I think E has an incredible mother. It’s something bittersweet for her to be so good that it hurts me by comparison. But at the same time I still have E and I even have her mother in a way, and I’m so grateful for that. We agreed to play until we won one, and it’s not 3:20 AM and we hugged eachother when we finally won.

数ヶ月前から髪型をベリーショートで美容師に頼んでいるが、短くするとサイドがトゲトゲしてしまう髪質のせいで、思っていたような形にうまくまとまらなかった。 まあそんなものかと思い、完璧に気に入っているわけではない髪型にもなんだかんだで慣れていって、何か他の事に夢中になっていれば特に問題がなかった。

昔よく聴いていた、展開の多いポップな曲をネットで見つけ、懐かしさを感じながら次々と再生していった。 もうとっくに耳が受け付けなくなっていて、懐かしさと苦しさを感じながら楽しんでいく。 精神がアンビエントに逆らうときに放出されるエネルギーを感じる。 こういうコンテンツを耳が受け付けなくなるのは構わないが、このエネルギーすら感じなくなる段階が来ると思うと少し怖さを覚える。

洋食屋ではハンバーグとエビフライの定食を頼み、妻はナスとハンバーグの定食を選んでいた。 店内には子供連れではない夫婦が多く、彼らは総称して鮮やかな黒色のように見えた。

試しに、普段通っているチェーン店よりも、薄暗い黄色が強めに出ているパン屋に入ってみた。 最初はフランスパンに目がいったが、妻が食パンが気になると言うので、それを買うことにした。 パン屋の食パンは、二人で食べるには少し多く、その量のわりに賞味期限が短いことが多いが、買わない理由にはぎりぎりならない。 食パンが置かれていた台が良い木のような素材で、良く感じたのは実は下の板なんじゃないのかと思いながら店を後にする。

普段行かないスーパーにも寄ってみた。野菜も肉も鮮度が良く感じたが、いつものスーパーのほうが配置を把握しているという理由で、何も買わずに出た。 その判断から、もうその日の自分には体力が残っていないことに気づく。

妻のお腹が痛くなり、近くの建物のお手洗いに入った。そこは昭和の婦人服店が並ぶアーケードで、駅の地下通路の途中にあるような雰囲気だが、入り口からは地上の光が差し込んでいた。 横に長い建物だったので、妻が戻るまで端まで歩いて折り返すことにした。 通路の途中には、占い館から人間と水晶玉だけを取り除いたような空間があり、「ここはレンタルスペースです」とだけ書かれた看板が置いてある無人の店があった。 今のところ自分がお世話になりそうな店は一つもないことを確認し、妻と合流した。

家に帰るまで半信半疑だったが、昼に買った食パンは、今まで食べた中でもトップクラスの美味しさだった。 焼かなくても良いし、焼いても良い。 食パンを食べたあと、俺はアヒージョを思いつき、夕飯でそのパンの最高点を叩き出した。 家に誰も来たことがないのに、これならホームパーティできるねと二人で喜んだ。

今日もうちの洗濯機は調子が悪く、入れたタオルを何時間もかけて乾かしていた。

A zine chronicling the Conquering the Barbarian Altanis D&D campaign.

This issue details sessions 94, 95, 96, 97, and 98.

Adventurers deal with curses, logistics, death, and webs.

Lots of webs.

You can download the issue here.

Overlord's Annals zine is available as part of the Ever & Anon APA, issue 8:

#Zine

Je vindt iemand heel leuk en je merkt aan alles dat er chemie is.

Maar als je een stap dichterbij zet om deze magie concreet te maken, rent die ander hard weg.

Als je dit intikt in je browser, is er maar één verklaring mogelijk.

De ander heeft een hechtingsstoornis.

Of er is een ingewikkeld zielsverbond aan de hand waar je nu in de ‘runner-chaser’-fase zit.

Of, heel misschien, vindt de ander je gewoon niet zo leuk.

‘Hunnie. That man isn’t busy. He's just not that into you!’

Weet je wat het is?

We leven in een tijd waarin bijna al ons gedrag direct gepsychologiseerd of gespiritualiseerd wordt. We geven de ander meteen een label of een diepere universele betekenis, zodat we de afwijzing niet bij onszelf hoeven te zoeken.

Terwijl we volledig voorbijgaan aan het feit dat liefde gewoon iets heel complex is. Het brengt het mooiste en het slechtste in ons allen naar boven.

Is die stap achteruit van iemand niet gewoon een instinctieve reactie?

Vluchten voor intimiteit hoeft geen stoornis te zijn. Het kan net zo goed een natuurlijke manier van zelfbescherming zijn.

Zelfbescherming tegen de last van de verwachtingen van liefde.

==

Er is een theorie dat sinds de kerk zich heeft teruggetrokken in onze samenleving, we de liefde op het hoogste podium hebben gezet.

Dat is waar we voor leven, waar we zingeving vinden en waar we goddelijkheid ervaren.

Relatie-expert Esther Perel (1958) constateert nuchter dat we al onze verwachtingen in het leven projecteren op één persoon. Je geliefde moet je beste vriend zijn, een goede ouder, leuk omgaan met je vrienden, een romanticus zijn, een seksbeest, een gevoelige prater, een sterke beschermer.

En oh ja, het moet altijd leuk zijn.

Come on! Dat is het recept voor teleurstellingen. Niemand voldoet hieraan. Ook jij niet.

En dan hebben we het nog niet eens over de manische kant van liefde.
(ik gooi er toch maar even een psychologische term in de strijd).

De kracht is verwoestend.

• Je kunt jezelf compleet verliezen in iemand anders.
• Je kunt een leven opbouwen met iemand en alles opeens vernietigen omdat je je hart aan iemand anders verliest.
• Je kunt het contact met jezelf totaal kwijtraken om de relatie te redden.
• Je kunt gek worden van verlangen naar die ene bevestiging.

En dan is er nog dat ongemakkelijke moment, wanneer je in de ogen van de ander ziet hoeveel die van je houdt. Hoe die zich volledig aan jou overgeeft en je begrijpt niet eens zo goed waarom.

Iemands hart krijgen, dat is echt een zware verantwoordelijkheid.

Het lijkt me niet meer dan normaal dat sommige mensen daar zenuwachtig van worden. Van die verwachtingen, van de manie, van de verantwoordelijkheid.

Dat ze liever even iets meer afstand nemen om zichzelf te behouden.

Zoals in de Griekse mythe van Apollo en Daphne.

==

Thomas Moore (1940), een van mijn favoriete spirituele denkers, ziet in de Griekse mythes altijd onze meest menselijke tekortkomingen terug. Een bevestiging dat we niet raar zijn, maar dat onze ziel zich niet zo snel laat vangen.
(pun intended)

Daphne is een vrouw die zich niets aantrekt van de maatschappij. Ze leeft het liefst alleen, midden in de natuur. Omringd met dieren en bloemen en muggen.

Maar dan ziet Apollo met zijn harp haar. Hij wordt verblind door verlangen en wil maar één ding: haar bezitten. Voor Daphne is dat echt het laatste wat ze wil.

Ze heeft helemaal geen zin in zijn aandacht. Dus ze vlucht. Apollo voelt zich daardoor nog meer aangemoedigd om haar te veroveren en gaat erachteraan.

Daphne wordt helemaal koekewous van Apollo. Ze komt maar niet van hem af. Dus besluit ze om te smeken naar een god om haar te helpen.

En die god ziet maar één logische oplossing mogelijk: haar in een boom veranderen.

Een laurier.

En daar staat Apollo dan. Een boom te omarmen (en te berijden, stel ik me zo voor, de smeerlap).

Volgens Moore staat dit verhaal symbool voor de menselijke behoefte om zich terug te trekken, als een statige boom die standhoudt.

Niet iedereen wil zich overgeven aan de liefde of zin vinden in een relatie. Sommige mensen willen gewoon een vast, onbeweeglijk bestaan met zichzelf hebben.

Dat is niet raar.

Het verhaal eindigt best wel tragisch. Als een paradox van verlangen en onbereikbaarheid.

Apollo krijgt niet wat hij wil. En Daphne offert haar vrijheid op om haar autonomie te behouden.

Maar er is ook hoop. Want Daphne geeft haar blaadjes aan Apollo, wat hij aangrijpt om haar voor altijd te eren.

De laurierbladeren worden een symbool van overwinning, eer en prestatie. Tot op de dag van vandaag is het een symbool van respect bij het uitreiken van prijzen en prestaties.

Het is een eerbetoon aan iets dat je nooit volledig kunt bezitten.

Zoals roem.

Of je geliefde.

Tessa vertelde me dat ze was veranderd.

• Geen foute mannen meer in haar leven.
• Geen eenzame avonden meer op het balkon met een joint in haar hand.

Ze zei dat ze aan het groeien was. Dat ze haar ware zelf aan het worden was. Vrij van verdoving. Vrij van bevestiging via anderen.

Haar stem klonk zo hoopvol en in haar ogen zag ik een glinstering van oprechtheid in haar eigen verhaal.

Ze sprak de waarheid, maar toch geloofde ik haar niet.

Omdat woorden woorden zijn. Hoe overtuigend en mooi ze ook klinken. Op het moment dat we ze uitspreken, voelen we ons goed, gemotiveerd en hoopvol.

Maar dat is niet de echte test. Dat is niet de realiteit.

Er zit iets dieps in ons verborgen.

Iets dat we als mens nooit volledig zullen begrijpen. Laat staan dat we het kunnen beheersen. Het zijn verlangens die we niet eens hardop durven uitspreken. Het zijn behoeftes waar we nooit helemaal vanaf komen.

Juist als we zwak zijn, ons gekrenkt voelen, de eenzaamheid opkomt en we uitgeput raken, doen we juist datgene wat we hadden afgezworen nooit meer te doen.

Omdat het zo vertrouwd voelt.

Nee.

Je dacht dat je veranderd was. Totdat je beseft dat je hier al eens bent geweest.

==

Denk maar eens aan hoe vaak je terugkeert naar hetzelfde punt, ook al zitten er soms jaren tussen:

• Weer een conflict op je werk.
• Weer een misverstand met iemand die dichtbij je staat.
• Weer die twijfels over je relatie.
• Weer het gevoel dat je niet goed genoeg bent.
• Weer de angst om iemand kwijt te raken die je lief is.
• Weer die impuls om alles achter je te laten en opnieuw te beginnen.
• Weer jezelf verliezen in iets of iemand om de leegte te vullen.
• Weer dezelfde beloftes aan jezelf die je uiteindelijk toch breekt.
• Weer dat ongemakkelijke gevoel dat je iets belangrijks mist in het leven.

Maar zie deze herhaling niet als een mislukking.

Nee.

Het is een verdieping van je eigen ziel.

Je komt terug bij hetzelfde punt, ja. Dezelfde twijfel, dezelfde drang, dezelfde leegte. Maar de ogen die het aanschouwen, zijn wel veranderd.

Je bent ouder geworden. Je krijgt meer levenservaring. De littekens op je ziel nemen toe. Er komt meer wijsheid in de woorden die je uitspreekt.

Je bent niet verloren. Slechts een beetje verdwaald.

==

Ik weet heel goed waarom Tessa haar donkere patronen achter zich wil laten. Ik heb ook een duidelijk vermoeden waar de pijn vandaan komt. En ik hoop ook echt dat het haar lukt. Oprecht.

Weg met de foute mannen. Weg met de drugs.

