistent advantage that frontier models hold over open-source alternatives has less to do with architecture and more to do with what happens after pre-training. Reinforcement Learning from Human Feedback and its variants form a substantial part of this gap, and the economics of closing it are unfavourable for the open-source community.

RLHF works by having human annotators evaluate pairs of model outputs and indicate which response better satisfies criteria like helpfulness, accuracy, and safety. Those preferences train a reward model, which then guides further optimisation of the language model through reinforcement learning. The process turns a base model that just predicts the next token into something that follows instructions well, pushes back when appropriate, handles edge cases gracefully, and avoids the confident-but-wrong failure mode that plagues undertrained systems.

The cost of doing this well at scale is staggering. Research from Daniel Kang at Stanford estimates that high-quality human data annotation now exceeds compute costs by up to 28 times for frontier models, with the data labelling market growing at a factor of 88 between 2023 and 2024 while compute costs increased by only 1.3 times. Producing just 600 high-quality RLHF annotations can cost approximately $60,000, which is roughly 167 times more than the compute expense for the same training iteration. Meta’s post-training alignment for Llama 3.1 alone required more than $50 million and approximately 200 people.

The frontier labs have also increasingly moved beyond basic RLHF toward more sophisticated approaches. Anthropic’s Constitutional AI has the model critique its own outputs against principles derived from human values, while the broader shift toward expert annotation, particularly for code, legal reasoning, and scientific analysis, means the humans providing feedback need to be domain practitioners rather than general-purpose annotators. This is expensive, slow, and extremely difficult to replicate through the synthetic and distilled preference data that open-source projects typically rely on.

The 2025 introduction of RLTHF (Targeted Human Feedback) from research surveyed in Preprints.org offers some hope, achieving full-human-annotation-level alignment with only 6-7% of the human annotation effort by combining LLM-based initial alignment with selective human corrections. But even these efficiency gains don’t close the fundamental gap: frontier labs can afford to spend tens of millions on annotation because they recoup it through API revenue, while open-source projects face a collective action problem where the cost of annotation is concentrated but the benefits are distributed.

Where the gap genuinely is closing

The picture is not uniformly bleak for open-source, and understanding where the gap has closed is as important as understanding where it hasn’t.

Code generation is the domain where convergence has happened fastest. Qwen3-Coder’s 61.8% on Aider Polyglot at full precision puts it within striking distance of frontier coding models, and the Unsloth project’s dynamic quantisation of the same model achieves 60.9% at a quarter of the memory footprint, which represents remarkably small degradation. For writing, editing, and iterating on code, a well-configured local model running on capable hardware is now a genuinely viable alternative to an API, provided you’re not relying on long-context reasoning across an entire codebase.

Classification, summarisation, and embedding tasks have been viable on local models for some time, and the performance gap for these workloads is now negligible for most practical purposes. Document processing, data extraction, and content drafting all fall into the category where open-source models deliver sufficient quality at dramatically lower cost.

The OpenRouter State of AI report’s analysis of over 100 trillion tokens of real-world usage data shows that Chinese open-source models, particularly from Alibaba and DeepSeek, have captured approximately 13% of weekly token volume with strong growth in the second half of 2025, driven by competitive quality combined with rapid iteration and dense release cycles. This adoption is concentrated in exactly the workloads described above: high-volume, well-defined tasks where cost efficiency matters more than peak reasoning capability.

Privacy-sensitive applications represent another area where local models have an intrinsic advantage that no amount of frontier improvement can overcome. MacStories’ Federico Viticci noted that running vision-language models locally on a Mac Studio for OCR and document analysis bypasses the image compression problems that plague cloud-hosted models, while keeping sensitive documents entirely on-device. For regulated industries where data sovereignty matters, local inference is a feature that frontier APIs cannot match.

What convergence would actually require

If the question is whether open-source models running on consumer hardware will eventually match frontier models across all tasks, the honest answer requires examining several conditions that would need to hold simultaneously.

The first is that Mixture of Experts architectures and similar efficiency innovations would need to continue improving at their current rate, allowing models with hundreds of billions of total parameters to activate only the relevant subset for each task while maintaining quality. The early evidence from DeepSeek’s MoE approach and Qwen3’s hybrid reasoning is encouraging, but there appear to be theoretical limits to how sparse activation can get before coherence suffers on complex multi-step problems.

The second condition is that the quantisation problem would need a genuine breakthrough rather than incremental improvement. The ACL 2025 finding that better-trained models are more sensitive to quantisation is a structural headwind that current techniques are not on track to solve. Red Hat’s evaluation of over 500,000 quantised model runs found that larger models at 8-bit quantisation show negligible degradation, but the story at 4-bit, where you need to be for consumer hardware, is considerably less encouraging for anything beyond straightforward tasks.

