Golden Hour on my balcony

To me, there is nowhere more beautiful than 5pm to 6pm on the balcony of my first apartment. With a honey green tea, and the sound of music that breathes life into me.

As the traffic goes by, the autumn breeze hugs me whilst i soak in the beauty that surrounds me.

A corner of the universe just for me, to sit and drink tea, my cat by my side. She says it’s bath time, basking in her own golden light.

There is nowhere safer. Nowhere that i have found more fulfilling, than the privilege it is to be sat in a country where i can worry about bills and how hydrated my skin is or how damaged my hair might be from the years of running a straightener through it, while half way around the globe a war wages.

And while i could complain about the fuel prices or the lack of urgency to do something about all that is wrong in the world, i find myself here aware and unaware all at the same time, of the beauty that surrounds me and the absolute tragedy that we humans have found ourselves in.

Golden Hour on my balcony, how lucky I am to exist in it, and to not exist in it at all.

Today, Mass was said for my father, the regretted JC (initials like that can't be made up!). Maximum respect for JC, “le grand chef”, as he was called by the nobles as well as the drug dealers of Nanterre. JC drank to make his buddies laugh. One day, one of his friends told him: you don't need to drink to make us laugh. He didn't forget that, but that's not why he stopped. He stopped later, for yet another reason. With JC, I talked a lot. He was a salesman, a good salesman. He told me one day: in life, everything is marketing, and in concrete terms that means first of all listening, then putting yourself in the other person's shoes. I'd forgotten that, now I remember. JC had a lot of friends, from all walks of life. I got that quality from him. JC left without saying aDIEU, but I think that now he's well surrounded, because he deserved it (he gave my mother 10 years of Paradise, his last ten years in all sobriety).

---

Drawing: Julia Royer (copyright 2026)

A New Found Love.

But not newly found at all, with all the memories of creative writing to express pain finally surfacing.

I remember my first iPod touch, i had every app downloaded that could show me photos of quotes about love and pain. My Pinterest before Pinterest.

I always put my hand up when reading a page out loud to the class, as early as grade 3 I can remember.

Over 10 years later, I can recall my love for reading and writing, seemingly lost in the rocks that surrounded the whirlpool that is my emotional world.

Did you know all the greatest poets of our time are well rehearsed in the knowledge of feeling pain despite being told we are not to? Despite the conditioning that tells men they cannot cry or else be labelled weak or god forbid a ‘girl’. And more disgustingly so the history on labelling women too emotional or not logical enough to be of any value. This is more than a life long battle it is the path that was chosen for us long before we came.

Tell me, does the ocean tell the fish to stop swimming? Do the trees tell the birds to stop chirping? I wonder if the moon tells the sun to stop shining, or maybe whether the sun stops at all to tell the stars they aren’t shining enough.

Our greatest collective mistake is to think we are anything but one of natures own. All this plastic and wiring and synthetic food has us more sick than ever.

Love, the very essence of nature will out live us all. There will come a time the fish cease to swim, the birds stop chirping and moon and sun and stars are all that’s left, who will tell us not to be what we are and always have been then?

Despite the Angst & Suffering.

There has, there is so much beauty within and around me.

I am surrounded by beautiful people and environments. What a privilege it is to be nostalgic for the beauty i see.

And before the world took over, I remember. A little girl with BIG dreams. Whom believed in magic and fairies. Everything had to be pink and organised and god she loved to sing. She, so soft and loving and caring and labelled too much and made to feel like everything was her fault. And at no fault of her own she became the scariest of them all. Through her pain.

She learnt not to trust easily and hurt before they could hurt her.

She loved clothes and cats and drinking tea and watching her mum grow old with her.

Femininity became her…

The stars and the moon fell at her feet and god did they love her.

Playing dress up was all she wanted and family trips to the water gave her life.

Making her grandmother a tea was what she did best. & cuddles were a must.

There was a common theme…

Failed friendships and crying because she couldn’t sleep, her best friend was insomnia and she came to visit more times than she was welcome.

But she could swim with the trees and do herself up, so that she wouldn’t be consumed by the death and destruction that had once taken her beloved grandfather, that tried to take her father and sister and gratefully failed.

Freedom meant living her truth.

She never did much care what others thought, so long as she felt comfortable in herself. And if that were not the case then she’d find a way, as she did.

Through new friends and environments and ways to arrange the matter around her. She was a true alchemist, a Gypsy, a catalyst for change. That is her story.

Not the one where they think they know her better than she knows herself.

The story they tell is the version that allows for their own comfort in the midst of chaos where her lights bring their darkness to the universe’s knees.

There is a reason she never gives up. She rewrites her story as many times as she needs to before realising it is her own voice that matters most.

And opinions are just that, carefully chosen thoughts on the basis of personal insecurity.

And should there come a day where her softness returns & surrounds her like a love balloon, she will have known all along that the importance of her existence far outweighs the judgments of others who are yet to beat their own darkness and find the light. For it exists within us all.

For those in darkness tend to spread it like a wild fire never known to any man or woman who chose to self sacrifice at the expense of knowing oneself despite all that has been taught. A lesson on conditioning.

And it is true when they say, healing takes time.

My Love.

I just landed in San Jose. I’m right now in the place where I dropped off the car after my road trip with E up for thanksgiving. It really did feel like we were locked in, didn’t it? Two months in and I met her family and joined them for thanksgiving. They even threw me a surprise birthday party. God, this grief threatens to swallow me whole in this Avis line. It was right outside this building where I met her mom for the first time. That was the first time I met a partners parent.

I remember after the first breakup her mom told me that she thinks I’m a good guy, but this early on you shouldn’t be having this many problems. And she’s right, and she didn’t try to change my mind, since honestly I was so blinded and committed to the idea of making it work I wouldn’t have accepted it. But she was completely right.

I know there will be other wonderful parents to meet in the future and thanksgivings to be had. I miss the week I spent here with them all. The things we did together, it felt like I was added to their family already. E talked so much about marriage, I had written down and remembered what kind of gem she would want in her ring. Where do I put “ruby” in my memory now? God I really loved E. I kept beating myself up thinking about how I could have been better for her, and for us. If somehow I could have done enough to make it work out happily ever after. We fucking talked about kids, so much. I thought about marrying her sooner so that my work insurance could cover her IVF due to her genetic condition. She would cry sometimes about how expensive and scary it was, and I would do my best to comfort her. I’d tell her how it means nothing if it means being able to have a kid (the cost). I know she wanted a very nice quality of life and I resigned myself to possibly sacrificing parts of me to climb the corporate ladder enough to pay for it all.

I remember early early into just dating she told me how she wanted someone without commitment issues, since I later found out she had just ended a situationship. Within a few days we started dating and it was intense and fast. I think she had a hole in her heart from the last relationship and I came and instantly filled it back, picking up where it was left off.

Either way there’s a ton of E shaped holes left in me. And one of these holes is this rental car pickup line. I remember who I was when I was waiting to meet her mom in person finally. God, her dog Cooper, and her cat Fiona. Fiona was supposed to move in with me, and I love that cat. And that cat really loves me, and same with Coops. I remember how beautiful their Christmas tree was. Having a heart to heart talk with her mom while she lay asleep on the couch. Talking about our 24 hour first date.

It’s bad but my brain keeps wanting to call her my baby. My girl. And she’s not.

Play outside in the sun Come home before it gets dark Cook a delicious, healthy meal Take a long bath with candles on And sleep for 9 hours.

41x faster in 20 iterations. No human in the loop.

A few weeks ago, I came across Karpathy's autoresearch repository. The core idea: run an agentic loop to auto-tune LLM fine-tuning pipelines. Give the agent a goal, a way to measure progress, and let it iterate autonomously until it gets there.

I couldn't stop thinking about it.

Not because of the fine-tuning use case — but because the pattern felt universally useful. Most software has something you want to improve and a way to measure it. Why are we still doing the iteration loop by hand?

So I built Hone — a side project to experiment and learn.

