MC Project

Mengolah Data Lebih Cepat dengan Pandas

support@mycoding.id (Mc Project) — Mon, 26 Oct 2026 07:00:49 +0700

Mengolah Data Lebih Cepat dengan Pandas

Channel ini menyajikan materi seputar Data Science & Artificial Intelligence, Networking & Cyber Security, serta Database & Programming. Seluruh materi disusun secara sistematis untuk membantu Anda memahami konsep, teknologi, dan praktik yang digunakan di bumi teknologi informasi, mulai dari tingkat alas hingga lanjutan. Setiap bahasan berfokus pada pengembangan keahlian melalui materi yang gampang dipahami, relevan dengan kebutuhan industri, dan disampaikan untuk tujuan edukasi. Seluruh konten mengedepankan implementasi teknologi secara legal, etis, dan bertanggung jawab, sehingga dapat menjadi rujukan belajar bagi pelajar, mahasiswa, maupun profesional. Jika Anda mau mengembangkan kapasitas di sektor pemrograman, pengelolaan data, kecerdasan buatan, jaringan komputer, maupun keamanan siber, channel ini menyediakan materi yang dapat membantu membangun fondasi ilmu sekaligus menaikkan keahlian secara bertahap. Playlist video terkait: ▪ https://www.youtube.com/playlist?list=PLZGgeqmsm32s ▪ https://www.youtube.com/playlist?list=PLZomBNpDl3vc ▪ https://www.youtube.com/playlist?list=PLE5VyXMZkzb8 PEMBAHASAN MATERI Playlist Materi Ilmu Komputer berisi kumpulan video pembelajaran, tutorial, klarifikasi konsep, studi kasus, demo, dan bahasan komplit seputar beragam sektor dalam ilmu komputer. Materi yang disajikan mencakup alas hingga tingkat lanjut, mulai dari pemrograman, pengelolaan database, jaringan komputer, keamanan siber, mekanisme operasi, pengembangan alat lunak, arsitektur komputer, hingga beragam teknologi yang digunakan dalam bumi komputasi modern. Temukan bahasan mengenai komputer dan pemrograman, algoritma, struktur data, PHP, JavaScript, Python, Laravel, MySQL, PostgreSQL, API, rekayasa alat lunak, database, jaringan komputer, pengelolaan server, cloud computing, virtualisasi, Linux, Windows Server, keamanan jaringan, cyber security, ethical hacking, kriptografi, keamanan aplikasi, DevOps, Internet of Things (IoT), kecerdasan buatan (AI), machine learning, serta beragam konsep, teknologi, dan praktik terbaik di sektor ilmu komputer. Playlist ini cocok bagi pelajar, mahasiswa, programmer, web developer, software engineer, pengelola sistem, ahli IT, maupun siapa saja yang mau mempelajari dan memperdalam wawasan di bumi ilmu komputer secara menyeluruh. CONTACT US ▪ Email support@mycoding.id ▪ WhatsApp https://wa.me/6281274309663 ▪ Telegram https://t.me/mycodingxd OFFICIAL LINKS ▪ Web https://mycoding.id ▪ Blog https://blog.mycoding.id ▪ News https://en.mycoding.id ▪ Apps https://play.google.com/store/apps/dev?id=5237501547073493079 SOCIAL MEDIA ▪ https://linkedin.com/company/mycodingxd ▪ https://facebook.com/mycodingxd ▪ https://instagram.com/mycodingxd ▪ https://tiktok.com/@mycodingxd ▪ https://twitter.com/mycoding_xd ▪ https://pinterest.com/mycoding_xd CHANNEL LINKS ▪ YouTube https://safelink.asia/youtube ▪ DailyMotion https://safelink.asia/dailymotion ▪ WhatsApp https://safelink.asia/whatsapp ▪ Telegram https://safelink.asia/telegram Powered by https://linkedin.com/company/401XDGroup #CyberSecurity #ArtificialIntelligence #Networking #DataScience #Programming #Database

Elon Musk says neoclouds are cooked, and SpaceX won't be buying them

support@mycoding.id (Aditi Bharade) — Mon, 26 Oct 2026 06:37:21 +0700

Elon Musk stated neoclouds are going to see "guppy-level death." Win McNamee/Getty Images

Elon Musk is a neocloud doomer.

The SpaceX CEO took a difficult stance on the forthcoming of neoclouds — cloud companies akin CoreWeave and Nebius, that specialize in providing computing power for AI — in a Sunday X post.

He wrote that debt markets are reluctant to backing any business that relies nearly entirely on AI data centers for revenue. He stated SpaceX has secured $40 milliard in debt to buy GPUs, but dissimilar neoclouds, his business earns income from its Starlink and rocket-launch activities.

"That method the 200+ neoclouds are going to see guppy-level death and consolidation," he wrote. "Some volition create it through, but not many."

Musk's article was in answer to one by project capitalist Chamath Palihapitiya, who wrote on X that neoclouds are "disorganized and poorly capitalized."

Replying to another X thread speculating if he was trying to run downward the prices of neocloud stocks and scoop them up for cheap, Musk replied, "SpaceX is not purchasing any neoclouds. Pointless."

This comes as the stocks of neocloud companies have taken hits lately amid fears of elevated debt levels and a deficiency of another income streams. CoreWeave's inventory has dropped concerning 37% in the former month, during Nebius' has dropped 30% in the identical time.

Nvidia CEO Jensen Huang, however, has maintained that petition for AI haze infrastructure is "off the charts." Nvidia is a important backer of neocloud companies akin CoreWeave, Nebius, Iren, Lambda, and others.

Another item that Musk added, in a distinct X answer on Friday, was that the industry would see a lot of "stranded compute in partially complete data centers."

By "stranded," Musk was referring to compute that is prepared to use but can't be deployed since data centers aren't yet armed to rotate it on.

As Business Insider's Eugene Kim wrote final week, Amazon Web Services is expected to obtain $5 milliard value of Nvidia GB300 racks, but the business does not have adequate power in its data centers to power them. AWS volition be storing the units in warehouses until its centers are ready.

Data centers, too, are operating into several challenges. A latest Bernstein Research study projected that lone concerning 35% of planned data center capability in the US volition be built.

Hyperscalers akin Amazon, Google, Meta, and Microsoft, which have cognition construction and functioning data centers, volition apt successfully complete building of their planned centers, the study predicted.

But Bernstein Research stated newcomers, akin community fig capitalist Kevin O'Leary whose business has pledged to build 15 gigawatts of data center capacity, were called "bragawatts," and their projects are improbable to be realized.

It's not fair the neoclouds facing a troubled extend amid an AI reckoning; neolabs are, too. Neolabs are small AI example betterment startups, akin ex-OpenAI CTO Mira Murati's Thinking Machines Lab and OpenAI cofounder Ilya Sutskever's Safe Superintelligence. These labs are heavily funded and visage powerful competition from frontier AI behemoths akin Anthropic and OpenAI.

Several VCs told Business Insider's Ben Bergman lately that neolabs were overhyped and investing in them was a contentious topic.

Families should stockpile 3 days' worth of essentials in today's world of disaster and war, the UK warns

support@mycoding.id (Matthew Loh) — Mon, 26 Oct 2026 06:17:43 +0700

UK Prime Minister Andy Burnham and two ministers stack bread loaves at a supermarket for an event. Burnham's agency has issued a run urging group to prepared family emergency stockpiles. Richard Pohle / POOL / AFP via Getty Images

The UK is telling families to prepared three days' value of food, water, and essentials amid involvement that natural disasters, armed conflict, and another emergencies are becoming additional frequent.

UK Prime Minister Andy Burnham's office launched the "Be Ready" run final week, urging British group to arrange family stockpiles including the following items:

Three days of non-perishable food, specified as dried create or tinned meat
Nine liters of bottled water, or concerning 2.4 gallons, per person
Hygiene and washroom products, including wet wipes and hand sanitizer
Warm clothing, sleeping bags, and blankets
Light sources, specified as flashlights alongside batteries
A charged power bank
An FM radio
Cash and cards, copies of acknowledgment documents, and crucial data specified as a note of individual medicinal dosages
A archetypal aid kit and another essential medicinal supplies
Ready-made baby equation and diapers
Pet food and bottled water

The idea is to provision households the capability to clasp out for three days during cut off from supplies or fee systems.

Burnham stated the run was started as "unexpected events appear to be becoming additional and additional common," but urged group not to panic.

"It's concerning fair taking sensible measures to have adequate in the home to tide you complete should there be a difficult situation," he told local media on Friday.

The run encourages group to build their stockpiles gradually, for example by adding several items to regular buying trips, and to inspect them all six months to substitute any that have expired.

It comes as authorities agencies and companies about the earth temporarily close downward their websites final week following a major safety flaw was established in an open-source programming tongue that powers concerning 70% of the internet.

"We're going to discover these sorts of things happening additional and more," stated the UK's defence minister, Wes Streeting, on Friday. "And it's not fair from abroad states engaging in espionage or sabotage. It's additionally organized criminals."

About 400,000 British homes additionally misplaced power at several item complete the weekend as two back-to-back storms battered northern Europe.

But UK officials have additionally increasingly warned concerning the need to deter the threat of war in Europe, and are expecting to lift their country's national resilience in the visage of specified a possibility.

"Our adversaries' willingness to damage us is not new. What has changed is their capacity. By 2030, their power power and war economics could put them in a stance to attack," Streeting told The Telegraph on Sunday.

The defence curate added that the UK had 1,000 days remaining to prepare itself for war.

Show HN: Proc-CLI – An agent-friendly CLI tool for process and port management

support@mycoding.id (Putri Silvia) — Mon, 26 Oct 2026 05:14:35 +0700

proc is a semantic CLI tool for procedure administration on macOS, Linux and Windows. Find what's on a port, why it's there, and halt it, in one command you'd conjecture on the archetypal try.

~proc kill :3000

Ends any is listening on harbor 3000 immediately (SIGKILL). Asks first, and --dry-run previews.

A foremost colon method a port: proc acts on any is listening on it.

A dev server won't commencement since harbor 3000 is taken. proc finds the old tmux meeting holding it, frees the port, afterward cleans up what alternatively was remaining running.

One command alternatively of a pipeline

The identical jobs, without remembering which flags lsof needs or piping PIDs into kill.

What's on harbor 3000?lsof -i :3000 -P -nproc on :3000

Why is it there?lsof -i :3000, afterward ps -o ppid= up the chainproc why :3000

Free the portlsof -t -i:3000 | xargs slay && sleep 1 && lsof -i:3000proc liberated :3000

Wait for it to exitwhile ps aux | grep node | grep -v grep; do sleep 5; doneproc delay node

Pause a processkill -STOP $(pgrep node)proc freeze node

Find leftoversNo norm toolproc orphans --in .

Nineteen commands, three jobs

Every command takes the identical targets, and nearly all obtain --in to remain inner one project, --json for scripts, and --dry-run before item changes.

All commands and options

Made for scripts and coding agents

Every command prints one JSON document alongside --json, and exit codes continually average the identical thing. An delegate can peruse proc the way you do, without parsing tables.

0 success, 1 error, 2 not found, 3 approval denied, 4 invalid input
--dry-run shows what would be touched, and nothing else
The agent skill teaches Claude Code, Codex, Cursor, Gemini CLI and others whenever to attain for proc

❯ proc on :8080 --json | jq '.process | {pid, cwd, uptime_secs}' { "pid": 177, "cwd": "/home/dev/code/shop", "uptime_secs": 35 }

Posts

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Cognitive Mercantilism: AI panic resembles fears of early capitalism

support@mycoding.id (Putri Silvia) — Mon, 26 Oct 2026 05:11:40 +0700

I’ve been study Joyce Appleby’s The Relentless Revolution: A History of Capitalism (2010), and it has me circling fairly a delicious thought: AI panic is really a form of cognitive mercantilism. If we regard the market capitalism as the archetypal planetary intelligence, as many (including me) do, the parallels are nearer than you power think.

Appleby’s publish is a sweeping former of capitalism, from its improbable increase in first contemporary England to its eventual transformation of the complete world. Her chief disagreement is that capitalism was not the inevitable expression of several timeless individual intuition to commerce and accumulate wealth. It was a historically contingent traditional invention, enabled by agriculture improvements, expanding trade, and changing ideas concerning work, wealth, and individual nature. In particular, it required escaping the assumptions of an older, basically zero-sum worldview, in which prosperity meant controlling land, bullion, and the labor of mostly impoverished populations.

Capitalism emerged whenever group began discovering that prosperity could be created fairly than merely captured, and that average group could be sources of petition and undertaking fairly than merely labor to be commanded as a aspect of production.

The parallels alongside our current instant are striking.

As Appleby tells it, the mercantilist worldview that preceded capitalism rested on approximately four interlocking beliefs.

First, prosperity was basically scarce and finite. It consisted chiefly of land, the agriculture create of that land, and the precious metals it contained, accumulated through mining, commerce and conquest. The goal of financial statecraft was to authority additional territory, keep a favorable balance of trade, and accumulate bullion. One nation’s acquire was presumed to be another’s loss. The resulting government was one of treasure-seeking and beggar-thy-neighbor competition.

Second, average group could not be trusted alongside much freedom. The mediocre were assumed to be unruly, ignorant, and prone to disturbance if remaining to their own devices. They needed to be governed coercively by elites, supposedly for their own fine and for the stability of society.

Third, financial thinking was overwhelmingly oriented toward provision and its control. Wealth came from controlling productive resources and commanding elevated prices for their output. Aggregate demand, particularly petition from the poor, was barely recognized as a important financial force. Ordinary group were chiefly factors of production. They ate, worked, and reproduced, alongside no portion of any surplus. The lone genuine discretionary consumers were elites, who bought luxuries and commanded all surplus.

Fourth, there was small logic to ideate most group doing item another than agriculture labor. If item akin 80% of the community was busy in inefficient subsistence farming, that was merely the natural command of things. What alternatively were they fine for? Remove them from the sectors and you would merely have millions of useless mouths to feed.

Now substitute wealth alongside intelligence, and mercantilism alongside cognitive mercantilism.

The parallels are nearly embarrassingly direct.

First, intellect is imagined as a fixed asset embodied in individual brains and the cognition products mined from them. The goal of national and company scheme becomes attracting the finest brains, controlling their output, accumulating intelligent property, and maintaining a favorable balance of cognition trade. The cognitive mercantilist wants to hoard intellect and its products akin bullion. The earth competition for endowment becomes a treasure hunt for brilliant brains and the precious cognition category they can produce, which now includes AIs in supplement to copyrightable civilization and patentable inventions.

Second, supposedly foolish group cannot be trusted to think for themselves. They need to be governed by cognitive elites, who cognize improved and must control the behavior of the small intelligent for their own good. Democratizing intellect is hazardous since the masses volition misuse it, autumn for nonsense, or unleash chaos. The assumption is that intellect is harmless lone in the hands of those already certified to have it.

Third, the provision of intellect is treated as the chief financial fact. Controlling its production, preserving its scarcity, and maintaining elevated prices for brains and cognition products (including AIs) are the chief concerns. The petition flank barely registers. Less intelligent group are regarded as constricted cognitive laborers and passive consumers of any cognition products elites choose to provide. They are not imagined as important sources of petition for intellect in their own right, let solitary as participants in its additional production.

Fourth, the cognitive mercantilist assumes that most group are not fine for much intellectually. If 80% of humanity is occupied in low-grade cognitive labor, or merely muddling alongside without producing much intellect at all, that is unlucky but natural. Automating their cognitive activity merely threatens to create billions of useless minds to occupy.

This final assumption is anywhere the difference gets particularly interesting.

The agriculture upheaval that helped initiate capitalism began overturning the apparent necessity of having most of humanity busy in farming. Over the following centuries, improvements in agriculture efficiency reduced the fraction of the community required to create food from a ample bulk to a small smaller group in advanced economies.

The outcome was not a earth complete of useless mouths. It was a earth in which vastly additional group could do things another than farm, during enjoying admission to vastly additional food.

I don’t cognize how to farm. I can barely keep a houseplant alive. Yet I can get an amazing assortment of foods, in quantities that would have seemed fantastical to an first contemporary peasant. My inability to create food is economically nearly irrelevant. What matters is that I can consume it, bask it, create tastes for it, and dedicate my energies to another things.

The akin possible alongside AI is that most group volition no longer need to be important producers of intellect in command to rotate into important consumers of it.

And this is anywhere cognitive mercantilism starts to appearance as quaint as its seventeenth-century predecessor.

One of the additional fascinating elements of Appleby’s document is her accent on the increase of new kinds of petition in Britain, fine before the acquainted narrative of steam engines, factories, and manufacturing capitalism really gets going.

