The promotional materials are breathtaking. Artificial intelligence systems that can analyse medical scans with superhuman precision, autonomous vehicles that navigate complex urban environments, and vision-language models that understand images with the fluency of a seasoned art critic. The benchmark scores are equally impressive: 94% accuracy here, state-of-the-art performance there, human-level capabilities across dozens of standardised tests.

Then reality intrudes. A robotaxi in San Francisco fails to recognise a pedestrian trapped beneath its chassis and drags her twenty feet before stopping. An image recognition system confidently labels photographs of Black individuals as gorillas. A frontier AI model, asked to count the triangles in a simple geometric image, produces answers that would embarrass a primary school student. These are not edge cases or adversarial attacks designed to break the system. They represent the routine failure modes of technologies marketed as transformative advances in machine intelligence.

The disconnect between marketed performance and actual user experience has become one of the defining tensions of the artificial intelligence era. It raises uncomfortable questions about how we measure machine intelligence, what incentives shape the development and promotion of AI systems, and whether the public has been sold a vision of technological capability that fundamentally misrepresents what these systems can and cannot do. Understanding this gap requires examining the architecture of how AI competence is assessed, the economics that drive development priorities, and the cognitive science of what these systems actually understand about the world they purport to perceive.

The Benchmark Mirage

To understand why AI systems that excel on standardised tests can fail so spectacularly in practice, one must first examine how performance is measured. The Stanford AI Index Report 2025 documented a striking phenomenon: many benchmarks that researchers use to evaluate AI capabilities have become “saturated,” meaning systems score so high that the tests are no longer useful for distinguishing between models. This saturation has occurred across domains including general knowledge, reasoning about images, mathematics, and coding. The Visual Question Answering Challenge, for instance, now sees top-performing models achieving 84.3% accuracy, while the human baseline sits at approximately 80%.

The problem runs deeper than simple test exhaustion. Research conducted by MIT's Computer Science and Artificial Intelligence Laboratory revealed that “traditionally, object recognition datasets have been skewed towards less-complex images, a practice that has led to an inflation in model performance metrics, not truly reflective of a model's robustness or its ability to tackle complex visual tasks.” The researchers developed a new metric called “minimum viewing time” which quantifies the difficulty of recognising an image based on how long a person needs to view it before making a correct identification. When researchers at MIT developed ObjectNet, a dataset comprising images collected from real-life settings rather than curated repositories, they discovered substantial performance gaps between laboratory conditions and authentic deployment scenarios.

This discrepancy reflects a phenomenon that economists have studied for decades: Goodhart's Law, which states that when a measure becomes a target, it ceases to be a good measure. A detailed 68-page analysis from researchers at Cohere, Stanford, MIT, and the Allen Institute for AI documented systematic distortions in how companies approach AI evaluation. The researchers found that major technology firms including Meta, OpenAI, Google, and Amazon were able to “privately pit many model versions in the Arena and then only publish the best results.” This practice creates a misleading picture of consistent high performance rather than the variable and context-dependent capabilities that characterise real AI systems.

The problem of data contamination compounds these issues. When testing GPT-4 on benchmark problems from Codeforces in 2023, researchers found the model could regularly solve problems classified as easy, provided they had been added before September 2021. For problems added later, GPT-4 could not solve a single question correctly. The implication is stark: the model had memorised questions and answers from its training data rather than developing genuine problem-solving capabilities. As one research team observed, the “AI industry has turned benchmarks into targets, and now those benchmarks are failing us.”

The consequence of this gaming dynamic extends beyond misleading metrics. It shapes the entire trajectory of AI development, directing research effort toward whatever narrow capabilities will boost leaderboard positions rather than toward the robust, generalisable intelligence that practical applications require.

Counting Failures and Compositional Collapse

Perhaps nothing illustrates the gap between benchmark performance and real-world competence more clearly than the simple task of counting objects in an image. Research published in late 2024 introduced VLMCountBench, a benchmark testing vision-language models on counting tasks using only basic geometric shapes such as triangles and circles. The findings were revealing: while these sophisticated AI systems could count reliably when only one shape type was present, they exhibited substantial failures when multiple shape types were combined. This phenomenon, termed “compositional counting failure,” suggests that these systems lack the discrete object representations that make counting trivial for humans.

This limitation has significant implications for practical applications. A study using Bongard problems, visual puzzles that test pattern recognition and abstraction, found that humans achieved an 84% success rate on average, while the best-performing vision-language model, GPT-4o, managed only 17%. The researchers noted that “even elementary concepts that may seem trivial to humans, such as simple spirals, pose significant challenges” for these systems. They observed that “most models misinterpreted or failed to count correctly, suggesting challenges in AI's visual counting capabilities.”

Text-to-image generation systems demonstrate similar limitations. Research on the T2ICountBench benchmark revealed that “all state-of-the-art diffusion models fail to generate the correct number of objects, with accuracy dropping significantly as the number of objects increases.” When asked to generate an image of ten oranges, these systems frequently produce either substantially more or fewer items than requested. The failure is not occasional or marginal but systematic and predictable. As one research paper noted, “depicting a specific number of objects in the image with text conditioning often fails to capture the exact quantity of details.”

These counting failures point to a more fundamental issue in how current AI architectures process visual information. Unlike human cognition, which appears to involve discrete object representations and symbolic reasoning about quantities, large vision-language models operate on statistical patterns learned from training data. They can recognise that images containing many objects of a certain type tend to have particular visual characteristics, but they lack what researchers call robust “world models” that would allow them to track individual objects and their properties reliably.

The practical implications extend far beyond academic curiosity. Consider an AI system deployed to monitor inventory in a warehouse, assess damage after a natural disaster, or count cells in a medical sample. Systematic failures in numerical accuracy would render such applications unreliable at best and dangerous at worst.

The Architectural Divide

The question of whether these failures represent fundamental limitations of current AI architectures or merely training deficiencies remains actively debated. Gary Marcus, professor emeritus of psychology and neural science at New York University and author of the 2024 book “Taming Silicon Valley: How We Can Ensure That AI Works for Us,” has argued consistently that neural networks face inherent constraints in tasks requiring abstraction and symbolic reasoning.

Marcus has pointed to a problem he first demonstrated in 1998: neural networks trained on even numbers could generalise to some new even numbers, but when tested on odd numbers, they would systematically fail. He concluded that “these tools are good at interpolating functions, but not very good at extrapolating functions.” This distinction between interpolation within known patterns and extrapolation to genuinely novel situations lies at the heart of the benchmark-reality gap.

Marcus characterises current large language models as systems that “work at the extensional level, but they don't work at the intentional level. They are not getting the abstract meaning of anything.” The chess-playing failures of models like ChatGPT, which Marcus has documented attempting illegal moves such as having a Queen jump over a knight, illustrate how systems can “approximate the game of chess, but can't play it reliably because it never induces a proper world model of the board and the rules.” He has emphasised that these systems “still fail at abstraction, at reasoning, at keeping track of properties of individuals. I first wrote about hallucinations in 2001.”

Research on transformer architectures, the technical foundation underlying most modern AI systems, has identified specific limitations in spatial reasoning. A 2024 paper titled “On Limitations of the Transformer Architecture” identified “fundamental incompatibility with the Transformer architecture for certain problems, suggesting that some issues should not be expected to be solvable in practice indefinitely.” The researchers documented that “when prompts involve spatial information, transformer-based systems appear to have problems with composition.” Simple cases where temporal composition fails cause all state-of-the-art models to return incorrect answers.

The limitations extend to visual processing as well. Research has found that “ViT learns long-range dependencies via self-attention between image patches to understand global context, but the patch-based positional encoding mechanism may miss relevant local spatial information and usually cannot attain the performance of CNNs on small-scale datasets.” This architectural limitation has been highlighted particularly in radiology applications where critical findings are often minute and contained within small spatial locations.

Melanie Mitchell, professor at the Santa Fe Institute whose research focuses on conceptual abstraction and analogy-making in artificial intelligence, has offered a complementary perspective. Her recent work includes a 2025 paper titled “Do AI models perform human-like abstract reasoning across modalities?” which examines whether these systems engage in genuine reasoning or sophisticated pattern matching. Mitchell has argued that “there's a lot of evidence that LLMs aren't reasoning abstractly or robustly, and often over-rely on memorised patterns in their training data, leading to errors on 'out of distribution' problems.”

Mitchell identifies a crucial gap in current AI systems: the absence of “rich internal models of the world.” As she notes, “a tenet of modern cognitive science is that humans are not simply conditioned-reflex machines; instead, we have inside our heads abstracted models of the physical and social worlds that reflect the causes of events rather than merely correlations among them.” Current AI systems, despite their impressive performance on narrow benchmarks, appear to lack this causal understanding.

An alternative view holds that these limitations may be primarily a consequence of training data rather than architectural constraints. Some researchers hypothesise that “the limited spatial reasoning abilities of current VLMs is not due to a fundamental limitation of their architecture, but rather is a limitation in common datasets available at scale on which such models are trained.” This perspective suggests that co-training multimodal models on synthetic spatial data could potentially address current weaknesses. Additionally, researchers note that “VLMs' limited spatial reasoning capability may be due to the lack of 3D spatial knowledge in training data.”