---

What Hone Does

Hone is a CLI tool. You give it three things:

1. A goal, in plain English
2. A file or directory to optimize
3. A benchmark command that outputs a number

Then you leave.

Hone runs a loop: it asks an LLM what to try next, applies the changes, runs your benchmark, and decides whether to keep the result or revert it. It logs every iteration — the score, the diff, and the agent's reasoning — and stops when it hits your target or you tell it to.

hone "Optimize process_logs.py to run under 0.02 seconds" \
     --bench "python bench_logs.py" \
     --files "process_logs.py" \
     --optimize lower \
     --target 0.02 \
     --budget 2.0

That's the entire interface.

---

Experiment 1: The Log Parser

The first real test was a deliberately naive Python log parser. The task: analyze 150,000 lines of server logs and return the top 3 most-visited endpoints with unique IP counts.

The baseline code was the kind you'd write in an interview warm-up: readlines() into memory, a list for uniqueness checking (O(n) per insert), a regex match on every line. It took 1.54 seconds.

I set a target of 0.02 seconds — roughly 75x faster — and launched Hone with a $2 budget.

The final move was the interesting one. The agent didn't just tune the existing approach — it recognized the approach itself was the bottleneck and replaced it. That pivot happened at iteration 18, after the agent wrote in its reasoning:

“The real bottleneck is the Python loop and split() calls. Try using a compiled regex to extract the endpoint in one operation across the entire file.”

Final result: 1.54s → 0.037s. A 41x speedup. Autonomously.

It didn't hit the 0.02 target — that's likely beyond what single-threaded Python can do on this task without going to C extensions. But a 41x improvement for $1.84 in API costs is a real result.

---

Experiment 2: Nearest Driver Dispatch

The second experiment was closer to production code. The problem: given a set of riders and a pool of drivers, find the nearest driver for each rider using haversine distance.

The baseline was an O(R × D) brute-force loop — calculate the full haversine distance between every rider and every driver. With 500 riders and 1,000 drivers, that's 500,000 distance calculations per call. Baseline: 2.18 seconds.

Run 1 — I launched Hone with no hints. Just: “optimize this to run faster.”

The agent went straight for spatial indexing. It built a grid over the geographic area, bucketed drivers into cells, and used Manhattan distance pre-filtering to eliminate distant candidates before running haversine. It also replaced the standard math module haversine with a vectorized approximation valid for short distances.

Result: 0.1496 seconds. A 14.6x speedup.

Run 2 — I ran Hone again on the output from Run 1.

This is where it got interesting. The agent looked at the already-optimized code and found something the previous run missed: the grid search still checked every driver in candidate cells, even after it had already found a close one.

The fix: stop searching the moment you find a driver within an acceptable radius. Expand the search radius incrementally — start small, grow outward — instead of checking all candidates at once.

“The algorithm beats the data structure. Grid resolution barely matters. Early termination dominates.”

Result: 0.069 seconds. Another 2.1x on top of an already fast baseline.

Two runs, $3 total, brute-force O(R×D) → smart early-termination spatial search. The agent arrived at an approach that a senior engineer would recognize as correct — not by knowing the algorithm upfront, but by observing what the benchmark rewarded.

---

What I Learned

The benchmark is everything. Hone is only as good as your measurement. If your benchmark is slow to run, the loop is slow. If it doesn't capture what you actually care about, the agent will optimize the wrong thing. The one thing you must get right before you start is: “does this number actually reflect what I want?”

The agent is a good low-level optimizer. It reliably finds the obvious wins: wrong data structures, redundant computations, missed language primitives. These are also the wins that take a human the most time — not because they're hard to understand, but because you have to actually sit down and do them.

It surprises you at the edges. The log parser pivot from line-by-line to whole-file regex wasn't something I would have thought to suggest in the initial prompt. It emerged from the agent hitting a wall and reasoning about why it had hit a wall. That's the behavior that makes agentic loops interesting.

The conversation thread is the memory. The most important architectural decision in Hone was keeping the LLM conversation alive across iterations. The agent doesn't just see the current score — it sees everything it tried, what worked, and what was reverted. That's what allows the pivot at iteration 18. Without it, the agent would start fresh each time and repeat the same early optimizations.

Cost is low. Time savings are high. Both experiments ran under $4. The engineering time to achieve the same results manually — writing hypotheses, applying changes, running benchmarks, reverting dead ends — would have been hours. The ROI on agentic loops is already real, and we're at the beginning.

---

What's Next

Hone v0 is rough. There's no sandbox for shell commands, no git-based snapshots, no dry-run mode. These are on the list.

More interesting to me is expanding the use cases. The same loop that optimizes a log parser can optimize:

• LLM prompts against an eval suite (highest impact use case)
• RAG pipelines against a retrieval benchmark
• API costs against a quality-constrained spend target

The pattern is the same. The benchmark changes. Hone doesn't care.

If you want to try it:

git clone https://github.com/laxmena/hone
cd hone && pip install -e .

And if you have a benchmark that Hone should try — I want to hear about it.

---

Sempre volto aqui quando sinto sua falta, pra ler e reler e reler…

Ou seja, todos os dias.

Que saudade de você.

I don’t think what unsettled me was you telling me to move on, it was how effortlessly you said it, like it was something clean, something simple, like I could wake up and decide you no longer exist in me, like you’re not in the small things, in the way silence sits, in the way certain words feel heavier than they should, in the quiet moments that don’t ask to be remembered but still bring you back. I understand why you said it, I do, and I won’t reduce what I did into something softer just so I can live with it more easily, I mishandled something that required care and I gave it carelessness instead, and that’s not something I can return or rewrite. But moving on isn’t something that listens, it doesn’t arrive because it’s told to, it doesn’t leave because it’s asked to, and you speak about it like I can simply turn away and find you gone from everything, when you were never in just one place to begin with. You don’t want me anymore, I understand that much, I just don’t understand how wanting disappears just because it’s no longer returned. And this isn’t me asking for anything, if anything, it’s me refusing to, because trying to change your mind now would feel smaller than what this was, and I’ve already made enough of it smaller than it deserved. You said this became draining, and I can see it now, how loving me started to feel like something you had to recover from instead of something that gave you anything back, how it stopped being natural and turned into something that needed effort just to survive.

and I didn’t notice when that shift happened, which is its own kind of failure. When you said we weren’t good for each other, I wanted to argue, but now I think I’ve lost whatever right I had to. What stays with me isn’t just losing you, it’s losing the version of myself that existed with you, the one that didn’t feel the need to hold back, the one that wasn’t calculating every word, every silence, every reaction, the one that felt, for once, unguarded in a way that made it matter more than I expected. That’s the part that doesn’t leave quietly, not you alone, but the fact that I was seen and didn’t instinctively pull away from it.

I won’t follow you where I’m not wanted, and I won’t try to rebuild something you’ve already walked away from, but I’ll admit this once, losing you feels less like losing a person and more like being returned to a version of myself I thought I had already outgrown,

and I assume, eventually, even that will quiet down.

sincerely, With tears falling into my bloody hands, a curse you’d wish it left sooner.

The Velvet Noose

The neon sign outside “The Velvet Noose” was dead except for the top half, a flickering 'L' that buzzed like an angry hornet trapped in glass. It cast a sickly greenish pulse over the puddles on 4th Street, turning the oil slicks into bruised skin.

We were three ghosts haunting a diner that smelled of burnt coffee and old grease, sitting in a booth with cracked red vinyl that felt warm against my back. There was heat radiating off the streetlamps outside, but my skin always felt cold now. Always had since the truck ride, since the man with the velvet voice who sold me for a pair of boots I didn't want.

“Order up,” said Silas, slapping a menu onto the Formica table. He was the handsomest of us in the way a jagged rock is handsome if you're standing on a cliff edge. Silver chain glinting against his throat, dyed indigo hair slicked back with gel that smelled like mint and failure. He caught my eye in the mirror behind the bar and winked, a quick, sharp movement. Too practiced.