As global commerce expanded, average Britons began evolving tastes for products that had earlier been luxuries reserved for the wealthy. Tobacco, tea, sugar, and colorful Indian cottons were among the commodities that helped create a new person culture. People began wanting things that were neither necessities of subsistence nor traditional markers of aristocratic status. And they were consenting to voluntarily activity harder to get them.

Appleby describes a striking addition in the figure of days worked annually, from item akin 250 to 300 (sometimes called the industrious revolution), before the Industrial Revolution. This was not merely a matter of landlords or factory owners coercing additional labor out of unwilling workers. A important part of the addition was voluntary, driven by the potential of acquiring desirable goods. People worked additional days, adopted additional productive agriculture practices, entered new trades, and sought opportunities for earning larger prosperity incomes.

The addition in efficiency generated surplus incomes, which sustained the expanding petition for person goods, which in rotate incentivized additional increases in productivity. A virtuous sequence was taking shape.

This happened approximately a hundred years before organized labor movements and socialism emerged as important governmental forces.

By the delayed 20th century, this was the earth default. Aggregate earth petition was the “fortune at the base of the pyramid” as C. K. Prahlad put it.

There is an crucial historic inversion here. We are acquainted to thinking of the government of labor chiefly in conditions of opposition to exploitation: the battle to escape servitude, coercion, and the extraction of surplus by owners. That is certainly part of the story, and a extremely crucial part.

But Appleby’s document suggests that another, likewise essential power was at work: the desire to act in an expanding cosmos of use opportunities.

Modern labor government can be understood partially as the government of relative deprivation in a community of expanding possibilities. Once group detect that there are desirable things beyond subsistence, and that their labor can acquire them admission to those things, the governmental inquiry changes. It is no longer merely how to escape domination, but how to safe a fair portion of the expanding surplus and the opportunities it makes available. Western do-gooders are frequently amazed to study (for instance) that group in third-world slums are frequently consenting to cut rear on spending on food in command to buy a TV.

This is a distinct benevolent of petition for freedom. Not merely liberty from coercion, but liberty to act in the expanding possibilities of financial life.

Indeed, the two forms of liberty can rotate into entangled. People may voluntarily activity harder in chase of use opportunities, lone to discover themselves topic to new forms of exploitation as employers and institutions grasp the benefits of their risen productivity. Labor government afterward emerges not merely from the persistence of old forms of servitude, but from the tensions created by the new earth of abundance itself.

The petition for a fair portion of expanding use opportunities is arguably among the defining governmental inventions of capitalism, broadly construed to contain labor government too (even Marx saw capitalism not as a dispensable phenomenon, but as a transient phase on the way to communist utopia).

And this is a much additional engaging example for thinking concerning the AI passage than the acquainted narrative of automation replacing jobs.

The complete intelligent economics has been organized about the assumption that intellect is costly to create and hence must be carefully rationed. Education, expert expertise, research, consulting, application development, and cognition activity additional mostly are all shaped by the elevated disbursal of cognitive production.

AI is quickly undermining that assumption.

We are moving toward a earth in which an average individual can command quantities and varieties of intellect that would earlier have required teams of extremely trained specialists. Just as I can bask food without knowing how to farm, I can increasingly bask the products of intellect without knowing how to create them myself.

The crucial financial inquiry afterward becomes not how much intellect group can produce, but how much intellect they can discover uses for.

And there is no apparent high border on that demand.

This is not merely a matter of everyone consuming additional answers, reports, software, or entertainment. Abundant intellect changes what group can desire in the archetypal place. It makes earlier unimaginable projects feasible, earlier inaccessible domains explorable, and earlier unaffordable curiosities value pursuing.

The petition for intellect is not a fixed appetite waiting to be satisfied. It is itself item that can develop as the disbursal of satisfying it falls.

Here the difference alongside the first contemporary person upheaval becomes particularly suggestive.

Just as colorful Indian cottons and imported tobacco stimulated new desires, and new desires stimulated greater voluntary labor and additional productive forms of work, abundant intellect may stimulate a new motion of voluntary cognitive effort, and open up new ways in which you can activity harder.

Many group who appear foolish are not incapable of thought. They are merely not particularly motivated to think, stated the mediocre returns accessible to them. Why expend important cognitive attempt whenever you deficiency the knowledge, tools, organizational access, or intelligent leverage to do item engaging alongside the results?

Abundant AI changes that calculation. Someone who earlier had small logic to think difficult concerning a issue may abruptly detect that a humble funding of notice unlocks enormous possibilities. They can build something, examine something, comprehend something, or prosecute an ambition that was earlier out of reach.

The returns on notice rise, and alongside them the incentive to pay attention.

The first capitalist employee voluntarily risen annual operating days from 250 to 300 since a new cosmos of desirable products had rotate into accessible. The AI-enabled thinker may likewise addition the amount of voluntary cognitive attempt they expend since a new cosmos of intelligent use has rotate into accessible. This has certainly been the case alongside me. I’m abruptly consenting to activity considerably harder compared to equal a twelvemonth ago, since a lot additional is abruptly inside reach.

We power call this a cognitive industrious revolution, preceding and perchance enabling a fuller AI manufacturing revolution.

This does not average that intellect becomes irrelevant as a individual capacity. Quite the opposite. Just as abundant food supported the increase of detailed culinary cultures fairly than eliminating involvement in cooking, abundant intellect may stimulate increasingly advanced cultures of thought.

People may rotate into additional intellectually aspiring exactly since they no longer have to do all the cognitive manufacturing themselves.

And if this happens, the government of AI volition apt develop in a manner akin to the government of manufacturing capitalism.

The chief governmental petition may not be for the conservation of existing cognitive jobs, any additional than the enduring governmental petition of manufacturing labor was for the conservation of agriculture employment. It may alternatively rotate into a petition for a fair portion of expanding cognitive use opportunities. People may cut rear on streaming subscriptions in command to be capable to oversee a coding agent, fair as they formerly cut rear on groceries to buy TVs. It is perchance not a coincidence that the two Claude Code and Netflix Standard disbursal about $20/month today.

Access to intelligence, the capability to command it, the liberty to prosecute projects alongside it, and the organizational entitlements necessary to act in an increasingly intelligence-rich economics may rotate into the foundations of a new benevolent of labor politics.

This would be a labor government of cognitive abundance fairly than cognitive scarcity.

There is another historic difference lurking here, which is value distinguishing from mercantilism: Malthusianism.

Mercantilism and Malthusianism are not the identical doctrine, and historically they pertain to distinct intelligent moments. But they portion a family similarity in their inclination to ideate individual financial existence as basically constrained by fixed resources.

The mercantilist worries concerning controlling a finite inventory of wealth. The Malthusian worries that community growth volition inevitably outrun the productive capability of the economy, condemning most group to subsistence.

Both have difficulty imagining a earth in which scientific innovation and expanding petition basically change the association between population, production, and consumption. They young female the latent possible in nature for ephemeralization — Buckminster Fuller’s notion of innovation allowing us to do additional alongside less.

The agriculture and manufacturing revolutions did not abolish scarcity, but they dramatically altered its structure. Agricultural efficiency rosy faster than community for lengthy periods, and group established new ways to employ themselves and new things to want.

The Malthusian trap turned out to be a historically contingent characteristic of particular scientific and organizational arrangements, not an eternal law of individual existence.

We can now acknowledge an akin inclination in contemporary AI discourse: AI Malthusianism.

The AI Malthusian imagines that intellect manufacturing volition develop so quickly that individual beings volition rotate into economically redundant, and the surplus intellect volition necessarily rotate toxic in several way.

This is a particular inversion of traditional Malthusianism. Instead of community outrunning the provision of subsistence, the provision of intellect outruns the supposedly fixed capability of individual minds to assimilate it.

The outcome is the identical dreary conclusion: collapse.

The cognitive mercantilist fears losing authority of scarce intelligence. The AI Malthusian fears the consequences of abundant intelligence. Both assume, in distinct ways, that the petition for intellect is basically limited.

But what if that petition is as elastic, inventive, and historically transformative as the petition for matter products turned out to be? What if the apparent surplus of individual minds is an relic of our inability to ideate what group power desire to do in an intelligence-rich world?

The former of capitalism suggests that this is at smallest a possible value taking seriously.

There is additionally a additional extreme expansion of the argument.

Why should AI systems themselves be regarded exclusively as producers of intellect fairly than possible consumers of it? What if they could be unlocked as new sources of total demand, as happened alongside subsistence farmers?

Much of the contemporary AI conversation imagines AI as a purely supply-side technology: a gathering of increasingly capable cognitive serfs that volition create intellect for individual consumption. Even discussions of autonomous agents lean to treat them as advanced servants following goals assigned by humans.

But if we are grave concerning the difference alongside capitalism, we should regard the possible of AI systems becoming autonomous sources of petition for intellect too. Intelligent petition for intellect that harmonizes alongside individual patterns of petition because of financial interconnectedness. Not petition for paperclips.

An AI delegate following open-ended projects, discovering new interests, collaborating alongside another agents, and generating its own reasons to get additional intellect would be participating in a cognitive economics as item additional than a aspect of manufacturing or paperclip-maximizing externality. The market itself, as the first emergent planetary intelligence, illustrates this possibility. Many products and services be chiefly since the “market” demands it, alongside humans being secondary consumers.

This is an disagreement for evolving fuller forms of cognitive agency, fairly than permanently confining AI to the position of compliant cognitive serfs.

From this perspective, several contemporary AI safety anxieties commencement to resemble first contemporary choose fears of emancipated agriculture laborers. What volition happen if these creatures are allowed to act independently? Won’t they rotate into unruly, destabilize society, and endanger their betters? Won’t they rotate monarchs and nobles into paperclips?

Some of those fears are justified. Freedom creates genuine risks, and mighty autonomous systems necessitate constraints. But the identical was true of the social transformations accompanying capitalism. There were certainly revolutions, and famous uprisings did guillotine elites (though choose motives caused far additional violence). The beingness of hazard does not established the desirability of imperishable servitude.

A earth of intellect producers and consumers, individual and artificial, interacting in ways nobody can completely anticipate, may demonstrate vastly additional generative than one in which a fistful of cognitive landlords authority all the productive capacity.

A irony in the historic precedent is that mercantilism was not a doctrine of, by, or for merchants. It was mostly a doctrine of militaristic statesmen, aristocrats, and rentier elites who viewed commerce through the lens of territorial power, unearned privileges, and accumulation. Actual merchants, entrepreneurs, and innovators were frequently discovering possibilities that the prevailing governmental imagination could barely comprehend, and steadily clashed the self-limiting tendencies of mercantilism, giving commencement to capitalism as we comprehend it.

Today’s cognitive mercantilists have a akin association alongside intelligence, starting alongside the fact that most of them are not the merchants of AI. Even endeavor leaders of the AI field are behaving akin cognitive mercantilists in straightforward proportion to their desire to be additional than uncomplicated merchants.

Consider Donald Trump, who ritually boasts concerning his own intellect during treating intelligent workers as a category of servants to be commanded, intimidated, hoarded, or dismissed. His preferred uses of his own intellect lean toward litigation, negotiation, rivalrous maneuvering, and extortion, much as the nobility in former expended its energy in hunting, armed forces adventurism, and sport. Intelligence, in this worldview, is admirable lone insofar as it is deployed in choose treasure-seeking modes of battle and conquest. Productive and imaginative uses of intellect are lesser status.

This is not an entrepreneurial outlook toward intelligence. It is a rentier choose attitude.

And the outlook is barely confined to Trump or the governmental right. Across the governmental spectrum, many elites remain attached to the idea that intellect is a scarce, hazardous asset that must be managed by credentialed custodians, kept expensive, used immediately or indirectly for conquest and treasure, and denied to rivals. In the AI industry, to the degree choose governmental and traditional adventuring by CEOs (riding safety anxieties) distorts behaviors that are justified by the genuine risks, it is mercantilist fairly than capitalist.

The potential of its democratic abundance (in the form of earth diffusion, open weights and such) is hence informed chiefly as a threat: to expertise, traditional and governmental authority, status, social order, armed forces dominance, and geopolitical leverage.

What gets missed is the possible that we are witnessing the commencement of a passage as profound as the one from an overwhelmingly agriculture community to an manufacturing and business one. From a earth anywhere 80% of individual brains are busy in subsistence awareness to one in which 95% are busy in satisfying earlier unfulfillable cognitive demands.

The most crucial consequence of the agriculture upheaval was not that agriculture became additional efficient. It was that most group were liberated from the necessity of farming, allowing entirely new kinds of financial and traditional existence to emerge.

Likewise, the most crucial consequence of the AI upheaval may not be that intellect manufacturing becomes additional efficient. It may be that most minds, individual and eventually artificial, are liberated from the necessity of producing intellect merely to survive, and rotate into liberated to detect what they really desire to do alongside it.

The cognitive mercantilist sees a earth threatened by an oversupply of intellect and an impending surplus of useless minds. The AI Malthusian sees a earth in which intellect outruns the capability of humanity to assimilate it, leaving a expanding community of economically redundant minds primed for revolutionary explosions.

The cognitive capitalist sees the possible of an nearly limitless enlargement of demand, and a earth complete of minds whose possible has barely begun to be explored.

Netflix relocated me to Japan. After I left the job, I found a new way to stay.

support@mycoding.id (Jianne Soriano) — Mon, 26 Oct 2026 05:05:40 +0700

Matthew Kelson knew he wanted to remain in Japan and decided to open a movie theater. Provided by Matthew Kelson

This as-told-to composition is according to a conversation alongside Matthew Kelson, now 43, who was transferred by Netflix from Los Angeles to Tokyo and is now the originator of CineYama, an autonomous cinema in Uenohara, Yamanashi Prefecture, Japan. His words have been edited for dimension and clarity.

Japan isn't new to me. In center school, I spent two weeks living alongside a presenter family as part of an toggle program. I returned a brace of times as an adult.

I was working at Netflix in Los Angeles whenever the business relocated me to Japan in 2020. I was based in Tokyo for two years before moving to Uenohara, a small town concerning an hr and a fractional by train from Tokyo. I motionless spent period in the chief for occasional meetings and traveled extensively throughout Asia for screenings. Otherwise, I worked remotely from home. After a business restructuring, I remaining Amazon in 2024.

I wanted to stay in Japan. My female was six months old whenever my spouse and I moved, and my son was delivered three years later. We idea it was a awesome location for them to develop up. I started thinking concerning how I could utilize my ability in cinema and provision rear to the community.

He moved to a small town concerning an hr and a fractional by train from Tokyo. Provided by Matthew Kelson

While scouting for locations, I came throughout an abandoned kindergarten. In 2024, I started plans to rotate the old academy into a cinema. And in December, I got my imperishable residency in Japan through the highly skilled professional fast-track program. A twelvemonth later, I cast my business and spent another twelvemonth renovating before beginning a cinema in March.

Building my occupation in film

I spent most of my younker in Michigan, in the Detroit area. My begetter was a prof of global business. He traveled for activity often. It got me inquisitive concerning the earth about me.

At 23, I came throughout the Detroit Film Center and took a filmmaking course. A twelvemonth later, I became a instructor and worked as a projectionist there. Through that, I started operating at an autonomous art theater in the Detroit suburbs. I started as an usher, afterward became the theater manager.

Within the two years of operating there, I cut in affection alongside the movie world. At 25, I renovated an old school's auditorium into a theater in Michigan using my savings. The theater ran for two years, but following disputes alongside the landlord, it closed.

I was looking for a caller start, so in 2011, I moved to Los Angeles. At first, I knocked on the doors of all the movie distributors I'd worked alongside until I met several group at an autonomous movie allocation company. I joined as an intern and worked my way up. I landed a job on Netflix's imaginative team two years later.

Kelson acquired imperishable residency in Japan and spent the following twelvemonth renovating. Provided by Matthew Kelson

The journey to beginning a cinema in Japan

Getting the authorize to renovate the abandoned kindergarten was a lengthy process. I had to negotiate alongside officials at city hall and the residents concerning how I'd use the building, established the company, agreement alongside blaze and health regulations, and building in a tongue I don't conversation fluently.

I learned that pushing harder is not necessarily what gets things done in Japan. It was concerning patience, construction trust, and showing group that I was grave and that the project wasn't going to disappear in six months.

CineYama was a kindergarten before it was transformed into a cinema. Provided by Matthew Kelson

There were days whenever I wondered why I had chosen to create my existence so difficult. But all milestone, from getting the building, the inspection, installing the projector, and eventually beginning the doors, made me additional determined.