When Failures Cause Harm

The gap between benchmark performance and real-world capability becomes consequential when AI systems are deployed in high-stakes domains. The case of autonomous vehicles provides particularly sobering examples. According to data compiled by researchers at Craft Law Firm, between 2021 and 2024, there were 3,979 incidents involving autonomous vehicles in the United States, resulting in 496 reported injuries and 83 fatalities. The Stanford AI Index Report 2025 noted that the AI Incidents Database recorded 233 incidents in 2024, a 56.4% increase compared to 2023, marking a record high.

In May 2025, Waymo recalled over 1,200 robotaxis following disclosure of a software flaw that made vehicles prone to colliding with certain stationary objects, specifically “thin or suspended barriers like chains, gates, and even utility poles.” These objects, which human drivers would navigate around without difficulty, apparently fell outside the patterns the perception system had learned to recognise. Investigation revealed failures in the system's ability to properly classify and respond to stationary objects under certain lighting and weather conditions. As of April 2024, Tesla's Autopilot system had been involved in at least 13 fatal crashes according to NHTSA data, with Tesla's Full Self-Driving system facing fresh regulatory scrutiny in January 2025.

The 2018 Uber fatal accident in Tempe, Arizona, illustrated similar limitations. The vehicle's sensors detected a pedestrian, but the AI system failed to classify her accurately as a human, leading to a fatal collision. The safety driver was distracted by a mobile device and did not intervene in time. As researchers have noted, “these incidents reveal a fundamental problem with current AI systems: they excel at pattern recognition in controlled environments but struggle with edge cases that human drivers handle instinctively.” The failure to accurately classify the pedestrian as a human being highlighted a critical weakness in object recognition capabilities, particularly in low-light conditions and complex environments.

A particularly disturbing incident involved General Motors' Cruise robotaxi in San Francisco, where the vehicle struck a pedestrian who had been thrown into its path by another vehicle, then dragged her twenty feet before stopping. The car's AI systems failed to recognise that a human being was trapped underneath the vehicle. When the system detected an “obstacle,” it continued to move, causing additional severe injuries.

These cases highlight how AI systems that perform admirably on standardised perception benchmarks can fail catastrophically when encountering situations not well-represented in their training data. The gap between laboratory performance and deployment reality is not merely academic; it translates directly into physical harm.

The Gorilla Problem That Never Went Away

One of the most persistent examples of AI visual recognition failure involves the 2015 incident in which Google Photos labelled photographs of Black individuals as “gorillas.” In that incident, a Black software developer tweeted that Google Photos had labelled images of him with a friend as “gorillas.” The incident exposed how image recognition algorithms trained on biased data can produce racist outputs. Google's response was revealing: rather than solving the underlying technical problem, the company blocked the words “gorilla,” “chimpanzee,” “monkey,” and related terms from the system entirely.

Nearly a decade later, that temporary fix remains in place. By censoring these searches, the service can no longer find primates such as “gorilla,” “chimp,” “chimpanzee,” or “monkey.” Despite enormous advances in AI technology since 2015, Google Photos still refuses to label images of gorillas. This represents a tacit acknowledgement that the fundamental problem has not been solved, only circumvented. The workaround creates a peculiar situation where one of the world's most advanced image recognition systems cannot identify one of the most recognisable animals on Earth. As one analysis noted, “Apple learned from Google's mistake and simply copied their fix.”

The underlying issue extends beyond a single company's product. Research has consistently documented that commercially available facial recognition technologies perform far worse on darker-skinned individuals, particularly women. Three commercially available systems made by Microsoft, IBM, and Megvii misidentified darker female faces nearly 35% of the time while achieving near-perfect accuracy (99%) on white men.

These biases have real consequences. Cases such as Ousmane Bah, a teenager wrongly accused of theft at an Apple Store because of faulty face recognition, and Amara K. Majeed, wrongly accused of participating in the 2019 Sri Lanka bombings after her face was misidentified, demonstrate how AI failures disproportionately harm marginalised communities. The technology industry's approach of deploying these systems despite known limitations and then addressing failures reactively raises serious questions about accountability and the distribution of risk.

The Marketing Reality Gap

The discrepancy between how AI capabilities are marketed and how they perform in practice reflects a broader tension in the technology industry. A global study led by Professor Nicole Gillespie at Melbourne Business School surveying over 48,000 people across 47 countries between November 2024 and January 2025 found that although 66% of respondents already use AI with some regularity, less than half (46%) are willing to trust it. Notably, this represents a decline in trust compared to surveys conducted prior to ChatGPT's release in 2022. People have become less trusting and more worried about AI as adoption has increased.

The study found that consumer distrust is growing significantly: 63% of consumers globally do not trust AI with their data, up from 44% in 2024. In the United Kingdom, the situation is even starker, with 76% of shoppers feeling uneasy about AI handling their information. Research from the Nuremberg Institute for Market Decisions showed that only 21% of respondents trust AI companies and their promises, and only 20% trust AI itself. These findings reveal “a notable gap between general awareness of AI in marketing and a deeper understanding or trust in its application.”

Emily Bender, professor of linguistics at the University of Washington and one of the authors of the influential 2021 “stochastic parrots” paper, has been a prominent voice challenging AI hype. Bender was recognised in TIME Magazine's first 100 Most Influential People in Artificial Intelligence and is the author of the upcoming book “The AI Con: How to Fight Big Tech's Hype and Create the Future We Want.” She has argued that “so much of what we read about language technology and other things that get called AI makes the technology sound magical. It makes it sound like it can do these impossible things, and that makes it that much easier for someone to sell a system that is supposedly objective but really just reproduces systems of oppression.”

The practical implications of this marketing-reality gap are significant. A McKinsey global survey in early 2024 found that 65% of respondents said their organisations use generative AI in some capacity, nearly double the share from ten months prior. However, despite widespread experimentation, “comprehensive integration of generative AI into core business operations remains limited.” A 2024 Deloitte study noted that “organisational change only happens so fast” despite rapid AI advances, meaning many companies are deliberately testing in limited areas before scaling up.

The gap is particularly striking in mental health applications. Despite claims that AI is replacing therapists, only 21% of the 41% of adults who sought mental health support in the past six months turned to AI, representing only 9% of the total population. The disconnect between hype and actual behaviour underscores how marketing narratives can diverge sharply from lived reality.

Hallucinations and Multimodal Failures

The problem of AI systems generating plausible but incorrect outputs, commonly termed “hallucinations,” extends beyond text into visual domains. Research published in 2024 documented that multimodal large language models “often generate outputs that are inconsistent with the visual content, a challenge known as hallucination, which poses substantial obstacles to their practical deployment and raises concerns regarding their reliability in real-world applications.”

Object hallucination represents a particularly problematic failure mode, occurring when models identify objects that do not exist in an image. Researchers have developed increasingly sophisticated benchmarks to evaluate these failures. ChartHal, a benchmark featuring a taxonomy of hallucination scenarios in chart understanding, demonstrated that “state-of-the-art LVLMs suffer from severe hallucinations” when interpreting visual data.

The VHTest benchmark introduced in 2024 comprises 1,200 diverse visual hallucination instances across eight modes. Medical imaging presents particular risks: the MediHall Score benchmark was developed specifically to assess hallucinations in medical contexts through a hierarchical scoring system. When AI systems hallucinate in clinical settings, the consequences can be life-threatening.

Mitigation efforts have shown some promise. One recent framework operating entirely with frozen, pretrained vision-language models and requiring no gradient updates “reduces hallucination rates by 9.8 percentage points compared to the baseline, while improving object existence accuracy by 4.7 points on adversarial splits.” Research by Yu et al. (2023) explored human error detection to mitigate hallucinations, successfully reducing them by 44.6% while maintaining competitive performance.

However, Gary Marcus has argued that there is “no principled solution to hallucinations in systems that traffic only in the statistics of language without explicit representation of facts and explicit tools to reason over those facts.” This perspective suggests that hallucinations are not bugs to be fixed but fundamental characteristics of current architectural approaches. He advocates for neurosymbolic AI, which would combine neural networks with symbolic AI, making an analogy to Daniel Kahneman's System One and System Two thinking.

The ARC Challenge and the Limits of Pattern Matching

Francois Chollet, the creator of Keras, an open-source deep learning library adopted by over 2.5 million developers, introduced the Abstraction and Reasoning Corpus (ARC) in 2019 as a benchmark designed to measure fluid intelligence rather than narrow task performance. ARC consists of 800 puzzle-like tasks designed as grid-based visual reasoning problems. These tasks, trivial for humans but challenging for machines, typically provide only a small number of example input-output pairs, usually around three.

What makes ARC distinctive is its focus on measuring the ability to “generalise from limited examples, interpret symbolic meaning, and flexibly apply rules in varying contexts.” Unlike benchmarks that can be saturated through extensive training on similar problems, ARC tests precisely the kind of novel reasoning that current AI systems struggle to perform. The benchmark “requires the test taker to deduce underlying rules through abstraction, inference, and prior knowledge rather than brute-force or extensive training.”