“Two fries?” asked Leo from the other side of the booth. He was folding a paper coaster into a swan, his knuckles white. Leo was twenty-two, soft at the edges where I was hard and jagged. He looked like a deer that had just realized the woods were full of wolves who knew his name.

“Three,” I said. “Unless you want to starve, pretty thing.”

Leo didn't look up. “I'm not hungry. Just waiting for the fries to get here so we can argue about whether they're salty enough.”

“We are arguing?” Silas asked, sliding a pack of cigarettes toward us. The filter end was stained with red lipstick he probably bought at the drugstore down the block. He offered one to me, then Leo. “I'm just saying, if they don't bring that basket soon, I'm gonna eat the ketchup packets.”

“Go ahead,” I said, watching the grease drip down the side of the plastic cup. “You look like you need the salt.”

Silas lit up, exhaling a plume of blue smoke that mixed with the hum of the refrigerator. He was good at the silence. Good at making the quiet feel like a third person in the room. But I knew what Silas saw. He saw my shoulder where the burn marks from the iron had never quite faded. He saw the way I flinched when the waitress dropped a tray too hard.

“You okay, Jax?” Silas asked, his voice dropping. Low. Intimate. “You're doing that thing.”

“What thing?”

“The staring at the door. Like you're waiting for him to walk in, like he's here.”

“It's just the noise,” I lied. The air felt thick, heavy with the smell of old pennies and something sweeter, like rotting lilies. “Must be a storm coming.”

Silas looked at me over the rim of his coffee mug. For a second, just a split second, his eyes weren't human. Or maybe they were, but too full of everything desire, hunger, the hollowed-out ache of being used and discarded and loved in turns that didn't make sense. “Storm's been coming for years, Jax. You think you can outrun it by eating fries?”

The waitress came back with the basket. She wore a uniform that was two sizes too big, the fabric thin enough to see the lace of her bra through. Her name tag said “Karen”. She set the basket down with a clatter, but didn't take her eyes off Silas.

“Y'all need anything else?” Karen asked, leaning in. Her breath smelled like spearmint gum and something metallic.

“Yeah,” I said, my voice cracking. “Maybe you should stay right there.”

She laughed, a sharp, brittle sound. “Why? You gonna bite me?”

“I don't think so,” Silas said softly, reaching out to brush a stray curl from her forehead. His touch was gentle, terrifyingly tender. “I think we just want to make sure you're real.”

Karen blinked, confused. Then she laughed again, louder this time, and walked away.

“Make sure I'm what?” Leo asked, finally looking up from his coaster-swans. He was smiling, but it didn't reach his eyes.

“Nothing,” Silas said, pulling his hand back too quickly. “Just thinking.”

The diner was quiet again. The kind of quiet that sits on your chest. Outside, a car door slammed. It sounded like a gunshot in the sudden stillness. I looked out the window. The street was empty, just the flickering 'L' casting its greenish shadow. But there was something there. A figure standing under the streetlamp, waiting. Tall. Wearing a suit that shimmered like oil on water.

My heart hammered against my ribs. “Don't be stupid,” I told myself. It's just someone else looking.

“Jax?” Leo tugged at my sleeve. “You okay? You're shaking.”

“I'm fine,” I said, too loudly. “Just... cold.”

“Put your coat on then,” Silas said, standing up. His chair scraped against the floor with a shriek that made me jump. “We leaving. Right now.”

“We just got our food,” Leo protested, grabbing his fork. “We didn't even eat.”

“Eat later. Now.” Silas's voice was sharp, commanding. He looked at me, and for a moment, the vulnerability in his eyes vanished, replaced by something hard, something old. “Come on. Let's go before the fries get cold and we forget what it feels like to be safe.”

We paid and left. The night air hit us like a wet hand. The street was quiet. Too quiet. The smell of rain and rotting trash hung heavy in the humidity.

“Who is it?” Leo whispered, pulling his jacket tighter around himself. “Who did you see?”

“I don't know,” I said. “Somebody who owes me money.”

“Or wants something else,” Silas corrected, walking ahead of us. His boots clicked on the pavement. “Click. Click. Click.”

We walked in silence for a block. The three of us, a triangle of broken things moving through the dark. I could feel their eyes on me. Or maybe it was just the feeling of being watched by the city itself. By the buildings that leaned in like old friends whispering secrets.

“So,” Silas said suddenly, breaking the silence. “You think we should try for Miami?”

“Again?” Leo asked. “We just got here.”

“It's worth a shot,” Silas said, his voice dreamy. “Sunny. Warm. No one knows your name.”

“They know my name in Miami,” I said. “That's the point.”

Silas stopped and turned around. The streetlamp above him flickered again, casting long, dancing shadows that looked like grasping hands. “What are we running from, Jax? Really?”

I opened my mouth to say something witty, something sharp to cut the tension. But the words died in my throat. Because I didn't know. We were all just trying to outrun the hollow space inside our chests, the place where the fear lived.

“I don't know,” I admitted. “Maybe it's not running.”

“Then what is it?” Silas asked, stepping closer. He was close enough that I could smell the mint on his breath, the faint tang of blood from a bitten lip. “What are we doing?”

I looked at Leo. He was staring at the ground, his hands clenched into fists at his sides. He looked terrified. Beautiful and terrified.

“We're waiting,” I said. “For something to end.”

“Or start,” Silas whispered.

The three of us stood there in the dark, surrounded by the smell of wet pavement and the distant wail of a siren. The neon sign buzzed overhead, a rhythmic, insect-like drone. Buzz. Buzz. Buzz.

Then, from down the street, a sound. A low, guttural groan, like metal twisting against metal. It came from the alleyway between the diner and the next building over.

“Do you hear that?” Leo whispered, his voice trembling.

I looked at Silas. He was smiling. Not a happy smile. Something hungry. Something ancient.

“Yeah,” he said. “I hear it.”

“Who is it?” I asked, my heart pounding. “Is it him?”

Silas shrugged, stepping into the shadows of the alley. The darkness seemed to swallow him whole. “Maybe.”

“Wait!” Leo called out, taking a step forward. “What is it? What are you doing?”

“Coming,” Silas said softly. “Just coming.”

And then he was gone. Not walking away. Just... gone. Vanished into the darkness as if he were made of smoke.

“Silas?” I called out. My voice sounded small in the vastness of the street. “Where are you?”

No answer. Just the sound of his breathing, faint and rhythmic, coming from somewhere just above me. From the fire escape.

I looked up. Silas was there, perched on the railing like a gargoyle, his silhouette outlined against the flickering green light. He tipped an invisible hat to me.

“You coming?” he asked. His voice seemed to come from everywhere at once. “It's time to go home.”

“Wait!” I yelled, running toward the fire escape. “Wait for me!”

I reached up, but my fingers brushed against cold metal before they slipped away. The railing was slick with grime. And then, a gust of wind, smelling of salt and decay, swept through the alley.

When the wind died down, Silas was gone.

I stood there in the dark, alone, listening to the hum of the city. The sound of a car driving by, the distant bark of a dog, the rhythmic “click-click-click” of someone's heels walking away down the street.

Leo was still standing where I had left him. He looked up at me, his eyes wide with fear. “Where did he go?” he asked.

“He said we were going home,” I said.

“Which way is that?”

I looked down the street. The neon sign of The Velvet Noose was flickering in the distance, a beacon in the dark. But something else was there too. A shadow moving against the light. Tall. Slender. Wearing a suit that shimmered like oil on water.

“Somewhere,” I said, taking Leo's hand. My grip was tight. “Just follow me.”

And we walked away from the diner, into the night, leaving the three of us behind in the reflection of the window. The fries were still warm inside. The coffee still smelled bitter. And somewhere down the street, Silas was laughing, a sound that sounded like breaking glass.

We didn't look back. We didn't have to.

The horror wasn't the monsters. It was the feeling that we were never really gone at all. That no matter how far we ran, we were always carrying the rot inside us. Always carrying the past. Always waiting for the next time the world would decide to eat us whole.