Maar ik weet ook dat ze nog heel vaak opnieuw geconfronteerd gaat worden met zichzelf. Want dat is wat het leven doet.

Er zit iets in ons dat zich blijft vastklampen aan dat wat ons pijn deed.

Er zit iets in Tessa dat elke keer opnieuw naar boven gaat komen.

De niet te stoppen drang naar bevestiging van buitenaf als je je minderwaardig voelt. Haar gevoel van machteloosheid wanneer emoties elkaar te snel opvolgen.

Ze hoeft zich niet schuldig te voelen als ze tijdens zo’n moment van zwakte toch weer haar stomme ex appt. Of dat ze in een emotionele periode toch weer in de rij bij de coffeeshop gaat staan.

Maar ik hoop ook dat ze iets anders vindt om te doen als ze zich zo voelt.

Praat niet over spirituele groei of dat je nu echt veranderd bent. Stop met die zelfbevlekking omdat je bepaald gedrag van jezelf zat bent. Nee. Vertel me gewoon wat je de volgende keer gaat proberen als je weer in een dieptepunt zit.

We groeien niet. We verdiepen ons via zelfinzicht.

Ben je bereid te accepteren dat sommige delen van jezelf nooit zullen veranderen?

When some people think of HOA neighborhoods, they picture sameness. The grounds are decorated with rows of plants. The community is free of trash and other debris. The houses look more or less the same. The lawns appear perfectly maintained. The cars are nicely hidden in the garages. And there are white picket fences, two-parent households with 2.5 children and a cat and or a dog.

Oh yeah, and everyone is white.

Let’s not pretend that the origins of Homeowners Associations in the United States wasn’t based on racism. Of course it was! Land developers wrote deed restrictions and covenants built on exclusionary practices: in-group versus out-group. And who could be more “out” in the great USA other than Black people?

Under the guise of “protecting property values,” white people excluded Black people from purchasing homes in white communities; and they created HOAs to accomplish this. To top it off, the federal government backed it. And although Black skin is the easiest to visually discriminate against, there were other groups of people that white people did not want living amongst them:

• Jewish
• Chinese
• Japanese
• Spanish-speakers
• Catholics
• Persons of non European descent
• Non Christians
• Persons of foreign birth

Here’s an example that you can find in the Richard Rothstein’s The Color of Law. It is from the 1920s in a subdivision in Missouri:

“No person of Negro, Mongolian, or Semitic race shall be permitted to purchase, lease, or occupy any lot or dwelling within this subdivision. All lots shall be conveyed only to persons of the Caucasian race.”

Apparently, some of this wording is still present in deeds and CC&Rs across the U.S., but it is just too physically taxing and expensive to purge racist verbiage from the books.

The next time you are looking for homogeneity in a community—I mean when you say you want to live in an HOA community—ask yourself what you are really seeking. Be honest…because it is a myth that they protect property values.

Train Dreams, Clint Bentley 2025

IMDB Film Details

Train Dreams is haunting and poetic, capturing America when the old ways transitioned to the new. Bentley’s story is difficult to place on a historical timeline, leaving only visual hints, such as the introduction of chainsaws to logging or an astronaut orbiting Earth.

Logger Robert Grainier's melancholy journey allows us to witness the lives of those on the Northwest Frontier, from homesteaders to foreign workers to blue-collar philosophers to a feminist intellectual. Robert experiences trauma from white supremacist violence and tragedy from an unforgiving land. The film's message? Rugged individualism was a boondoggle that has always ensnared us.

Watch it.

#Film #Oscars2026 #TrainDreams #ClintBentley #AmericanWest #NickCave #LiteraryAdaptation #MovieReview #Larrys100 #100WordReview #100DaysToOffload

Tags: #música #Delaossa

Estrella es mi canción favorita del disco y no me he dado cuenta de que no ha sonado hasta que he leído un comentario en IG.

No es pose. Es q me daba igual. Es que me he pasado medio concierto sufriendo por alguien a quien no conozco pero sí.

Es solo un concierto. Da igual aunque nos importe tanto a los 17.000. Da igual aunque sea inolvidable por todas las razones equivocadas y también por todas las correctas.

Al empezar yo pensaba en lo bien que sonaba todo. Luego el salto. He dicho automáticamente “Dani, por dios, dinos que estás bien. Una prueba de vida” no sé por qué. Ver no he visto nada. El cerebro procesa cosas inconscientemente. El chico a mi lado izquierdo “tranquila está todo controlado”. Yo no estaba tranquila ni me parecía que nada estuviese controlado.

El mensaje sobre “una incidencia” no me ha parecido muy acertado por ningún lado que lo pienso. Es una gestión muy “masculina” de la situación. Una que no entiendo ni quiero entender. Aspiro a un mundo donde alguien sale y dice “Dani se ha hecho daño. Quiere seguir. Lo va a intentar pero no sabemos”

La gente, de todas formas, ha aprendido a aceptar “pulpo” e “incidencia” como animal de compañía y a esperar como si todo fuese normal.

15 min después Dani sale haciendo como si nada. Zeta me pregunta a mi si estoy bien. Varias veces. Lo seguirá haciendo a lo largo del concierto como si fuese yo la que se ha dado la hostia. Lo q no deja de hacerme gracia. No. No estoy bien. Cuando él grita con dolor “no busqué ser un líder” tengo ganas de chillar. Otra vez lo de siempre. Qué es ser un líder y por qué volvemos a dar por sentadas las lógicas de los señores sobre el liderazgo como algo solitario donde el abandono no existe. El abandono es simplemente necesario. Para los líderes también.

Empieza vulnerable. Le digo a Zeta otra vez lo de la fragilidad. Igual hoy se entiende bien la diferencia. Todos estamos a un milissegundo de rompernos.

Acaba la canción. Saluda y se disculpa por la interrupción. Yo grito a la nada “pero estás bien?”. Dice que se le ha salido el hombro. O sea que no.

Si tu supieras... Pero cómo te lo contamos. Incidencias e interrupciones. Santísimo cristo bendito que decía mi abuelo. Pepe y Vizio son majisimos.

Todos hacemos como si nada. A todos nos sale regular.

Dani sirve vino. En la barra del bar termina y viene la segunda pausa. Otra vez el mensaje sobre la incidencia. 20 min después vuelve con el brazo en cabestrillo. Empieza Bling Bling. Una de mis favoritas. La chaqueta por encima. Quiero creer que le han dado algo para el dolor. Anda que estás tú pa trios ahora mismo, pienso en voz alta.

El concierto no va a ser una mierda ni va a ser histórico ni falta que hace que nada sea histórico. Va a ser el mejor concierto q podías hacer. Y eso es lo único que importa.

Ojos verdes. Todos estamos sobrecompensando en el Movistar Arena. Me parece bonito. No sé si funciona, pero es lo único que podemos hacer.

Seguimos porque él sigue. Y con eso vale. Todo el mundo en la producción está muy nervioso. Pesadillas que no empiezan bien. Que terminan mejor. Rounders. A veces parece que la rabia ayuda. Tengo la edad suficiente como para saber que es un espejismo. Que no te ahorra ni un poco de dolor. Mal agüero.

Algo tiene que pasar. Lo que pasa es que se suben los Space Hammu en bloque. Y Dani empieza a respirar más lento por fin.

Viene entonces Fernando Costa. Estuve mucho tiempo enganchada a Fumando serio. Sigue gustandome muchísimo.

A veces te pasas meses contando cuánto falta para algo y luego ese día nada sale según el plan. La vida es eso. Que dé igual el plan mientras esté lo demás.

Se abre de golpe la puerta del coche. Yo me giro para decir “por favor, alguien puede ir y abrazarle”?

Nosotros no podemos abrazarle así que gritamos su nombre. Pero Jorge si puede. Y le abraza después de cantar Demonios. Bendito seas, Jorge. Desde aquí te lo digo. No sé si Dani podría haber cantado así Pájaros de barro sin ese abrazo.

Y para mi ha sido lo mejor del concierto. El día en que alguien que no es un cantante de la hostia, busca dentro de sus tripas algo y lo encuentra. Y sale. Y tú estás ahí abajo, invisible entre la masa, mirando muy fijo cómo eso ocurre. Inesperado. Ese “que me lleva a tu casa” se clava en alguna parte.

Habría pagado el doble de la entrada por solo ese momento. El triple porque nadie saliese herido en el proceso. Yo y mis ideas peregrinas del arte. Ya tu sabe.

Veneno.

Limón y sal es una salvajada de canción en todas sus versiones. Esta noche quizá un poco más que nunca.

Su gente abrazada en Nueva season. En este momento él ya sabe que lo ha conseguido. Que lo peor ha pasado.

El patio. Mariposas rojas volando desde el cielo, papel de seda doblado en nuestros bolsillos. Still luvin. El mega éxito mundial del disco. Sale Quevedo. Da igual. Que Quevedo me perdone.

Otro amanecer. Sigo diciendo que necesito una versión de esta canción sin Calamardo. Y no pienso disculparme por esto jamás.

Necesito otro amanecer sin calamardo del mismo modo en que necesito un concierto de esta gira en el que nadie resulte herido. Ya veremos cómo y cuándo.

The promise was elegant in its simplicity: AI agents that could work on complex software projects for hours, reasoning through problems, writing code, and iterating toward solutions without constant human supervision. The reality, as thousands of development teams have discovered, involves a phenomenon that researchers have begun calling “context rot,” a gradual degradation of performance that occurs as these agents accumulate more information than they can effectively process. And the strategies emerging to combat this problem reveal a fascinating tension between computational efficiency and code quality that is reshaping how organisations think about AI-assisted development.

In December 2025, researchers at JetBrains presented findings at the NeurIPS Deep Learning for Code workshop that challenged prevailing assumptions about how to manage this problem. Their paper, “The Complexity Trap,” demonstrated that sophisticated LLM-based summarisation techniques, the approach favoured by leading AI coding tools like Cursor and OpenHands, performed no better than a far simpler strategy: observation masking. This technique simply replaces older tool outputs with placeholder text indicating that content has been omitted for brevity, while preserving the agent's reasoning and action history in full.

The implications are significant. A simple environment observation masking strategy halves cost relative to running an agent without any context management, while matching or slightly exceeding the task completion rate of complex LLM summarisation. The researchers found that combining both approaches yielded additional cost reductions of 7% compared to observation masking alone and 11% compared to summarisation alone. These findings suggest that the industry's rush toward ever more sophisticated context compression may be solving the wrong problem.

The Anatomy of Forgetting

To understand why AI coding agents struggle with extended tasks, you need to grasp how context windows function. Every interaction, every file read, every test result, and every debugging session accumulates in what functions as the agent's working memory. Modern frontier models can process 200,000 tokens or more, with some supporting context windows exceeding one million tokens. Google's Gemini models offer input windows large enough to analyse entire books or multi-file repositories in a single session.

But raw capacity tells only part of the story. Research from Chroma Labs has verified a troubling pattern: models that perform brilliantly on focused inputs show consistent performance degradation when processing full, lengthy contexts. In February 2025, researchers at Adobe tested models on what they called a more difficult variant of the needle-in-a-haystack test. The challenge required not just locating a fact buried in lengthy text, but making an inference based on that fact. Leading models achieved over 90% accuracy on short prompts. In 32,000-token prompts, accuracy dropped dramatically.