From its introduction in 2019 until late 2024, ARC remained essentially unsolved by AI systems, maintaining its reputation as one of the toughest benchmarks available for general intelligence. The ARC Prize competition, co-founded by Mike Knoop and Francois Chollet, saw 1,430 teams submit 17,789 entries in 2024. The state-of-the-art score on the ARC private evaluation set increased from 33% to 55.5% during the competition period, propelled by techniques including deep learning-guided program synthesis and test-time training. More than $125,000 in prizes were awarded across top papers and top scores.

While this represents meaningful progress, it remains far below human performance and the 85% threshold set for the $500,000 grand prize. The persistent difficulty of ARC highlights a crucial distinction: current AI systems excel at tasks that can be solved through pattern recognition and interpolation within training distributions but struggle with the kind of abstract reasoning that humans perform effortlessly.

Trust Erosion and the Normalisation of Failure

Research on human-AI interaction has documented asymmetric trust dynamics: building trust in AI takes more time compared to building trust in humans, but when AI encounters problems, trust loss occurs more rapidly. Studies have found that simpler tasks show greater degradation of trust following errors, suggesting that failures on tasks perceived as easy may be particularly damaging to user confidence.

This pattern reflects what researchers term “perfect automation schema,” the tendency for users to expect flawless performance from AI systems and interpret any deviation as evidence of fundamental inadequacy rather than normal performance variation. The marketing of AI as approaching or exceeding human capabilities may inadvertently amplify this effect by setting unrealistic expectations.

Research comparing early and late errors found that initial errors affect trust development more negatively than late ones in some studies, while others found that trust dropped most for late mistakes. The explanation may be that early mistakes allow people to adjust expectations over time, whereas trust damaged at a later stage proves more difficult to repair. Research has found that “explanations that combine causal attribution (explaining why the error occurred) with boundary specification (identifying system limitations) prove most effective for competence-based trust repair.”

The normalisation of AI failures presents a concerning trajectory. If users come to expect that AI systems will periodically produce nonsensical or harmful outputs, they may either develop excessive caution that undermines legitimate use cases or, alternatively, become desensitised to failures in ways that increase risk. Neither outcome serves the goal of beneficial AI deployment.

Measuring Intelligence or Measuring Training

The fundamental question underlying these failures concerns what benchmarks actually measure. The dramatic improvement in AI performance on new benchmarks shortly after their introduction, documented by the Stanford AI Index, suggests that current systems are exceptionally effective at optimising for whatever metrics researchers define. In 2023, AI systems could solve just 4.4% of coding problems on SWE-bench. By 2024, this figure had jumped to 71.7%. Performance on MMMU and GPQA saw gains of 18.8 and 48.9 percentage points respectively.

This pattern of rapid benchmark saturation has led some researchers to question whether improvements reflect genuine capability gains or increasingly sophisticated ways of matching test distributions. The Stanford report noted that despite strong benchmark performance, “AI models excel at tasks like International Mathematical Olympiad problems but still struggle with complex reasoning benchmarks like PlanBench. They often fail to reliably solve logic tasks even when provably correct solutions exist.”

The narrowing performance gaps between models further complicate the picture. According to the AI Index, the Elo score difference between the top and tenth-ranked model on the Chatbot Arena Leaderboard was 11.9% in 2023. By early 2025, this gap had narrowed to just 5.4%. Similarly, the difference between the top two models shrank from 4.9% in 2023 to just 0.7% in 2024.

The implications for AI development are significant. If benchmarks are increasingly unreliable guides to real-world performance, the incentive structure for AI research may be misaligned with the goal of building genuinely capable systems. Companies optimising for benchmark rankings may invest disproportionately in test-taking capabilities at the expense of robustness and reliability in deployment.

Francois Chollet has framed this concern explicitly, arguing that ARC-style tasks test “the ability to generalise from limited examples, interpret symbolic meaning, and flexibly apply rules in varying contexts” rather than the ability to recognise patterns encountered during training. The distinction matters profoundly for understanding what current AI systems can and cannot do.

Reshaping Expectations and Rebuilding Trust

Addressing the gap between marketed performance and actual capability will require changes at multiple levels. Researchers have begun developing dynamic benchmarks that are regularly updated to prevent data contamination. LiveBench, for example, is updated with new questions monthly, many from recently published sources, ensuring that performance cannot simply reflect memorisation of training data. This approach represents “a close cousin of the private benchmark” that keeps benchmarks fresh without worrying about contamination.

Greater transparency about the conditions under which AI systems perform well or poorly would help users develop appropriate expectations. OpenAI's documentation acknowledges that their models struggle with “tasks requiring precise spatial localisation, such as identifying chess positions” and “may generate incorrect descriptions or captions in certain scenarios.” Such candour, while not universal in the industry, represents a step toward more honest communication about system limitations.

The AI Incidents Database, maintained by the Partnership on AI, and the AIAAIC Repository provide systematic tracking of AI failures. The AIAAIC documented that in 2024, while incidents declined to 187 compared to the previous year, issues surged to 188, the highest number recorded, totalling 375 occurrences, ten times more than in 2016. Accuracy and reliability and safety topped the list of incident categories. OpenAI, Tesla, Google, and Meta account for the highest number of AI-related incidents in the repository.

Academic researchers have proposed that evaluation frameworks should move beyond narrow task performance to assess broader capabilities including robustness to distribution shift, calibration of confidence, and graceful degradation when facing unfamiliar inputs. Melanie Mitchell has argued that “AI systems ace benchmarks yet stumble in the real world, and it's time to rethink how we probe intelligence in machines.”

Mitchell maintains that “just scaling up these same kinds of models will not solve these problems. Some new approach has to be created, as there are basic capabilities that current architectures and training methods aren't going to overcome.” She notes that current models “are not learning from their mistakes in any long-term sense. They can't carry learning from one session to another. They also have no 'episodic memory,' unlike humans who learn from experiences, mistakes, and successes.”

The gap between benchmark performance and real-world capability is not simply a technical problem awaiting a technical solution. It reflects deeper questions about how we define and measure intelligence, what incentives shape technology development, and how honest we are prepared to be about the limitations of systems we deploy in consequential domains. The answers to these questions will shape not only the trajectory of AI development but also the degree to which public trust in these technologies can be maintained or rebuilt.

For now, the most prudent stance may be one of calibrated scepticism: appreciating what AI systems can genuinely accomplish while remaining clear-eyed about what they cannot. The benchmark scores may be impressive, but the measure of a technology's value lies not in how it performs in controlled conditions but in how it serves us in the messy, unpredictable complexity of actual use.

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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

Mark does not ease us into the story. He does not warm us up. He does not clear his throat and offer context the way other writers might. He opens the door and shoves us straight into movement. “The beginning of the gospel of Jesus Christ, the Son of God.” No genealogy. No childhood. No soft music. Just a declaration and then action. Mark writes like someone who knows time is short and truth matters more than polish. From the very first line of Mark 1, the reader is confronted with urgency. Something has begun, and it will not wait for anyone to feel ready.

That urgency is not accidental. It mirrors the way God so often moves in real life. God rarely announces Himself with long explanations. He breaks into ordinary routines, interrupts settled assumptions, and forces people to respond. Mark 1 is not just the beginning of a book; it is the beginning of disruption. It is the moment when heaven steps into history and refuses to be ignored. If you read it carefully, you realize that nothing in this chapter allows for passive faith. Everything demands movement, repentance, obedience, or resistance. There is no neutral ground.

Mark opens by grounding the moment in prophecy, reminding us that what is unfolding did not come out of nowhere. God had been speaking long before He started moving. Isaiah had declared that a messenger would prepare the way, that a voice would cry out in the wilderness. This matters because it shows us something essential about God’s character. God does not act randomly. He is intentional. Even when His timing feels sudden to us, it is rooted in long-established purpose. The problem is not that God moves without warning; it is that people stop listening.

John the Baptist appears in the wilderness, not in the centers of power, not in religious institutions, not in palaces or synagogues. The wilderness is uncomfortable. It is exposed. It is inconvenient. Yet that is where God chooses to begin. John’s message is simple and confrontational: repent. Not feel sorry. Not explain yourself. Not blame circumstances. Repent. Turn around. Change direction. Prepare yourself, because something holy is approaching.

People respond. Not because John is gentle, but because he is honest. He does not flatter them or promise comfort. He calls them out. He tells them they are not ready, and somehow that truth draws crowds. This is important to notice, because it challenges a modern assumption that people only want encouragement. Deep down, people want truth. They want clarity. They want someone to tell them what is wrong and how to get right again. John offers that, and people come from everywhere to hear it.

John’s humility is just as striking as his boldness. He knows exactly who he is, and more importantly, who he is not. He refuses to let the attention confuse him. He does not build a following for himself. He points away from himself entirely. He says that the one coming after him is greater, so much greater that John is unworthy even to loosen his sandals. In a world obsessed with recognition, John stands as a rebuke. His entire purpose is to prepare the way and then step aside.

Then Jesus appears.