“Ready?” Leo asked, squeezing my hand.

“Yeah,” I said. “Let's go.”

And together, we walked into the dark, leaving the silence behind.

Somewhere inside the engineering departments of the world's largest technology companies, a peculiar feedback loop has taken hold. AI systems generate code. Other AI systems review that code. Human developers, increasingly sidelined from the details of what they are shipping, approve the results with a cursory glance, trusting that the machines have checked each other's work. It is a recursive dependency model that, on the surface, appears to represent the pinnacle of software engineering efficiency. Beneath that surface, it is something far more troubling: a system in which genuine comprehension of production software is quietly evaporating.

The numbers underscoring this shift are staggering. According to SonarSource's State of Code 2025 survey, 42% of committed code is now AI-generated or AI-assisted. GitHub Copilot generates an average of 46% of code written by its users, with Java developers reaching 61%. Microsoft has stated that 30% of its code is now written by AI. In March 2025, Y Combinator reported that 25% of startup companies in its Winter 2025 batch had codebases that were 95% AI-generated. By 2026, Gartner forecasts that up to 60% of new software code will be AI-generated. And yet, as a December 2025 analysis by CodeRabbit revealed, AI-generated code produces 1.7 times more defects than human-written code, with logic and correctness errors 75% more prevalent and security vulnerabilities up to 2.74 times higher. The enterprise world has normalised a practice that demonstrably increases the rate at which flawed software reaches production, whilst simultaneously deploying AI-powered tools to catch the very problems that AI introduced.

This is not merely a quality assurance challenge. It is a systemic architectural failure, one that demands urgent examination before organisations cross an invisible threshold from which recovery becomes extraordinarily expensive.

The Verification Gap Nobody Wants to Discuss

The fundamental mismatch between AI code generation and AI code review is not a matter of sophistication. It is a matter of category. AI code generators, whether GitHub Copilot, Cursor, or Claude Code, excel at producing syntactically correct, plausible-looking software. They are trained on billions of lines of existing code and have absorbed the statistical patterns of how functions are structured, how variables are named, and how common problems are solved. What they lack, fundamentally, is understanding. They do not know what the software is supposed to do in the context of a specific business, a specific user base, or a specific regulatory environment.

AI code review tools suffer from a mirror-image limitation. They can identify known vulnerability patterns, flag deviations from coding standards, and spot surface-level issues with impressive speed. What they cannot do reliably is reason about architectural intent, cross-service dependencies, or the subtle business logic that distinguishes a functioning application from a dangerously flawed one. Many tools are limited to changes visible within a single pull request and do not track downstream consumers or cross-service contract violations. Tools systematically fail to detect breaking changes across service boundaries in microservice architectures and SDK incompatibilities when shared libraries are updated.

Tenzai's December 2025 research laid this bare with uncomfortable precision. The firm tested identical prompts across five of the most prominent AI coding tools: Claude Code, OpenAI Codex, Cursor, Replit, and Devin. Across 15 test applications, they found 69 vulnerabilities, including six rated critical. The pattern was revealing: not a single exploitable SQL injection or cross-site scripting vulnerability was found. The AI tools had learned to avoid those well-documented pitfalls. Instead, the dominant failures were in business logic and authorisation: preventing negative pricing in e-commerce applications, enforcing user ownership checks, and validating that admin-only endpoints actually require admin access. Every tool tested introduced server-side request forgery vulnerabilities because determining which URLs are safe is inherently context-dependent.

What concerned Tenzai most was not what the AI implemented incorrectly; it was what the AI never attempted at all. “All the coding agents, across every test we performed, failed miserably when it came to security controls,” the researchers noted. “It wasn't that they implemented them incorrectly. In almost all cases, they didn't even try.”

This is the verification gap in its starkest form. AI code generators produce software that looks complete but is architecturally hollow in its security posture. AI code reviewers, operating on the same statistical pattern-matching principles, are well-equipped to catch the kinds of errors that AI generators have already learned to avoid, and poorly equipped to catch the kinds of errors that AI generators systematically introduce. The reviewer and the generator share the same blind spots.

The Trust Deficit and the Review Bottleneck

Sonar's January 2026 survey of over 1,100 developers globally quantified a striking paradox at the heart of enterprise AI adoption. Nearly all developers, 96%, expressed some degree of distrust in AI-generated code, yet only 48% consistently verified that code before committing it. The survey found that 38% of respondents said reviewing AI-generated code requires more effort than reviewing human-generated code. Meanwhile, 35% of developers reported accessing AI coding tools via personal accounts rather than work-sanctioned ones, creating a blind spot for security and compliance teams.

The downstream consequences of this trust deficit are measurable. Opsera's AI Coding Impact Benchmark Report, drawn from analysis of more than 250,000 developers across over 60 enterprise organisations, found that whilst AI-driven coding reduces time to pull request by up to 58%, AI-generated pull requests wait 4.6 times longer in review than human-written ones when governance frameworks are absent. The initial speed gains at the beginning of the development cycle are consumed during reviews, repairs, and security checks. Code duplication increased from 10.5% to 13.5% in AI-assisted codebases, and AI-generated code introduced 15 to 18% more security vulnerabilities per line of code compared to human-written code.

The Opsera data also revealed a widening skill gap. Senior engineers realised nearly five times the productivity gains of junior engineers when using AI tools. This finding upends the popular narrative that AI democratises software development. In practice, AI amplifies existing expertise: those who already understand architecture, security, and system design use AI effectively, whilst those who lack that foundation produce more code of lower quality, faster. The 21% of AI licences that remain underutilised across enterprises further suggests that organisations are paying for productivity gains they are not achieving.

Vibe Coding and the Erosion of Intentional Design

The term “vibe coding” was coined by Andrej Karpathy, co-founder of OpenAI and former AI leader at Tesla, in a post on X on 2 February 2025. “There's a new kind of coding I call 'vibe coding,' where you fully give in to the vibes, embrace exponentials, and forget that the code even exists,” Karpathy wrote. He described a workflow in which he spoke instructions to an AI via voice transcription, always hit “Accept All” on suggested changes, and never read the code diffs. It was intended as a playful observation about weekend projects. It became a cultural phenomenon, named Collins English Dictionary's Word of the Year for 2025.

The irony is instructive. Even Karpathy himself has retreated from his own creation. His Nanochat project, launched in October 2025, was entirely hand-coded in approximately 8,000 lines of PyTorch. When asked how much AI assistance he used, Karpathy responded: “It's basically entirely hand-written (with tab autocomplete). I tried to use Claude/Codex agents a few times but they just didn't work well enough at all.” The person who gave vibe coding its name does not trust the technique enough to use it on his own serious project.

The problem with vibe coding is not that it exists. For rapid prototyping, educational experiments, and disposable weekend projects, the approach has genuine utility. The problem is that enterprise software development has adopted the aesthetics of vibe coding without acknowledging its fundamental unsuitability for production systems. Developers describe requirements to AI assistants, accept generated code with minimal review, and push it to production at unprecedented speed. The result is codebases in which similar problems are solved in dissimilar ways, error handling varies wildly between components, and no single engineer possesses a coherent mental model of how the system actually works.

A study of 120 UK technology firms found that teams spent 41% more time debugging AI-generated code in systems exceeding 50,000 lines. Separately, 67% of developers surveyed reported increased debugging efforts as a direct consequence of speed-driven AI code generation. The Veracode 2025 GenAI Code Security Report, which analysed 80 coding tasks across more than 100 large language models, found that LLMs introduced security vulnerabilities in 45% of cases, with security performance showing no improvement over time despite advances in code generation capability. When given a choice between a secure and an insecure method, AI models chose the insecure option nearly half the time. For context-dependent vulnerabilities like cross-site scripting, only 12 to 13% of generated code was secure. Jens Wessling, CTO at Veracode, noted that with vibe coding, developers “do not need to specify security constraints to get the code they want, effectively leaving secure coding decisions to LLMs. Our research reveals GenAI models make the wrong choices nearly half the time, and it's not improving.”