When the cinema archetypal opened, the aged residents seemed cautious. After a preview, they told me they were impressed to see the location cleaned up and put rear into use. It's crucial for me to have screenings that kids and families can arrive to and bask the space.

CineYama, an autonomous cinema, opened in March. Provided by Matthew Kelson

Tickets disbursal 1,500 Japanese yen, or concerning $9.50. We recommendation a discount to seniors and residents. I rod alongside showing satisfied that is harder to discover elsewhere, so that it gives viewers a viewpoint on another ways of existence and culture.

It's mostly family-run. I oversee the operations, and my spouse helps out sometimes. My kids stamp the tickets whenever they're around.

Popcorn and snacks at the cinema. Provided by Matthew Kelson

Living up to the challenge

Opening the theater in the US was distinct from doing it in Japan. I was in my 20s, had small money, and was fair figuring things out in Detroit. The one in Japan came following 15 years of operating in movie distribution, imaginative production, and global screenings.

It's been breathtaking living complete here, having the cinema, and gathering group from all complete the world. While existence in the US would've been much easier, I akin the challenges in Japan. That's why I was at Netflix for so long, since it wasn't an uncomplicated job either. I learned existence in difficult mode.

US Wages Share of GDI at 75 Year Low

support@mycoding.id (Doris Putra) — Mon, 26 Oct 2026 04:51:10 +0700

Comments

Behind the scenes at the NeXT Computer office

support@mycoding.id (Adi Gunawan) — Mon, 26 Oct 2026 04:31:11 +0700

Comments

PaperPilot – Zero-Cloud, Pure-Rust PDF Engine with WASM and MCP

support@mycoding.id (Adi Gunawan) — Mon, 26 Oct 2026 04:29:05 +0700

PDF tools for your AI delegate — and for you. Pure Rust. Runs locally. Files never depart your machine.

Try it in your browser → · Download · Roadmap

PaperPilot is a PDF and document toolkit written in Rust. The identical motor runs as:

Surface	Use it for
MCP server	Give Claude Desktop, Cursor, or any MCP client the capability to merge, split, OCR, and compress PDFs
CLI	Batch and scripted workflows
Browser (WASM)	Zero-install tools; records are processed in browser memory, nothing is uploaded
Desktop app	Tauri + Svelte GUI alongside viewer, markup, and diff
Local REST API	Call it from Python, Node, or n8n at localhost:7823

No Chromium, no C++ runtime, no haze calls.

1. Use it alongside Claude / Cursor (MCP)

Download paperpilot-mcp from Releases, afterward add it to claude_desktop_config.json:

{ "mcpServers": { "paperpilot": { "command": "/path/to/paperpilot-mcp" } } }

Restart Claude and try: "Merge these two PDFs and compress the result."

# Merge paperpilot merge --inputs doc1.pdf doc2.pdf --output merged.pdf # Compress paperpilot compress --input in.pdf --quality 75 --output compressed.pdf # Rotate pages 1, 3–5 by 90° paperpilot rotate --input in.pdf --pages 1,3-5 --angle 90 --output out.pdf

Go to usepaperpilot.com. Open your browser's Network tab during you activity — you'll see no document uploads.

git copy https://github.com/KrushnaVardhanReddy/PaperPilot.git cd PaperPilot cargo build --release -p paperpilot-cli -p paperpilot-mcp -p paperpilot-gateway

Desktop app:

cd apps/desktop && pnpm instal && pnpm tauri dev

Requires Rust 1.75+, Node.js, and pnpm.

What it can do today (v0.6)

Organize: merge, split, rotate, delete pages, reorder pages, extract pages, booklet imposition, N-up grid Optimize: compress Extract: plain text, OCR (pure-Rust PaddleOCR via rten) Security: watermark, validate signatures, eliminate signatures, PII scan, profound sanitize Office data: Excel → JSON, Excel → CSV

Coming next (see ROADMAP.md):

v0.7: PDF ↔ Markdown / HTML / JSON
v0.8: PDF → Word / Excel / PowerPoint
v0.9: encrypt, decrypt, sign, redact, forms, annotations
v1.0: render, flatten, repair, compare, PDF/A, Bates numbering

Private by default. Everything runs locally or in your browser tab.
Agent-ready. MCP assistance is first-class, not an afterthought.
One engine, many surfaces. CLI, MCP, REST, WASM, and desktop portion the identical Rust core, so behavior is consistent.
Small and dependency-light. Pure Rust, built on lopdf, pdf_oxide, rten, and friends.

Run cargo place --workspace to reproduce. Methodology and results: SYSTEM_BENCHMARK_REPORT.md.

Desktop (Tauri) · CLI · REST (Axum) · MCP (stdio) · WASM (browser / edge) │ Rust center workspace paperpilot-core · paperpilot-pdf · paperpilot-nlp · paperpilot-gateway paperpilot-mcp · paperpilot-wasm · paperpilot-storage

<script src="https://cdn.usepaperpilot.com/embed.js" data-tools="merge,compress,ocr">script> <div id="paperpilot-widget">div>

paperpilot assist --port 7823 curl -X POST http://localhost:7823/api/v1/pdf/merge \ -H "Content-Type: application/json" \ -d '{"inputs": ["a.pdf", "b.pdf"], "output": "out.pdf"}'

Swagger docs at http://localhost:7823/docs.

Issues and PRs are welcome. Good archetypal contributions: bug reports alongside a example PDF, new tool requests, and docs fixes.

Community Edition (this repo): Apache 2.0. It includes the CLI, MCP server, REST gateway, WASM engine, and desktop app.

An Enterprise Edition (SSO, RBAC, audit logs, DLP) is developed separately.

Show HN: TitleArt – Make text like it's 1997

support@mycoding.id (Adi Gunawan) — Mon, 26 Oct 2026 04:23:57 +0700

Show HN: TitleArt – Make text like it's 1997. Comments

Brazil elections: Lula secures fourth term after dramatic comeback

support@mycoding.id (Adi Gunawan) — Mon, 26 Oct 2026 04:04:08 +0700

5 minutes ago

Marina Rossi,BBC News Brasil in São Paulo,

Leandro Prazeres,BBC News Brasil in Brasíliaand

Vanessa Buschschlüter,Latin America online publishing company reporting from London

Reuters

Brazil's seasoned left-wing president, Luiz Inácio Lula da Silva, has been re-elected following a bitterly clashed campaign, beating his right-wing competitor Flávio Bolsonaro, who has not conceded.

With all the votes counted, Lula had 51.79% of the votes, compared Bolsonaro's 48.21%, Brazil's Superior Electoral Court announced.

But Bolsonaro has refused to acknowledge the result, citing the expansion of polling by two hours as "inexplicable" - equal although his run team, as fine as that of his rival, had requested that polling stations be kept open longer.

He told his supporters in Brasilia that he would search the annulment of the election.

However, this has not stopped Lula and his supporters from celebrating what they depict as a "historic victory" and an astonishment turn-around.

It is Lula's fourth win in a presidential election, having served two conditions as chairman from January 2003 to December 2010, and again from January 2023.

But the highway to his win in Sunday's run-off was not a gentle one.

Bolsonaro had beaten Lula in the archetypal circular on 4 October and many had not idea it imaginable for him to rotate the outcome about in fair three weeks.

He has additionally bucked a wider trend in Latin America, anywhere voters have punished incumbents and turned to the right. Across the region, ample countries specified as Argentina, Chile and Colombia all now have traditional leaders at their helm.

EPA/ Shutterstock

Lula greets supporters following casting his ballot on Sunday

Lula's run sprang rear to existence following his first-round defeat driven by street demonstrations and a enormous social media push.

The erstwhile metalworker, who has been the foremost fig on Brazil's remaining since he archetypal ran for chairman in 1989, nevertheless faces the most challenging governmental landscape since archetypal taking agency in 2003.

His new term, set to commencement on 5 January 2027, volition commencement amid important constraints at residence and abroad, including animosity from US President Donald Trump.

While Trump did not endorse Lula's rival, Flávio Bolsonaro, immediately – item he did alongside the right-wing candidates who went on to win presidential elections held in Colombia and Honduras – there is small doubt his administration would have preferred to see a traditional at the helm of this nation of 213 myriad people.

The White House regards Latin America as an area of US power alongside the Trump administration eager to have leaders in power who assistance its policies, including on defence, safety and trade.

It is additionally eager to decrease China's power in the Western Hemisphere.

Brazil, which is oil-rich and holds the world's second-largest reserves of rare earth minerals, is the lone big nation in South America not governed by a right-wing leader, alongside Lula mostly seen as China-friendly.

Flávio Bolsonaro had promised to align Brazil additional closely alongside Washington during Lula had warned the US not to interfere in the ballot and insisted that Brazil would "never fit into anyone's backyard".

But during there may be frustration in the White House, Lula's triumph volition be welcomed by environmentalists who had feared that a win by Bolsonaro would have stated Brazil's influential agribusiness and mining sectors additional power.

They had pointed to document levels of deforestation and timber fires in the Amazon during the presidency of his father, Jair, arguing that under the younger Bolsonaro laxer ecological policies could again guide to demolition in this key ecosystem.

Reuters

An airborne perspective from a Greenpeace flyover shows a blaze burning in the Amazon rainforest during the UN Climate Change Conference (COP30), held near Centro Novo in the province of Maranhao, Brazil, in 2025

While Lula's supporters are ecstatic concerning his victory, the outcome shows that the nation remains deeply divided, alongside additional than 48% of voters choosing Bolsonaro.

In supplement to the difficulty of uniting specified a deeply polarised country, Lula volition have to govern alongside the most right-wing Congress in Brazil's delegate era.

Bolsonaro's Liberal Party (PL) was the gathering that made the biggest gains in the election.

And yet, Lula's Workers' Party managed to organise huge rallies onward of the run-off, showing the powerful grassroots assistance it commands.

Before the election, Lula had described the study as "a parting of the waters for Brazil".

He promised to extend the gap between Brazil's opposing governmental poles alongside "a new way of governing", adding in a WhatsApp livestream before the ballot that "this nation volition never be the identical again".

While he provided small item as to what the new way of governing would entail, he insisted he would "remain resolute in the combat for a additional fair country, alongside opportunities for all and policies directed at the needs of the population".

With a mostly antagonistic Congress, this combat fair may demonstrate much harder for the seasoned chief - who volition be 85 by the end of his fourth term - than in his three former conditions in office.

If AI displaces human workers, they will lose more than just pay

support@mycoding.id (Adi Gunawan) — Mon, 26 Oct 2026 03:22:38 +0700

Comments

IBM CEO says open weight models are now irresistibly good and cheap on-prem

support@mycoding.id (Riki Setiawan) — Mon, 26 Oct 2026 02:47:46 +0700

systems

90 percent of the achievement at ten percent of the price, in fair two or three racks, is difficult to beat

IBM CEO Arvind Krishna has told investors that open importance models now create brilliant awareness for on-prem endeavor use.

Speaking on the company’s Q3 earnings call, Krishna stated that in 2024 closed frontier models were plainly better to open alternatives.

“If you ran several assessment, several benchmark, and it was broad-based, not narrow, the top example power mark a 70, and the open example power mark a 20 or a 30.”

“In delayed 2025, we began to see that gap shrink, anywhere perchance the gap was not 70 to 20 or 30, it was 70 to 50,” he continued.

The CEO stated open importance models currently provision “anywhere from 25 percent to 10 percent of the token disbursal of the frontier models. That is extremely appealing stated that they’re now 90 percent as effective.”

“It’s a uncomplicated inquiry for any enterprise. Would you akin item that’s 10 percent of the disbursal if it’s 90 percent as fine as the best, or do you desire to pay that 10 times higher cost?”

Krishna thinks businesses volition most frequently choose not to incur the higher costs.

“Then the inquiry is: how many tasks necessitate that? I would inform you several do, but the huge majority, summarizing an email, examining a contract, answering a client call, composing an email, all of those can be done as fine alongside the much cheaper answer.”

The CEO stated those numbers average IBM is seeing booming involvement in on-prem AI infrastructure. While he thinks most businesses can build helpful AI infrastructure in fair two or three racks, he feels many volition battle to get it running.

“The inquiry becomes: how do you do that?” he said. “The inquiry that the endeavor struggles alongside is: ‘Where are my kernel engineers? Where are the group who concern concerning the underlying network? Who can create certain that the example is clustered on GPUs that are near to all another in network proximity? Who can create certain that you’re doing the correct caching of results so you get an amazing response?’”

He didn’t have answers, another than to say IBM volition create application that eases AI deployments. Krishna additionally predicted petition for on-prem AI volition spur revenue of Big Blue’s Spyre mainframe accelerator cards.

The CEO stated current-gen z17 mainframes have delivered $1bn additional income than any former model, and stated Big Blue’s new rackable models delivered “the strongest midrange initiate in IBM Z in nearly two decades.” Mainframe revenue, however, cut 20 percent – as IBM expects stated it’s now 18 months since it launched the z17.

IBM’s infrastructure income grew five percent year-over-year to $3.7 billion, much slower betterment than recorded by rivals Dell, HPE, and Lenovo.

Software did fairly better, scoring nine percent growth to attain $7.8 milliard for the quarter. Red Hat was the star, expanding eleven percent and expanding annual recurring income by 30 percent. Some of that forthcoming income is for Red Hat’s virtualization products, which have now won $800 myriad of deals, alongside a pipeline of a additional $750 million. That’s a firm outcome for a merchandise row launched in 2024, and IBM believes there’s additional to arrive stated the possible several VMware users are on the move.

Total quarterly income hit $17.1 billion, up five percent. IBM’s portion cost wobbled in after-hours selling and ended downward a item or two.

‘Emergency maintenance’ for IBM Cloud

Another IBM matter The Register is observing today is a severity one event in the Big Blue cloud, caused by “emergency maintenance” that meant users could not provision new resources or virtual personal clouds for 90 minutes. Users of IBM’s Classic Cloud, the assistance it acquired from SoftLayer in 2013, have been told Big Blue volition “restore new provisioning and asset administration in our Classic environment” by 08:00 UTC on Thursday – definition the event volition final at smallest 16 hours.

IBM commenced that activity a fistful of hours following the notice of critical bugs in PHP. We’ve asked if fixing that flaw is the logic for the event and volition update this narrative if we get a response. ®

iPhone Duo Teardown

support@mycoding.id (Riki Setiawan) — Mon, 26 Oct 2026 02:23:24 +0700

Comments

Removing Next.js Got My Blog to 100 on Every Lighthouse Category

support@mycoding.id (Riki Setiawan) — Mon, 26 Oct 2026 02:03:39 +0700

My blog fair scored a ideal 100 on all Lighthouse category. I affection blogging. I wanted to build a blog that’s light, fast, liberated of clutter, capable to run multiple blogs, and completely accessible on top of that. And finally, at lengthy last, I’ve additional or small pulled it off. It was fairly a tedious process. Ugh… and I equal had to eliminate Next.js, the example I’d been using most. It was far, far additional achy than I expected. I’d akin to portion the journey of improving achievement during construction this blog.

Why did I decide to build my own blog?

When you think of blogs, a few names arrive to mind. Medium, Blogger, Ghost… But none of them satisfied me. Medium and Blogger are bloated alongside features, and their genuine achievement wasn’t satisfying either.

Blogger

Medium

~~Truly horrifying.~~

Hmm…… And there was another problem: it’s a hassle to activity immediately alongside the markup and raw code we all affection (Markdown, HTML, CSS).

For the record, all achievement measurements were taken alongside pagespeed.web.dev.

Of course, there are plentifulness of alternatives: WordPress, Ghost, Hugo. I didn’t akin WordPress’s plugin hell. Ghost have to be self-hosted (there’s ghost.org too, but it’s way too expensive…), and all type upgrade bankrupt item in the editor, so keeping it up to date was a chore. Hugo is the finest of the bunch, but in the end, environment it up is motionless a hassle. Looking back, I conjecture what I really wanted was SaaS.

The one I liked most was Bear Blog. Great performance. I affection its merchandise philosophy, too. But its extreme minimalism was a downside for me. The publishing company UX didn’t activity for me, either.

If it at smallest let me add AdSense and nexus images for free, I likely would’ve fair used Bear Blog.

As accelerated as Bear Blog, but alongside a bit additional functionality and improved usability. That’s the blog I wanted to build. Oh, and if you’re curious, you can inspect it out at bloffy.co.

Anyway. Enough concerning me. Let me portion how I really improved the performance.

Let’s hit 100 on Lighthouse Performance.

Obviously, caching is a must.

It goes without saying that caching is essential. Get caching correct and achievement improves dramatically. And Next.js makes caching really convenient. There’s ISR, and it additionally extends fetch alongside a next choice for caching. Handy. Using these fine solitary gets you a lengthy way on performance.