There is no dramatic entrance. No announcement from the crowd. No reaction shot. Jesus simply comes from Nazareth of Galilee and is baptized by John in the Jordan. If you read too quickly, you might miss how shocking this is. The sinless one submits to a baptism of repentance. The one who needs no cleansing steps into the water with those who do. This is not weakness. It is identification. From the very beginning, Jesus aligns Himself with humanity in its brokenness.

As Jesus comes up out of the water, the heavens are torn open. Not gently parted. Torn. The language Mark uses is violent, deliberate, and irreversible. God does not politely peek into the world; He rips the barrier open. The Spirit descends like a dove, and a voice speaks from heaven, declaring pleasure and identity. “Thou art my beloved Son, in whom I am well pleased.” Before Jesus preaches a sermon, performs a miracle, or calls a disciple, His identity is affirmed. This is crucial. Jesus does not earn the Father’s approval through performance. He receives it before doing anything publicly at all.

That order matters more than many people realize. So many believers spend their lives trying to earn what God offers freely. Mark 1 quietly dismantles that lie. Identity comes before assignment. Belonging comes before obedience. Approval comes before action. When we reverse that order, faith becomes exhausting and joyless. Jesus begins His ministry from a place of affirmation, not insecurity.

Immediately, Mark says, the Spirit drives Jesus into the wilderness. Not gently leads. Drives. The same Spirit who descended in affirmation now pushes Jesus into isolation and testing. This too is unsettling, because it challenges the idea that God’s pleasure guarantees ease. It does not. Sometimes God’s affirmation is followed by testing, not because He doubts us, but because He is preparing us.

Jesus is in the wilderness forty days, tempted by Satan, among wild beasts, attended by angels. Mark gives no details of the temptations themselves. He does not linger. He simply states the reality. Temptation is not an anomaly. It is part of the story. Even Jesus faces it. The difference is not the absence of temptation, but the presence of obedience. Jesus does not negotiate with evil. He endures, resists, and remains faithful.

After John is arrested, Jesus begins His public ministry. The timing is significant. When one voice is silenced, another rises. God’s work does not stop because a servant is removed. It continues through obedience. Jesus comes into Galilee preaching the gospel of God, proclaiming that the time is fulfilled and the kingdom of God is at hand. Repent and believe the gospel. This is not a suggestion. It is a declaration. Something has changed in the fabric of reality, and the appropriate response is repentance and belief.

As Jesus walks by the Sea of Galilee, He sees Simon and Andrew casting a net. They are fishermen. Ordinary men doing ordinary work. Jesus does not approach scholars first. He does not recruit religious elites. He calls working people in the middle of their routines. “Follow me, and I will make you fishers of men.” Mark tells us they immediately leave their nets and follow Him. No debate. No delay. No exit strategy.

This immediacy should make us uncomfortable. It confronts the illusion that obedience requires perfect understanding. They do not know where Jesus will lead. They do not know how long they will be gone. They do not know what the future holds. They only know who is calling. That is enough.

James and John are next. They leave their father in the boat with the hired servants and follow Jesus. This is not just a career shift. It is a relational rupture. Following Jesus often means redefining loyalties. Not abandoning love, but reordering it. Jesus does not apologize for the cost. He simply calls.

From there, Mark moves quickly into action. Jesus enters Capernaum, goes into the synagogue, and teaches. The people are astonished because He teaches with authority, not like the scribes. Authority here is not volume or aggression. It is alignment. Jesus speaks as one who knows God, not one who merely discusses Him. Truth sounds different when it comes from someone who lives it.

A man with an unclean spirit interrupts the service, crying out in recognition and fear. The demon knows exactly who Jesus is. This is one of the most sobering moments in Mark 1. The spiritual realm recognizes Jesus before many people do. The demon calls Him the Holy One of God. Jesus silences the spirit and commands it to come out. There is no struggle. No ritual. Just authority. The spirit obeys.

The people are amazed, not only by the deliverance, but by the manner in which it happens. Jesus does not rely on formulas or traditions. His authority is intrinsic. It flows from who He is. News spreads quickly. Mark emphasizes this again and again. Jesus cannot remain hidden, not because He seeks fame, but because power cannot be concealed.

After leaving the synagogue, Jesus goes to Simon’s house, where Simon’s mother-in-law is sick with a fever. Jesus takes her by the hand and lifts her up. The fever leaves, and she begins to serve them. This is not exploitation; it is restoration. Healing returns people to purpose. The same hand that casts out demons lifts up the sick. Authority and tenderness coexist in Jesus without contradiction.

That evening, the whole city gathers at the door. The sick, the possessed, the desperate all come. Jesus heals many and casts out many demons, but He does not allow the demons to speak because they know who He is. Jesus controls the narrative. Revelation is not forced; it unfolds according to God’s timing.

Very early the next morning, while it is still dark, Jesus goes out to a solitary place to pray. This detail matters. After a night of intense ministry, Jesus does not sleep in. He withdraws to pray. Power flows from communion, not exhaustion. If Jesus needs solitude with the Father, we cannot pretend we do not.

The disciples search for Him, telling Him that everyone is looking for Him. This could have been a moment of expansion, of consolidation, of building momentum. Instead, Jesus says they must go on to other towns, because that is why He came. He refuses to be trapped by popularity. Purpose determines His movement, not demand.

Jesus continues preaching and casting out demons throughout Galilee. Then a leper comes to Him, kneeling and begging, saying that Jesus can make him clean if He is willing. This is one of the most emotionally charged moments in the chapter. Lepers are untouchable. They are isolated, feared, and forgotten. The man’s question is not about ability, but willingness.

Jesus is moved with compassion. He stretches out His hand and touches him. This touch is scandalous. Jesus does not need to touch him to heal him. He chooses to. In that moment, Jesus takes on ritual uncleanness to restore the outcast. “I will; be thou clean.” The leprosy leaves immediately.

Jesus tells the man to say nothing to anyone and to show himself to the priest, but the man goes out and spreads the news freely. The result is ironic. Jesus can no longer openly enter towns and stays in deserted places, while people come to Him from everywhere.

This is where Mark 1 leaves us, with roles reversed. The cleansed man moves freely. Jesus bears the cost. From the very beginning, the pattern of the cross is already present. Jesus heals by taking upon Himself the consequences of restoration. He does not merely fix problems. He absorbs them.

Mark 1 is not a gentle invitation to religious reflection. It is a declaration of invasion. God has entered the world, authority has arrived, and everything must respond. There is no room for delay, no space for neutrality, no comfort in half-hearted belief. The beginning of the gospel is not the beginning of information. It is the beginning of transformation.

And it is only just beginning.

Mark 1 ends in a place that feels unresolved, almost uncomfortable. Jesus is pushed to the outskirts. The healed man walks freely while the Healer withdraws into deserted places. That tension is intentional. Mark wants us to sit with it. He wants us to feel that following Jesus is not about personal comfort or religious polish. It is about collision. When God enters human history, something always gets displaced.

One of the great mistakes we make when reading Mark 1 is treating it like an introduction rather than a warning. We read it as the opening chapter of a book instead of the opening chapter of a life-altering reality. But Mark does not write introductions. He writes thresholds. He writes moments where you either step forward or stay behind. Mark 1 is not asking if you find Jesus interesting. It is asking if you are willing to be changed.

What stands out when you slow down and sit with the chapter is how little time Jesus spends explaining Himself. He declares, He acts, He moves on. The gospel is not built on persuasion techniques or clever arguments. It is built on authority that reveals itself through action. Jesus does not argue demons into submission. He commands them. He does not negotiate with sickness. He touches it. He does not wait for disciples to feel qualified. He calls them while they are still holding nets.

This confronts a deeply ingrained belief many people carry: that we must become ready before we respond. Mark 1 dismantles that idea piece by piece. Readiness does not precede calling; calling creates readiness. Simon and Andrew do not attend a seminar on discipleship. James and John do not receive a five-year plan. They hear a voice, and they move. Obedience begins with trust, not clarity.

Another striking theme in Mark 1 is the relentless pace. The word “immediately” appears again and again. Immediately the Spirit drives Jesus into the wilderness. Immediately the disciples leave their nets. Immediately the demons obey. Immediately the fever leaves. Mark is showing us something about the nature of the kingdom of God. It does not drift in slowly. It arrives decisively. Delay is almost always human, not divine.

This does not mean God is impatient. It means He is purposeful. When God moves, He does so with intent. Our hesitation often comes from fear of loss. The fishermen leave nets. James and John leave their father. Jesus leaves popularity. Every movement forward involves leaving something behind. Mark does not soften this reality. He simply presents it as fact.

The wilderness scenes in Mark 1 deserve special attention, because they frame the entire chapter. John preaches in the wilderness. Jesus is driven into the wilderness. Jesus retreats to a solitary place to pray. The wilderness is not a detour; it is a classroom. It strips away distractions. It exposes motives. It reveals dependence. The wilderness is where identity is tested and clarified.

For Jesus, the wilderness confirms what was already declared at baptism. He is the Son. He does not need to prove it. For us, the wilderness often feels like punishment or abandonment, but Mark 1 suggests otherwise. The wilderness is where God prepares His servants for public faithfulness. What is shaped in solitude sustains obedience in crowds.