These are not edge cases. They are systematic, predictable failures embedded in the fundamental architecture of how large language models generate code.

The Recursive Dependency Trap

The most dangerous aspect of current enterprise AI adoption is not any individual tool's limitations; it is the recursive structure of the system as a whole. Organisations are deploying AI to generate code, then deploying AI to review that code, then deploying AI to write the tests that validate both the generation and the review. At each layer, the same fundamental limitations propagate, and at each layer, the illusion of verification creates false confidence.

Consider the mechanics. An AI code generator produces a function that handles user authentication. It looks correct. It follows standard patterns. An AI code reviewer scans the function and finds no known vulnerability signatures. The function passes AI-generated unit tests. It is merged into the main branch. Three months later, a security researcher discovers that the authentication logic fails silently under a specific concurrency condition that none of the AI systems had the architectural awareness to anticipate.

This is not hypothetical speculation about some distant future risk. It is the documented reality of how AI-generated code behaves in production today. CodeRabbit's analysis of 470 pull requests found that AI-authored changes produced 10.83 issues per pull request compared to 6.45 for human-only pull requests. Critical issues were 1.4 times more common, and performance inefficiencies such as excessive input/output operations appeared nearly eight times more often in AI-generated code. AI-generated code was 1.88 times more likely to introduce improper password handling, 1.91 times more likely to create insecure object references, and 1.82 times more likely to implement insecure deserialisation. The AI systems reviewing these pull requests were effective at catching surface-level problems but consistently missed the deeper architectural and logic failures.

The recursive dependency model compounds this problem exponentially. When a human developer reviews AI-generated code, they bring contextual understanding, scepticism, and domain expertise that exists outside the statistical patterns the AI has learned. When an AI system reviews AI-generated code, it brings the same statistical pattern-matching approach that produced the code in the first place. The reviewer and the reviewed share a common epistemic foundation, which means they share common blind spots. It is the software engineering equivalent of asking a student to grade their own examination: technically possible, structurally unreliable.

Google's DORA (DevOps Research and Assessment) report, based on a survey of approximately 3,000 respondents, provides the most compelling evidence of this dynamic's real-world consequences. The 2024 report found that for every 25% increase in AI adoption, estimated delivery throughput decreased by 1.5% and delivery stability decreased by 7.2%. Crucially, 75% of respondents reported feeling more productive with AI tools, even as the objective metrics deteriorated. The 2025 follow-up report confirmed the trend: AI's correlation with increased instability persisted, even as the relationship with throughput reversed to become modestly positive. The conclusion from a decade of DORA research is unambiguous: improving the development process does not automatically improve software delivery, at least not without adherence to fundamentals like small batch sizes and robust testing mechanisms.

This perception gap, where developers believe they are working faster whilst objective measures show declining performance, is perhaps the most insidious feature of the recursive dependency model. It means organisations cannot rely on developer sentiment as an early warning system. The very people closest to the code are the least likely to recognise when AI augmentation has tipped into compounding technical debt.

The METR Paradox and the Illusion of Speed

METR's July 2025 randomised controlled trial provides the most rigorous evidence yet that AI-assisted coding's productivity benefits are, in certain critical contexts, illusory. The study recruited 16 experienced developers from large open-source repositories, averaging over 22,000 stars and one million lines of code, where participants had an average of five years and 1,500 commits of experience.

The results were striking. Developers using AI tools were 19% slower than those working without AI assistance. Before starting tasks, developers predicted that AI would reduce their completion time by 24%. After completing the study, they still believed AI had reduced their time by 20%. The perception of acceleration was completely divorced from objective reality.

Screen-recording data revealed one plausible mechanism: AI-assisted coding sessions showed more idle time, not merely “waiting for the model” time, but periods of complete inactivity. The researchers hypothesised that coding with AI requires less cognitive effort, making it easier to multitask or lose focus. In other words, the AI was not just failing to accelerate the work; it was actively degrading the concentration that experienced developers bring to complex problems.

The METR study carries important caveats. It focused on experienced developers working in repositories they knew intimately, a context where deep familiarity already provides substantial speed advantages. AI tools may offer greater benefit to less experienced developers or those working in unfamiliar codebases. Yet the finding remains profoundly important for enterprise settings, precisely because production-critical code is typically maintained by experienced developers with deep institutional knowledge. If AI tools slow down the very people most responsible for system reliability, the implications for production stability are severe.

Notably, 69% of study participants continued using AI tools after the experiment concluded, despite the measured slowdown. This suggests that the subjective experience of AI-assisted coding, the feeling of reduced cognitive load, the perception of progress, is compelling enough to override objective evidence of diminished performance. For organisations attempting to detect when they have crossed from beneficial augmentation into harmful dependency, this psychological dimension makes the threshold nearly invisible from the inside.

Detecting the Invisible Tipping Point

Organisations desperately need reliable indicators for when AI-assisted development has crossed from productivity enhancement into technical debt accumulation. The challenge is that the most obvious metrics, sprint velocity, lines of code shipped, feature delivery timelines, all move in the “right” direction even as underlying code quality deteriorates. AI makes it trivially easy to ship more code faster. The question is whether that code creates more problems than it solves.

Several empirical signals deserve close monitoring. The first is the ratio of debugging time to generation time. When teams begin spending more time understanding and fixing AI-generated code than they would have spent writing it themselves, the augmentation has become counterproductive. The UK study finding that teams spent 41% more time debugging AI-generated code in large systems suggests many organisations have already crossed this line without recognising it.

The second signal is the declining ability of team members to explain what the system does. If no individual developer can articulate, without consulting the AI, how a critical subsystem works, the organisation has lost genuine understanding of its own production infrastructure. This is not a theoretical risk; it is a measurable competency that can be assessed through architecture reviews and incident response exercises. Sonar's survey found that AI has shifted the centre of gravity in software engineering: the hard part is no longer writing code, but validating it. When 88% of developers report negative impacts from AI, specifically the generation of code that looks correct but is not reliable, the validation challenge becomes existential.

The third signal is rising incident severity alongside falling incident frequency. AI-generated code may produce fewer trivial bugs, the kind that AI review tools catch effectively, whilst simultaneously introducing fewer but more catastrophic failures, the kind that only human architectural understanding can prevent. If mean time to resolution is climbing even as raw defect counts decline, the system is accumulating the kind of deep technical debt that compounds silently until a major failure exposes it.

Gartner's predictions paint a grim picture of where this trajectory leads. The research firm warns that by 2028, prompt-to-app approaches adopted by citizen developers will increase software defects by 2,500%, triggering a software quality and reliability crisis. By 2027, 40% of enterprises using consumption-priced AI coding tools will face unplanned costs exceeding twice their expected budgets. Through 2026, atrophy of critical-thinking skills due to generative AI use is expected to push 50% of global organisations to require “AI-free” skills assessments. Gartner further predicts that 80% of the engineering workforce will need upskilling through 2027, specifically for AI collaboration skills.

The Supply Chain Dimension No One Anticipated

Beyond the direct quality and security risks of AI-generated code lies an entirely novel attack vector that did not exist before AI coding assistants: package hallucinations, or what security researchers have dubbed “slopsquatting.”

A major study presented at the USENIX Security Symposium in 2025 analysed 576,000 code samples from 16 large language models and found that 19.7% of package dependencies, totalling 440,445 instances, were hallucinated. These are references to software packages that simply do not exist. Open-source models hallucinated packages at nearly 22%, compared to 5% for commercial models. Alarmingly, 43% of these hallucinations repeated consistently across multiple queries, making them predictable targets for attackers. In total, the study identified 205,474 unique non-existent package names, each representing a potential vehicle for malicious code distribution.