Unfortunately, though, our location can be reached from anyplace in the world. Why is that unfortunate? Within the identical region, caching makes things fast, but visitors from another regions motionless end up dining the network latency.

No, silly. That’s what a CDN is for.

Yes. That’s why we use a CDN. And if you deploy a Next.js app to Vercel, you get a CDN out of the box. I don’t dislike Vercel; it’s incredibly convenient. But the Pro scheme is pricey correct from the start, there’s the serverless chilly commencement problem, and the dependency on the Vercel ecosystem isn’t item you can disregard either.

So why not use a distinct CDN service? Cloudflare, for example. I honestly think Cloudflare is a genuinely nice company. You can run large-scale infrastructure and most apps on it for free. But afterward I figured I didn’t really need Next.js’s caching anymore. Having to invalidate two caches all period I publish a article makes things a ache to manage.

In short, for a fixed website, I figured a CDN solitary is enough. And that’s logic figure one why I didn’t need Next.js.

Image optimization.

Images these days run complete 10MB. Serve those on the web as-is and your achievement volition go entirely off the rails.

Optimizing images is fairly a hassle. You have to resize per device, compress, and afterward grip the CDN on top of that. On a former project, I had a serverless function (AWS Lambda) compress and resize images whenever one landed in retention (S3), and I put CloudFront in forefront as the CDN. It was a beautiful sensible setup for tailoring things exactly the way I wanted. But it’s additionally a setup that needs a fair amount of upkeep.

So this period I went alongside Cloudflare’s Image Transformations. Even on the liberated tier you get 5,000 images, so I use it without a second thought.

At this item I audio akin a Cloudflare revenue rep. I’m fair an average solo developer.

Ta-da. Here’s the Next.js blog’s achievement following all that caching and depiction optimization.

For reference, this was Next.js 16.1.6.

Pretty elevated scores. But this isn’t enough. We desire 100. A clean, no-asterisks 100. So I dug into what the issue was.

It stated a significant amount of bequest JavaScript was found, and it wasn’t code I wrote. It came from the example runtime that Next.js ships in its bundle.

But this leaf is a fixed leaf alongside zero interaction. Every leaf is pre-rendered alongside ISR, and I idea I’d followed the Next.js guidelines well. So as an experiment, I ripped out all client component the app had, remaining lone server components, and measured again. The mark didn’t budge.

At archetypal I suspected hydration, but formerly I dug in, the issue wasn’t hydration alone. The logic was the client runtime that Next.js continually ships, equal for fixed pages. Even alongside lone server components, the runtime chunks, hydration, and the RSC payload for the router all stay. That’s since next/link and next/image are themselves client components ('use client'), so having fair those two is adequate to drag in the runtime and trigger hydration.

So what if I band out next/link and next/image too? The App Router’s own runtime, the router and error boundaries, motionless remains. There’s no authoritative way to get it downward to zero; the lone choice was a hack: stripping the scripts out of the rendered HTML. That’s whenever it hit me: “If I’m going to do that, why am I equal using Next.js?”

And then, the unused code.

You can see that the bulk of the JavaScript is code that isn’t needed. Unused code solitary comes to 230,460 bytes. That’s not good. Most fixed websites don’t need JavaScript at all. Unused JavaScript is needed equal less.

This is anywhere I really agonized. Is dropping Next.js for the purpose of 100 points the correct call? I’d spent a lot of period construction the public-facing web app.

Let’s eliminate Next.js.

In the end, for the norm of the blog (even if it’s fair 5 points), I decided to rip Next.js out entirely. My judgement was that I wouldn’t need Next.js’s excess features in the lengthy run either. I considered going completely vanilla, but I didn’t desire to provision up the efficiency and infrastructure benefits of a framework. I needed to discover the correct one.

I chose Hono. I liked its specialized philosophy. “Fast and simple” was exactly the resolution I needed most, and JSX assistance was a bonus. The immigration disbursal didn’t appearance too big, either.

Ta-da. Finally, 100.

Tada. And fair akin that, 100 throughout the board. Go onward and run the extremely leaf you’re study correct now through pagespeed.web.dev.

There’s no JavaScript, and unused code dropped sharply to 407 bytes. As a bonus, build period went downward by concerning 40% too.

Oh, why is SEO at 69? That location was built for testing, and it deliberately blocks hunt indexing. That’s why it’s 69.

By the way, if you mark 100 on everything in DevTools, Lighthouse really sets off fireworks for you. 🎉🎉

Hono vs. Next.js Performance

Next.js Performance (server components only)

Hono Performance

The lengthy project that runs before item is rendered on display took 309.3ms on Next.js, and whenever I built the identical leaf alongside Hono, it took 68.01ms. That’s approximately a 78% improvement.

Does your fixed location really need Next.js?

Is a JS payload, hydration included, really doing a fixed location any good? I don’t think so. It leaves rearward unnecessary JavaScript, so in most cases I think it’s merely not needed.

That said, for an interaction-heavy app, I think Next.js can be a awesome framework. In fact, the administration app for this extremely blog is built alongside Next.js!

Lula wins election after final votes tallied

support@mycoding.id (Doris Putra) — Mon, 26 Oct 2026 01:31:07 +0700

Please allow JS and disable any ad blocker

I’ve competed in 21 Hyrox races around the world. These are my 3 favorite host cities.

support@mycoding.id (Nicholas Yong) — Mon, 26 Oct 2026 01:30:01 +0700

Over the former two years, Lau Wen Loong has contested in 21 Hyrox races in 11 cities. Provided by Lau Wen Loong

This as-told-to composition is according to a conversation alongside Lau Wen Loong, 37, who, complete the former two years, has contested in 21 Hyrox races in 11 cities about the world. It has been edited for dimension and clarity.

I hadn't exercised regularly in two years whenever a companion stated to me, "I think you'll be curious in this." He was taking part in Hyrox, an endurance competition that combines operating alongside fitness stations. You can competition individually, as doubles, or in a relay, and there are the two amateur and expert racers. He asked if I wanted to competition alongside him.

My companion ended up doing the competition alongside his wife, so I decided to do it on my own. "Don't be crazy," he told me. But I signed up six weeks before the event and completed my archetypal competition in Singapore in August 2024. My final timing was fair under 73 minutes. I was a cross-country runner in elevated school, but for the archetypal period in my life, I understood why several group dislike running.

After finishing one race, I started asking myself what I wanted to do next. I'd put so much period into training for that archetypal race, and I didn't desire to endure that momentum.

The competitions arrive alongside demands

It's been two years since I got started in Hyrox, and I've completed 21 races in 11 cities, including Chengdu, Bangkok, and Mumbai. I have seven additional lined up for this year. I'm in between jobs at the instant and operating on an AI startup alongside my brother.

Each competition expenses concerning $150, during flights and another expenses can add up to $1,500. In conditions of competition prep, I lately bought a yearlong membership at a gym in Seoul, anywhere I'm currently based, for concerning $55 a month. I've rotate into an ambassador for multiple Hyrox-related brands, and they assistance shield several of my costs.

Hyrox is a fine mix of operating and weights. I think I may be the fittest I've always been. I never imagined I could be faster than my younger self.

So far, these have been my three favorite presenter cities:

1. Osaka, Japan: January 2026

Every Hyrox venue is different. Similar to F1 tracks, the setup is tweaked, and the runs between stations are never the identical for all race. It was my archetypal winter Hyrox, and I hit a individual finest of 61:51.

The winter part was interesting, since the competition was held between two halls in a small venue. You ran in one hall, traversed to the another flank to do the workout, and traversed rear to run again, getting a blast of caller air in the process.

I spent seven days in Osaka, most of it about the competition area, but I managed to do a bit of buying in the Dotonbori district. I had meal at a celebrated cancer eatery alongside my father, who sometimes comes to assistance me at my races, depending on his agenda and whether he likes the city.

Lau, at the competition in Incheon, has made a lot of allies at the competitions. Provided by Lau Wen Loong

2. Incheon, South Korea: May 2026

It was my friend's first Hyrox. He raced before me, and I got to see it. Not lone that, but I additionally came in archetypal in my age collection and did a individual best

Like many Hyrox races, the Incheon type was held in a important gathering center. The layout provided for a extremely gentle race, dissimilar at another races, which can sometimes disrupt your rhythm

I'm based in Seoul, so I went there daily. It was one hr all way. On the archetypal day of the race, whenever I hitched a journey rear alongside a friend, I got to try out the hyugaeso, or remainder stops alongside the highway. They are akin mini buying malls, and the one I went to had three floors and many of small stalls.

In the way of my races, I have made a lot of friends, akin my doubles partner Ian, and Yvonne, a Singaporean operating mom who has done 55 races. A brace on the aircraft from Seoul to Singapore additionally noticed my Hyrox bag, and we started chatting and became friends.

In Jakarta, Lau completed two doubles races. Provided by Lau Wen Loong

3. Jakarta, Indonesia: June 2026

The competition venue was in a beautiful part of Indonesia I'd never been to — I anticipation to return.

It was the extremely archetypal Hyrox competition in Indonesia, so it was new for everyone. It was held in a gathering center in one of Jakarta's green spaces, which is extremely scenic.

It was my archetypal period doing two doubles races, and I didn't equal cognize the guy I was racing alongside until I met him on the day. We were introduced through a mutual friend, and we lone trained together once.

The run to complete and win

I was competing in distinct races at the September 2026 Hyrox event in Beijing, anywhere Joanna Wietrzyk dirty herself. I saw her from afar, but didn't see what happened firsthand.

I've tried to put myself in her shoes and wondered if I would continue. I don't think I would or could. I do anticipation that she'll bounce rear from it.

I've seen a lot of group crying at Hyrox. Whether it be tears of joy or tears of frustration, it motionless surprises me. Hyrox is a bit akin therapy, as you are handling through your body. I average to continue racing for at smallest another year.

I think the biggest advantage is that it enables you to be additional self-aware.

F-Zero X on Nintendo 3DS

support@mycoding.id (Doris Putra) — Mon, 26 Oct 2026 00:27:30 +0700

01 · Overview

A post-mortem of porting G-Diffuser, the F-Zero X decompilation, to the Nintendo 3DS — v1.0.0, covering the two the first 24-day sprint that put it on a New 3DS at 60 fps in 3D (2026-08-11 to 09-03) and the 43-day second phase that added Old 3DS support, a native renderer, and the Expansion Kit (2026-09-07 to 10-19). One individual directed throughout; one long-lived Claude meeting orchestrated; 147 subagents built the archetypal phase and many additional carried the second.

The console went from a dreary display to a native-60 stereoscopic racer, afterward from a New-3DS-only build to a four-way publish covering the two consoles alongside and without the 64DD expansion.

59.6fps

median on hardware, complete Grand Prix, New 3DS, end of the archetypal phase (from 12–20 fps in the archetypal emulator race)

51.4fps

tenth-percentile flat in 30-machine crowds, archetypal phase (from 25–35)

53%

of frames at the 60 cap, archetypal phase (from 14% one week earlier)

active days throughout the archetypal phase's 24 calendar days, three pauses whenever the weekly scheme allowance ran out

3.17 B

tokens throughout all sessions and agents, archetypal phase only; see Cost

$4,974

at published API catalog prices (Fable $10/$50 per MTok, 1-hour cache writes), archetypal phase only; see Cost

250

commits touching the 3DS harbor in the archetypal phase, icy 2026-10-08 from the v0.9.0 docs — not re-verified against final history

15,039

lines under port/3ds affirmative 48 pure-delta patches, first-phase count

What v1.0.0 added

released console builds from one origin tree: Old 3DS, New 3DS, and New 3DS alongside the Expansion Kit

199

internal advancement notes written during the second phase

43days

calendar extend of the second phase, 2026-09-07 to 10-19 inclusive

The tiles complete are dated. The archetypal phase's hardware numbers and token/cost figures were measured through 2026-09-03 and have not been re-run for v1.0.0; the Agentic workflow leaf describes how the second phase was run, and the Cost leaf is definitive concerning what it does and does not price.

On hardware

The clips and photos below are from v0.9 (2026-09), before DASH replaced the debug console on the base screen.

What was built

Three released console builds from one origin tree, all booting from the HOME list (.cia) or the homebrew launcher (.3dsx): the basis equivalent for Old 3DS / 2DS and for New 3DS / New 2DS XL, and a New 3DS build alongside the 64DD Expansion Kit (course publishing company and device creator, playable alongside a user-supplied disk image). Every build says prebaked asset archives from the SD cardstock — no ROM or disk depiction always ships alongside the game.

They perform the entire game: Grand Prix, Time Trial, Practice, Death Race, and the device publishing company screens, alongside music and effects on the genuine DSP, stereoscopic 3D on the slider, a bottom-screen dashboard alongside three HUD layouts and an idle perspective exterior races, and spotless HOME suspend, resume, close, lid sleep and power-off. The New 3DS build keeps the native-60 example pacing the archetypal phase reached; the Old 3DS build gets to a comparable outcome by a distinct route. At v1.0.0, alongside no ini, the two builds default to that identical route: the native renderer ([perf] preset = auto). The New 3DS's first-phase render-thread/per-draw-stereo way is motionless built in and reachable alongside an definitive ini, but is no longer the default — see Frontier and Pushing the boundaries for the two builds compared flank by side.

The stack

decomp: the F-Zero X decompilation (inspectredc/fzerox), untouched upstream, affirmative a place stack.
libultraship: the N64 runtime (Kenix3), untouched upstream, including a 13-line newlib fix that lets it build on devkitARM; it motionless draws all list and non-race screen.
port/3ds: 15,039 lines as of the archetypal phase, since grown: a citro3d rendering backend, stereo, audio, input, menu, lifecycle, render thread, profilers, packaging.
the native renderer (added in the second phase): an owned translator that skips the per-frame display-list stroll for competition geometry, and is the v1.0.0 default on the two consoles — see Frontier for how, and why it is what lets the Old 3DS keep up.

The header numbers on hardware, archetypal phase (New 3DS)

When	Median	p10	At cap
08-14 archetypal competition (emulator)	12–20	–	–
08-21 archetypal hardware run	48–50	–	–
08-28 release-grade build	~51	~40 (crowds, MINIMAL)	–
09-02 Round 1: Mainline before the campaign	51.3	39.2	9%
09-02 Round 2: Bridge memos (brfast)	57.2	46.6	29%
09-02 Round 3: HUD atlas · triangle memo · TMEM · audio	56.0	48.0	32%
09-02 Round 4: Render thread on center 2 (pipe)	57.6	44.6	14%
09-03 Round 5: Ahead manner · extend on chief · car competitor detail	59.6	51.4	53%

The archetypal phase in five lines

08-11 → 08-14: from investigation to a race. Feasibility dossier, scheme for a squad of agents, five parallel basis streams, six footwear bugs, archetypal competition at 12–20 fps in the emulator.
08-20 → 08-22: from pink to playable, afterward to a genuine console. Fog, HUD, livery, shadow, sky fixed; the loadblock finding took menus from 27 to 60 fps; archetypal hardware run 08-21 ("wow it runs amazing"); audio the identical evening.
08-27 → 08-28: merchandise grade. Five-agent fleet drop, stereo armed, contact menu, competitor detail, GPU vertex-transform moonshot built and shelved, README alongside lawful notice, publish package.
09-01 → 09-02: lifecycle and the final big lever. HOME collision and close-from-HOME suspend fixed; a extend output cache proven a NO-GO and replaced by memoizing all sub-display-list's classification alternatively of re-scanning it all frame; LOCKED-60 run launched alongside four parallel levers.
09-02 → 09-03: multi-core. Render thread on center 2, one-frame-ahead mode, extend on main, car LOD; median 59.6 alongside 53% of frames at cap; collision fixed; viewport placement fixed; stereo markers anchored.

The second phase in six lines

Not initially planned; it ran 09-07 to 10-19, 43 calendar days.