Notice also how Mark balances Jesus’ authority with His compassion. He casts out demons with command, yet He touches the leper with tenderness. He heals with power, yet He prays in quiet places. Too often, people try to separate strength and gentleness, as if one cancels the other. Jesus embodies both fully. Mark 1 refuses to let us domesticate Him into a one-dimensional figure.

The leper’s encounter near the end of the chapter is especially revealing. The man does not question Jesus’ power. He questions His willingness. That question echoes through history. People often believe God can help, but doubt that He cares enough to do so personally. Jesus answers that question not with theology, but with touch. He crosses a boundary no one else will cross.

In doing so, Jesus models the cost of compassion. He becomes ceremonially unclean so that the leper can be restored. This exchange foreshadows the cross. From the very beginning, Jesus absorbs the consequences of healing others. Salvation is not a transaction where everyone walks away untouched. Someone always bears the weight. In Mark 1, that someone is already Jesus.

The ending of the chapter leaves us with movement outward. Jesus is still healing. People are still coming. The gospel is spreading, not because of a marketing strategy, but because lives are being changed. Even disobedience plays a role, as the healed man spreads the news despite Jesus’ instructions. Mark is not endorsing disobedience, but he is showing that the power of what Jesus does cannot be contained.

So what does Mark 1 demand of us now?

It demands honesty. Repentance is not optional. Turning toward God requires turning away from something else. There is no version of Christianity that avoids this reality.

It demands movement. Faith is not a mental agreement with ideas. It is a response to a call. Nets are left. Paths are changed. Direction shifts.

It demands humility. John knows his place. Demons know their limits. Disciples learn they are not in control. Pride has no place in the presence of real authority.

It demands trust. Jesus does not give full explanations. He gives commands. Following Him means trusting who He is more than understanding where He is going.

And it demands surrender. From the wilderness to the leper’s touch, Mark 1 shows us that God’s work involves cost. Jesus bears it willingly. Those who follow Him must be prepared to bear it too.

Mark 1 is the beginning of the gospel, but it is also the end of comfortable religion. It introduces a Savior who cannot be managed, predicted, or contained. He interrupts routines, exposes hearts, heals deeply, and then moves on, calling others to follow.

If this is the beginning, then everything that follows makes sense. The cross. The empty tomb. The cost of discipleship. The power of resurrection. None of it is surprising if you have been paying attention since the wilderness, since the nets, since the touch of a leper.

The gospel begins here, but it does not stop here.

It keeps moving.

And it is still calling.

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

Your friend, Douglas Vandergraph

In Summary: * Another quiet Sunday winds down. Looking ahead to the upcoming week I see one phone call I'll need to make tomorrow morning, two other calls I'll probably need to make on Wednesday, and other than that ... smooth sailing. After a good night's sleep, which I anticipate having, Monday morning will find me ready and willing to take it on.

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= 219.03 lbs. * bp= 143/85 (67)

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

Diet: * 07:45 – crispy oatmeal cookies * 09:00 – lasagna * 10:20 – toast and butter * 13:10 – egg rolls, spinach, egg plant, pancit, white rice * 15:45 – Ensaymada

Activities, Chores, etc.: * 06:30 – bank accounts activity monitored * 06:45 – read, pray, follow news reports from various sources, surf the socials, nap * 08:00 – pray the The Propers of the Day according to the 1962 Roman Missal for The Second Sunday after Epiphany, January 18th, 2026. * 08:30 – follow news reports from various sources, surf the socials * 12:30 – watching NFL Gameday on NFL Network * 14:00 – watching the Texans / Patriots Game on my phone because that's the only way my NFL+ membership lets me follow the game * 17:19 – game over, Patriots win, 28 to 16. * 18:00 – listening to relaxing music

Chess: * 13:10 – moved in all pending CC games

Autre lieu où l'on se déporte à l'est de tout, où l'on peut sans craindre bannir le moi, le nous, tribunaux féroces, et dans les rythmiques de ce dehors que l'on écoute, jeter son corps entier.

Je l'entends toujours dire, malgré ma désertion, et parler sans reproche, de cette voix si claire que sur son texte je me retourne encore. Mais j'ai trop écrit d'elle, qui n'est plus que le vide en moi, de moi, l'espace de sa parole à taire.

Nombre d’occurrences : 15

#VoyageauLexique

Dans son ouvrage How I Take Photographs, Daido Moriyama présente quelques-unes de ses démarches. Une des premières présentée consiste pour lui à parcourir dans les deux sens une rue fréquentée. Pour lui, > «There is no better place to start than an ordinary shopping street – the kind you find in front of railway stations in any town or city in Japan.*»

Pas de rue commerçante ordinaire, puisque c'est dimanche, mais le bord de quai à Montreux, du côté de Territet, que nous avons parcouru dans les deux sens pour cette flânerie photographique inspirée par Daido Moriyama. Sans prétention.

Premier passage

La descente vers le bord du lac.

Le départ du quai près de l'Auberge de jeunesse de Montreux

Le port de Territet

Le Contre Temps, hors-saison et dans l'attente de la saison estivale

Le pêcheur

L'appel du large ou la joie espérée du pêcheur

Que serait Montreux sans ses palmiers et la promesse d'un doux séjour?

Sur le chemin du retour ou le re-passage

Piscator lacustrus. Labubu des Espaces

Le texte suivant accompagnait cette réalisation de la commune de Montreux: > Personnages issus de l’univers fantastique de l’artiste Kasing Lung. L’expression de cette peluche est souvent décrite comme espiègle, malicieuse, ou même légèrement sauvage, ce qui lui donne une personnalité forte et attachante. Ces figurines se déclinent sous divers coloris et formes tout en possédant leur propre nom. Ces sculptures végétales ont été imaginées et réalisées par les jardinier.ère.s de la Commune de Montreux.

« Pour une cause pure avec une épée pure »

Si je suis passé de nombreuses fois sur ce quai, c'est la première fois que je suis attardé sur ce monument et que j'y ai prêté attention. Probablement que le côté hors-saison de cette promenade dominicale a mis plus particulièrement en évidence le monument. Le texte sur la face présentée de cet obélisque est le suivant :

A LA GLOIRE DE LA FINLANDE ET DE SON PEUPLE HÉROÏQUE A LA MÉMOIRE DU NOBLE CHEVALIER LE BARON CARL GUSTAF MANNERHEIM MARECHAL DE FINLANDE 1867–1951 CANDIDA PRO CAUSA ENSE CANDENDO

En recherchant sur internet à l'aide du texte du document, il est possible d'arriver sur une page de l'armée suisse présentant le monument. On y apprend que le monument a été réalisé en 1955. Le baron Carl Gustaf Mannerheim (1867 – 1951), maréchal de Finlande, est devenu le premier commandant en chef de la jeune armée finlandaise créée lors de l’accession du pays à l’indépendance après la Révolution russe de 1917. Le Dictionnaire historique de la Suisse nous apprend que, durant la “guerre d'hiver” (1939-1940), il organisa la résistance de son pays en 1940-1941 contre les unités soviétiques et devint ainsi le symbole de l'indépendance nationale. Sous son influence, la Finlande se rapprocha de l'Allemagne dès le milieu de l'année 1940 et entra en guerre à ses côtés contre l'Union soviétique (“guerre de continuation”, 1941-1944). Enfin, il devint président de la République jusqu’en 1946. À partir de 1943, il vint régulièrement faire des séjours de santé à Lugano, Lausanne et Montreux (sanatorium de Valmont où il rédigea ses mémoires). L'article de Wikipedia le concernant me permet de comprendre que la citation Pro causa candida Ense candido (« Pour une cause pure avec une épée pure ») figurant sur le monument de Territet est la devise des Mannerheim. En effet, Wikipedia m'indique que cette citation figure également sur son tombeau du cimetière militaire de Hietaniemi à [Helsinki].(https://fr.wikipedia.org/wiki/Helsinki).

Voilà pour le côté week-end studieux de cette flânerie. Sur place, nous arrivons presque au terme de ce parcours.

Un dernier coup d’œil sur les quais.

Avant d'entreprendre la remontée…

J'espère que cette promenade vous aura plu et vous incitera tant à utiliser votre appareil photo dans vos pérégrinations qu'à entreprendre ce type de ballade.

Tags : #aucafé #Histoire #Roadbook #suisse🇨🇭 #montreux #photographie #twice #sonya6000 #sigma1850f28

In last week's post, I mentioned that my main current writing goal is to finish the draft of a novel that I've been thinking about for a couple of years now but have been struggling to get down on paper. Normally, I'm a pantser. I might have a rough idea of where I want a story to go when I start it (though I don't always), but I don't sit down and plan it out. My preferred approach is to discover the story as I write it, then refine the arc and give it a more intentional-feeling pacing and flow during edits. This has worked for me thus far for the majority of my projects. It works especially well for short stories, but I've also written a couple of novellas and four novels this way, so I have tangible proof that it can work for longer stories, too. 