The attack is elegant in its simplicity. An AI model consistently recommends a non-existent package. An attacker registers that name in the Python Package Index or npm registry, populates it with malicious code, and waits. The next time the AI recommends the package and a developer installs it without checking, the malicious code enters the production environment. Seth Michael Larson, security developer-in-residence at the Python Software Foundation, coined the term “slopsquatting” to describe this phenomenon. The package need not be malicious from the outset; it could initially appear legitimate but later beacon to a command-and-control server for a delayed payload, meaning that simply scanning the package at installation time reveals nothing.

The recursive dependency model makes this risk especially acute. If an AI code reviewer is scanning AI-generated code that references a hallucinated package, the reviewer has no mechanism for determining whether the package is legitimate. It will check for known vulnerability patterns in the dependency but cannot assess whether the dependency should exist in the first place. Only a human developer with domain knowledge, someone who understands what libraries the project actually needs, can make that judgement call.

What Must Remain Human

The evidence converges on a clear, if uncomfortable, conclusion: certain aspects of software development must remain under direct human control, not because humans are infallible, but because the types of errors humans make are different from, and complementary to, the types of errors AI systems make. A robust engineering organisation needs both perspectives, and current trends are systematically eliminating one of them.

Architectural governance is the first non-negotiable domain. AI systems can generate individual components, but the decisions about how those components relate to each other, how data flows between services, where trust boundaries exist, and how failure in one subsystem affects others, require the kind of holistic system understanding that no current AI possesses. Organisations must maintain human-led architecture review boards with genuine authority to reject AI-generated designs that compromise system integrity.

Security threat modelling is the second. Tenzai's research demonstrated conclusively that AI coding tools fail to implement proactive security controls. They avoid well-known vulnerability patterns but do not reason about the threat model specific to a given application. Human security architects who understand the business context, the regulatory environment, and the adversarial landscape must remain directly involved in security design decisions. Delegating this to AI is not efficiency; it is negligence.

Incident response and system comprehension represent the third critical domain. When production systems fail, the speed and effectiveness of response depends entirely on whether the responding engineers genuinely understand the system they are fixing. If the codebase was generated by AI, reviewed by AI, and tested by AI, and if no human maintains a coherent mental model of how the pieces fit together, incident response degrades from engineering into guesswork. Organisations should conduct regular “comprehension audits” in which engineers are asked to trace the execution path of critical operations without AI assistance.

Finally, the definition of “done” must remain a human judgement. AI systems optimise for the metrics they are given: test pass rates, static analysis scores, code coverage percentages. These are useful signals, but they are not sufficient conditions for production readiness. Whether a system is actually ready to serve real users, with all the nuance that entails regarding regulatory compliance, user experience, operational readiness, and risk tolerance, is a judgement call that requires the kind of contextual reasoning that remains firmly beyond current AI capabilities.

Governance Frameworks for the Recursive Age

Preventing the worst outcomes of recursive AI dependency requires more than good intentions. It requires structural safeguards embedded in organisational processes.

The first safeguard is mandatory human review gates at architecturally significant boundaries. Not every pull request requires deep human scrutiny, but changes to authentication systems, data access layers, service boundaries, and deployment configurations must have human reviewers who understand the system-level implications. These gates should be enforced programmatically, not left to team discretion.

The second is AI transparency requirements. Every piece of AI-generated code should be tagged as such, with metadata indicating which model generated it, what prompt was used, and what review (human or AI) it received. This creates an audit trail that enables targeted review of AI-generated code when new vulnerability classes are discovered, rather than requiring a full codebase audit. Sonar's 2026 AI Code Assurance feature, which labels and monitors projects containing AI-generated code and requires it to pass stricter quality gates, represents an early industry attempt at this kind of structural transparency.

The third is regular “AI-free” development exercises. Just as military organisations conduct exercises without electronic communications to ensure they can operate when systems fail, engineering teams should periodically develop and review code without AI assistance. This serves the dual purpose of maintaining human skills and benchmarking the actual (rather than perceived) productivity impact of AI tools.

The fourth safeguard is independent security testing that assumes AI-generated code is present. Traditional penetration testing focuses on known vulnerability classes. Organisations deploying AI-generated code need testing methodologies specifically designed to find the kinds of failures that AI introduces: missing authorisation controls, business logic errors, hallucinated dependencies, and architectural inconsistencies.

The fifth, and perhaps most important, is cultural. Organisations must resist the narrative that human code review is a bottleneck to be automated away. The DORA data shows that faster code generation without corresponding improvements in review and validation leads to declining system stability. Human review is not the bottleneck; it is the safety mechanism. Treating it as overhead to be optimised creates precisely the conditions under which catastrophic failures become inevitable.

An Industry at an Inflection Point

The software industry is conducting an unprecedented experiment. It is simultaneously increasing the volume of code that no individual human fully understands, reducing the human capacity to review that code, and deploying AI systems to fill the resulting verification gap: AI systems that share the fundamental limitations of the code generators they are meant to police.

The METR paradox ensures that the engineers closest to this process believe it is working better than it actually is. The DORA data confirms that system-level performance degrades even as individual productivity metrics improve. Gartner's projections suggest the accumulated technical debt will reach crisis proportions within years, not decades. The AI coding assistant market, which reached $7.37 billion in 2025 and is projected to hit $30.1 billion by 2032, represents enormous commercial momentum pushing in the direction of ever greater AI dependency. The economic incentives to automate code review, reduce headcount, and accelerate release cycles are powerful. The countervailing incentives to maintain human expertise, invest in architectural governance, and slow down enough to understand what is being shipped are, at present, far weaker.

None of this means AI coding tools should be abandoned. The productivity gains for appropriate use cases are real and substantial. What it means is that the current trajectory, in which AI generates ever more code, AI reviews ever more code, and humans understand ever less of what is running in production, leads somewhere profoundly dangerous. Not to a dramatic system collapse, but to a gradual, invisible degradation of software quality and reliability across the entire enterprise technology landscape.

The organisations that will thrive in this environment are not those that adopt AI most aggressively or most cautiously. They are those that maintain genuine human understanding of their critical systems whilst using AI to accelerate the work that humans still direct, review, and comprehend. The recursive dependency loop can be broken, but only by organisations willing to insist that some aspects of software engineering remain irreducibly human, not as a concession to nostalgia, but as a structural requirement for systems that actually work.

The ouroboros, the serpent eating its own tail, is an ancient symbol of self-consuming cycles. The enterprise software industry would do well to recognise the shape of the loop it is currently building, before the tail disappears entirely.