The Chroma research revealed several counterintuitive findings. Models perform worse when the surrounding context preserves a logical flow of ideas. Shuffled text, with its lack of coherent structure, consistently outperformed logically organised content across all 18 tested models. The researchers found that Claude models exhibited the lowest hallucination rates and tended to abstain when uncertain. GPT models showed the highest hallucination rates, often generating confident but incorrect responses when distracting information was present. Qwen models degraded steadily but held up better in larger versions. Gemini stood out for starting to make errors earlier with wild variations, but Claude models decayed the slowest overall.

No model is immune to this decay. The difference is merely how quickly and dramatically each degrades.

Two Philosophies of Context Management

The industry has coalesced around two primary approaches to managing this degradation, each embodying fundamentally different philosophies about what information matters and how to preserve it.

Observation masking targets the environment observations specifically, the outputs from tools like file readers, test runners, and search functions, while preserving the agent's reasoning and action history in full. The JetBrains research notes that observation tokens make up around 84% of an average SWE-agent turn. This approach recognises that the most verbose and often redundant content comes not from the agent's own thinking but from the systems it interacts with. By replacing older tool outputs with simple placeholders like “Previous 8 lines omitted for brevity,” teams can dramatically reduce context consumption without losing the thread of what the agent was trying to accomplish.

LLM summarisation takes a more comprehensive approach, compressing entire conversation histories into condensed representations. This theoretically allows infinite scaling of turns without an infinitely scaling context, as the summarisation can be repeated whenever limits approach. The yellow-framed square in architectural diagrams represents the summary of previous turns, a distillation that attempts to preserve essential information while discarding redundancy.

The trade-offs between these approaches illuminate deeper tensions in AI system design. Summarisation adds computational overhead, with summarisation calls accounting for up to 7% of total inference cost for strong models according to JetBrains' analysis. More concerning, summaries can mask failure signals, causing agents to persist in unproductive loops because the compressed history no longer contains the specific error messages or dead-end approaches that would otherwise signal the need to change direction.

Factory AI's research on context compression evaluation identified specific failure modes that emerge when information is lost during compression. Agents forget which files they have modified. They lose track of what approaches they have already tried. They cannot recall the reasoning behind past decisions. They forget the original error messages or technical details that motivated particular solutions. Without tracking artefacts, an agent might re-read files it already examined, make conflicting edits, or lose track of test results. A casual conversation can afford to forget earlier topics. A coding agent that forgets it modified auth.controller.ts will produce inconsistent work.

The Recursive Summarisation Problem

Sourcegraph's Amp coding agent recently retired its compaction feature in favour of a new approach called “handoff.” The change came after the team observed what happens when summarisation becomes recursive, when the system creates summaries of summaries as sessions extend.

Among several findings, the Codex team had noted that its automated compaction system, which summarised a session and restarted it whenever the model's context window neared its limit, was contributing to a gradual decline in performance over time. As sessions accumulated more compaction events, accuracy fell, and recursive summaries began to distort earlier reasoning.

Handoff works differently. Rather than automatically compressing everything when limits approach, it allows developers to specify a goal for the next task, whereupon the system analyses the current thread and extracts relevant information into a fresh context. This replaces the cycle of compression and re-summarisation with a cleaner break between phases of work, carrying forward only what still matters for the next stage.

This architectural shift reflects a broader recognition that naive optimisation for compression ratio, minimising tokens per request, often increases total tokens per task. When agents lose critical context, they must re-fetch files, re-read documentation, and re-explore previously rejected approaches. Factory AI's evaluation found that one provider achieved 99.3% compression but scored lower on quality metrics. The lost details required costly re-fetching that exceeded token savings.

The Technical Debt Accelerator

The context management problem intersects with a broader quality crisis in AI-assisted development. GitClear's second-annual AI Copilot Code Quality research analysed 211 million changed lines of code from 2020 to 2024 across a combined dataset of anonymised private repositories and 25 of the largest open-source projects. The findings paint a troubling picture.

GitClear reported an eightfold increase in code blocks containing five or more duplicated lines compared to just two years earlier. This points to a surge in copy-paste practices, with duplication becoming ten times more common. The percentage of code changes classified as “moved” or “refactored,” the signature of code reuse, declined dramatically from 24.1% in 2020 to just 9.5% in 2024. Meanwhile, lines classified as copy-pasted or cloned rose from 8.3% to 12.3% in the same period.

Code churn, which measures code that is added and then quickly modified or deleted, is climbing steadily, projected to hit nearly 7% by 2025. This metric signals instability and rework. Bill Harding, GitClear's CEO and founder, explains the dynamic: “AI has this overwhelming tendency to not understand what the existing conventions are within a repository. And so it is very likely to come up with its own slightly different version of how to solve a problem.”

API evangelist Kin Lane offered a stark assessment: “I don't think I have ever seen so much technical debt being created in such a short period of time during my 35-year career in technology.” This observation captures the scale of the challenge. AI coding assistants excel at adding code quickly but lack the contextual awareness to reuse existing solutions or maintain architectural consistency.

The Google 2025 DORA Report found that a 90% increase in AI adoption was associated with an estimated 9% climb in bug rates, a 91% increase in code review time, and a 154% increase in pull request size. Despite perceived productivity gains, the majority of developers actually spend more time debugging AI-generated code than they did before adopting these tools.

Anthropic's Systematic Approach

In September 2025, Anthropic announced new context management capabilities that represent perhaps the most systematic approach to this problem. The introduction of context editing and memory tools addressed both the immediate challenge of context exhaustion and the longer-term problem of maintaining knowledge across sessions.

Context editing automatically clears stale tool calls and results from within the context window when approaching token limits. As agents execute tasks and accumulate tool results, context editing removes obsolete content while preserving the conversation flow. In a 100-turn web search evaluation, context editing enabled agents to complete workflows that would otherwise fail due to context exhaustion, while reducing token consumption by 84%.

The memory tool enables Claude to store and consult information outside the context window through a file-based system. The agent can create, read, update, and delete files in a dedicated memory directory stored in the user's infrastructure, persisting across conversations. This allows agents to build knowledge bases over time, maintain project state across sessions, and reference previous learnings without keeping everything in active context.

Anthropic's internal benchmarks highlight the impact. Using both the memory tool and context editing together delivers a 39% boost in agent performance on complex, multi-step tasks. Even using context editing alone delivers a notable 29% improvement.

The company's engineering guidance emphasises that context must be treated as a finite resource with diminishing marginal returns. Like humans, who have limited working memory capacity, LLMs have an “attention budget” that they draw on when parsing large volumes of context. Every new token introduced depletes this budget by some amount, increasing the need to carefully curate the tokens available to the model.

Extended Thinking and Deliberate Reasoning

Beyond context management, Anthropic has introduced extended thinking capabilities that enable more sophisticated reasoning for complex tasks. Extended thinking gives Claude enhanced reasoning capabilities by allowing it to output its internal reasoning process before delivering a final answer. The budget_tokens parameter determines the maximum number of tokens the model can use for this internal reasoning.

This capability enhances performance significantly. Anthropic reports a 54% improvement in complex coding challenges when extended thinking is enabled. In general, accuracy on mathematical and analytical problems improves logarithmically with the number of “thinking tokens” allowed.

For agentic workflows, Claude 4 models support interleaved thinking, which enables the model to reason between tool calls and make more sophisticated decisions after receiving tool results. This allows for more complex agentic interactions where the model can reason about the results of a tool call before deciding what to do next, chain multiple tool calls with reasoning steps in between, and make more nuanced decisions based on intermediate results.

The recommendation for developers is to use specific phrases to trigger additional computation time. “Think” triggers basic extended thinking. “Think hard,” “think harder,” and “ultrathink” map to increasing levels of thinking budget. These modes give the model additional time to evaluate alternatives more thoroughly, reducing the need for iterative correction that would otherwise consume context window space.

The Rise of Sub-Agent Architectures

Beyond compression and editing, a more fundamental architectural pattern has emerged for managing context across extended tasks: the sub-agent or multi-agent architecture. Rather than one agent attempting to maintain state across an entire project, specialised sub-agents handle focused tasks with clean context windows. The main agent coordinates with a high-level plan while sub-agents perform deep technical work. Each sub-agent might explore extensively, using tens of thousands of tokens or more, but returns only a condensed, distilled summary of its work.

Gartner reported a staggering 1,445% surge in multi-agent system enquiries from Q1 2024 to Q2 2025, signalling a shift in how systems are designed. Rather than deploying one large LLM to handle everything, leading organisations are implementing orchestrators that coordinate specialist agents. A researcher agent gathers information. A coder agent implements solutions. An analyst agent validates results. This pattern mirrors how human teams operate, with each agent optimised for specific capabilities rather than being a generalist.

Context engineering becomes critical in these architectures. Multi-agent systems fail when context becomes polluted. If every sub-agent shares the same context, teams pay a massive computational penalty and confuse the model with irrelevant details. The recommended approach treats shared context as an expensive dependency to be minimised. For discrete tasks with clear inputs and outputs, a fresh sub-agent spins up with its own context, receiving only the specific instruction. Full memory and context history are shared only when the sub-agent must understand the entire trajectory of the problem.

Google's Agent Development Kit documentation distinguishes between global context (the ultimate goal, user preferences, and project history) and local context (the specific sub-task at hand). Effective engineering ensures that a specialised agent, such as a code reviewer, receives only a distilled contextual packet relevant to its task, rather than being burdened with irrelevant data from earlier phases.

Sub-agents get their own fresh context, completely separate from the main conversation. Their work does not bloat the primary context. When finished, they return a summary. This isolation is why sub-agents help with long sessions. Claude Code can spawn sub-agents, which allows it to split up tasks. Teams can also create custom sub-agents to have more control, allowing for context management and prompt shortcuts.

When Compression Causes Agents to Forget Critical Details

The specific failure modes that emerge when context compression loses information have direct implications for code quality and system reliability. Factory AI's research designed a probe-based evaluation that directly measures functional quality after compression. The approach is straightforward: after compression, ask the agent questions that require remembering specific details from the truncated history. If the compression preserved the right information, the agent answers correctly.

All tested methods struggled particularly with artefact tracking, scoring only 2.19 to 2.45 out of 5.0 on this dimension. When agents forget which files they have modified, they re-read previously examined code and make conflicting edits. Technical detail degradation varied more widely, with Factory's approach scoring 4.04 on accuracy while OpenAI's achieved only 3.43. Agents that lose file paths, error codes, and function names become unable to continue work effectively.

Context drift presents another challenge. Compression approaches that regenerate summaries from scratch lose task state across cycles. Approaches that anchor iterative updates preserve context better by making incremental modifications rather than full regeneration.

The October 2025 Acon framework from Chinese researchers attempts to address these challenges through dynamic condensation of environment observations and interaction histories. Rather than handcrafting prompts for compression, Acon introduces a guideline optimisation pipeline that refines compressor prompts via failure analysis, ensuring that critical environment-specific and task-relevant information is retained. The approach is gradient-free, requiring no parameter updates, making it usable with closed-source or production models.

The Productivity Paradox

These technical challenges intersect with a broader paradox that has emerged in AI-assisted development. Research reveals AI coding assistants increase developer output but not company productivity. This disconnect sits at the heart of the productivity paradox being discussed across the industry.

The researchers at METR conducted what may be the most rigorous study of AI coding tool impact on experienced developers. They recruited 16 experienced developers from large open-source repositories averaging over 22,000 stars and one million lines of code, projects that developers had contributed to for multiple years. Each developer provided lists of real issues, totalling 246 tasks, that would be valuable to the repository: bug fixes, features, and refactors that would normally be part of their regular work.