That said, I have written some select projects in the past that I planned out before writing. Any time I write a choose-your-own-adventure style story, for instance, I at least have a big-picture plan for how the pieces are going to flow together from the start. And when I'm ghostwriting novels, those always start from the outline first—it's the only way to wrangle the project in and make sure me and the client are on the same page from the start.

Of course, just because I know how to outline doesn't mean I enjoy it. To me, pantsing feels more organic and allows for more natural points of surprise. When I write a character into a corner, I need to be creative to get them out of it, in exactly the same way the character needs to be creative to get out of whatever bind I've put them into. What I've been reminding myself of lately, though, is that outlining doesn't need to mean putting rigid controls on what you write. There's a middle ground where you can get the thought-organizing, momentum-driving, rewrite-reducing benefits of an outline while still letting your story breathe and surprise you. With that in mind, here is my top advice for pantsers who are outline-curious on how to make the technique work for you.

#1: Don't limit yourself to just plot movement. 

One issue with stories written from an outline is that they can feel formulaic or overly architected. Sometimes you read them and can see the author moving the pieces around, or the characters feel like they're being directed through a series of actions rather than having those choices seem like their own, ones that arise out of their motivations, beliefs, and identity rather than something imposed by the person creating the story. 

While I can't confirm exactly why this happens in every instance, I suspect the problem often starts with what the writer focuses on when creating their outline. If you only think about how the plot will move, you're missing a critical ingredient of a compelling story: the development of the characters, and how their emotions, relationships, and motivations influence the choices they make and actions they take. 

An outline does need to clarify the plot movement, but that's not the only thing that should be in it. At each stage of the outline, think about the key players involved, how their prior experiences and beliefs influence what actions they'd take, how those actions move them closer to (or away from) their ultimate goal, and what impact each plot point would have on their emotional state, their relationships with other characters, and the choices they'd make in the future. An authentic character that's well-integrated into the plot and setting shouldn't be static. They change in response to the experiences you write for them, and planning out that evolution is just as important to creating a fully realized story as plotting out the story's action. 

#2: Let yourself take tangents.

One of the exciting things about pantsing is that I sometimes end up discovering new ways for the story to play out as I write, things that were never even in my brain when I first sat down to work on it. But you don't need to sacrifice this when you start from an outline if you take the same exploratory approach to writing it. 

Instead of just seeing the outline as a straight line from A to B to C, let yourself linger at each step and think about the different ways your characters might approach the situation. If you find yourself at a plot crossroads where you could take multiple paths forward, use the outline process to “audition” those paths and see which one will serve the story the best. One might stand out as the best option once you've finished the outline and know where you want the story to go. In other cases, you can wait to decide which path you’ll take until you're in the writing stage, when you'll be able to better assess which one seems like the most logical decision for your characters at that point of their journey.

The same idea can apply to filling in details of the characters' and world's history. When you reach a point that this backstory feels necessary to understand the choices characters make, or to get a full sense for the cultural, political, economic, etc. landscape that they're operating in, give yourself permission to go on a sidebar. Outline those backstory details the same way you would forward plot movement. That doesn't mean you'll necessarily include all of that information at this specific point in the story—these may be worldbuilding details that you want to sprinkle in through descriptions, or character context that you'll establish through conversations and flashbacks as you're building their identity on the page. But by brainstorming those background details as they come up, you'll give yourself a roadmap for which aspects of the world history or characters' past are actually need-to-know for the reader. Once you know that, all you need to do is find the right time and place to bring readers in on that knowledge. 

#3: Use whatever format makes sense for your brain.

Most people hear the word “outline” and think about the specific structured document that we're all taught to write for high school English, the kind that involves various levels of numbering and indenting and bullet points. This is one way to approach outlining a creative work, but that's not the only option. If thinking about things that way automatically kills all of your creativity or gives you flashbacks to writing five-paragraph essays, you can still get the same value out of doing things in a different format.

I’ll give some examples of other options. One way you can approach it is by writing a script-style outline. This can be especially effective for character-driven stories where conversations are going to be key plot drivers. With this style of outline, you write out many of the dialogue passages, surrounded by scene direction style summaries of their actions and expressions, as well as the setting and any other background information that you plan to work into the narrative. This is a kind of middle ground between outlining and pantsing. On your next pass, you convert this into full prose by filling in the narration and descriptive details around the dialogue, using the scene directions you wrote as a guide. 

Another option is to use a notecard outlining system. The basic idea here is that each chunk of the story (scene, chapter, plot point, etc.) gets its own notecard, where you can also write down things like the characters involved, where it's happening, and other details you'll want to make sure to include. This is the approach I default to when I'm doing choose-your-own narratives, since it makes it easier to visualize how the different plot choices branch off from each other, but it can be just as useful for other types of stories. I would say this is an especially good approach for more complex novels that have multiple plot threads or large casts of characters, because it also allows you to easily isolate each of these threads and experiment with different approaches to weaving them together. 

There are other options too, I'm sure, or you could come up with your own if none of the other approaches that people have tried seem like they'd work. The big-picture takeaway here is that there's more than one way to outline, and you don't need to lock yourself into anybody else's system. 

Outlines are tools, not rules

This was the big thing I needed to get into my own head before I could start to take advantage of outlining as a part of my process. I've heard a similar thing from other pantsers—that the idea of writing an outline feels restrictive, like it's preventing your creativity from having full room to blossom. But here's the thing about an outline: literally nobody else is going to see it. It doesn’t matter if it follows the rules or adheres to someone else’s standard. It's just a way to plan and organize your story before you start writing it. If you feel too constrained with a chapter-by-chapter outline, for instance, then you don't need to use that format. Maybe instead you just give yourself some key plot points to shoot for, and wait until you're writing it to decide where the chapter breaks will go. 

For the current novel, I'm starting with a big-picture outline divided into three acts. I've sketched out the basic plot movement and which characters will be involved, as well as how their motivations or allegiances will change over the course of the book. I plan to gradually reveal certain aspects of the world and characters to the reader, so I've also marked in the points where key info bits are going to be dropped. But there are some places where I haven't yet planned out exactly how the characters are going to get from one plot moment to the next—I know where I want them to end up, but I'm going to let myself figure out exactly how they get there when I sit down to write the thing. This kind of half-outlining gives me the structure I need to construct a complex plot involving a large cast of characters, so I'm not just stumbling around in the metaphorical woods for 30,000 words (like I did on my first attempt to write this novel a couple of years back), but it still leaves me some room to play when it's time for writing. That's important, because the actual act of writing a novel can be obnoxiously long and tedious, and it's even more so for me when I'm following a detailed outline and know exactly what comes next. Leaving myself some places to explore and make creative decisions during the writing phase I know is going to be crucial to forcing myself through those points where the writing doesn't feel exciting. 

That's the last tidbit of advice I'll end on. Writing a book takes a while. Experienced writers on the fast side of things can churn out a manuscript in 2-3 months, but for most people I'd say 6 months to a year is more realistic. In either case, though, you're going to be following this outline for a while, so it's smart to think about your writing process. Structure the outline in a way that will be easy for you to follow and matches up with how you prefer to write. If you like to work in chunks instead of writing through chronologically, for example, then doing a notecard system might be smart because it'll let you isolate or rearrange sections easily. The goal is to organize your thoughts and the story's structure, so whatever strategy will allow you to do that the best is the right tool for you, whether or not it matches with someone else's idea of what an outline should be.

See similar posts:

#WritingAdvice #NovelWriting

At the concert, I bought an extra beer for my friend, couldn’t find him, so ended up having to drink both.

There is a lesson to be learned from this.

This is my docker-compose.yaml for beszel:

services:
  beszel:
    image: henrygd/beszel:latest
    x-ports:
      - beszel.your-domain.com:8090/https
    volumes:
      - ./beszel_data:/beszel_data
      - ./beszel_socket:/beszel_socket

• Deploy the beszel webapp with uc deploy bezel.yml
• Signup and login
• Go to settings/tokens and activate “Universal Token”
• Under the ••• drop-down menu, select “Copy Docker Compose”. This will give you something like this:

services:
  beszel-agent:
    image: henrygd/beszel-agent
    container_name: beszel-agent
    restart: unless-stopped
    network_mode: host
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock:ro
      - ./beszel_agent_data:/var/lib/beszel-agent
      # monitor other disks / partitions by mounting a folder in /extra-filesystems
      # - /mnt/disk/.beszel:/extra-filesystems/sda1:ro
    environment:
      LISTEN: 45876
      KEY: 'ssh-ed25519 xxxxxxxxxxxxxxxxxxxxxxxxx'
      TOKEN: xxxx-xxxxx-xxxxx-xxxxx
      HUB_URL: https://beszel.your-domain.com

Add this line to the bottom of it:

    deploy:
      mode: global

This will ensure that the agent is installed on all your machines.