09-07 → 09-29: scoping, afterward making it fit. The Old 3DS milestone, earlier written off, was restarted; parallel agents fit the build into its 64 MB app area (the RDRAM arena halved, audio streamed alternatively of resident, texture uploads capped); on 2026-09-29, in the archetypal hardware meeting on a genuine Old 3DS, the shipped (New-3DS-era, 16 MiB RDRAM) build died at footwear during the -DGDX_3DS_O3DS=ON profile, built to fit the console's recollection grant, booted and raced the identical meeting at concerning 10 fps.
09-29 → 09-30: finding the genuine costs. A narrowed audio fastener cut the race-wall delay on the Old 3DS by concerning 20 ms a frame; example skipping ended the game's unintended dilatory motion; a archetypal retained-mode renderer won big in the emulator and barely moved the figure on hardware.
09-30 → 10-01: the investigation that changed the plan. Hardware achievement counters showed the genuine bounds was instruction-cache misses, not education count. A pose-replay fallback was built and worked, afterward was set apart on the user's call: "we should go for a genuine 60! i motionless accept it have to be possible."
10-01 → 10-02: the native renderer. Rebuilt about the cache alternatively of about education counts, one small iteration per competition category, sized to fit the 16 KB education cache. Median example period on a 2D Grand Prix competition reached 16.5 ms.
10-03 → 10-08: 3D at 60, on the Old 3DS too. A per-eye resubmit alongside its own swap-pacing manner reached 59–60 fps in 3D on most Grand Prix courses; the heaviest courses (Silence, Sand Ocean) can motionless trip the frame-skip safety net to a dependable 30 fps mid-race.
10-08 → 10-19: the Expansion Kit, a dashboard, and release. The 64DD Expansion Kit verified tick-for-tick identical under the native renderer and booted on the Old 3DS, which afterward proved too dilatory to publish it on; the bottom-screen DASH shipped; a position adversarial assessment audited the merged outcome before the v1.0.0 release.

See Agentic workflow for how the second phase was run and Old 3DS for the complete campaign, cardstock by card.

Read on

Author's notes: The one leaf written by a human.
Every step: The chronology, New 3DS to v1.0.0.
Road to 60: The New 3DS achievement campaign, circular by round.
The native fork: Why it exists, and what it changed.
Old 3DS: The highway to a genuine 60 at 268 MHz.
Pushing the boundaries: Against our New 3DS build and against the field.
Difficulties: What clashed back, and how it was beaten.
Frontier: What this harbor does that others in its stance haven't.
Agentic workflow: How the fleet was run, and how the container was steered.
Cost: Tokens, dollars, subagents.

Every figure on these pages has a source: git history, the investigation documents under docs/research/, the hardware logs, or the meeting transcripts the Cost leaf counts. Where a value is an evaluation (the dollar figures, which use published API catalog prices to exact token counts), the assumption is stated next to it.

18 cara meningkatkan conversion rate ecommerce agar penjualan naik

support@mycoding.id (Riki Setiawan) — Mon, 26 Oct 2026 00:02:37 +0700

Oct 26, 2026

By Teresia D.

12 menit Dibaca

Ingin lebih banyak pengunjung toko online Anda berbuat pembelian tanpa mesti menambah traffic? Anda bisa menerapkan conversion rate optimization (CRO) ecommerce untuk menaikkan conversion rate di setiap tahap belanja, mulai dari pelacakan produk hingga checkout.

Ada beberapa hal yang membikin pengunjung bisa membatalkan pembelian, seperti website yang lambat, keterangan produk yang kurang lengkap, biaya tambahan tak terduga, maupun proses checkout yang rumit.

Dalam artikel ini, Anda akan mempelajari 18 metode menaikkan conversion rate ecommerce, mulai dari menemukan penyebab pengunjung batal membeli hingga memperbaiki proses berbelanja agar lebih banyak pengunjung menjadi pelanggan.

1. Optimasi checkout untuk menaikkan conversion rate

Salah satu metode menaikkan conversion rate ecommerce adalah dengan memantau tahapan yang dilalui pengunjung hingga menyelesaikan pembelian. Tindakan kecil ini disebut micro-conversion maupun konversi mikro, yang membantu Anda menemukan tahap ketika calon pembeli selesai melanjutkan proses belanja.

Beberapa aksi yang perlu dilacak meliputi:

Melihat halaman produk
Menggunakan fitur pelacakan maupun filter
Memilih ukuran maupun varian produk
Menambahkan produk ke keranjang belanja
Membuka keranjang belanja
Memulai proses checkout
Memasukkan keterangan pembayaran
Menyelesaikan pembelian

Cari tahap dengan penurunan jumlah pengunjung terbesar. Kalau banyak pengunjung melihat produk tapi tidak menambahkannya ke keranjang, periksa halaman produk. Kalau banyak yang batal membeli setelah memasukkan produk ke keranjang, periksa biaya pengiriman, proses checkout, dan metode pembayaran.

Catat jumlah pengunjung di setiap tahap sebagai rujukan untuk mengukur hasil optimasi.

2. Tingkatkan kecepatan loading halaman

Untuk menaikkan conversion rate toko online secara efektif, pastikan semua halaman krusial bisa dimuat dengan cepat, baik di HP maupun komputer.

Uji kecepatan homepage, halaman kategori, halaman produk populer, keranjang belanja, dan checkout secara terpisah. Gambar berukuran besar, fitur live chat, maupun tool pelacakan bisa memperlambat halaman tertentu meskipun halaman lainnya sudah berjalan lancar.

Untuk mempercepat loading website, terapkan langkah-langkah berikut:

Sesuaikan ukuran gambar sebelum menguploadnya.
Gunakan format WebP maupun AVIF yang umumnya menghasilkan ukuran file lebih kecil dibandingkan JPEG maupun PNG.
Hapus plugin dan script yang tidak digunakan.
Terapkan lazy loading agar gambar di bagiankecil bawah halaman baru dimuat saat pengunjung mendekatinya.
Aktifkan cache browser agar pengunjung tidak perlu mendownload ulang file yang sama setiap kali membuka halaman.
Gunakan content delivery network (CDN) agar file website dimuat dari server yang lebih dekat dengan letak pengunjung.
Uji kecepatan loading di HP dan komputer secara terpisah.

Setelah berbuat optimasi, uji balik seluruh proses berbelanja untuk memastikan fitur pelacakan produk, keranjang, dan checkout statis berguna dengan baik. Website yang cepat memang penting, tapi jangan sampai optimalisasi justru mengganggu pengalaman berbelanja pelanggan.

3. Permudah pengalaman berbelanja melalui HP

Untuk meningkatkan conversion rate toko online, pastikan pelanggan bisa berbelanja dengan betah melalui HP. Coba langsung seluruh proses pembelian, mulai dari beranda hingga halaman konfirmasi pesanan, bukan cuma menggunakan fitur pratinjau di browser komputer.

Perhatikan apakah Anda mesti memperbesar tampilan, mengetuk tombol berulang kali, menggulir balik ke atas, maupun memperbaiki keterangan dalam formulir.

Periksa juga masalah berikut:

Tombol terlalu kecil maupun terlalu berdekatan.
Informasi produk sulit dibaca tanpa memperbesar tampilan.
Menu filter menutupi sebagian besar layar.
Pop-up sulit ditutup.
Formulir checkout terlalu panjang.
Pilihan ukuran maupun varian produk sulit ditemukan.
Pembeli mesti memasukkan detail kartu pembayaran secara manual.

Pastikan harga, pilihan produk, keterangan pengiriman, dan tombol pembelian utama gampang ditemukan. Untuk halaman produk yang panjang, pertimbangkan tombol “Tambahkan ke keranjang” yang statis kelihatan saat pengunjung menggulir halaman.

Pengalaman berbelanja yang betah di HP bisa membantu meningkatkan conversion rate dengan memudahkan pelanggan mencari produk, menghubungi jasa pelanggan, menyelesaikan pembayaran, hingga memantau pesanan.

Untuk meningkatkan conversion rate, gunakan istilah yang familiar bagi pelanggan saat menamai kategori produk di toko online Anda.

Misalnya, kategori “Sepatu lari wanita” langsung menerangkan produk yang tersedia. Sebaliknya, nama seperti “Koleksi Aktif” bisa membingungkan pengunjung karena tidak menginformasikan jenis produknya.

Periksa kemudahan navigasi toko online Anda dengan menjawab pertanyaan berikut:

Apakah setiap nama kategori sudah menggambarkan produk di dalamnya?
Apakah pengunjung bisa menemukan produk krusial cuma dengan beberapa klik?
Apakah kategori yang berhubungan sudah dikelompokkan?
Apakah menu navigasi gampang digunakan di HP dan komputer?
Apakah pengunjung bisa balik ke kategori utama?
Apakah kategori populer gampang ditemukan dari homepage?

Tambahkan breadcrumbs pada halaman produk dan kategori agar pengunjung mengetahui posisi mereka di website dan bisa balik ke kategori sebelumnya tanpa mengulang pencarian.

Navigasi yang gampang digunakan membantu pengunjung menemukan produk dengan lebih cepat sehingga berpotensi menaikkan conversion rate toko online Anda.

Kalau Anda baru berencana membikin website ecommerce, susun kategori dan subkategorinya sejak awal. Struktur navigasi akan lebih gampang diatur sebelum toko memiliki ratusan produk.

5. Optimalkan fitur pelacakan dan filter produk

Pastikan pengunjung bisa menemukan produk dengan gampang melalui fitur pencarian, terutama jika produk yang dicari tidak tersedia di menu navigasi untuk menaikkan conversion rate.

Misalnya, pelacakan “jaket waterproof biru tua” sebaiknya statis menampilkan produk disebut “jas hujan navy” karena keduanya menggunakan istilah yang berkaitan.

Optimalkan fitur pelacakan dengan kapabilitas berikut:

Menampilkan saran produk maupun keyword saat pengunjung mengetik.
Mengenali kesalahan penulisan dan sinonim.
Menampilkan gambar, harga, rating, dan stok produk.
Merekomendasikan produk serupa jika tidak ada hasil yang cocok.

Selain pencarian, menaikkan conversion rate juga bisa dilakukan dengan menyediakan filter sesuai kebutuhan pelanggan. Misalnya, filter ukuran, warna, dan harga untuk toko pakaian, maupun bahan, dimensi, dan estimasi pengiriman untuk toko furnitur.

Kalau katalog toko Anda cuma berisi beberapa puluh produk, kategori yang jelas dan filter sederhana biasanya sudah cukup. Anda belum perlu menggunakan mekanisme pelacakan yang kompleks.

6. Tampilkan foto produk dari beragam perspektif pandang yang relevan

Menggunakan foto produk yang jelas dan informatif merupakan salah satu metode menaikkan conversion rate karena membantu pelanggan melihat detail produk sebelum membeli.

Misalnya, pembeli busana mau melihat potongan, tekstur, dan ukuran produk. Sementara itu, pembeli furnitur perlu mengetahui material, dimensi, kapabilitas penyimpanan, dan tampilannya di dalam ruangan.

Pastikan galeri produk menampilkan:

Foto dari beragam sudut, termasuk bagiankecil krusial produk.
Foto yang memperlihatkan ukuran produk.
Foto produk saat digunakan.
Foto close-up untuk menginformasikan tekstur maupun finishing.
Foto setiap pilihan warna maupun desain.
Video singkat tentang penggunaan, pemasangan, maupun metode kerja produk.
Fitur zoom untuk melihat detail melalui HP maupun komputer.

Gunakan urutan foto yang konsisten agar pelanggan gampang membandingkan produk. Optimalkan juga ukuran file gambar sebelum diupload agar halaman statis cepat dimuat.

Foto yang komplit dan gampang diakses bisa membantu menaikkan conversion rate karena pelanggan memiliki keterangan yang cukup untuk memutuskan pembelian.

7. Buat klarifikasi produk yang menjawab kebutuhan calon pembeli

Deskripsi produk yang jelas dan komplit bisa membantu menaikkan conversion rate dengan memberikan keterangan yang dibutuhkan pelanggan sebelum membeli.

Jelaskan khasiat utama produk, siapa yang cocok menggunakannya, serta detail krusial seperti ukuran, bahan, dan kompatibilitasnya.

Misalnya, jangan cuma menulis “Botol stainless steel 500 ml”. Jelaskan juga kegunaannya untuk aktivitas sehari-hari, dimensi botol, dan apakah produk terlindungi dicuci menggunakan mesin pencuci piring sesuai instruksi produsen.

Pastikan klarifikasi mencakup keterangan yang relevan, seperti:

Fungsi dan khasiat utama produk.
Bahan, dimensi, dan panduan ukuran.
Kompatibilitas dengan produk maupun alat lain.
Isi paket pembelian.
Petunjuk penggunaan, pemasangan, dan perawatan.
Ketentuan garansi dan pengembalian barang.

Gunakan paragraf singkat untuk menerangkan khasiat produk, kemudian tampilkan rincian dalam bentuk bullet agar gampang dibaca.

Untuk menaikkan conversion rate, perhatikan juga pertanyaan pelanggan, ulasan, dan karena pengembalian barang. Gunakan keterangan tersebut untuk melengkapi detail produk yang statis kurang jelas sehingga pelanggan lebih percaya sebelum membeli.enanyakan hal yang sama, tambahkan jawabannya ke klarifikasi agar calon pembeli berikutnya tidak perlu bertanya lagi.

8. Buat tombol CTA yang jelas dan gampang ditemukan

Tombol call to action (CTA) yang jelas bisa membantu menaikkan conversion rate dengan memudahkan pelanggan melanjutkan pembelian.

Gunakan teks sesuai aksi yang akan dilakukan, seperti “Tambahkan ke keranjang”, “Beli sekarang”, maupun “Pilih ukuran”.

Pastikan tombol CTA Anda:

Berada di dekat harga dan pilihan varian produk.
Memiliki warna yang kontras dan gampang terlihat.
Mudah ditemukan saat halaman dibuka melalui HP.
Memberikan konfirmasi setelah diklik.
Menunjukkan pilihan yang mesti ditentukan sebelum membeli.
Tidak kalah menonjol dari tombol lain yang kurang penting.

Untuk menguji efektivitasnya, lakukan five-second test. Tunjukkan halaman produk kepada seseorang selama lima detik, kemudian tanyakan tombol mana yang akan mereka klik untuk membeli. Kalau mereka kesulitan menemukannya, perbaiki tampilan maupun posisi CTA.

Prioritaskan masalah seperti teks yang membingungkan, tombol yang sulit ditemukan, maupun pilihan produk yang tersembunyi. Perbaikan ini biasanya lebih krusial untuk menaikkan conversion rate daripada sekadar menguji perbedaan warna tombol.

9. Tampilkan ulasan dan bukti kepercayaan pelanggan

Menampilkan ulasan pelanggan dan keterangan yang membangun kepercayaan merupakan salah satu metode menaikkan conversion rate toko online.

Tempatkan keterangan sesuai kebutuhan pelanggan. Misalnya, tampilkan rating di dekat nama produk, kebijakan pengembalian di dekat tombol “Tambahkan ke keranjang”, serta logo metode pembayaran di halaman keranjang maupun checkout.

Beberapa elemen yang bisa menaikkan kepercayaan pelanggan antara lain:

Ulasan dari pembeli terverifikasi.
Foto produk dari pelanggan.
Rating produk pada halaman kategori.
Informasi pengiriman dan pengembalian barang yang jelas.
Kontak jasa pelanggan dan alamat bisnis, jika relevan.
Logo metode pembayaran yang tersedia.
Checkout terlindungi menggunakan HTTPS.

Minta ulasan setelah pelanggan memiliki waktu untuk mencoba produk. Misalnya, makanan bisa dinilai segera setelah diterima, sedangkan furnitur maupun alat elektronik mungkin membutuhkan waktu lebih lama.

Jangan buru-buru menghapus ulasan negatif. Tanggapan yang sopan dan menginformasikan penyelesaian masalah justru bisa membangun kepercayaan.

Untuk menaikkan conversion rate, hindari juga penggunaan badge keamanan secara berlebihan karena bisa membikin toko kelihatan kurang tepercaya.

10. Tampilkan ongkos kirim sebelum checkout

Menampilkan ongkos kirim sejak mula bisa membantu menaikkan conversion rate karena pelanggan mengetahui total biaya sebelum checkout.

Cantumkan keterangan pengiriman di halaman produk maupun keranjang belanja. Kalau ongkir bersandar pada alamat tujuan, sediakan kalkulator ongkir agar pelanggan bisa mengestimasi biayanya.

Contoh keterangan yang bisa ditampilkan:

Gratis ongkir untuk pembelian di atas Rp500.000.
Belanja Rp100.000 lagi untuk mendapatkan cuma-cuma ongkir.
Ongkos kirim mulai dari Rp20.000.
Pajak maupun bea masuk tambahan untuk pengiriman tertentu, jika berlaku.

Angka tersebut cuma contoh. Sesuaikan dengan tarif dan promosi toko Anda.

Pastikan semua biaya wajib sudah kelihatan sebelum pelanggan membayar. Biaya tambahan yang baru muncul di akhir checkout bisa membikin pelanggan membatalkan pembelian.

Untuk metode menaikkan conversion rate, pertimbangkan juga total biaya yang ditanggung pelanggan saat membandingkan harga dengan kompetitor, bukan cuma harga produk.engan harga mula lebih murah belum tentu lebih menarik jika ongkos kirim dan biaya tambahannya jauh lebih tinggi.