The finding shocked the industry. When developers were randomly assigned to use AI tools, they took 19% longer to complete tasks than when working without them. Before the study, developers had predicted AI would speed them up by 24%. After experiencing the actual slowdown, they still believed it had helped, estimating a 20% improvement. The objective measurement showed the opposite.

The researchers found that developers accepted less than 44% of AI generations. This relatively low acceptance rate resulted in wasted time, as developers often had to review, test, and modify code, only to reject it in the end. Even when suggestions were accepted, developers reported spending considerable time reviewing and editing the code to meet their high standards.

According to Stack Overflow's 2025 Developer Survey, only 16.3% of developers said AI made them more productive to a great extent. The largest group, 41.4%, said it had little or no effect. Telemetry from over 10,000 developers confirms this pattern: AI adoption consistently skews toward newer hires who use these tools to navigate unfamiliar code, while more experienced engineers remain sceptical.

The pattern becomes clearer when examining developer experience levels. AI can get you 70% of the way, but the last 30% is the hard part. For juniors, 70% feels magical. For seniors, the last 30% is often slower than writing it clean from the start.

The Human Oversight Imperative

The Ox Security report, titled “Army of Juniors: The AI Code Security Crisis,” identified ten architecture and security anti-patterns commonly found in AI-generated code. According to Veracode's 2025 GenAI Code Security Report, which analysed code produced by over 100 LLMs across 80 real-world coding tasks, AI introduces security vulnerabilities in 45% of cases.

Some programming languages proved especially problematic. Java had the highest failure rate, with LLM-generated code introducing security flaws more than 70% of the time. Python, C#, and JavaScript followed with failure rates between 38 and 45%. LLMs also struggled with specific vulnerability types. 86% of code samples failed to defend against cross-site scripting, and 88% were vulnerable to log injection attacks.

This limitation means that even perfectly managed context cannot substitute for human architectural oversight. The Qodo State of AI Code Quality report found that missing context was the top issue developers face, reported by 65% during refactoring and approximately 60% during test generation and code review. Only 3.8% of developers report experiencing both low hallucination rates and high confidence in shipping AI-generated code without human review.

Nearly one-third of all improvement requests in Qodo's survey were about making AI tools more aware of the codebase, team norms, and project structure. Hallucinations and quality issues often stem from poor contextual awareness. When AI suggestions ignore team patterns, architecture, or naming conventions, developers end up rewriting or rejecting the code, even if it is technically correct.

Architectural Decision-Making Remains Human Territory

AI coding agents are very good at getting to correct code, but they perform poorly at making correct design and architecture decisions independently. If allowed to proceed without oversight, they will write correct code but accrue technical debt very quickly.

The European Union's AI Act, with high-risk provisions taking effect in August 2026 and penalties reaching 35 million euros or 7% of global revenue, demands documented governance. AI governance committees have become standard in mid-to-large enterprises, with structured intake processes covering security, privacy, legal compliance, and model risk.

The OWASP GenAI Security Project released the Top 10 for Agentic Applications in December 2025, reflecting input from over 100 security researchers, industry practitioners, and technology providers. Agentic systems introduce new failure modes, including tool misuse, prompt injection, and data leakage. OWASP 2025 includes a specific vulnerability criterion addressing the risk when developers download and use components from untrusted sources. This takes on new meaning when AI coding assistants, used by 91% of development teams according to JetBrains' 2025 survey, are recommending packages based on training data that is three to six months old at minimum.

BCG's research on human oversight emphasises that generative AI presents risks, but human review is often undermined by automation bias, escalation roadblocks, and evaluations based on intuition rather than guidelines. Oversight works when organisations integrate it into product design rather than appending it at launch, and pair it with other components like testing and evaluation.

Emerging Patterns for Production Systems

The architectural patterns emerging to address these challenges share several common elements. First, they acknowledge that human oversight is not optional but integral to the development workflow. Second, they implement tiered review processes that route different types of changes to different levels of scrutiny. Third, they maintain explicit documentation that persists outside the agent's context window.

The recommended approach involves creating a context directory containing specialised documents: a Project Brief for core goals and scope, Product Context for user experience workflows and business logic, System Patterns for architecture decisions and component relationships, Tech Context for the technology stack and dependencies, and Progress Tracking for working features and known issues.

This Memory Bank approach addresses the fundamental limitation that AI assistants lose track of architectural decisions, coding patterns, and overall project structure as project complexity increases. By maintaining explicit documentation that gets fed into every AI interaction, teams can maintain consistency even as AI generates new code.

The human role in this workflow resembles a navigator in pair programming. The navigator directs overall development strategy, makes architectural decisions, and reviews AI-generated code. The AI functions as the driver, generating code implementations and suggesting refactoring opportunities. The critical insight is treating AI as a junior developer beside you: capable of producing drafts, boilerplate, and solid algorithms, but lacking the deep context of your project.

Research from METR shows AI task duration doubling every seven months, from one-hour tasks in early 2025 to eight-hour workstreams by late 2026. This trajectory intensifies both the context management challenge and the need for architectural oversight. When an eight-hour autonomous workstream fails at hour seven, the system needs graceful degradation, not catastrophic collapse.

The Hierarchy of Memory

Sophisticated context engineering now implements hierarchical memory systems that mirror human cognitive architecture. Working memory holds the last N turns of conversation verbatim. Episodic memory stores summaries of distinct past events or sessions. Semantic memory extracts facts and preferences from conversations and stores them separately for retrieval when needed.

Hierarchical summarisation compresses older conversation segments while preserving essential information. Rather than discarding old context entirely, systems generate progressively more compact summaries as information ages. Recent exchanges remain verbatim while older content gets compressed into summary form. This approach maintains conversational continuity without consuming excessive context.

Claude Code demonstrates this approach with its auto-compact feature. When a conversation nears the context limit, the system compresses hundreds of turns into a concise summary, preserving task-critical details while freeing space for new reasoning. Since version 2.0.64, compacting is instant, eliminating the previous waiting time. When auto-compact triggers, Claude Code analyses the conversation to identify key information worth preserving, creates a concise summary of previous interactions, decisions, and code changes, compacts the conversation by replacing old messages with the summary, and continues seamlessly with the preserved context.

However, the feature is not without challenges. Engineers have built in a “completion buffer” giving tasks room to finish before compaction, eliminating disruptive mid-operation interruptions. The working hypothesis is that Claude Code triggers auto-compact much earlier than before, potentially around 64-75% context usage versus the historical 90% threshold.

The emerging best practice involves using sub-agents to verify details or investigate particular questions, especially early in a conversation or task. This preserves context availability without much downside in terms of lost efficiency. Each sub-agent gets its own context window, preventing any single session from approaching limits while allowing deep investigation of specific problems.

Balancing Efficiency and Quality

The trade-offs between computational efficiency and code quality are not simply technical decisions but reflect deeper values about the role of AI in software development. Organisations that optimise primarily for token reduction may find themselves paying the cost in increased debugging time, architectural inconsistency, and security vulnerabilities. Those that invest in comprehensive context preservation may face higher computational costs but achieve more reliable outcomes.

Google's 2024 DORA report found that while AI adoption increased individual output by 21% more tasks completed and 98% more pull requests merged, organisational delivery metrics remained flat. More concerning, AI adoption correlated with a 7.2% reduction in delivery stability. The 2025 DORA report confirms this pattern persists. Speed without stability is accelerated chaos.

Forecasts predict that on this trajectory, 75% of technology leaders will face moderate to severe technical debt by 2026. The State of Software Delivery 2025 report found that despite perceived productivity gains, the majority of developers actually spend more time debugging AI-generated code. This structural debt arises because LLMs prioritise local functional correctness over global architectural coherence and long-term maintainability.

Professional developers do not vibe code. Instead, they carefully control the agents through planning and supervision. They seek a productivity boost while still valuing software quality attributes. They plan before implementing and validate all agentic outputs. They find agents suitable for well-described, straightforward tasks but not complex tasks.

The Discipline That Enables Speed

The paradox of AI-assisted development is that achieving genuine productivity gains requires slowing down in specific ways. Establishing guardrails, maintaining context documentation, implementing architectural review, and measuring beyond velocity all represent investments that reduce immediate output. Yet without these investments, the apparent gains from AI acceleration prove illusory as technical debt accumulates, architectural coherence degrades, and debugging time compounds.

The organisations succeeding with AI coding assistance share common characteristics. They maintain rigorous code review regardless of code origin. They invest in automated testing proportional to development velocity. They track quality metrics alongside throughput metrics. They train developers to evaluate AI suggestions critically rather than accepting them reflexively.

Gartner predicts that 40% of enterprise applications will embed AI agents by the end of 2026, up from less than 5% in 2025. Industry analysts project the agentic AI market will surge from 7.8 billion dollars today to over 52 billion dollars by 2030. This trajectory makes the questions of context management and human oversight not merely technical concerns but strategic imperatives.

The shift happening is fundamentally different from previous developments. Teams moved from autocomplete to conversation in 2024, from conversation to collaboration in 2025. Now they are moving from collaboration to delegation. But delegation without oversight is abdication. The agents that will succeed are those designed with human judgment as an integral component, not an afterthought.

The tools are genuinely powerful. The question is whether teams have the discipline to wield them sustainably, maintaining the context engineering and architectural oversight that transform raw capability into reliable production systems. The future belongs not to the organisations that generate the most AI-assisted code, but to those that understand when to trust the agent, when to question it, and how to ensure that forgetting does not become the defining characteristic of their development process.

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References and Sources

1. JetBrains Research, “The Complexity Trap: Simple Observation Masking Is as Efficient as LLM Summarization for Agent Context Management,” NeurIPS 2025 Deep Learning for Code Workshop (December 2025). https://arxiv.org/abs/2508.21433

2. JetBrains Research Blog, “Cutting Through the Noise: Smarter Context Management for LLM-Powered Agents” (December 2025). https://blog.jetbrains.com/research/2025/12/efficient-context-management/

3. Chroma Research, “Context Rot: How Increasing Input Tokens Impacts LLM Performance” (2025). https://research.trychroma.com/context-rot

4. Anthropic, “Managing context on the Claude Developer Platform” (September 2025). https://www.anthropic.com/news/context-management

5. Anthropic, “Effective context engineering for AI agents” (2025). https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents

6. Anthropic, “Building with extended thinking” (2025). https://docs.claude.com/en/docs/build-with-claude/extended-thinking

7. Factory AI, “Evaluating Context Compression for AI Agents” (2025). https://factory.ai/news/evaluating-compression

8. Amp (Sourcegraph), “Handoff (No More Compaction)” (2025). https://ampcode.com/news/handoff

9. METR, “Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity” (July 2025). https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/

10. Qodo, “State of AI Code Quality Report” (2025). https://www.qodo.ai/reports/state-of-ai-code-quality/

11. Veracode, “GenAI Code Security Report” (2025). https://www.veracode.com/blog/genai-code-security-report/

12. Ox Security, “Army of Juniors: The AI Code Security Crisis” (2025). Referenced via InfoQ.

13. OWASP GenAI Security Project, “Top 10 Risks and Mitigations for Agentic AI Security” (December 2025). https://genai.owasp.org/2025/12/09/owasp-genai-security-project-releases-top-10-risks-and-mitigations-for-agentic-ai-security/