I usually just paste the beszel-agent bit into the first docker-compose, then re-run:

uc deploy -f beszel.yml

This will give you some output like this:

[+] Deploying services 8/8
 ✔ Container beszel-agent-xmai on eon    Started         1.4s 
 ✔ Container beszel-agent-os6i on itx    Started         0.6s 
 ✔ Container beszel-agent-hkhd on node2  Started         0.6s 
 ✔ Container beszel-agent-w84p on node3  Started         1.4s 
 ✔ Container beszel-agent-qd42 on node4  Started         0.6s 
 ✔ Container beszel-agent-c79q on pico   Started         0.5s 
 ✔ Container beszel-agent-v7ff on rock4  Started         0.8s 
 ✔ Container beszel-agent-odec on rock5  Started         0.7s 

Then you might want to rename the nodes in the beszel web UI for easier machine identification. I still haven't worked out how to make that process automatic, but it's not a big deal.

Nayavia is an early-stage project exploring how students experience college learning environments. It begins from a simple observation: the same college can feel enabling to some students and quietly misaligned for others, even when preparation and ability appear similar. Rather than focusing on rankings, predictions, or outcomes, Nayavia is interested in understanding what learning environments actually feel like from the inside.

Where this work currently stands At the moment, Nayavia exists as a research notebook. This work is focused on: thinking carefully about how college environments shape day-t0-day learning, listening to student experience without rushing to conclusions, questioning assumptions that are often taken for granted in college guidance. No data has been collected yet No analysis has been completed This emphasis is on forming the right questions before attempting answers. What this is not Nayavia is not a ranking system. It is not a recommendation engine. It is not a promise of better outcomes or a guide to choosing the right college. There is no advice being offered here, and no decisions being optimized. Research The core work currently lives in an ongoing research notebook. The writing is primarily for internal clarity. External readers may follow along, but the purpose is to document how the thinking evolves over time, including uncertainty, revisions, and dead ends.

So it was a bit longer since last time… a lot longer, but that’s fine, I had in it a break and now I worked on it the last days again and made big progress, here everything I did, compressed.

Paging

I made the Paging System in Sv39, in short Terms, this Describes how the Page Table Entry (Short PTE) is built, in my case I used first Sv39, it looked like a default thing to use, at least from what I saw and it was good to learn, but its a bit more complicated to explain, at least for me, but to make it short, the MMU take an address, makes it in 3 Parts, all 9 bits, called (VPN2, VPN1, VPN0) and the last 12 Bits are the offset for the address (For me right now, I use 4KiB Pages). If you wanna know more about this, here you can find more: https://riscv.github.io/riscv-isa-manual/snapshot/privileged/#sv39

Lily

Lily is the Name of my Bootloader, currently it was that OpenSBI loads my kernel Directly, In future I want it so that it loads my Bootloader (from now on Lily) and Lily loads my Kernel, also Lily should be capable of installing the OS if its not already installed. Currently both are in the same Binary because i’m concentrating on getting the Kernel ready, Lily will get an overwork if my Kernel is stable and I have more experience with RISC-V in general.

General Overwork

I made more documentation and made some code more clean and MANY bugs found and fixed.

What Next?

I’m currently working on a Kernel Allocator and on the internal memory Map.

Thank you for Reading :D

Littleclone (Mastodon) (Twitter)

City est un roman de science-fiction de l’écrivain américain Clifford D. Simak, publié pour la première fois en 1952. En français, il a été traduit sous le titre Demain les chiens, et c’est ce titre français qui a été ma première raison pour lire ce classique de la SF des années 1950.

On a far future Earth, mankind's achievements are immense: artificially intelligent robots, genetically uplifted animals, interplanetary travel, genetic modification of the human form itself. But nothing comes without a cost. Humanity is tired, its vigour all but gone. Society is breaking down into smaller communities, dispersing into the countryside and abandoning the great cities of the world. As the human race dwindles and declines, which of its great creations will inherit the Earth? And which will claim the stars?

Ce roman se compose de huit “nouvelles”, présentées comme des légendes que se racontent autour du feu des chiens qui, dans un futur lointain, ont beaucoup évolué et ont remplacé l'humanité comme espèce dominante. Ces légendes racontent l'évolution parallèle de la race humaine, à travers la lignée de la famille Webster et de leur robot Jenkins, et celle des chiens, qui acquièrent la capacité de parler suite à une expérimentation humaine et qui développent ainsi leur intelligence sociale au point de prendre le relais de l'humanité en déclin.

L'un des points saillants du livre, ce sont les notes critiques qui précèdent chaque nouvelle et relatent les débats philologiques qui agitent la communauté savante des chiens concernant la véracité et l'origine des légendes, et en particulier l’existence ou non de ces Hommes et leur lien avec la civilisation canine. Plusieurs chiens que l'on devine être des spécialistes de l’étude des légendes sont cités à plusieurs reprises et portent des visions très différentes : l'un prend au sérieux l'existence de cette humanité et considère que ces légendes constituent une vérité historique, quand un autre estime qu'il ne s'agit que de récits mythologiques écrits par des chiens pour expliquer leur origine. Ces courts chapitres fonctionnent comme un paratexte fictif particulièrement drôle pour les lecteurs humains contemporains que nous sommes.

Les deux premières nouvelles m'ont semblé un peu faibles mais les six suivantes sont absolument géniales, tout comme l’épilogue émouvant rédigé par l’auteur en 1973 et présent dans les éditions ultérieures.

À travers les huit nouvelles, Clifford D. Simak dépeint une humanité condamnée à réinventer la violence, la domination, les armes, et la guerre, et à disparaître pour laisser place à une civilisation canine qui saura faire mieux qu'elle, sur de nouvelles bases d'empathie, de pacifisme et de solidarité. La civilisation menée par les chiens du futur constitue en effet une Fraternité des animaux où le meurtre est interdit et où la communication entre les espèces est sacrée. C'est donc un récit à la fois pessimiste sur la destinée et la nature de l'espace humaine, et optimiste pour le vivant dans son ensemble.

Après avoir relu et beaucoup aimé les Chroniques Martiennes de Ray Bradbury, je suis heureux d’avoir poursuivi avec un autre classique de l’âge d’or de la science-fiction. Et quel classique ! J’ai adoré ce livre, et hormis ses deux premières nouvelles un peu plus faibles que les autres, la perfection n’est pas très loin.

There are places in America that never make the news. Towns you can drive through in four minutes if you blink too long. Places where the sidewalks roll up early, the diner closes at eight, and the quiet is so complete you can hear your own thoughts echo back at you. These towns are not famous, not fast, not impressive. They are faithful in a quiet way. They endure. They wait. And sometimes, they become the stage for the most important lessons a human soul can learn.

This story begins in one of those towns.

It had one main street and one church that still rang its bell every Sunday even though fewer people came each year. There was a hardware store that smelled like oil and wood, a post office where the same woman had worked for decades, and a café that stayed open later than it should have. No one could quite explain why the café remained open past midnight. It never made much money. It never had a line. But the lights were always on, and the door was never locked.

People joked that the owner just hated going home.

But those who had ever walked in on a hard night knew better.

The café didn’t look like much. Old booths. Scratched tables. Mismatched mugs. A bell over the door that rang a little too loud. The coffee wasn’t special, but it was hot. The kind of hot that warmed your hands before it ever reached your lips. The kind of warmth you forgot you needed until it showed up.

On a winter night when the town had already gone to sleep, a man named Thomas pushed that door open.

He didn’t come for coffee. He didn’t come for food. He came because he didn’t know where else to go.

Thomas had lived in that town his whole life. He was the kind of man people described as “good” without thinking much about it. He worked hard. He showed up. He tried. But the thing no one saw was the weight he carried when the lights were off and the noise was gone. The way his thoughts turned on him the moment he was alone. The way shame replayed old memories like evidence in a trial that never ended.

Depression had settled into him slowly. Quietly. It didn’t announce itself. It just took more and more space until everything else felt crowded out. Prayer became difficult. Hope felt distant. God felt silent. And silence, when mixed with guilt, becomes something else entirely.

Punishment.

Thomas had started to believe that God wasn’t quiet because He was close, but because He was done.

He slid into a booth and stared at his hands. They shook just slightly. He didn’t notice until the mug appeared in front of him.

“On the house,” a voice said.

Thomas looked up. The man behind the counter wasn’t what he expected. No uniform. No forced smile. Just someone present. Fully present. The kind of presence that doesn’t rush you or try to fix you.

“I didn’t order,” Thomas said.

“Most people don’t,” the man replied. “Not at first.”

Thomas frowned. “What does that mean?”

“It means people usually come in here because they’re carrying something,” the man said. “They sit down before they even know what they need.”

Thomas let out a breath he didn’t know he was holding. “I think God’s angry with me.”

The man didn’t flinch. Didn’t correct him. Didn’t quote Scripture. Just nodded, as if he’d heard that sentence many times before.

“Anger is a loud emotion,” the man said. “Silence usually isn’t.”

Thomas stared into the coffee. “Feels like punishment. Everything going wrong. Can’t feel God. Can’t hear Him. Feels like He’s turned His back.”

“Punishment always tells you the story is over,” the man replied. “Love never does.”

Thomas shook his head. “You don’t know what I’ve done.”

The man leaned on the counter. “I know what everyone says when they’re hurting.”

Outside, snow drifted past the windows. The town was still. The kind of still that makes you feel small.

“I’m afraid,” Thomas said quietly. “Afraid I’m condemned. Afraid this is just how it ends.”