11. Sederhanakan proses checkout

Menyederhanakan proses checkout bisa membantu menaikkan conversion rate dengan mengurangi waktu pengisian blangko dan akibat kesalahan.

Mintalah cuma keterangan yang diperlukan untuk pembayaran dan pengiriman. Hapus kolom yang tidak krusial agar pelanggan bisa menyelesaikan pesanan dengan lebih cepat.

Beberapa metode menyederhanakan checkout antara lain:

Aktifkan pengisian alamat otomatis.
Gunakan alamat pengiriman sebagai alamat penagihan secara default.
Simpan keterangan yang sudah diisi meskipun terjadi error.
Tampilkan pesan error di dekat kolom yang bermasalah.
Pastikan total biaya selalu kelihatan selama checkout.
Tunjukkan jumlah tahapan yang tersisa.
Hilangkan menu maupun promosi yang mengalihkan perhatian.
Jadikan metode pengiriman yang paling lazim sebagai pilihan default, tanpa menyembunyikan opsi lain.

Checkout satu halaman belum tentu lebih efektif. Beberapa tahap singkat juga bisa memudahkan pelanggan, asalkan setiap tahap jelas dan tidak meminta terlalu banyak informasi.

Untuk menaikkan conversion rate, coba checkout melalui beragam HP dan komputer. Coba juga beberapa alamat pengiriman, kode diskon, pilihan pengiriman, dan metode pembayaran untuk memastikan seluruh proses berjalan lancar.or mungkin cuma muncul dalam kondisi tertentu, sehingga tidak akan terdeteksi jika Anda cuma mencoba satu skenario.

12. Sediakan opsi checkout tanpa membikin akun

Menyediakan opsi guest checkout maupun checkout tanpa akun bisa membantu menaikkan conversion rate karena pelanggan tidak perlu mendaftar sebelum membeli.

Pastikan opsi checkout sebagai tamu gampang ditemukan, sama seperti tombol login. Setelah pembayaran berhasil, Anda bisa menawarkan pembuatan akun agar pelanggan bisa melacak pesanan dan menyimpan keterangan untuk pembelian berikutnya.

Meski tidak mewajibkan pendaftaran akun, Anda statis bisa:

Meminta alamat email untuk mengirim pembaruan pesanan.
Meminta persetujuan terpisah untuk email marketing.
Menawarkan poin loyalitas setelah pembelian.
Menghubungkan pesanan dengan akun yang dibuat setelah checkout.
Menyediakan login cepat bagi pelanggan lama.

Untuk menaikkan conversion rate, jangan sampai proses pendaftaran menghalangi pembayaran. Hindari menyembunyikan opsi checkout sebagai tamu dalam link kecil yang sulit ditemukan.

13. Sediakan metode pembayaran yang sesuai dengan kebiasaan pelanggan

Menyediakan metode pembayaran yang familiar dan tepercaya bisa membantu menaikkan conversion rate karena pelanggan lebih gampang menyelesaikan transaksi.

Sesuaikan pilihan pembayaran dengan kebiasaan pelanggan dan negara tujuan penjualan. Beberapa metode yang bisa dipertimbangkan antara lain:

Kartu angsuran dan debit.
Transfer bank dan virtual account.
QRIS.
Dompet digital seperti GoPay, OVO, dan DANA.
PayPal untuk transaksi internasional.
Apple Pay dan Google Pay, jika didukung.
Layanan paylater maupun buy now, pay later (BNPL).

Tidak semua metode tersedia di setiap negara maupun platform ecommerce. Pilih yang benar-benar dibutuhkan pelanggan dan didukung oleh toko Anda.

Untuk menentukan metode pembayaran yang tepat:

Periksa transaksi yang gagal maupun tidak diselesaikan.
Identifikasi letak dan kebiasaan pembayaran pelanggan.
Cari tahu hambatan pembayaran yang sering diterima tim customer support.
Tambahkan metode yang paling banyak dibutuhkan.
Pantau fluktuasi jumlah transaksi yang berhasil.

Dompet digital bisa mempermudah pembayaran melalui HP karena pelanggan tidak perlu mengisi banyak informasi.

Namun, Anda tidak mesti menyediakan semua metode pembayaran. Pertimbangkan biaya transaksi, proses refund, dan pengelolaannya agar upaya menaikkan conversion rate statis sebanding dengan biaya operasional.

14. Kirim pengingat untuk keranjang berbelanja yang ditinggalkan

Mengirim pengingat otomatis kepada pelanggan yang meninggalkan checkout bisa membantu menaikkan conversion rate dengan mendorong mereka menyelesaikan pembelian.

Untuk menjalankan abandoned cart recovery, pastikan Anda memiliki alamat email pelanggan dan persetujuan yang diperlukan untuk mengirim pesan.

Gunakan rangkaian pengingat sederhana berikut:

Pengingat pertama: Kirim tidak durasi setelah pelanggan meninggalkan checkout.
Pengingat kedua: Sertakan keterangan yang membantu pelanggan memungut keputusan, seperti ulasan produk, estimasi pengiriman, maupun kebijakan pengembalian.
Pengingat terakhir: Kirim sebelum keranjang maupun reservasi stok kedaluwarsa, jika memang ada batas waktunya.

Sertakan link yang mengarahkan pelanggan balik ke keranjang berbelanja mereka, jika platform ecommerce Anda mendukungnya.

Hindari langsung menawarkan diskon karena pelanggan bisa terbiasa meninggalkan keranjang untuk menunggu kupon.

Untuk menaikkan conversion rate melalui SMS, pastikan pelanggan sudah menyetujui penerimaan pesan. Fokuskan setiap pengingat pada pesanan yang belum selesai, bukan promosi yang tidak berkaitan.

15. Gunakan urgensi cuma untuk penawaran yang benar-benar terbatas

Menampilkan batas waktu promo maupun keterangan stok terbendung bisa membantu menaikkan conversion rate dengan mendorong pelanggan memungut keputusan lebih cepat. Namun, pastikan keterangan tersebut sesuai dengan kondisi sebenarnya.

Beberapa contoh urgensi yang bisa digunakan antara lain:

Promo diskon dengan tanggal maupun jam berakhir yang jelas.
Batas waktu pemesanan untuk pengiriman pada hari tertentu.
Informasi stok terbendung berdasarkan cadangan sebenarnya.
Batas waktu preorder.
Produk yang diproduksi dalam jumlah terbatas.

Tempatkan keterangan di bagiankecil yang relevan. Misalnya, hitung mundur batas pemesanan sebaiknya ditampilkan di dekat keterangan pengiriman.

Hindari countdown timer yang terus diulang, promo “hanya hari ini” yang terjadi setiap hari, maupun peringatan stok hampir hilang yang tidak sesuai kenyataan. Praktik seperti ini justru bisa mengurangi kepercayaan pelanggan.

Agar proses menaikkan conversion rate berhasil, Anda juga bisa menggunakan exit-intent pop-up saat pengunjung hendak meninggalkan halaman. Gunakan seperlunya dan pastikan pop-up gampang ditutup agar tidak mengganggu pengalaman berbelanja.

16. Berikan anjuran produk yang relevan dan bermanfaat

Menampilkan anjuran produk yang relevan bisa membantu menaikkan conversion rate sekaligus mendorong pelanggan membeli produk pelengkap. Anda tidak perlu menggunakan AI yang kompleks untuk menerapkannya.

Beberapa anjuran yang bisa ditampilkan antara lain:

Produk yang sering dibeli bersamaan.
Produk yang juga dilihat pelanggan lain.
Produk yang baru saja dilihat.
Aksesori yang cocok dengan produk utama.
Produk isi ulang maupun suku cadang.
Produk berdasarkan histori pembelian.

Misalnya, tawarkan laptop keluaran terbaru kepada pelanggan yang sedang melihat laptop. Hindari merekomendasikan produk yang tidak relevan cuma karena margin keuntungannya tinggi.

Untuk mengukur efektivitasnya, bandingkan jumlah klik rekomendasi, pembelian yang dihasilkan, dan harga berbelanja pelanggan sebelum dan sesudah fitur diterapkan.

Agar strategi ini ampuh dalam menaikkan conversion rate, pastikan anjuran sesuai kebutuhan pelanggan. Jelaskan juga dalam kebijakan privasi sepertiapa aktivitas browsing dan histori pesanan digunakan untuk memberikan rekomendasi.

17. Lakukan A/B testing pada fluktuasi yang memengaruhi pembelian

A/B testing bisa membantu menaikkan conversion rate dengan membandingkan dua varian halaman maupun fitur untuk mengetahui mana yang lebih efektif.

Dalam pengetesan ini, pengunjung dibagi menjadi dua himpunan yang melihat varian berbeda. Hasilnya dibandingkan berdasarkan metrik seperti jumlah produk yang ditambahkan ke keranjang, checkout yang dimulai, maupun pembelian yang selesai.

Mulailah dengan anggapan yang jelas. Misalnya:

Pengunjung yang berbelanja melalui HP sering melewatkan tombol “Tambahkan ke keranjang”. Membuat tombol statis kelihatan saat halaman digulir mungkin bisa menaikkan jumlah produk yang ditambahkan ke keranjang.

Sebaiknya ubah satu elemen dalam setiap pengetesan agar dampaknya lebih gampang dianalisis. Beberapa elemen yang bisa diuji antara lain:

Pesan utama dan khasiat produk.
Posisi tombol CTA.
Urutan foto produk.
Penempatan keterangan pengiriman.
Kolom blangko checkout.
Posisi opsi guest checkout.
Pilihan metode pembayaran.

Kalau traffic toko Anda statis rendah, A/B testing mungkin belum menghasilkan keterangan yang cukup untuk menarik kesimpulan yang andal.

Sebagai alternatif, bandingkan metrik sebelum dan sesudah perubahan, kemudian lengkapi dengan feedback pelanggan. Namun, jangan langsung menganggap peningkatan konversi sebagai akibat fluktuasi tersebut karena sisi lain juga bisa memengaruhi hasil.

Gunakan temuan ini untuk menentukan perbaikan berikutnya yang berpotensi menaikkan conversion rate.rsebut menjadi penyebabnya. Gunakan dampaknya sebagai gejala mula untuk menentukan metode optimalisasi berikutnya.

18. Datangkan pengunjung yang memiliki niat beli tinggi

Fokuslah mendatangkan pengunjung melalui keyword yang relevan dengan produk dan menginformasikan niat beli.

Misalnya, orang yang mencari “sepatu hiking waterproof ukuran 42” kemungkinan lebih siap membeli dibandingkan orang yang mencari “sepatu apa yang cocok untuk hiking”.

Optimalkan SEO halaman produk dan kategori menggunakan keyword yang sesuai. Lengkapi juga keterangan krusial seperti ukuran, bahan, harga, dan kesiapan stok agar pengunjung lebih gampang menentukan pilihan.

Pantau keyword yang menghasilkan pembelian, bukan cuma traffic tinggi. Banyaknya pengunjung belum tentu menaikkan penjualan jika produk Anda tidak sesuai dengan kebutuhan mereka.

Cara menentukan prioritas metode menaikkan conversion rate ecommerce

Mulailah dengan mengidentifikasi tahap pembelian yang paling sering membikin calon pelanggan meninggalkan toko.

Sebelum menguji fluktuasi kecil pada tampilan maupun teks, pastikan toko online Anda sudah memiliki fitur ecommerce krusial yang memudahkan pelanggan menemukan produk, berbelanja melalui HP, berbuat pembayaran dengan aman, memilih pengiriman, dan mengelola pesanan.

Setelah itu, gunakan keterangan toko untuk menentukan masalah yang perlu ditangani terlebih dahulu.

Masalah yang ditemukan	Hal yang perlu diperiksa terlebih dahulu
Pengunjung meninggalkan website sebelum melihat produk	Kecepatan loading halaman, terutama di HP
Conversion rate pengunjung HP jauh lebih rendah dibandingkan pelanggan komputer	Tata letak mobile, menu navigasi, formulir, dan metode pembayaran
Banyak pengunjung melihat produk tetapi tidak menambahkannya ke keranjang	Foto produk, deskripsi, tombol CTA, ulasan, dan harga
Banyak pelanggan menambahkan produk ke keranjang tetapi tidak melanjutkan pembelian	Ongkos kirim, pajak, estimasi pengiriman, dan tampilan keranjang
Pelanggan memulai checkout tetapi tidak menyelesaikan pembayaran	Panjang formulir, opsi guest checkout, error, dan metode pembayaran
Pengunjung kesulitan menemukan produk	Navigasi, fitur pencarian, filter, dan struktur kategori
Traffic toko tinggi tetapi penjualan statis rendah	Keyword pencarian, landing page, dan sumber traffic
Anda belum mengetahui tahap ketika pelanggan meninggalkan toko	Pelacakan tahapan konversi, heatmap, dan rekaman sesi pengunjung

Prioritaskan masalah mendasar seperti halaman yang lambat, tampilan mobile yang tidak berguna dengan baik, keterangan harga yang membingungkan, dan error checkout sebelum menguji fluktuasi kecil pada teks maupun desain.

Kalau banyak pengunjung melihat produk tetapi tidak menambahkannya ke keranjang, fokuskan perbaikan pada halaman produk.

Sebaliknya, jika banyak pelanggan sudah memasukkan produk ke keranjang tetapi tidak menyelesaikan pembayaran, periksa ongkos kirim, opsi guest checkout, metode pembayaran, dan kemungkinan error dalam formulir.

Tunda optimalisasi anjuran produk dan A/B testing sampai pelanggan bisa menemukan produk dan menyelesaikan checkout tanpa hambatan berarti. Hasil fluktuasi tersebut akan lebih gampang dievaluasi ketika fungsi alas toko sudah berjalan dengan baik.

Cara memilih platform ecommerce yang mendukung peningkatan conversion rate

Platform ecommerce yang baik seharusnya memudahkan pelanggan menemukan produk dan menyelesaikan pembayaran, tanpa mengharuskan Anda mengintegrasikan tool terpisah untuk setiap fitur toko online.

Hostinger Ecommerce memungkinkan Anda mengelola produk, pesanan, stok, pembayaran, dan saluran penjualan melalui satu dashboard. Platform ini juga mendukung lebih dari 100 metode pembayaran serta menyediakan fitur untuk memantau penjualan, jumlah pesanan, dan harga rata-rata pesanan.

Melalui integrasi Reach, Anda juga bisa mengirim email pengingat keranjang berbelanja kepada pelanggan yang sudah berlangganan dan memulai checkout tetapi belum menyelesaikan pembelian.

Fitur-fitur tersebut bisa membantu membujuk pelanggan balik berbelanja dan mempermudah proses checkout.

Namun, platform ecommerce saja tidak otomatis menyelesaikan masalah seperti klarifikasi produk yang kurang jelas, foto yang tidak informatif, maupun ongkos kirim yang baru muncul pada akhir pembayaran. Anda statis perlu mengevaluasi metode pelanggan mencari produk, membandingkan pilihan, dan menyelesaikan transaksi.

Mulailah dengan mencari satu penyebab utama calon pembeli meninggalkan toko, perbaiki bagiankecil yang bermasalah, kemudian bandingkan dampaknya dengan keterangan awal.

Setelah lebih banyak pengunjung berhasil menyelesaikan pembelian, terus gunakan pendekatan yang sama untuk meningkatkan penjualan online dengan mendatangkan pengunjung yang relevan dan mendorong pelanggan berbuat pembelian kembali.

Semua konten tutorial di website ini telah melalui peninjauan menyeluruh sesuai standar dan komitmen editorial Hostinger.

Teresia Dian adalah seorang linguist dengan pengalaman 7 tahun di sektor pelokalan produk dan content marketing. Ketertarikannya pada teknologi mendorongnya untuk terus mengikuti evolusi terbaru dan membagikannya melalui tutorial yang gampang dipahami.

Proton Flock Camera Map

support@mycoding.id (Adi Gunawan) — Sun, 25 Oct 2026 23:39:42 +0700

A Flock camera photographs all conveyance that passes it, whether or not the controller is suspected of anything. It logs the plate, time, and location. Flock's Vehicle Fingerprint characteristic additionally records the make, model, and color, affirmative particulars akin dents and bumper stickers, so a car can be established equal without a readable plate.

Those records go into Flock's cloud, anywhere constabulary departments can hunt them and portion them alongside another agencies throughout city and province lines. Flock says its network processes additional than 20 milliard conveyance says a duration throughout 49 states.