14. Google DORA, “State of DevOps Report” (2024, 2025). https://dora.dev/research/

15. GitClear, “AI Copilot Code Quality: 2025 Data Suggests 4x Growth in Code Clones” (2025). https://www.gitclear.com/ai_assistant_code_quality_2025_research

16. Gartner, Multi-agent system enquiry data (2024-2025). Referenced in multiple industry publications.

17. BCG, “You Won't Get GenAI Right if Human Oversight is Wrong” (2025). https://www.bcg.com/publications/2025/wont-get-gen-ai-right-if-human-oversight-wrong

18. JetBrains, “The State of Developer Ecosystem 2025” (2025). https://blog.jetbrains.com/research/2025/10/state-of-developer-ecosystem-2025/

19. Stack Overflow, “2025 Developer Survey” (2025). https://survey.stackoverflow.co/2025/

20. Google Developers Blog, “Architecting efficient context-aware multi-agent framework for production” (2025). https://developers.googleblog.com/architecting-efficient-context-aware-multi-agent-framework-for-production/

21. Faros AI, “Best AI Coding Agents for 2026” (2026). https://www.faros.ai/blog/best-ai-coding-agents-2026

22. Machine Learning Mastery, “7 Agentic AI Trends to Watch in 2026” (2026). https://machinelearningmastery.com/7-agentic-ai-trends-to-watch-in-2026/

23. Arxiv, “Acon: Optimizing Context Compression for Long-horizon LLM Agents” (October 2025). https://arxiv.org/html/2510.00615v1

24. ClaudeLog, “What is Claude Code Auto-Compact” (2025). https://claudelog.com/faqs/what-is-claude-code-auto-compact/

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Tim Green UK-based Systems Theorist and Independent Technology Writer

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

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

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

In Summary: * Wow! What an incredible win! IU wins in double-overtime, 98 to 97! Now to relax myself down and work through my night prayers. Hoping for a long, restful sleep tonight.

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

Health Metrics: * bw= 221.68 lbs. * bp= 140/81 (74)

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

Diet: * 08:15 – nacho chips with cheese sauce * 11:30 – Big buffet meal at Lin's * 14:55 – meat loaf, white bread

Activities, Chores, etc.: * 07:50 – bank accounts activity monitored * 08:05 – read, pray, follow news reports from various sources, surf the socials * 09:35 – listening to streaming “Happy Morning Jazz” * 10:30 – Go to bank, take care of business * 11:30 – lunch with the wife at a favorite restaurant * 13:00 – shopping with the wife * 15:20 – tuned into The Flagship Station for IU Sports for the Pregame Show ahead of this evening's men's basketball game between my Indiana University Hoosiers and the UCLA Bruins

Chess: * 17:00 – moved in all pending CC games

Mark 14 is not just a chapter about betrayal and arrest. It is the chapter where love is weighed against fear, where devotion is measured against convenience, and where silence becomes louder than sermons. It is the night where everything holy is pressed under the full weight of human weakness, and yet nothing holy breaks. This chapter does not rush. It lingers. It walks slowly through rooms heavy with perfume and heavier with plotting. It moves from a table of friendship to a garden of agony, and every step feels intentional, as if God Himself is making sure we notice how fragile people become when the cost of obedience finally becomes visible.

The story begins in tension. The religious leaders are already decided. They want Jesus gone, but they want Him gone quietly. Not during the feast. Not in public. Not in a way that causes unrest. Their fear of the people reveals how much of their authority depends on appearances. They believe themselves righteous, yet they scheme like thieves. They wear robes of holiness while whispering plans of murder. This is not accidental. Scripture does not soften this contrast. It shows us how religion without surrender can become more dangerous than open rebellion. They are not attacking Jesus because He is immoral. They are attacking Him because He exposes them. Truth is not usually hated for being false. It is hated for being inconvenient.

Then suddenly, the story shifts from men plotting in the shadows to a woman kneeling in the light. A jar of perfume. Spikenard. Rare. Expensive. Years of value sealed in a fragile container. She breaks it. Not carefully. Not a drop at a time. She breaks it open. That sound alone must have startled the room. The fragrance fills everything. It interrupts conversation. It marks the moment as something that cannot be taken back. This is not a practical gift. This is not a cautious act. This is devotion without calculation. And immediately, the criticism comes. It always does. Someone says it could have been sold. Someone says it could have helped the poor. Someone measures worship with a calculator.

Jesus does not join their math. He joins her meaning. He calls her act beautiful. Not efficient. Not strategic. Beautiful. He connects her gesture to His burial. She anoints Him while He is still alive because soon He will not be. Her love reaches into the future before the disciples do. They argue theology and budgets. She recognizes mortality. She honors Him while she still can. And Jesus says something staggering. Wherever the gospel is preached, her story will be told. In other words, love that looks wasteful to the world becomes eternal to God. This woman does not preach. She does not argue. She does not organize. She pours. And her pouring becomes part of the gospel itself.

Immediately after this act of devotion, Judas goes to betray Him. The contrast is deliberate. One pours out treasure. One sells out truth. One gives without counting. One counts coins. One recognizes the worth of Jesus. One prices Him. The gospel does not hide the ugliness of this exchange. Judas is not forced. He chooses. He looks at the same Jesus the woman honored and decides He is worth silver instead of surrender. The tragedy is not just betrayal. The tragedy is proximity. Judas has walked with Him. Heard Him. Watched Him heal. And still trades Him away. This is not ignorance. This is disappointment hardened into decision.

Then comes the Passover meal. The setting is sacred. The story is ancient. Israel’s history is thick in the air. The lamb. The blood. The deliverance. And now Jesus sits among His disciples and redefines the symbols. He says one of them will betray Him. They do not immediately point at Judas. They look inward. One by one they ask, “Is it I?” That question matters. It reveals that even those closest to Jesus do not trust themselves fully. They know their weakness. They sense something dark in the room and cannot assume they are immune. That is humility. That is realism. Faith is not pretending you are incapable of failure. Faith is staying near the One who can hold you up when you fall.

Jesus takes bread and breaks it. He gives thanks for what is about to become suffering. He calls it His body. He takes the cup and calls it His blood. Covenant language. Not symbolic poetry. Relational promise. This is not only about sacrifice. It is about connection. His life poured into them. Their lives carried in Him. Communion is not a ritual of distance. It is a declaration of closeness. Yet even here, betrayal sits at the table. Grace is offered in the presence of treachery. Jesus does not stop the meal. He does not cancel the covenant. He does not withhold Himself because someone will reject Him. Love is still offered, even when it will not be received.

After the meal, they sing. Imagine that. They sing after hearing that betrayal is coming. They sing after being told that blood will be shed. There is something defiant about that. Worship in the shadow of death is not denial. It is trust. Then Jesus tells them they will all fall away. Peter, strong and loud and certain, insists he will not. Even if everyone else does, he says, he will not. Jesus does not argue loudly. He simply predicts gently. Before the rooster crows twice, Peter will deny Him three times. This is not condemnation. It is knowledge. Jesus sees the coming fear. He sees the human instinct to survive. And He names it.

They go to Gethsemane. The garden. A place of prayer. A place of oil presses. A place where olives are crushed to release what is inside them. The symbolism is unbearable. Jesus brings Peter, James, and John further in. He does not want to be alone. That itself matters. The Son of God wants human company in His sorrow. He says His soul is overwhelmed to the point of death. He asks them to watch. To stay awake. To be present. This is not about protection. It is about companionship. And then He goes a little farther and falls to the ground.

Here, the chapter slows into agony. Jesus prays. Abba. Father. Intimacy and terror in the same breath. He acknowledges God’s power. All things are possible for You. Then He asks. Remove this cup from Me. This is not a lack of faith. This is honesty. The cup is suffering. The cup is separation. The cup is judgment. And He does not pretend it is light. He does not romanticize it. He does not rush past it. He asks if there is another way. And then He submits. Not what I will, but what You will. This is the heart of obedience. Not silence. Not stoicism. But surrender after struggle.

He returns and finds them sleeping. He wakes them. He warns them. Watch and pray so that you do not fall into temptation. The spirit is willing, but the flesh is weak. This is not an insult. It is a diagnosis. Desire alone is not enough. Intention is not enough. Proximity is not enough. Without prayer, the body will choose rest over righteousness every time. He goes back and prays again. Same words. Same agony. This is not repetition for drama. It is persistence in pain. And again He finds them sleeping. Their eyes are heavy. Their loyalty is sincere but exhausted.

The third time, He says it is enough. The hour has come. The betrayer is near. And then Judas arrives. With a crowd. With swords and clubs. The irony is thick. They come armed for a teacher. They bring weapons against the Word. Judas approaches and kisses Him. A sign of friendship turned into a signal of capture. Betrayal does not always look violent. Sometimes it looks affectionate. Someone strikes with a sword and cuts off an ear. Jesus does not praise the defense. He questions the arrest. Why come like this? Why not arrest Me in the temple? But this happens so Scripture may be fulfilled. He is not surprised. He is not outmaneuvered. He is not trapped. He is obedient.

Then they all flee. Every single one. The men who swore loyalty scatter into the night. A young man runs away naked, leaving his garment behind. Even the unnamed cannot hold on. Jesus is alone now. Not because He was abandoned by God, but because He has stepped fully into the place of humanity’s isolation. The Son stands where we fall. He walks where we run. He stays when we scatter.

He is taken to the high priest. False witnesses come. Their stories do not agree. Lies are clumsy. Truth does not need coordination. They accuse Him of destroying the temple and rebuilding it. He stays silent. Then the high priest asks directly if He is the Christ, the Son of the Blessed. Jesus answers plainly. I am. And you will see the Son of Man seated at the right hand of Power and coming with the clouds of heaven. It is not defiance. It is revelation. They tear their clothes. They call it blasphemy. They spit on Him. They blindfold Him. They beat Him. They mock Him and demand prophecy. The hands that healed are tied. The mouth that taught is struck. The face that revealed God is covered.

Meanwhile, Peter follows at a distance. Close enough to watch. Far enough to deny. He sits by the fire with servants. A servant girl recognizes him. He denies it. Another does. He denies again. Others accuse him. He swears. He curses. He insists he does not know Him. And then the rooster crows. And Peter remembers. And he breaks. The bravest voice becomes the bitterest weeping. This is not cowardice alone. This is shattered self-image. Peter believed he was stronger than this. The collapse hurts because it exposes the truth about him.

Mark 14 does not resolve this pain yet. It ends with failure and silence and sorrow. It leaves Jesus beaten and Peter broken. It does not rush to resurrection. It makes us sit in the night. Because before victory, there is surrender. Before redemption, there is cost. Before forgiveness, there is fracture. This chapter teaches us that devotion and betrayal can exist in the same room. That prayer can be met with sleep. That promises can evaporate under pressure. And yet, none of this stops the mission. None of this surprises God. None of this cancels grace.

What makes this chapter unbearable is not just what is done to Jesus. It is what is revealed about us. We see ourselves in the woman with perfume. We see ourselves in Judas. We see ourselves in Peter. We see ourselves asleep in the garden. We see our good intentions and our weak follow-through. We see our love mixed with fear. Our worship mixed with calculation. Our courage mixed with self-preservation.

And still, Jesus walks forward.

He does not turn back from the table. He does not withdraw from the garden. He does not retract His words. He does not refuse the cup. He does not curse His betrayer. He does not abandon His disciples in return. He stands. He answers. He endures. He submits.