The third and most fundamental condition is that the post-training gap would need to close, which requires either a dramatic reduction in the cost of expert human annotation or a breakthrough in synthetic preference data that produces equivalent alignment quality. The emergence of techniques like RLTHF and Online Iterative RLHF suggests the field is working on this, but the frontier labs are investing in these same efficiency gains while simultaneously scaling their annotation budgets. It’s a race where both sides are accelerating, and the side with revenue-funded annotation budgets has a structural advantage.

The fourth condition is that inference hardware would need to improve enough to make unquantised or lightly quantised large models viable on consumer devices. Apple’s unified memory architecture is the most promising path here, and the progression from M1 to M4 chips has been impressive, but even the top-spec M3 Ultra at 512GB can only run the largest MoE models at aggressive quantisation levels. The next generation of Apple Silicon with 1TB+ unified memory would change the calculus significantly, but that’s likely several years away, and memory costs just shot through the ceiling.

Given all of these dependencies, a realistic timeline for broad convergence across most production tasks is probably three to five years, with coding and structured data tasks converging first, creative and analytical tasks following, and complex multi-step reasoning with tool use remaining a frontier advantage for the longest.

The hybrid approach and what it means in practice

The most pragmatic position right now (which is also the least satisfying one to post about), is that the future is hybrid rather than either-or. The smart deployment pattern routes high-volume, lower-stakes tasks to local models where the cost savings compound quickly and the quality gap is negligible, while reserving frontier API calls for the work that demands peak reasoning: complex multi-step planning, high-stakes domain-specific analysis, nuanced tool orchestration, and anything where being confidently wrong carries real cost.

This is approximately what the Menlo Ventures survey data suggests enterprise buyers are doing already, with model API spending more than doubling to $8.4 billion while open-source adoption stabilises around 13% of production workloads. The enterprises that are getting value from local models are not using them as wholesale API replacements; they’re using them as a complementary layer that handles the grunt work while the expensive models handle the hard problems.

There’s also the operational burden that is rarely mentioned in relation to model use. When you run models locally, you effectively become your own ML ops team. Model updates, quantisation format compatibility, prompt template differences across architectures, memory management under load, and testing when new versions drop, all of that falls on you. The API providers handle model improvements, scaling, and infrastructure, and you get a better model every few months without changing a line of code. For a small team that should be spending its time on product rather than infrastructure, that operational overhead has real cost even if it doesn’t show up on an invoice.

The future of AI probably does involve substantially more local compute than we have today. Costs will come down, architectures will improve, hardware will get more capable, and the hybrid model will become standard practice. The question is not who removes the constraints first, it’s who understands which constraints actually matter.

Audible link

This, as were the last two, is a book I discovered in Tiny Bookshop. I like a good love story and the game's blurb sounded pretty good.

Story

Actually listening to the book, I found it has a bit too much drama for my taste. Why are all the protagonist together with these absolute garbage people?

The love story though is well told and charming. Even if some of thoughts Harriet is having toward her crush gave me «Good Intentions» vibes in a way that did not feel appropriate for the book.

I'm also not sure we needed to hear the outcome in quite so many words. The book comes to an epic climax. Stopping there and leaving the rest to the listener's imagination would have been fine, too. It's something we do not get nearly enough of these days. At least in the books I listen to.

The letter

I want to take a moment to talk about the letter Harriet writes. In the story it goes that she sits down, in the middle of the night no less and writes the letter in one go, even refuses to proof read it. Remember too, that she is a wedding photographer and not an author or a journalist who has a lot of practice. I am sorry, but that is bullshit. That letter is so well written. Clearly, these are the carefully written words of Mhairi McFarlane and not those of Harriet. Now, I am sure that this is necessary. The letter is pretty long and we get to hear all of it and were it written in a more realistic fashion, that part of the story might be hard to get through. Nonetheless, it shattered my suspension of disbelief.

But also, it is an interesting way of doing exhibition. I've not had that in too many books before, so fair enough I guess.

Social media

Social media plays a big role in this book. It gets mentioned from the beginning, reminding us that this is a contemporary piece of work.

Anyone can make up a story, paint themselves as a victim and their adversary as the abuser. Online mobs are quick to judge and ruthless in their damnation. They don't wait around to ask if there might be another side to the story.

By making this a central plot point, the book serves as a warning, to not believe everything you see online, just because it sounds sincere and plausible. A warning that can't be made often enough in these times.

Recording

The audio quality is good, as you'd expect from any modern recording. I am, however, not too happy with the performance of Chloe Massey in reading the book.

Yes, the different people do all get their own voices. But they are not very pronounced and, worse, not very consistent either. It is especially hard to distinguish what Harriet is saying out loud and what she is merely thinking in her head, sometimes making conversations hard to follow.

This might be to blame on the book in parts — there are some books that are suited better to being made into an audiobook than others.

Overall it's still an enjoyable listen, but it could definitely be better. If you're going to listen to the book, maybe check out this other recording here. It doesn't have as many reviews as the one I listened to, but they are better, particularly concerning the recording.

Who is it for

If you're looking for a romantic story and are not turned off by a bit of drama, then this is definitely for you!