The man stepped closer. “Let me tell you something about Jesus,” he said. “He never used fear as a doorway to God. Not once. Fear closes people. Love opens them.”

Thomas swallowed. “Then why does it feel like God left?”

“Because pain lies,” the man said gently. “It lies in God’s voice.”

That sentence landed heavier than anything else. Pain lies. It speaks with authority. It uses your own memories as evidence. It quotes your past like Scripture and convinces you the verdict has already been handed down.

Thomas felt something crack. Not relief. Not joy. Just recognition.

“Who are you?” he asked.

The man smiled. “Someone who’s very familiar with suffering.”

When Thomas looked down again, the man was gone. The mug was still warm. The café still quiet. The bell still hanging over the door.

Life did not suddenly get easier after that night. The depression did not disappear. The silence did not instantly lift. But something fundamental shifted.

Thomas stopped interpreting his pain as proof of rejection.

And that is where the lesson begins.

Because one of the most damaging lies many people believe is that suffering means separation from God. That silence means abandonment. That numbness means condemnation. And when depression enters the picture, those lies start to sound like truth.

But Scripture tells a very different story.

The Bible is filled with faithful people who could not feel God and assumed they were forgotten. David cried out asking why God seemed far away. Job believed God had turned against him. Elijah asked God to take his life because he felt alone and defeated. None of them were condemned. None of them were abandoned. Every one of them was still held, even when they could not feel it.

Jesus Himself entered silence.

On the cross, He cried out words that sound eerily familiar to anyone who has ever lived with depression: “My God, my God, why have you forsaken me?” Those words were not a confession of condemnation. They were a quotation of Scripture spoken from within suffering. They were the voice of someone fully human, fully faithful, and fully hurting.

If silence meant God had left, Jesus would not have known it.

The problem is that we often confuse feelings with facts. Depression dulls the senses. It numbs joy. It quiets emotion. It muffles spiritual awareness. And when that happens, the mind searches for meaning. If no comfort is felt, it assumes punishment. If no reassurance is heard, it assumes rejection.

But love does not withdraw because it is unseen.

Jesus did not come into the world to reward the emotionally strong or the spiritually confident. He came for the sick, the broken, the burdened, the ashamed, and the exhausted. He moved toward people who believed they were disqualified. He sat with those who thought they were beyond help.

Condemnation shouts. Mercy whispers.

And mercy almost always shows up in ordinary places. A café. A conversation. A quiet moment where someone finally feels seen instead of judged.

This is why Jesus so often taught in stories. Stories slip past our defenses. They don’t accuse. They invite. They allow truth to land gently where arguments would fail.

The lesson of the café is not that God removes pain instantly. It is that pain is not proof of God’s absence. Silence is not evidence of punishment. And depression is not a spiritual verdict.

If you are still breathing, the story is not over.

Jesus does not wait for you to feel worthy. He does not wait for your emotions to line up. He does not withdraw because you are numb, afraid, or exhausted. He sits with you in the quiet. He stays when you assume He has left. He remains present even when you cannot feel His presence.

That is not weakness. That is love.

And love, real love, never condemns the wounded for bleeding.

There is something deeply human about wanting proof that God is still near. Not theological proof. Not arguments. Just evidence that He hasn’t turned away. When the prayers feel flat, when worship feels empty, when Scripture feels distant, the heart starts to wonder if the problem is not the circumstance—but the soul itself.

That is where condemnation grows.

Condemnation does not usually arrive loudly. It slips in quietly and disguises itself as spiritual seriousness. It tells you that your suffering must mean something about your standing with God. It frames pain as punishment. It interprets silence as judgment. It rewrites grace into a probationary system where one mistake too many disqualifies you permanently.

But that voice does not belong to Jesus.

Jesus never spoke to the broken as if their pain proved their guilt. He never treated suffering as evidence of divine displeasure. In fact, He corrected that thinking repeatedly. When His disciples assumed blindness must be caused by sin, Jesus stopped them. When people believed tragedy meant God was angry, Jesus dismantled the assumption. Again and again, He redirected attention away from blame and toward mercy.

The Gospel does not teach that God withdraws from people in their darkest moments. It teaches the opposite—that God moves closer.

This is where the modern church sometimes struggles. We are good at talking about victory. We are less comfortable sitting with sorrow. We prefer testimonies that end quickly, stories that resolve neatly, faith that looks confident and clean. But Jesus did not limit His ministry to people who were emotionally regulated and spiritually certain.

He lingered.

He sat at wells with the ashamed. He ate meals with the accused. He allowed His feet to be washed by tears. He touched lepers before they were healed. He stood beside graves even though He knew resurrection was coming.

Jesus never rushed suffering out of the room.

Depression, anxiety, despair—these things do not scare Him. They do not repel Him. They do not offend Him. They are not evidence that faith has failed. They are part of the human condition He willingly entered.

That is why the idea that God punishes people by withdrawing His presence collapses under the weight of the cross. If God’s response to human brokenness was distance, Jesus would never have come at all. The incarnation itself is God’s answer to the lie of abandonment.

God came close.

And He stayed close.

Even when it cost Him everything.

This matters deeply for anyone who believes they are condemned because they cannot feel God. Feeling is not the same as truth. Emotional numbness does not equal spiritual separation. Silence does not mean rejection. Depression does not invalidate faith.

In fact, one of the cruelest aspects of depression is how convincingly it speaks in God’s voice. It uses religious language to reinforce despair. It says things like, “You’re being punished,” “You’ve gone too far,” “God is done with you.” And because those thoughts carry spiritual weight, they are harder to challenge.

But Jesus never speaks in hopeless absolutes.

Condemnation says, “There is no future.” Grace says, “There is still a story.”

Condemnation says, “You are beyond repair.” Grace says, “You are still being formed.”

Condemnation says, “God has left.” Grace says, “I am with you always.”

The café story is not meant to suggest that Jesus appears magically behind every counter or that suffering resolves through mysterious encounters. It is meant to remind us that Jesus specializes in meeting people where they least expect Him—and often in ways they do not recognize immediately.

Sometimes He shows up as presence rather than answers. Sometimes as companionship rather than correction. Sometimes as quiet endurance rather than instant relief.

And often, He shows up through other people.

This is where humility becomes holy. Needing help is not failure. Reaching out is not faithlessness. God has always worked through human hands, human voices, human compassion. To refuse help because you think you must suffer alone is not strength—it is isolation.

Jesus did not heal in private when crowds were present. He allowed witnesses. He allowed community. He allowed stories to spread. Healing was never meant to be hidden.

If you are struggling, staying connected is an act of faith. Talking is an act of courage. Continuing to breathe when everything inside wants to stop is not weakness—it is resistance against a lie that says you are finished.

The Gospel does not demand emotional certainty. It invites trust in the midst of uncertainty. It does not require you to feel God to belong to Him. It requires only that you keep turning toward Him, even when your steps are slow and your hands are empty.

Jesus never told anyone to clean themselves up before coming to Him. He said, “Come as you are.” Exhausted. Afraid. Ashamed. Confused. Numb. Angry. Silent.

Come anyway.

The café stayed open after midnight because some people don’t break down on schedule. Pain doesn’t punch a clock. And grace does not close early.

That is the lesson.

If you are still here, God is not done. If you are still breathing, grace is still active. If you are still reaching, mercy is still present.

Jesus does not abandon the wounded for bleeding. He does not condemn the suffering for struggling. He does not withdraw because the night feels long.

He stays.

And sometimes, staying is the miracle.

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

Your friend, Douglas Vandergraph

#Faith #Grace #Jesus #Hope #DepressionAndFaith #ChristianEncouragement #YouAreNotAlone #Mercy #SpiritualHealing #FaithInDarkness

In the darkness, feelings become visions that our minds make real.

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À l’abri de la nuit, la tendresse s’incarna.

Cette prise, refuge de tes plaies, non pas désir seulement,

mais soin — mon désir, baume offert

aux violences du jour qui veille.

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Our cover of night became gentleness.

This grip, a refuge for your wounds— not desire only,

but remedy: my desire, a balm

for the violences of waking hours.

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#poetry #madrid #wyst

OSRIC 3.0 PDFs are now available on DriveThruRPG:

• OSRIC 3.0 Player Guide
• OSRIC 3.0 Gamemaster Guide

Offset print version will be available from Mythmere Games later this year.

#OSR #OSRIC

courir pieds nus

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I’m not particularly interested in car racing or spectator sports in general. I did follow Formula 1 casually in the 90s and watched a race from start to finish once on TV, which I found to be an almost meditative experience. 

Yesterday, we watched the movie, and I loved it. It proves that it is possible to create big-screen excitement with an original story if you just follow time-tested rules. A rookie vs. seasoned pro trope, a Hollywood superstar in the lead, a believable love interest, and a Hans Zimmer soundtrack. Add some nail-biting action elements, and the movie is fun to watch.

The mastery of director Joseph Kosinski in F1 is evident in how well-measured these elements are. There were plenty of twists and turns, but the story always moved forward and left the big question open until the very end: Will Sonny Hayes win his first grand prix?