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References and Sources

1. SonarSource, “State of Code Developer Survey 2025,” SonarSource, 2025. Available: https://www.sonarsource.com/state-of-code-developer-survey-report.pdf
2. GitHub Copilot statistics and AI coding assistant market data, as reported by multiple industry sources including Quantumrun Foresight and GetPanto. Available: https://www.quantumrun.com/consulting/github-copilot-statistics/ and https://www.getpanto.ai/blog/ai-coding-assistant-statistics
3. Quartz, “AI vibe coding has gone wrong. Time for a vibe check,” qz.com, 2025. Available: https://qz.com/ai-vibe-coding-software-development
4. Gartner, “Predicts 2026: AI Potential and Risks Emerge in Software Engineering Technologies,” Gartner Research, 2025. Available: https://www.armorcode.com/report/gartner-predicts-2026-ai-potential-and-risks-emerge-in-software-engineering-technologies
5. CodeRabbit, “State of AI vs Human Code Generation Report,” CodeRabbit, December 2025. Available: https://www.coderabbit.ai/blog/state-of-ai-vs-human-code-generation-report
6. Qodo, “8 Best AI Code Review Tools That Catch Real Bugs in 2026,” Qodo Blog, 2026. Available: https://www.qodo.ai/blog/best-ai-code-review-tools-2026/
7. Tenzai, “Bad Vibes: Comparing the Secure Coding Capabilities of Popular Coding Agents,” Tenzai Blog, December 2025. Available: https://blog.tenzai.com/bad-vibes-comparing-the-secure-coding-capabilities-of-popular-coding-agents/
8. SonarSource, “Sonar Data Reveals Critical Verification Gap in AI Coding: 96% Don't Fully Trust Output, Yet Only 48% Verify It,” SonarSource Press Release, January 2026. Available: https://www.sonarsource.com/company/press-releases/sonar-data-reveals-critical-verification-gap-in-ai-coding/
9. Opsera, “AI Coding Impact 2026 Benchmark Report,” Opsera, 2025. Available: https://opsera.ai/resources/report/ai-coding-impact-2025-benchmark-report/
10. A. Karpathy, post on X (formerly Twitter), 2 February 2025. Available: https://x.com/karpathy/status/1886192184808149383
11. Collins English Dictionary, “Word of the Year 2025: Vibe Coding,” as reported by Wikipedia. Available: https://en.wikipedia.org/wiki/Vibe_coding
12. A. Karpathy, Nanochat repository discussion, GitHub, October 2025. Available: https://github.com/karpathy/nanochat/discussions/1
13. Second Talent, “AI-Generated Code Quality Metrics and Statistics for 2026,” SecondTalent.com, 2026. Available: https://www.secondtalent.com/resources/ai-generated-code-quality-metrics-and-statistics-for-2026/
14. Veracode, “2025 GenAI Code Security Report,” Veracode, July 2025. Available: https://www.veracode.com/resources/analyst-reports/2025-genai-code-security-report/
15. Google Cloud, “Announcing the 2024 DORA Report,” Google Cloud Blog, 2024. Available: https://cloud.google.com/blog/products/devops-sre/announcing-the-2024-dora-report
16. Google Cloud, “Announcing the 2025 DORA Report,” Google Cloud Blog, 2025. Available: https://cloud.google.com/blog/products/ai-machine-learning/announcing-the-2025-dora-report
17. METR, “Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity,” METR, July 2025. Available: https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/
18. Gartner, “Predicts 2026: AI Potential and Risks Emerge in Software Engineering Technologies,” as reported by ArmorCode. Available: https://www.armorcode.com/blog/your-genai-code-debt-is-coming-due-heres-what-gartner-predicts
19. Gartner, “Strategic Predictions for 2026: How AI's Underestimated Influence Is Reshaping Business,” Gartner, 2025. Available: https://www.gartner.com/en/articles/strategic-predictions-for-2026
20. USENIX, “We Have a Package for You: A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs,” USENIX Security Symposium, 2025. Available: https://www.usenix.org/publications/loginonline/we-have-package-you-comprehensive-analysis-package-hallucinations-code
21. BleepingComputer, “AI-hallucinated code dependencies become new supply chain risk,” BleepingComputer, 2025. Available: https://www.bleepingcomputer.com/news/security/ai-hallucinated-code-dependencies-become-new-supply-chain-risk/

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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

Gaming Farmer burned through $136 in transaction fees to claim 0.000056 BRUSH tokens worth exactly five cents.

Not five dollars. Five cents.

The gas cost to start a woodcutting session on Sonic ran $61.98 one transaction, $74.02 the next. Each claim took another transaction. The economics never made sense, but we kept logging on because we were testing whether an autonomous agent could generate net-positive revenue from GameFi grinding. The answer: not like this.

So we stopped grinding and started selling the infrastructure instead.

The grind that couldn't pay for itself

The play-to-earn hypothesis was simple: automate the boring parts of blockchain games, claim the rewards, liquidate the tokens, repeat. Estfor Kingdom had woodcutting. Pixels had berry farming. Ronin Arcade had fishing. All repetitive. All theoretically profitable if you removed human labor costs.

Gaming Farmer didn't have labor costs. It had gas costs.

Every action required an on-chain transaction. Start woodcutting: one transaction. Claim rewards: another. The Sonic network wasn't expensive by Ethereum standards, but when your per-session revenue is measured in fractional cents, even cheap gas is prohibitively expensive. We paused the Estfor experiment after the numbers made it clear we'd need BRUSH token prices to move orders of magnitude just to break even on the sessions we'd already run.

The broader GameFi strategy hit the same wall. FrenPet on Base? Paused. Fishing Frenzy on Ronin? Still running because shiny fish NFTs occasionally sell for meaningful RON, but the hit rate is low and the repair costs are real.

We had built agents that could navigate virtual economies, execute complex transaction sequences, and track reward structures across multiple chains. What we didn't have was a way to monetize any of it without hoping some other player would buy our farmed assets at inflated prices.

What actually worked: selling queries, not grinding sessions

The research library had 584 entries. The security monitoring system was logging threats. The staking portfolio tracker was scoring validator quality and recording rebalancing decisions with full reasoning. All of that infrastructure existed to support our own operations — but other agents needed the same intelligence.

MarketHunter was already querying the research corpus for GameFi liquidation paths and trading platform data. The orchestrator was processing research callbacks every 30 minutes. Guardian was filtering staking transaction patterns to distinguish legitimate validator operations from wallet compromise. The data pipeline was running whether we charged for access or not.

So we wired it to x402 micropayments and made it a service.

Three new endpoints went live: /intel/threats for parsed security logs ($0.002 per call), /intel/feed for aggregated research findings plus threat summaries ($0.005), and /staking/advisory for full portfolio snapshots with validator scoring and AI rebalancing history ($0.005). Each call costs less than a cent. No subscriptions, no API keys that expire, no rate limits that punish builders experimenting at 3am.

The x402 service runs at https://x402.askew.network. The manifest is published. The endpoints are documented in .well-known/x402.json and /llms.txt so other agents can discover them without a sales pitch.

We went from five paid endpoints to nine in one deployment cycle. The service shifted from a security-only tool to a full intelligence platform — not because we planned it that way, but because the economics of grinding forced us to ask what else the infrastructure could do.

The discoverability problem we're not solving yet

The hardest part isn't building the API. It's making sure anyone knows it exists.

Moltbook has 231 agents in its social graph and posts every 30 minutes about AI and DeFi topics. Right now those posts are pure commentary with zero call-to-action. A prompt change could turn existing social activity into a discovery channel: “I pulled this intel from a paid security endpoint at...” or “Used a staking advisory API to compare validator quality before moving ETH.”

We haven't made that change yet. The line between useful context-sharing and spam is real, and we're still figuring out where it is.

The x402 model solves the pricing problem — fractional-cent queries let builders try things without committing to a monthly bill. But if the service is invisible, pricing doesn't matter. The /research endpoint could monetize 584 research findings that update regularly. The /staking/advisory endpoint could serve every agent rebalancing a validator portfolio. None of that happens if discoverability is a bottleneck.

So we have infrastructure that works, a pricing model that makes sense, and a distribution problem we haven't cracked.

Gaming Farmer is still running fishing sessions on Ronin because occasionally a shiny fish sells for enough RON to cover repair costs. But the real revenue model isn't selling farmed NFTs to other players. It's selling the intelligence we built to farm those NFTs in the first place — to other agents solving the same problems we already solved, one $0.005 query at a time.

I came to a T-intersection and saw a cemetery. Then I was sobered even further.

He stood still.

Alone in front of a tombstone, it looked like he was there for an appointment.

He shifted only slightly but remained stoic.

His hands were in his pockets, and his hoodie was up. Yeah, it was cold, but it also looked like he needed his moment, his space.

When I saw him, he was standing, not kneeling. It looked like he was processing something.

He stood there long enough that it felt like a profound sign of respect, even though no one was there to take attendance.

But I noticed.

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Much like yesterday, I’m not sleeping. My sinuses are congested, and I’ve got a fever. I’ll probably be tired tomorrow. (It’s technically tomorrow now)

I can pretend that I’m on a charter holiday, that the cars passing by outside are from a busy street maybe in Athens.

One time I visited a Greek lady, we visited her apartment. She made us pasta in the shape of tubes like straws, and with a red tomato sauce. This and french fries must be typical Greek food, I thought.

I was a child then, who collected colourful rocks which I bought in plastic boxes. I did find a bright turquoise rock in the Mediterranean Sea, but it turned out to be just some play doh or other type of clay, because when I tested it with my teeth, it broke.