This is not the story of men being faithful. It is the story of God being faithful when men are not.

Mark 14 does not let us stay distant. It does not allow us to read like spectators watching history through glass. It pulls us inside the room. It places us at the table. It sets us in the garden. It warms us by the fire. And it forces us to ask not what Judas did or what Peter failed to do, but what kind of people we are when pressure replaces theory and fear replaces promise.

There is a reason this chapter does not end with a sermon. It ends with weeping. Peter does not receive a lesson yet. He receives a sound. A rooster. A memory. A sentence Jesus spoke earlier in the night. And that sound becomes a mirror. He sees himself clearly for the first time. Not as the brave disciple. Not as the bold spokesman. But as a man who loves Jesus and still chose himself when the moment came. That is the wound Mark leaves open at the end of the chapter. Not because God delights in shame, but because healing never begins with pretending.

One of the hardest truths in Mark 14 is that devotion does not erase vulnerability. The disciples truly loved Jesus. They had left their lives for Him. They had seen miracles. They had confessed Him as Messiah. And still, when fear showed up, love alone did not keep them standing. This does not mean love was fake. It means love had not yet been perfected by sacrifice. They had not yet learned how to lose something for Him. They had only learned how to gain something from Him. They followed Him while He healed and taught and fed and amazed. They had not yet followed Him into suffering.

This is where the woman with the perfume becomes more than a character. She becomes a prophecy of discipleship. She gives something that will not be replaced. She pours out something she cannot get back. The disciples argue about utility. She embodies surrender. The leaders plan murder. She prepares burial. And Jesus says she understands what others do not. She recognizes that love is not proven by intention but by cost. In a room full of men who will swear loyalty and run, a woman says nothing and gives everything.

Mark 14 is brutally honest about how close faith and failure live to each other. Judas does not betray Jesus from a distance. He betrays Him from a seat at the table. Peter does not deny Jesus in a palace. He denies Him by a fire meant to warm him. The disciples do not fall asleep in a tavern. They fall asleep while Jesus is praying for them. These details are not random. They tell us that collapse usually happens near holy places. It happens near prayer, not far from it. It happens near worship, not far from it. It happens when familiarity dulls urgency and comfort dulls watchfulness.

Jesus does not scold them for being tired. He diagnoses them. The spirit is willing, but the flesh is weak. This is one of the most compassionate sentences in the entire gospel. He does not say the spirit is fake. He does not say their love is a lie. He says their bodies are not built to carry temptation without prayer. He exposes the real battle. Not between devotion and betrayal, but between vigilance and exhaustion. Between attention and distraction. Between surrender and sleep.

The tragedy is not that they sleep. The tragedy is that they sleep while Jesus is choosing obedience over escape. They rest while He wrestles. They dream while He bleeds sweat. They close their eyes while He opens His will. This is not cruelty. It is humanity. But it is also warning. Love without endurance becomes collapse under pressure. Faith without prayer becomes panic when threatened.

And yet, Jesus does not wake them to shame them. He wakes them to prepare them. Watch and pray, He says, so you will not fall into temptation. He is not protecting Himself. He is protecting them. He already knows He will be arrested. He already knows He will be beaten. He already knows He will be killed. His concern is not His suffering but their collapse. He prays for Himself and warns them for them.

This is the paradox of Mark 14. Jesus is the one about to suffer, and He is still shepherding. He is about to be abandoned, and He is still teaching. He is about to be betrayed, and He is still loving. Even in His agony, His instinct is not self-preservation. It is preparation for others.

When Judas arrives, Jesus does not flinch. He does not argue. He does not expose him publicly. He lets betrayal complete its path. This is not weakness. It is authority. A man who is afraid fights. A man who is surrendered stands. Jesus is not overpowered. He is obedient. This is why He says the Scriptures must be fulfilled. He does not frame His arrest as failure. He frames it as purpose.

One of the most disturbing details is that the violence comes from a disciple, not an enemy. Someone draws a sword. Someone swings. Someone tries to defend Him. And Jesus does not praise it. He does not bless the blade. He does not call it loyalty. He calls it misunderstanding. Violence is the instinct of panic, not faith. The kingdom is not protected by steel. It is revealed by surrender. This is the moment where human strength tries to save divine will, and Jesus stops it. The cross is not an accident to be avoided. It is the mission to be fulfilled.

Then the disciples flee. This is not symbolic. It is physical. They run. The ones who swore never to abandon Him abandon Him in seconds. The young man running naked into the night becomes a picture of total loss. Even identity is stripped away. Fear leaves nothing intact. When survival becomes the only goal, dignity disappears. Promises evaporate. Community dissolves. And Jesus stands alone.

This is the heart of the chapter. Not that Jesus is arrested. But that He is arrested alone. He has no human shield. No loyal companion. No voice in His defense. The ones who said they would die with Him cannot even stay with Him. This is not to humiliate them. It is to show us that salvation is not a team effort. It is not a group project. It is not a shared burden. It is a solitary obedience. No one else can carry it. No one else can complete it. No one else can suffer it. This is why grace is grace. Because it is not built on our ability to stand with Him, but on His ability to stand for us.

At the trial, lies collapse under their own weight. Witnesses disagree. Accusations fail. And Jesus says nothing. This silence is not fear. It is restraint. Truth does not scramble. Truth waits. When He finally speaks, He does not defend Himself. He reveals Himself. I am, He says. And you will see the Son of Man seated at the right hand of Power. He does not deny His identity to avoid pain. He confirms it knowing it will cause pain. This is courage without noise. Authority without weapons. Power without escape.

They spit on Him. They blindfold Him. They strike Him. They mock Him. And the gospel does not rush past it. It forces us to see how easily humanity brutalizes holiness. The same mouth that taught them is now covered. The same hands that healed them are now hit. This is what happens when light enters systems built on shadow. It is not welcomed. It is punished.

Meanwhile, Peter is warming himself. This is not a small detail. Fire represents comfort. Safety. Belonging. He sits with the very people who are destroying Jesus. Not because he agrees, but because he is afraid to be alone. This is where denial is born. Not in hatred, but in fear of isolation. He denies Jesus not because he suddenly despises Him, but because he wants to survive. This is the most human betrayal of all.

And when the rooster crows, Peter does not curse God. He curses himself. He weeps. This is not repentance yet. This is devastation. He realizes he is not who he thought he was. The strength he trusted collapses. The courage he claimed dissolves. The loyalty he boasted disappears. And Mark leaves him there. Crying. Not restored yet. Not forgiven yet. Just exposed.

This is important. Because too often we rush past this moment. We skip to resurrection. We skip to forgiveness. We skip to commissioning. But Mark 14 ends in the wound. Because transformation requires truth. Peter will only become the man who preaches boldly later because this night destroys the illusion of his own strength. He cannot lead from pride anymore. He will lead from mercy.

Mark 14 teaches us that failure is not the opposite of discipleship. It is part of its formation. The chapter is not about perfect followers. It is about faithful purpose in the face of failing people. Jesus is not surprised by their collapse. He predicts it. He walks into it. He absorbs it. And He still goes forward.

This is why this chapter matters so much for ordinary faith. Because most of us do not betray Jesus with silver. We betray Him with silence. With comfort. With fear. With self-protection. We do not shout His name in the garden when it costs us safety. We deny Him quietly by the fire of social acceptance. We say we do not know Him when association would cost us something. We choose warmth over witness. We choose survival over surrender.

And yet, this chapter does not teach us that God is fragile. It teaches us that God is faithful. Jesus does not retreat because His disciples fail. He does not rewrite the plan because Judas sells Him. He does not abandon humanity because humanity abandons Him. He continues. That is the gospel inside this night. Not that humans hold on to God. But that God holds on to His mission even when humans fall apart.

The woman with the perfume shows us what love looks like. Judas shows us what disappointment becomes when it is not healed. Peter shows us what fear does to confidence. The disciples show us what exhaustion does to resolve. And Jesus shows us what obedience looks like when it is not easy, not safe, and not celebrated.

This chapter also teaches us something about prayer. Jesus prays three times. He asks honestly. He submits completely. He does not receive removal of the cup. He receives strength to drink it. This is the pattern of mature prayer. Not demanding escape, but asking for alignment. Not begging for comfort, but surrendering to calling. Not forcing outcomes, but yielding will.

The disciples, meanwhile, do not pray. They sleep. And when the trial comes, they collapse. This is not coincidence. Watch and pray, Jesus said. The reason they fall is not because they are evil. It is because they are unprepared. Prayer is not religious decoration. It is survival training. It is how the spirit stays awake while the body grows tired.

Mark 14 is therefore not just about what happened. It is about how we live. It asks us where we pour our perfume and where we count our coins. It asks us where we stay awake and where we sleep. It asks us whether we follow at a distance or stand in the garden. It asks us whether we warm ourselves by the fire of comfort or walk with Christ into the cold of obedience.

And it tells us something else that is easy to miss. Jesus does not discard His disciples because of this night. He does not choose new ones. He does not start over. He redeems them. The same Peter who denies Him will be the one He restores. The same men who flee will be the ones He sends. Their failure does not disqualify them. It reshapes them.

This is the deepest comfort of Mark 14. It shows us that God does not need our perfection to complete His purpose. He needs our surrender. The disciples fail loudly. Jesus succeeds quietly. And grace is born in between.

Mark 14 is the night where love chooses to bleed instead of break. It is the chapter where obedience walks into darkness without applause. It is the story of a Savior who knows exactly who will fail Him and still offers them bread. Still offers them covenant. Still offers them Himself.

This is not a chapter meant to make us feel strong. It is meant to make us feel honest. It strips away the fantasy of heroic faith and replaces it with something deeper: dependent faith. Faith that does not trust its own courage. Faith that clings to Christ’s obedience instead.

Because in the end, the gospel is not that Peter was loyal. The gospel is that Jesus was faithful.

And when the rooster crows in our own lives, when we realize who we are not, this chapter whispers who He is.

The One who stays.

The One who submits.

The One who saves.

Your friend, Douglas Vandergraph

Watch Douglas Vandergraph’s inspiring faith-based videos on YouTube

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See a video of the full story here: https://youtu.be/tBnoFZapbKk

There are towns you pass through and towns that pass through you. Maple Hollow was the second kind. It sat where the highway bent instead of cut straight, where the gas station doubled as the grocery store, and where the wind always carried the smell of cut grass or wood smoke depending on the season. It was not famous for anything. It did not appear on postcards. It did not have monuments. It had people. And sometimes that is enough to make a place holy without ever meaning to.

Eli Turner had lived in Maple Hollow for seventy-three years. He was born in the upstairs bedroom of the white house on Pine Street and had only ever lived two blocks away. He knew which porches sagged and which trees leaned. He knew which families argued loud and which ones hid it better. He knew which dogs barked at strangers and which ones barked because they were lonely. He worked three mornings a week at Fletcher’s Hardware, not because he needed the money, but because he needed a reason to wake up with purpose. When you have buried the person you planned to grow old beside, purpose does not come naturally. You have to practice it like a muscle.

Every morning before the sun showed its face, Eli walked from Pine Street to Main Street with a thermos in one hand and a Bible in the other. The bench outside the feed store was his destination. It had been placed there decades earlier by a man who wanted to rest while tying his boots. Eli claimed it by habit. He would sit with his back straight, his breath making small clouds in the cold months, and he would pray while the town slept. He prayed when the bakery lights flicked on and when the first delivery truck coughed its way down the street. He prayed when the bell at Saint Andrew’s rang noon and when the high school band practiced too loud. He prayed when rain soaked the pavement and when snow made everything sound like it had been wrapped in cotton.