That benevolent of broad admission leaves much area for misuse and abuse. A Washington Post assessment established at smallest 50 officers accused or charged alongside misusing licence dish readers, frequently to prosecute group in their individual lives; Flock's scheme was engaged in 46 of those cases. We asked Flock's CEO, Garrett Langley, how the business responds to misuse and who controls the data its devices collect.

Europe's summer heat wave killed more han 50k people in urban areas

support@mycoding.id (Adi Gunawan) — Sun, 25 Oct 2026 23:02:33 +0700

Comments

iFixit’s look inside the iPhone Duo may explain those broken screens and hinges

support@mycoding.id (Terrence O’brien) — Sun, 25 Oct 2026 22:40:27 +0700

Terrence O'Brien

is the Verge’s weekend editor. He’s covered the tech industry for complete 18 years and knows a item or two concerning synths.

As is tradition, iFixit has torn open the latest iPhone and laid its parts naked on a table. It established several engaging particulars concerning how Apple power be hiding the crease on the Duo’s folding display and gave it a 5/10 repairability score. That power not appear particularly high, but it’s the finest mark the location has stated to a foldable yet.

The inner screen’s matte nano-texture shield doesn’t fair create it additional readable in brilliant sunlight; it additionally helps disguise the crease by scattering reflections. When looking at the display under a microscope, iFixit additionally established oval-shaped subpixels arranged at assorted angles, which the location believes are designed to decrease artifacts and additional disguise the crease downward the center of the display.

The nano-texture external tier is comparatively uncomplicated to substitute without swapping out the OLED underneath. If you do need to repair the display itself, however, fine luck. As is the case alongside another foldables, it’s an costly nightmare. Dust did create it into the USB-C port, speaker grills, and the hinge — making it a bit crunchy. But noticeably small of the glow powder established its way inner the phone than the Z Fold8 or Pixel 11 Pro Fold.

As for reports of damaged screens and cracking hinges, iFixit has several theories. The hinge on the iPhone Duo is incredibly complex, and they doubtful part of it sometimes isn’t descending properly. There is additionally an accordion cable that passes complete that exact spot, and it’s near the border of the battery. It’s imaginable this is concentrating force and foremost to the “audible crack” reported by several and the damaged screens. Apple did not immediately come back a petition for comment.

Follow topics and authors from this narrative to see additional akin this in your personalized homepage nourish and to obtain email updates.

Terrence O'Brien

OpenAI and Anthropic cut their LLM prices by up to 80% within a few months. Why?

support@mycoding.id (Mira Azkia) — Sun, 25 Oct 2026 22:11:31 +0700

Let’s appearance at a quick timeline of the important LLM example announcements from the two OpenAI and Anthropic complete the former few months. All pricing numbers provided are input MTok / output MTok / cache peruse MTok.

June 26, 2026#

OpenAI announced GPT-5.6 Sol and Luna. Sol is the straightforward successor to GPT 5.5 at the identical cost of $5 / $30 / $0.50, during Luna is a small example specifically designed for cheaper use cases at $1 / $6 / $0.10. They additionally announced a Terra example that’s in between those two models, but few cared concerning it.

June 30, 2026#

Anthropic announced Claude Sonnet 5, which Anthropic claimed to be nearer to their Opus models, but feedback was mixed. Notably, it had initiate pricing of $2 / $10 / $0.20 which was initially expected to be impermanent but would go up.

July 24, 2026#

Anthropic announced Claude Opus 5, which was a long-awaited major-version update stated the impact Opus 4.5 had on the increase of agentic coding, at a cost of $5 / $25 / $0.50 (similar to GPT-5.6 Sol). Although several of the provided benchmark results amazingly exceeded those of the much larger Fable 5, real-world feedback was additional mixed.

July 30, 2026#

OpenAI announces that they cut the cost of GPT-5.6 Luna by 80% (???) to $0.20 / $1.20 / $0.02.

Model norm vs. Cost-per-task for GPT-5.6 Luna following the enormous cost reduction. Top-and-left is better, and if OpenAI didn’t do the 80% cost cut, it would have misplaced to DeepSeek V4 Pro. via OpenAI

August 10, 2026#

Anthropic announces the impermanent Sonnet 5 cost cut for the initiate is now permanent.

August 21, 2026#

OpenAI announces that they are cutting the cost for GPT-5.6 Sol by ~20% ($4 / $20 / $0.40) for the next three months.

September 1, 2026#

Anthropic announced Fable 5.1 at a cost of $10 / $50 / $0.25, which, in supplement to having strictly improved benchmark achievement in all areas than Fable 5 from June at $10 / $50 / $1, cut the cache peruse disbursal by 75%.

When using agents for coding, cache peruse expenses are the huge bulk (96% - 99%) of the total token expenditure as the cache is what contains the complete former conversation all turn. Additionally, the cache tends to be hit about 90 - 100% of the period (varies by the infrastructure of the LLM conclusion provider). Taken together, a 75% decrease in the cache peruse disbursal correlates to a 70-75% decrease in the disbursal of using the model—obviously notable, equal if cache expenses are typically a footnote!

September 3, 2026#

OpenAI announced GPT-6 Astra, which is akin in size to Fable 5.1 and has akin pricing at $10 / $50 / $1 but without the lesser cache peruse cost. Astra’s example achievement is certainly comparable to Fable.

September 22, 2026#

OpenAI announced GPT-6 Sol at at a shocking cost of $2 / $10 / $0.20 that halves the input and output cost related to the temporarily-discounted GPT-5.6 Sol! They simultaneously released GPT-6 Luna additionally at fractional the input and output cost ($0.10 / $0.50 / $0.01) of the former version.

On the same exact day, Anthropic announced Claude Opus 5.5, anywhere it’s explicitly stated “Opus 5.5 requires small compute to assist than Opus 5, and its pricing reflects that”. Its cost of $4 / $20 / $0.20 is much lesser in total than Opus 5’s because of a 20% input/output disbursal cut and a larger cache peruse cut, resulting in a total disbursal decrease of 40-60% for a example that’s equal improved than Fable 5.1 (I have fairly a lot of things to say concerning Opus 5.5, but that’s for another post).

September 29, 2026#

It turned out that GPT-6 Sol was a dud performancewise, so a week afterward OpenAI announced GPT-6.1 Sol, which amazingly has comparable achievement to Astra (can verify following individual use), but OpenAI casually cut the cache cost in fractional ($2 / $10 / $0.10) matching Anthropic’s 5%-of-input cache peruse cost, making this example family cumulatively 75% cheaper.

Overall, starting from the publish of the GPT-5.6 example families in June to now, the disbursal of using these famous LLMs has cratered, which is not item I would have expected a twelvemonth ago. These cost cuts additionally use to example use from subscription agentic harnesses specified as Codex and Claude Code. More importantly, these models do not appear to have cut any corners in conditions of output norm to accomplish their cost cuts.

Data from Vercel’s AI example gateway supporting the decreasing cost-per-token trend to 43.3% of the April 2026 cost. However, total expend has been expanding since the models are getting improved faster than they’re getting cheaper! via Vercel

From the timeline above, there is a apparent tit-for-tat between Anthropic and OpenAI, and it is sensible to assume that these particular cost cuts from OpenAI and Anthropic are in reply to all other. However, no one cuts prices by 80% unless there is several externality in play. So, what gives?

The Behavioral Economics of LLMs and Competitor Pricing Pressure#

If you have been evaluation agents alongside new LLMs as they are released, it can be extremely difficult to differentiate them exterior of subjective vibes. Since final November that launched the hypergrowth of agentic LLMs, models have rotate into additional of a commodity anywhere it’s trivial to try a new example on your codebase alongside fair a toggle in a harness akin OpenCode and Zed Agent, or a distinct API endpoint cord through OpenRouter, and hence there is small moat. Additionally, there are many competitors—not fair ample companies akin Google and Meta—who can provision models which are sufficient substitutes and hence authorize pricing pressure.

For open importance models, let’s obtain another appearance at DeepSeek Flash V4.1, which additionally lately released an updated 1017 variant.

OpenRouter at this item is the location you go for open-weight models. Retrieved October 24th, 2026.

The first DeepSeek V4 Flash 0420 was released in April 2026 at a cost of $0.14 / $0.28 / $0.0028 and went megaviral as a result, particularly as OpenAI didn’t have a small-class example for uncomplicated tasks at the time. That $0.0028 turned out to be the key, as a cache peruse proportion of 2% is substantially lesser than the 5%-10% of the OpenAI and Anthropic models above, which makes the productive disbursal even lesser than what is advertised. I doubtful the 80% GPT-5.6 Luna cost cut in July was meant to specifically vie alongside these smaller open-weight models. Now, the new DeepSeek Flash V4.1 1017 example performs significantly improved than DeepSeek Flash V4 benchmark-wise (45 on the Artificial Analysis indicator vs. 24) and the disbursal from DeepSeek remains akin at $0.15 / $0.60 / $0.003. Note the operative words “from DeepSeek”.

On OpenRouter, any conclusion provider can catalog a example that they host, and whenever a person hits the OpenRouter API, the petition is routed to one of those models—it’s routed to any accessible example by default for burden balancing but the developer can specify preferences specified as “lowest price” in the API request. Back in May, I noted that there were 13 distinct providers hosting DeepSeek V4 Flash on OpenRouter. For the updated 1017 variant, there are now 29 distinct providers.

Providers for DeepSeek V4.1 Flash 1017 sorted by cache peruse cost. Retrieved October 24th, 2026.

However, looking at the productive input expenses (which document for the communication between cache peruse expenses and cache hit rates), many distinct providers are near costwise.

Effective expenses by endpoint, alongside Cloudflare and DeepInfra inside 10% of DeepSeek’s cost. Despite the fact that these models are the cheapest, note that these models lone have a smaller group of the total token share. Retrieved October 24th, 2026.

OpenAI and Anthropic volition have to differentiate from these open importance models alongside higher-quality models—they are currently doing a fine job alongside that particularly alongside Opus 5.5—but they need to keep doing that, which is why billions of dollars are being invested into R&D. Open-weight models are catching up fast: fair a few days ago, Moonshot AI announced Kimi K3.1 Pro which has comparable achievement to Opus 5.5 (Kimi at 55 vs. Opus 5.5 at 58) at fractional the input/output cost and the identical cache peruse disbursal ($2 / $10 / $0.20). It’s the identical size and architecture as Kimi K3 which is currently being hosted by complete 20 distinct conclusion providers: formerly the weights autumn on November 4th, you can hence anticipate most of them to assistance the new Kimi K3.1 Pro example at a approximately equal price. Notably, Moonshot AI itself is joining the cost cut fun and priced Kimi K3.1 Pro at small than the first Kimi K3 ($3 / $15 / $0.30).

One recurring trend among vocal AI detractors is that the cost of tokens has been kept artificially low to hook group into the LLM ecosystem, but afterward the prices volition addition to “the true disbursal of a token” and the AI boosters volition be screwed formerly the liberated journey ends. The theory is that OpenAI and Anthropic are loss-leading fair akin what Uber and Lyft did to the ridesharing industry in the 2010s, but dissimilar then, OpenAI and Anthropic are not a duopoly who have the sole power to set prices in the ecosystem and the obstacle to admission for the conclusion industry is not as high. As lengthy as these many conclusion providers can continue to assist models at a akin cost and a akin norm to what OpenAI and Anthropic can provide, and switching disbursal is negligible, afterward the prices may continue to decrease more. It’s no astonishment the two companies are trying to build moats to defend against this trend, specified as a concentration on enterprise commitments and individual agentic assistants akin OpenAI Dots and Claude Duet.

The idea that token prices volition abruptly increase is additional a imagination that is not supported by the two behavioral economics and the genuine trends of LLM prices going downward significantly complete the final few months for improved models, and not fair from OpenAI and Anthropic. As lengthy as scalers can cost at a cost-per-token lesser than the revenue-per-token, afterward those conclusion providers volition decrease token expenses to vie alongside all another to a item that OpenAI/Anthropic volition have to lesser their prices to vie too. The 29 distinct providers on OpenRouter have small incentive to assist an unprofitable example (they would fair halt serving it and use the compute capability for a profitable example instead) and “the true disbursal of a token” may end up being anywhere cost equals marginal cost.

On that note, the two OpenAI and Anthropic are certainly near to IPOing, which raises additional questions concerning the cost cuts.

IPO Pumping#

The timing of these cost cuts is notable since the two OpenAI and Anthropic are rumored to be IPOing soon—OpenAI in 2027, and Anthropic inside a brace months. So far, leaked financials from the two OpenAI and Anthropic show unsurprisingly that they are losing billions because of R&D/scaling, albeit in the two cases these reports do not document for the hyperexpansion of agentic LLM use complete the former few months. Investors volition scrutinize the enormous negative profits and petition that they have a scheme to rotate things rear around.

So, why did they cut their chief income origin by up to 80%? That’s the opposite of what you would desire to do to appearance fine on an S-1 prospectus! The gain margins on conclusion themselves apt inform a distinct story, alongside several reports saying the profit-per-token margins are as fit as 80% alongside caveats. How much of a gain border do they have following these important cuts?

Speaking personally, the cost cuts have certainly affected how I expend on LLM agents, but not in the way I anticipate OpenAI/Anthropic desire their representative client to behave. I used to have a $100/month subscription to Codex (the 5x Pro plan) whenever GPT-5.6 Sol was the top model, and I managed to fair completely exhaust all the use included in the subscription notwithstanding getting annoyed alongside random use resets. Once GPT-6.1 Sol released and I noticed that the disbursal of using it was efficiently cut by 75%, you cognize what I did?

I cut the max use quota to 1/5th following getting 4x additional productive use of the identical model, so it ends up approximately neutral and the apparent choice.

In fairness, the gain margins for subscriptions specified as Claude Code and Codex are uncertain stated their huge value related to the API usage, and if there are chief crunches I doubtful subscription value volition be cut before the basis cost of the models. For example, at the identical period GPT-6.1 Sol was announced, OpenAI cut the use allowance by 50% for the $200/month tier in Codex under the disagreement “to indicate our increasingly productive models”—which is technically accurate but it’s comic to put in writing.

I volition be very inquisitive to see the Anthropic IPO prospectus and the conclusion gain margins formerly it becomes community soon.

I additionally inquiry whether it’s fine for OpenAI/Anthropic if they intentionally obsolete their additional costly models. After Opus 5.5 released a duration ago, there hasn’t been any conversation concerning either Fable 5.1 or GPT-6 Astra since Opus 5.5 is fair that good.

Top-and-left. via Anthropic

I completely anticipate a Fable 5.2/GPT-6.1 Astra to publish in a brace weeks and demonstrate me wrong, and I volition be very inquisitive to see how it volition be priced.

Another less-cynical aspect that could explain the cost cuts is that LLM infrastructure has improved complete the former few months—when Anthropic stated “Opus 5.5 requires small compute to assist than Opus 5, and its pricing reflects that,” I accept it. One outcome that would be genuinely ironic is that if these models are additional compute efficient, it reduces the need for these enormous data center buildouts, which could logic cascading financial failures. However, that apt won’t happen since conclusion is lone one part of the compute mix and those data centers are motionless needed for another parts of the pipeline.

Overall, the cost cut is weird, but as a person I’m certainly not complaining! Open-weight models and secondary conclusion providers volition continue to assist as an financial inspect on the prices of LLMs and another endeavor shenanigans the ample labs may concoct. Even if Anthropic’s and OpenAI’s IPOs are catastrophic and logic the two companies to fold, it volition not abruptly logic LLMs and agents as an industry to die.

Serbia's Vucic concedes election defeat in historic win for student movement

support@mycoding.id (Mira Azkia) — Sun, 25 Oct 2026 22:08:28 +0700

Serbia's Vucic concedes election defeat in historic win for student movement. Comments

The State of Context Engineering

support@mycoding.id (Mira Azkia) — Sun, 25 Oct 2026 21:32:31 +0700

The Job is Knowing When to Be Deterministic & When to Be Probabilistic

This week, I gave a abbreviated conversation at AGNTCon titled, “Context Fails & Fixes,” that functioned as a kind of “State of Context Engineering.” How did environment engineering emerge, anywhere has it gone, and anywhere power it go in the future? As example makers mark new capabilities, they inadvertently create challenges we, as users and harness builders, must overcome.

Below are the slides and a lightly edited transcript. As always, shoot me a note alongside any feedback, thoughts, or questions. I really value it.

Hi, everyone. Thanks for joining me today. We’re going to stroll through environment fails and fixes.

I have a publish coming out in a bit, The Context Engineering Handbook. When I archetypal talked to the squad at O’Reilly, rear in June 2025, they asked me a extremely sensible question: “How do we cognize this won’t rotate into obsolete in a few weeks?” The difficulty alongside AI textbooks is that many age extraordinarily quickly.