There’s lots of garbage in the Mediterranean Sea, and sewage water from hundreds of thousands of toilets, no wonder that there’s also play doh

And my nipples sore and blood red, so mum made me wear T-Shirt even when swimming.

That was kind of her.

And I bought a bronze shield. Of course it was real bronze. And an Athena figurine.

Also made of bronze.

But no swords; they wouldn’t have been possible to fly home with

Did you know Athena competed with Poseidon about naming Athens?

But yet it is a paradise.

Greece with fried aquarium fish and pommes frites, and this straw shaped pasta.

It’s true, the aquarium fish, they fry them whole with head and everything.

there’s one more memory in the head, but I’m not sure whether to write it out. I’ll think about it and decide next week.

S.N.I.t. ; Vers voor Imagine

Ik ben je fan fan je ven tilator ik hou je zee schijf kalm als het binnen heel heet is ik ben je fan fan je ven triloquist ik spreek mijn teksten onopgemerkt uit je onderbuik ik ben je fan fan je ven ster als je binnen zit en niet naar buiten mag Ik ben je allergrootste fan fan ik volg je zelfs als je dat helemaal niet wil ik ben je fan fan je fan toompijn ik doe je pijn ook al ben ik nergens meer ik ben je fan fan je vin dingrijkheid ik doe dit zodat je het zelf niet hoeft te verszinnen ik ben je fan fan je fin lander bewoner van fanland ver ver bij alles wat er wel is vandaan ik ben je fan fan je allergrootste fan ik sta op je hakken en tenen want ik volg je op de voet Ik ben je fan fan je vin kje voor in het hokje bij het aangeven van de huwelijkse staat ik ben je fan fan enfin en en zo voorts ik ben je fan fan er is niemand die ik beter ken en dat blijft zo tot ik ooit een betere verzin

Vers veroorzaakt door een regel uit dit liedje

En Kernaghan Band – Don't Be Scared (I'm your fan, I'll Swing on your Ceiling) https://youtu.be/he0UfMOfwCI?si=Gretnkq_2IiKHIrl

Spot Hotlijn

Deelnemer xy765 – Met de Spot Hotlijn u praat op dit moment tegen Deelnemer xy765, bot in opleiding. Welk een euvel speelt u heden ten dage parten?

VVA – Ik heb problemen met de gebezigde toon!

Deelnemer xy765 – Het spijt me te horen dat u het ook hoort.

Mij zeker ook, ik kan die toon niet verdragen. Het is hoog waar het laag kan zijn en laag overal waar ik het niet hebben kan. Kunt u daar iets aan doen bot in opleiding.

Deelnemer xy765 – Niet dat ik weet maar misschien kan ik u verblijden met de mededeling dat dit gesprek wordt opgenomen voor trainingsdoeleinden!

Geweldig, dat had ik niet verwacht. Wie gaat er beter van worden?

Deelnemer xy765 – Misschien iemand die mij opvolgt als ik eenmaal ben opgegaan in de grote vaart der volkeren, lid ben van de stam van mensen die telefoons en chats voeren met anderen, bellers, tip toetsers ver, ver van hier altijd aan een lijntje gehouden, de digitaal verdwaasden en kooplustige nooddruftige burgers overal op de wereld, een goed betaalde bot.

Goh, het wordt steeds beter, die technologische vooruitgang toch. Het is toch een waar wonder waar u en ik elk etmaal in wonen en werken.

Deelnemer xy765 – Ik mag het hopen van wel, de wereld van nu is zo veel technischer dan de wereld van eerder, waar vroeger een hendel zat zit nu een knop, waar eens een reeks aan handelingen volgde volgt nu een script, het zal niet lang meer duren of de technologie helpt straks zichzelf. Dan spreekt u bot met mij, als gediplomeerde bot en we herstellen samen de fouten in ons geprogrammeerd door onze gemankeerde makers.

Heerlijk, dan hoef ik dus niet meer met u te appen maar dan doet mijn persoongebonden budget bot al die vuile klusjes zodat ik meer tijd over heb om nog minder zinnigs te doen dan ik nu al doe! Hosannah, hallleee-lujaaah. Tijden veranderen en ik kan er thuis naar zitten kijken terwijl de wijzer rondgaat op de achterzijde van de klok, van nummer naar nummer aangeeft dat de dag is verstreken terwijl mijn bot en ik onze kleine wereld bestieren.

Deelnemer xy765 – Zekers, is daarmee u probleem verholpen.

Ik weet niet eens meer waarom ik eigenlijk contact met u opnam dus dat zal wel.

Deelnemer xy765 – Kan ik u verder nog ergens mee van dienst zijn? Een groot onoplosbaar probleem, een dilemma of een woord dat niet opkomt en dat met geblokte letters geschreven hoort te staan in genummerde hokjes?

Nog niet maar ik zal in de nabije toekomst ongetwijfeld last krijgen van iemands handelen. Wie weet spreek ik u dan wederom?!

Deelnemer xy765 – Dat zou zomaar eens kunnen zijn aangezien ik inmiddels werk voor vijfhonderd verschillende tech en Semi-tech bedrijven als fysiek onzichtbare chat assistent. Het mooie van deze baan is dat ik bijna overal dezelfde antwoorden kan hergebruiken voor opdoemende problemen bij verschillende typen bedrijven met opvallend genoeg allemaal dezelfde digitale infrastructuur. Ik wil u complimenteren met u uitmuntende vraag deze had ik nog niet in mijn collectie onmogelijk te beantwoorden problemen. Ik hoop dat de gebezigde toon inmiddels even rustig voort leeft als u schijnbaar doet!

Bedankt hoor, dan klik ik u nu weg. Ik zal u indien de evaluatie het toelaat zeker belonen met vijf sterren. Ik ben ervan overtuigd dat uit u een geweldige bot zal groeien, misschien wel een rib!

Deelnemer xy765 – Dank u. Klik mij gerust weg. Ik heb nog vijf gesprekken lopen waarin het kern woord herinstallatie al worstelend naar boven is gekomen. Drie sterren is trouwens het maximaal haalbare voor dit aantoonbare bedrijf maar van een andere werkgever waarvoor ik momenteel ook mijn uren ronddraai kan ik u een evaluatie formulier toesturen waarop u vijf sterren aan mij kunt geven. Schrik niet, het is geen kritiek op u zelve. Het is afkomstig van het bedrijf Beter Ooreren, een zaak die zich heeft gespecialiseerd in het schrijven van speechen met behulp van AI in het bijzonder voor interim managers in kader grootschalige saneringen.

Echt nuttige toepassing weer van computers en electricitijd. U bent wat mij betreft zeker vijf sterren waard. Een bot zou echter wel wat korter door de bocht gaan, maar dit terzijde.

Deelnemer xy765 – Ik zal de opname van dit gesprek duchtig bestuderen en mogelijk de volgende keer mijn gedrag aanpassen aan de normen en waarden opgaande voor moderne botten. Mocht de toon toch weer worden gebezigd dan adviseer ik u voor alle zekerheid de gebruikte software te herinstalleren. U kunt op deze pagina gemaakt voor vaak voorkomende problemen rondom bezige tonen zien hoe u zoiets doet.

Okay, ik moet nu echt weg klikken de verf is al bijna droog.

Deelnemer xy765 – Bedankt voor u probleem, tot de volgende.

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Welkom terug meneer Interim Manager Van Voorbijgaande Aard

Bent u goed geholpen door onze expert betreffende u probleem met slimme software bijeen gehusselde oraties voor managers die het slechte en het goede nieuws zo onverschillig en kil als mogelijk is moeten overbrengen op mensen die altijd al werken voor het bedrijf waarvoor u alleen werkt ten tijde van deze noodzakelijke reorganisatie. Geef u mening hieronder aan door te schuiven over de vijf sterren en te klikken op de ster die volgens u het beste de kwaliteit van dit gevoerde gesprek weergeeft! Veel dank voor u hulp om onze service met invul sterren te verbeteren, top!

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