People noticed. They always do when something happens the same way for long enough.

Some thought he was lonely. Some thought he was odd. A few thought he was holy. Most simply thought he was harmless. In small towns, harmlessness is a kind of social permission. You can be strange if you are kind. You can be quiet if you are useful. Eli was both.

Caleb Morris noticed too. Caleb was sixteen and had learned early how to look like he wasn’t carrying much. His mother worked nights at the nursing home. His father had left two years earlier with promises that never made the round trip back. Caleb rode his bike past the feed store every day on the way to school and every day back again. He saw Eli on the bench with his Bible and coffee, always still, like he was waiting for someone who never came.

One autumn afternoon, when the leaves were dry enough to crackle under tires and shoes, Caleb stopped. His bike leaned against the bench like it needed to hear the answer too.

“Why do you pray so much?” he asked.

Eli closed his Bible, not because he was finished, but because he had learned to listen when someone asked something honest.

“Why do you ask?” he said.

Caleb shrugged. “My mom says prayer changes things. But nothing around here changes. You still live alone. You still work at the hardware store. You still sit here every day. I don’t see what you gained.”

Eli smiled in a way that took time to arrive. “It’s not what I gained,” he said. “It’s what I lost.”

That sentence did not land like a sermon. It landed like a stone dropped into water. Caleb waited.

“I lost the heaviness,” Eli said after a moment. “The kind that makes breathing feel like work. After my wife died, I woke up every morning with a weight in my chest like someone had left a sack of gravel inside me. Prayer didn’t take away the grief. It gave it somewhere to go so it didn’t have to live in me.”

Caleb did not interrupt.

“I lost my anger,” Eli went on. “I used to think pain gave me permission to be sharp with the world. Prayer kept sanding me down when I wanted to be jagged.”

He looked at the feed store window where his reflection wavered in the glass. “I lost my greed. I thought if I filled the empty places with things, I’d feel whole. Prayer taught me how little I actually need.”

Caleb’s question did not turn into a debate. It turned into a door. Something about the way Eli spoke made the words feel like they had been carried for a long time before being set down.

That night, Caleb sat on his bed in a house that hummed with appliances and absence. He said no formal words. He said, “I don’t want to carry this alone.” It was the first prayer he could remember praying without being told.

Maple Hollow did not notice anything different the next day. The stoplight still blinked. The bakery still burned the first batch of rolls. The wind still moved the flags in front of the post office. But something invisible had changed, which is how most important things change.

Eli did not tell many people his story. He did not need to. His life told it quietly. But the truth was that prayer had become for him a daily practice of laying down what tried to climb back into his hands. He did not pray because he was strong. He prayed because he was not. He prayed because some burdens only get lighter when you stop pretending you can lift them yourself.

In his early years, Eli had believed prayer was a transaction. You said the right words and hoped for the right result. He had asked God for protection and gotten storms. He had asked for healing and learned what funerals felt like. Somewhere between disappointment and persistence, his prayers changed shape. They stopped being requests for control and became invitations for presence. He did not pray to escape pain. He prayed to survive it without becoming something bitter and small.

Maple Hollow had seen bitter men. They drank at noon and shouted at the television. They talked about the way things used to be as if the past were a weapon. Eli could have been one of them. Instead, he was the man who fixed loose steps for widows and replaced light bulbs in the sanctuary without being asked. He carried groceries for people who pretended they did not need help. He did not explain why he did these things. He just did them.

Caleb began sitting with him in the mornings. Not every day. Not like a vow. Just often enough that the bench felt like a place instead of a piece of wood. Sometimes they talked. Sometimes they did not. Prayer did not become a performance between them. It became a shared silence that did not feel empty.

One morning, Caleb said, “What else did you lose?”

Eli thought about it. “I lost my fear of being alone,” he said. “The house still gets quiet. But it doesn’t feel abandoned. Prayer taught me how to hear God in the quiet instead of just myself.”

He took a sip of coffee. “I lost my jealousy. I used to look at other men with their families and feel like I’d been cheated. Prayer taught me how to bless what I didn’t have instead of resenting it.”

Caleb nodded like someone who recognized a language.

“I lost my shame,” Eli said. “The kind that tells you you’re too late and too broken. Prayer showed me God does not throw away stories just because they have hard chapters.”

Caleb did not say much after that. He did not need to. Something inside him was rearranging its furniture.

Prayer did not change Maple Hollow into a place of miracles. It changed it into a place where burdens were spoken instead of swallowed. Where grief could sit on a bench and not be chased away. Where a boy could learn that faith was not a trick for fixing life but a way of staying human inside it.

Eli would never call himself wise. He would call himself practiced. Practiced at letting go of what tried to harden him. Practiced at laying down what made him heavy. Practiced at believing that God did not need perfect words, only honest ones.

Years later, after Eli’s bench was replaced and his coat hung in someone else’s closet, people would still talk about the man who prayed every morning. They would say he was faithful. They would say he was kind. They would say he changed lives.

Eli would have said something simpler.

He would have said prayer taught him what he could afford to lose.

And sometimes the most powerful testimony is not what God adds to your life, but what He teaches you to set down.

Eli never told Caleb about the winter when he almost quit praying. Some stories stay folded until the right season opens them. That winter had come after his wife’s funeral, after the casseroles stopped arriving and the cards stopped being mailed. The house had learned how to echo. Every room had learned how to repeat her name without using it. He had tried to pray the way he always had, but the words fell like stones into a well that felt too deep to hear them land. He would sit at the kitchen table in the dark and think about how strange it was that faith could feel heavier than doubt.

What kept him going was not a sudden vision or a voice from heaven. It was the memory of how prayer had once steadied him when nothing else could. He did not pray because it felt good. He prayed because stopping felt like giving grief the last word. He prayed because silence without God felt lonelier than silence with Him. Over time, prayer stopped being something he did and became something he returned to, the way you return to a road that once brought you home.

Caleb did not know this history. He only knew the man on the bench who did not rush him and did not preach. That was enough. In a world that spoke too fast and judged too quickly, Eli’s presence felt like a pause you could trust. The boy learned that prayer did not require the right tone or posture. It required honesty. Some mornings they prayed aloud. Some mornings they simply sat. Eli had learned long ago that listening could be a form of prayer too.

The town began to notice Caleb. Teachers said he seemed steadier. His mother said he was quieter in a way that did not feel withdrawn. He still struggled with school. His father still did not come back. Life did not rearrange itself around his prayers. But his heart did. He stopped trying to carry everything with clenched fists. He started naming what hurt. He started letting the future be something God could touch.

One day, when the wind came sharp off the fields and the sky looked like it had forgotten how to be blue, Caleb asked, “Do you ever stop losing things when you pray?”

Eli smiled. “No. You just lose better things.”

He explained that prayer had taught him to let go of the need to be right all the time. He had learned how costly certainty could be when it left no room for mercy. He had learned how to release the urge to replay old arguments as if they could be won after the people involved had moved on. Prayer had become the place where he set down his imaginary victories and picked up real peace.

He told Caleb that prayer had taken away his obsession with fairness. Not because fairness was wrong, but because it had been incomplete. He had wanted every hurt to be repaid and every wrong to be balanced like a ledger. Prayer taught him that justice and grace were not enemies, but they did not always arrive on the same schedule. He learned to trust that God kept books he could not see.

He said prayer had stripped him of the habit of pretending he was fine. That had been the hardest thing to lose. He had grown up believing that strength meant silence. Prayer had taught him that strength could mean telling the truth to Someone who would not use it against him. He lost the mask he wore when he did not want to admit he was afraid. He lost the voice that told him real men did not cry. He lost the lie that needing help was a failure instead of a human condition.

The bench became a classroom without chalk. The lessons were not formal. They were lived. When Eli’s hands shook too much to carry a bag of feed, Caleb carried it. When Caleb’s grades dipped, Eli helped him study without making him feel small. Prayer did not float above their lives. It sank into them, the way water sinks into dry ground and stays there even when the surface looks unchanged.

Maple Hollow had other benches. It had other men. It had other boys. But something about this small ritual made room for others to notice what they had been carrying. A woman who lost her job stopped one morning and asked if she could sit. A man whose marriage was unraveling slowed his truck and joined them. Nobody announced a gathering. It just happened, the way communities form when someone is brave enough to be still.

Eli never said prayer would fix everything. He said it would keep you from becoming someone you did not want to be while you waited for things to heal. He said prayer did not change the weather, but it changed how he stood in it. He said prayer did not erase the past, but it gave him a future that did not have to be afraid of remembering.

When his health began to fail, he did not stop coming to the bench. He came slower. He brought a heavier coat. He leaned more on the wood than he used to. But he kept praying. Not because he feared death, but because he wanted to meet it without bitterness. He had lost too much of that already to give it back.

Caleb grew older. He went to college. He came back on holidays. The bench was still there. Eli was thinner. Their prayers had changed. They no longer asked only for strength. They asked for gratitude. They asked for wisdom. They asked for the courage to lose what kept them small.

When Eli died, Maple Hollow gathered in a way it had not for years. The church was full. The hardware store closed for the morning. People spoke about the man who prayed. They spoke about his kindness. They spoke about how he listened. Caleb spoke last.

“He taught me prayer isn’t about gaining power,” he said. “It’s about losing fear. It’s not about adding things to your life. It’s about setting down what keeps you from living it.”

The bench outside the feed store was empty the next morning. It did not stay that way for long. Someone sat. Then someone else. The town did not put up a plaque. It did not rename the street. It simply kept the habit alive.

Prayer did not make Maple Hollow famous. It made it lighter.

And that is how you know a practice is holy. Not by how loud it is, but by what it teaches people to put down.

It teaches them to lose despair and pick up endurance. To lose resentment and learn mercy. To lose the illusion of control and find a steadier hope. It teaches them to stop carrying tomorrow like a threat and start carrying it like a promise.

Prayer does not give you a new life. It gives you freedom from the weight of the old one.

And sometimes the most important answer you will ever hear is not what you gained, but what you no longer have to hold.

Your friend, Douglas Vandergraph

Watch Douglas Vandergraph’s inspiring faith-based videos on YouTube

Support the ministry by buying Douglas a coffee

Microsoft seems to be repeating errors from its past in the pursuit of marketable “tools” and “features,” sacrificing safety and privacy for dominance. This is not a new pattern. In the late 1990s and early 2000s, Microsoft made a deliberate decision to integrate Internet Explorer directly into the operating system, not because it was the safest architecture, but because it was a strategic one. The browser became inseparable from Windows, not merely as a convenience, but as a lever to eliminate competition and entrench market control. The result was not only the well documented U.S. antitrust case, but a security disaster of historic scale, where untrusted web content was processed through deeply privileged OS components, massively expanding attack surface across the entire installed base. The record of that era is clear: integration was a business tactic first, and the security consequences were treated as collateral. https://www.justice.gov/

What is alarming is how directly this pattern is repeating today with Copilot. Microsoft is not positioning AI as an optional tool operating at the edge, but as a core operating system and productivity suite layer, embedded into Windows, Teams, Outlook, SharePoint, and the administrative control plane of the enterprise. This is not simply “an assistant.” It is an integrated intermediary designed to observe, retrieve, summarize, and act across the entire organizational data environment, often with persistent state, logging, transcripts, and cloud processing as defaults or incentives. This changes the risk model completely. With IE, the breach potential was largely about code execution. With Copilot, the breach potential becomes enterprise wide data aggregation and action at scale: mailboxes, chats, meetings, documents, connectors, tokens, workflows, all mediated through a vendor operated cloud layer. That is not a minor shift, it is a boundary collapse that turns governance, segmentation, least privilege, and managed security assumptions into fragile hopes rather than enforceable controls. Microsoft’s own documentation shows how rapidly these agent and integration surfaces are becoming enabled by default in Copilot licensed tenants.

https://learn.microsoft.com/

This is where the problem becomes existential for enterprise security. Windows is increasingly being positioned not as a stable, controllable endpoint, but as a marketing platform for AI driven features that require broad access, cloud mediation, and expanded telemetry. The job of IT and security teams becomes an endless exercise in ripping away functionality, disabling default integrations, restricting connectors, limiting retention, and then having difficult conversations with users about why the shiny new feature cannot be trusted in environments with real confidentiality requirements. Instead of enterprise computing becoming simpler and more governable, it becomes more complex, more fragile, and more sovereignty exposed by design. If this trajectory continues, Microsoft risks making Windows less and less defensible as a reasonable secure enterprise platform unless organizations are willing to invest significant effort just to undo what is being bundled in the name of market share.