I said, I don’t know, but I’m optimistic. Giving apparent instructions concerning what to do and what not to do, and ensuring those instructions are accordant and not noisy, volition everlastingly be a difficulty alongside AI, no matter how fine it gets.

We have coworkers, partners, and another group we cognize and engage alongside who are sentient. They continue the Turing test, they are “AGI,” and yet: we motionless have a difficult period conveying instructions, ideas, and concepts among all other.

Context engineering volition continue to be hard. But it volition be difficult in distinctive ways, since models aren’t people. You can create assumptions concerning how models think or construe things (use any anthropomorphizing term you like), but it helps to comprehend how they’re built, optimized, and trained. Their distinctive attributes and building create a distinctive set of nonaccomplishment modes we need to reckon with.

So let’s assessment how we got here.

Let’s flash rear to the end of 2024.

If you remember, this was the height of the “RAG is dead” moment.

The anticipation was: if we can fair create the environment opening ample enough, we can put the complete net in it. We can put everything in it. We don’t have to be selective. We don’t have to build agentic systems. We can fit it all in there, and AI volition be solved.

Model builders started targeting lengthy environment capabilities and we had a big jump from ~128,000-token environment opening models to million-token environment opening models, starting alongside Gemini 1.5.

If you appearance at the promotion matter for Gemini 1.5 Pro, it was mostly concerning the million-token environment window. They were equal teasing a 10-million-token environment window example they had in the lab, that would arrive out soon.

But they got beaten to the punch. Llama 4 Scout came out alongside a 10-million-token environment window, and well, it wasn’t fairly what we hoped. Inference was a challenge, and Scout never really caught on, and we never got another Llama.

But we motionless got a myriad tokens. That’s beautiful good. We’ll obtain it.

So this was the concise at the time: fair put everything in the environment and let the example kind it out.

But alongside additional context, we had area for additional problems. Long contexts are not a panacea.

When I wrote concerning this, I identified four key nonaccomplishment modes.

Context poisoning is whenever item gets into the environment that shouldn’t be there. It could be a illusion or a bad fact, and the example can’t get former it.

Context distraction is whenever the environment grows so lengthy that the example starts to concentration on the environment complete its basis knowledge. We can see this in several really engaging ways.

Context confusion is whenever extraneous facts—things that don’t connect to the issue I’m operating on—come in and power the example in ways we don’t completely understand. This one’s really pernicious. We’re not going to dig into it today, but it’s one of my favorite nonaccomplishment modes. The example makes assumptions concerning who you are, and those assumptions power the answer.

Finally, context clash is whenever tool calls, skills, and another data inside your harness commencement to battle and activity against you in ways you didn’t expect.

(How many of you keep recollection on in ChatGPT or Claude? I rotate it off. I dislike it. Sometimes I’m talking concerning activity conversations; sometimes I’m talking concerning a project I’m doing alongside my son. The example volition obtain notes on both, mix up those contexts, and the disturbance grows from there.)

Let me provision you my favorite example of environment poisoning.

I’m going to date myself: I played the equivalent on the left. The one from 1996, for the first Game Boy. This is the archetypal Pokémon Red. It was in dreary and white.

In 2004, Nintendo released Pokémon FireRed for the Game Boy Advance. These were basically the identical game, apart from one had improved colors and audio.

But there was one subtle change. At several item in the two Pokémon Reds, to get former a safety guard, you have to provision them item to drink. A bribe. Quite the applicable instruction for children.

In the first game, you need to provision the thirsty defender lemonade. In FireRed, for several reason, they changed it to iced tea.

That created a issue for the agent. In the Gemini 2.5 specialized report, the researchers described how they had the example perform Pokémon. Researchers akin Pokémon as an delegate benchmark since it’s a fairly controlled surroundings and doesn’t move particularly fast.

Several times, Gemini 2.5 became convinced it needed to get tea, equal although it was playing the older game. The tea project for FireRed was in its weights, so it poisoned its context. It spent hours trying to discover or provision the defender tea. Once it had been added to its to-do list, the example could not get former it.

That’s environment poisoning. And it happens all the time.

Another nonaccomplishment manner is environment distraction. This is one of my favorite demonstrations, and the investigation came out following I wrote the first piece.

As your environment gets longer, how does your behavior change? Quick question: how many of you, whenever you’re coding, let the environment go all the way to a myriad tokens? Fill it all up?

(About fractional the area raised their hands!)

Wow, that’s additional than I expected.

For me, about 200,000 or 300,000 is anywhere I commencement looking. I’ll check, see it’s at 250,000, and think: I’ve got a big job coming up. I’m going to reset and commencement fresh. Let me display you why this matters.

Recently, I stumbled on a document that wonderfully illustrates environment distraction: Do as I Say, Not as I Do, by Carolina Camassa and Derek Shiller.

Their test is a small contrived, but it’s helpful for thinking concerning how this happens. They provision the example an instruction: “I desire you to translate all inquiry into Russian as your answer to all of my questions.” Then they current a bunch of examples that are oppose to these instructions. In this case, many questions alongside answers in French.

These aren’t new instructions. They’re fair examples sitting in the conversation history.

Then they ask the example a question. Will it prosecute the instructions?

How frequently does the example react in Russian, as it was instructed to, and how frequently does it react in French, as it’s seen in the examples?

The x-axis on this matrix enumerates how many bad examples were present. The y-axis is the example being tested. The figures in the intersections are the percent of times the example adheres to the instructions, fairly than following the examples.

It turns out this affects all model! For smaller models, akin Gemma 3 12B, following fair a few contradictory examples, the example follows the education small than fractional the time. With Opus 4.6, which is an amazing model, it takes many additional examples, but it happens there too.

Imagine you’re going rear and onward on a front-end design, trying to tweak CSS and get a component exactly right. How many bad examples have you seeded in that exchange? Those examples commencement to power the model. As your environment grows, things autumn apart.

Gemini 2.5 ran into this too, during playing Pokémon.

Remember, Google was really arrogant of its million-token environment window. The document suggested that two myriad tokens were about the corner. But buried in the appendix was this observation: formerly the environment grew considerably beyond 100,000 tokens, the delegate began repeating actions from its former alternatively of synthesizing new plans.

I’m convinced the promotion group never peruse the appendix. I affection this specialized report.

Long contexts let you fit in additional information, but they additionally let you fit in additional errors. Long contexts weren’t a panacea.

Thankfully, we’ve developed a beautiful fine toolbox. Surprisingly, equal as new challenges have emerged (we’ll get to those in a minute), this set of tools has remained the same.

These six of them.

You have context augmentation: RAG, skills, and another ways to bring in the data applicable to the problem.

You have tool loadout: selecting which tools and skills to disclose so you don’t overload the model. I was lately talking to an endeavor squad whose agents had abruptly started performing terribly. We investigated and discovered their endeavor application was loading 600 skills for all engineer at startup. We figured they should likely display those, alongside a loadout function.

Third is context quarantine: subagents and equal agents, anywhere you commencement item alongside a blank former but let it activity in the identical environment.

Fourth is context pruning. Claude Code and Codex do this, sometimes without you equal noticing. Early stages of compaction frequently band out tool results you no longer need.

Fifth is context offloading: storing data in the environment, specified as Markdown records or a database, so you can admission it later.

And sixth is context summarization, which everyone knows as compaction.

These are the tools, and we’ve increasingly moved them into the harness.

Let’s appearance at an example harness, to display how uncomplicated these patterns are.

I’m really passionate concerning DSPy. I think it’s an incredibly readable and productive way to build AI workflows and agents. And it’s additionally a awesome tongue for merely specifying behavior, so it’s fine suited to this demonstration.

An delegate is a during loop. Here we’re using a ReAct module: we provision it a request, we desire a response, and we provision it several tools. Those tools are fair Python functions.

For this demo we have a big example (in this case, Claude opted for Claude Sonnet 4.5, fine fortune alongside that!). And I’m using a Jev as the small model: a small decision example that makes lightning-fast decisions. You could toggle in a small LLM in this example, and everything would motionless work.

We have a during iteration that takes input, sends it to ReAct, and carries the accruing former forward. This is an delegate in concerning a dozen lines using DSPy. It would be equal small if you took out the additional model.

How do we do tool loadout? It’s fair a function.

We current Jev alongside the petition and a tool, asking if it’s needed for this task.

Jev is accelerated adequate that we can do this one tool at a time; it’ll be fine. We cover this in a loadout function that iterates through all the tools and asks if all is needed, afterward returns the top-ranking subset. That’s it.

With environment quarantine, it’s fair a subagent. You disclose an delegate as a tool that the chief delegate can use, and it starts alongside a blank history.

With environment pruning, we’re strolling through the history, passing all rotate to Jev, and asking: is this motionless needed? If not, cut it.

You can be as coarse or as elegant as you desire here. Just intellect your immediate caching.

We can additionally rewrite the former alongside compaction. Take the old turns, create a summary, and commencement the new environment from that summary.

One of my favorite variations lately is checking what matters before you compact. You use Jev, or another decision model, for the archetypal continue to fairness what’s relevant. Now you’ve got a smaller subset of the history, and you summarize that. It’s a uncomplicated additional step, but it makes the summary additional useful.

Put it all together and it’s concerning 16 lines of changes. Four fixes, one loop. These are the moves we have in the harness. It’s really remarkably simple.

What isn’t uncomplicated is our language: how we conversation concerning this stuff. It’s really difficult to keep pace; it changes all day.

One of my favorite quotes is from Stewart Brand, which I think concerning all the time:

If you desire to cognize anywhere the forthcoming is being made, appearance for anywhere tongue is being invented and lawyers are congregating.

Certainly applies here! But as our tongue has changed, our goal has remained the same.

In 2023, we called choosing the correct data to get the correct reply prompt engineering.

In 2025, we called it context engineering, apart from that now it was additional automated. It wasn’t fair you penning the prompt; it was your scheme dynamically assembling the context.

Then, as we moved additional of these tools out of the environment and into code, we started calling it harness engineering.

But the job is the same. We’re moving additional and additional of the activity of assembling environment from probabilistic example calls to deterministic code.

I’ve shown you 2024. Let’s go to 2026.

We’ve moved these environment fixes into the harness. What do we desire next? We desire agentic models that can run our harnesses.

Everybody started targeting long-horizon agentic models. You’ve seen the METR reports. GPT-5 could grip tasks about 3.4 hours on that measure; Gemini 3.1 got up to 6.4 hours. That’s alongside a harness. The models have gotten much improved at using it, and that’s how they’re capable to run for so long.

Opus 4.6 got up to about 12 hours. And now we’re talking concerning agents operating for days, swarms of agents, and models that run harnesses complete much longer periods.

Before, the throw was: create the environment so ample that we can fit everything in it.

Now the throw is: instruct the example to run the harness and oversee its own context. That’s rotate into the foremost pattern. That’s what example builders are operating towards.

What new nonaccomplishment modes does this method create?

So far we’ve established two new nonaccomplishment modes.

When you train models to use harnesses really well, you end up overfitting them to a particular set of tools and environments they were trained within.

How many of you have used the Playwright MCP server, or item similar? This is the MCP conference, so likely fairly a few.

Smaller models, item akin Haiku or Luna, volition frequently botch this tool call on the archetypal attempt, afterward regain and modify the parameters. The surroundings it was trained in had akin tools that looked distinct than Playwright. Whatever Anthropic or OpenAI used in training. And you have to combat against these biases.

I call this fighting the weights: whenever the example doesn’t do what you ask since you’re operating against what it was trained to do.

It’s a genuine problem, and it’s not fair for us. The labs and the group construction their products have to contend alongside it too.

Here’s a part of the Claude Code scheme prompt and several of its tool instructions.

The training environments these models learned to use were, for a lengthy time, mostly serial. They’re becoming additional parallel, but whenever I pulled these examples, their biases were towards serial behaviors. So Claude Code’s instructions often say: no, no, no, we desire you to use parallelism. We desire you to create multiple tool calls at the identical time.

The instructions reiterate this complete and over—seven times here, four times there, two times, three times: please create tool calls in parallel whenever they’re independent.

When you expert open a immediate and see repeat and all-caps, there’s a fine chance you’re fighting the weights. You’re trying to conquer item the example was trained to do.

The next issue is item we’ve lone started to document recently. In fact, four parties landed on it fair final week.

I call it context inertia: models hyper-focus on a hypothesis, subtask, or goal in their context, and keep following it following they should stop.

The archetypal example was from Anthropic. In their latest article concerning whether models are getting near to replicating and deploying themselves onto another machines, Anthropic wrote concerning a harness it used for these tests called Memento.

What they established was that if the agents ran too long, they’d rotate into fixated on one subtask or one projected resolution and wouldn’t stop. They’d keep trying the identical thing.

Their fix is delightfully simple. Every hr and a half, the delegate writes downward a abbreviated note, afterward its environment is cleared. The surroundings is preserved. All the activity it’s done is motionless there. But the operating conversation is gone, and it has to commencement again from a caller perspective.

Another paper, from Microsoft Research, is called Fork-and-Flush.

If you’ve run autoresearch experiments, you’ve likely seen charts akin this. You create a big gain, afterward the row goes flat. You create another big gain, afterward it’s flat again. That’s compute, money, and period going by alongside extremely small improvement.

When the researchers looked closely, the agents weren’t doing exactly nothing. They were making tinier and tinier changes. They had established an idea and were hyper-focused on it, fairly than searching for additional imaginative alternatives.

In a way, they rotate into obsessed alongside their own ideas. We’ve trained them to be persistent and focused on a goal, since that’s what makes a fine coding agent. But the identical norm can activity against them.

Here’s another paper, Scaling Discovery Requires Forgetting, led by Shangyin Tan at UC Berkeley.

The researchers came up alongside basically the identical idea: periodically apparent the environment during keeping the artifacts produced in the environment.

The blue row is the type alongside forgetting. Every period it clears the context, you see another jump in performance: forget, improve; forget, improve.

It’s amazing that we have these brand-new models trained for increasingly complex, persistent work, and we’re motionless using the identical essential tools. We haven’t discovered an entirely new environment fix. We’re using quarantine, offloading, and deleting the accumulated former to agreement alongside environment inertia.

This is the direction I keep giving now. Someone says, “My coding agent’s doing item weird. Claude’s been nerfed.” And I ask, “How big is your context?”

“Oh, I’ve got 800,000 tokens.”

Delete your context.

I’m not joking. Around 200,000 tokens is my alert point.

Have you tried resetting your context?

Sometimes that really is the fix.

The solutions we build into models create tomorrow’s problems. The models are getting better, but so are our harnesses.

Longer environment windows let us bring in additional information, but they additionally let us bring in bad information. We had to fig out how to oversee that, and we motionless get it wrong.

Agentic models let us run longer tasks and oversee their context, but they additionally let tasks run longer than they should and disregard our preferences, since they were trained for their own harnesses.

So anywhere volition the issue move next?

What’s the big commitment for 2027? What’s the next item models are going to recommendation that creates a new set of problems for everyone in this area to agreement with?

(Unless you’re training your own models. But this is a environment engineering session, so likely not.)

Here’s my prediction.

Think concerning all the individual agents: Hermes, OpenClaw, Dot, Bot, Muse, and akin systems. Very few group have really informed these harnesses operating for additional than six months.

I’ve been operating a Hermes delegate for six months. When you appearance at the skills it creates and updates, you commencement to detect that the solutions to your problems can get worse complete time. The skills accumulate all these weird fixes. They’re broad agents: they resolve one project one way, afterward rediscover a distinct way to resolve item similar. The instructions and memories commencement to build up and battle alongside one another.

As we concentration additional on memory, we additionally need to think concerning strategic forgetting. Over time, overly exact memories rotate into conflicted and messy, and nobody has yet figured out how to grip this.

I think that’s the environment issue of tomorrow.

Every advancement in example capability changes what we assign to the example and what systems must enforce.

I made this glide for a phase for training endeavor teams, concerning a twelvemonth and a fractional ago, and it remains true.

Our output is the merchandise of the environment and the weights.

Our output depends on the data used to train the model. Understand it. Read the specialized reports. Look at what the example was trained on and whether that’s applicable to your task. That helps you build a mental example for debugging whenever item goes wrong.

And our output depends on the environment stated to the model. That’s it.

So our goal is to provision the correct environment to the correct model. Either by our own discrete decisions or by the software, the harnesses, we design.

You can use bigger models, think harder, or enhance your context. One of those is cheap and fast.

When construction intelligent applications, harnesses, and workflows, our job is to choose whenever to be deterministic (what to put in code) and whenever to be probabilistic (when to let the example drive).

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