When software was new, you would buy an application for a lot of money upfront and then get major new versions at discounted prices. There were no free minor updates because the software had to be delivered on physical media (which also meant that an application had to be bug-free out of the gate, an art that was lost in the age of weekly, automatic updates).

Today, software vendors love subscriptions because they guarantee a steady income. Many users are not so fond of them, so every time an app switches from a one-time purchase to a subscription model, it receives a slew of angry one-star reviews.

Maybe that’s why Apple’s Creator Studio subscription is confusing. While an incredible value by itself, it includes software that has been included for free with macOS and apps that many already bought as a one-time purchase. Nothing changes with the Creator Studio, but if you want, you can pay $12.90 per month or $129 per year to see what happens.

I own Logic Pro, Pixelmator Pro, and MainStage. I also subscribe to Logic on the iPad for $50/month. Should I get the Creator Studio to get Final Cut Pro, which I (currently) don’t really need? 

Apple has probably lined up subscription-only features to entice users to switch. I just wish for a little discount on licenses I already own, so I would not feel like throwing away good stuff.

This question has been running around my brain for a while, driven by two factors. First, building robust, production-ready enterprise agents that can handle scale, complexity and security is hard and complicated. Second, what if we could kind of abstract away all of that complexity in the way that AWS was so successful at?

The pitch sounds compelling: a managed platform that handles the gnarly infrastructure problems of deploying AI agents at enterprise scale. Security is baked in. Compliance, no problemo. Best practices are all there by default. Just bring your agent logic and go wild in the aisles!

I turned this into a sort of thought experiment, but the more I’ve considered the question, the more I think the AWS analogy breaks down in interesting ways. The hyperscalers are absolutely building toward this vision (AWS Bedrock AgentCore became generally available in October 2025, and Microsoft’s Azure AI Foundry is maturing rapidly), but what they’re creating is fundamentally different from the “neutral substrate” that made AWS transformative in cloud computing.

But first, the problem…

Building Enterprise Agents is a Mess

Before we get to the platform question, it’s worth understanding just how painful it is to ship production agents today, for those fortunate enough not to have had to do so. To be clear, we’re not talking about demo agents or “look what I built this weekend” prototypes. This is agents that handle sensitive data, integrate with business-critical systems, and need to satisfy compliance teams. The ones that if you’re not losing sleep over, you’re not doing it right.

The Security Problem Nobody Wants to Own

Every agent that can take actions is an attack surface. Prompt injection isn’t theoretical anymore; Lakera’s Q4 2025 data shows indirect prompt injection has become easier and more effective than direct techniques [1]. An agent that reads emails, queries databases, or browses websites is ingesting untrusted content that can manipulate its behaviour.

So you need input sanitisation. You need output filtering. Trust boundaries between different data sources are essential. You’ll probably want a separate security layer that operates outside the LLM’s reasoning loop entirely, because you can’t rely on the model to police itself. Unfortunately, most teams realise this after they’ve already built the “happy path”, only to then discover that retrofitting security is particularly brutal.

Identity and Authorisation

Your agent needs to act on behalf of users. That means OAuth flows, token management, scope limitations, and credential vaulting. It needs to access Salesforce “as Sarah”, but only read the accounts she’s allowed to see. It needs to query your data warehouse, but not the tables containing Personally Identifiable Information. This isn’t a solved problem, even for traditional applications. For agents that dynamically decide which tools to call based on user requests, it’s significantly harder.

Memory That Actually Works

Agents without memory are stateless assistants. Agents with memory need infrastructure to store it, retrieve it, scope it appropriately, and eventually forget it. Episodic memory (what happened in the conversation), semantic memory (facts about the user), and procedural memory (learned patterns) all require different storage and retrieval patterns. Build this yourself, and you’re suddenly maintaining a bespoke memory system alongside everything else.

Observability When You Can’t Predict Behaviour

Traditional application monitoring assumes you know what the system should do. Agent observability has to handle emergent behaviour, such as the agent deciding to try four different approaches before succeeding, or going down a rabbit hole that burned tokens for no good reason, or using a tool in a way you didn’t anticipate.

You need trace visibility at every step, cost tracking, and debugging tools that make sense of non-deterministic execution paths. Off-the-shelf Application Performance Monitoring tools don’t cut it.

Multi-Agent Orchestration

Single agents hit capability ceilings rather quickly. The current direction is toward multiple specialised agents coordinating themselves (a supervisor agent breaking down tasks, specialist agents handling specific domains, and handoffs between them). Gartner predicts that a third of agentic AI implementations will combine agents with different skills by 2027 [2], and to me, that seems conservative.

But orchestrating multiple agents means managing communication protocols, shared context, failure handling when one agent breaks, and preventing infinite loops when agents delegate to each other. More agents = More Complexity and Pain.

Compliance and Audit Requirements

In regulated industries, “the AI did something” isn’t an acceptable audit trail. You need to prove what data the agent accessed, what decisions it made, what actions it took, and that it operated within defined boundaries. This has to be tamper-evident and queryable.

Oh, and for bonus points, if you operate internationally, each jurisdiction will likely have its own requirements. For example, California’s new AI regulations took effect in January 2026, with enforcement shifting from policy to live production behaviour [3].

The point isn’t that any single problem described above is insurmountable. It’s that solving all of them simultaneously, whilst also building the actual agent functionality your business needs, is a massive undertaking. Most teams get stuck in what I’d call “prototype purgatory”. Impressive demos that never make it to production because the operational complexity is too high.

This is the gap that managed platforms are trying to fill. The mythical “AWS for AI Agents.”

Who’s Actually Building This?

The hyperscalers have moved aggressively into this space, as you’d expect. A few offerings stand out:

AWS Bedrock AgentCore

Amazon’s entry is the most developed. AgentCore is pitched as “an agentic platform for building, deploying, and operating effective agents securely at scale—no infrastructure management needed” [4].

The service suite covers most of the pain points I listed above:

• AgentCore Runtime: Serverless execution with session isolation using Firecracker microVMs. Each agent session runs in its own protected environment to prevent data leakage between users.
• AgentCore Gateway: Transforms existing APIs and Lambda functions into agent-compatible tools, with native MCP (Model Context Protocol) support. Handles the plumbing of connecting agents to enterprise systems.
• AgentCore Memory: Persistent memory management, including the recently added episodic memory, so agents can learn from interactions over time.
• AgentCore Identity: OAuth-based authentication for tool access, with support for custom claims in multi-tenant environments.
• AgentCore Observability: Step-by-step trace visualisation, cost tracking, debugging filters.
• AgentCore Policy: This is the interesting one. Natural language policy definitions that compile to Cedar (AWS’s open-source policy language) and execute deterministically at the gateway layer, i.e., outside the LLM reasoning loop [5].

That last point really matters. Policy enforcement that operates outside the model means constraints are hard limits, not suggestions. It doesn’t matter how cleverly a prompt injection tries to reason around a restriction; the gateway blocks it before execution. For compliance teams, this is the difference between “we hope the AI behaves” and “we can prove it can’t misbehave.”

Microsoft Azure AI Foundry

Microsoft’s approach is similarly ambitious but more tightly integrated with its existing stack. The headline feature is that over 1,400 business systems (SAP, Salesforce, ServiceNow, Workday, etc.) are available as MCP tools through Logic Apps connectors [6]. If your enterprise already runs on Microsoft, this level of built-in integration is compelling.

Their AI Gateway API Management handles policy enforcement, model access controls, and token optimisation. The positioning is less “build from scratch” and more “extend what you already have with agent capabilities.”

Google Vertex AI

Vertex AI Agent Builder is a genuine competitor to AgentCore. The platform follows the same “build, scale, govern” structure as AWS. The Agent Development Kit (ADK) is Google's open-source framework that has been downloaded over 7 million times and is used internally by Google for its own agents [9]. Agent Engine provides the managed runtime with sessions, a memory bank, and code execution. Agent Garden offers pre-built agents and tools to accelerate development.

Security and compliance capabilities are mature through VPC Service Controls, customer-managed encryption keys, HIPAA compliance, agent identity via IAM, and threat detection via the Security Command Centre. Sessions and Memory Bank are now generally available, and the platform is explicitly model-agnostic; you can use Gemini, as well as third-party and open-source models from their Model Garden.

Where Google really differentiates itself is ecosystem integration. They offer more than 100 enterprise connectors via Apigee for ERP, procurement, and HR systems. Grounding with Google Maps gives agents access to location data on 250 million places. If you're already running BigQuery, Cloud Storage, and Google Workspace, these integrations may be compelling.

Salesforce Agentforce

Agentforce is worth mentioning because it represents the most opinionated end of the spectrum. It’s not trying to be a general-purpose agent platform. It’s saying “agents exist to automate Salesforce workflows, and that’s it.”

Agentforce 2.0 embeds autonomous agents directly into Salesforce to manage end-to-end workflows, from qualifying leads to generating contracts. The agents have self-healing capabilities (automatically recovering from errors) and native human handoffs when escalation is needed [11].

The tradeoff is stark. If you’re all-in on Salesforce, the integration depth is unmatched. The agents understand your CRM data model, your workflow rules, and your permission structures. No translation layer is required. But if Salesforce isn’t your system of record, Agentforce is largely irrelevant.

However, this creates a useful reference point for thinking about the spectrum of approaches. Salesforce Agentforce offers maximum lock-in and deep integration for a narrow use case. Amazon’s AgentCore offers moderate opinions with broader applicability. Framework-level tooling offers maximum flexibility but also a significant operational burden. There’s no objectively correct position on this spectrum; it all depends on what you’re building and what constraints you’re willing to accept.

The Consultants Have Joined The Call

It’s also worth mentioning PwC who launched an “agent OS” that orchestrates agents across multiple cloud providers and enterprise systems [7]. They’re essentially packaging best practices and governance frameworks atop hyperscaler infrastructure. Accenture and others are doing similar things, as you’d expect.

This makes objective sense. Enterprises often want a trusted advisor to de-risk adoption rather than building expertise in-house. The consultancies are betting they can capture value at the integration layer. IBM, for example, is trying to leverage its success in helping clients with multi-cloud implementations into AI.

What About the Drag-and-Drop Builders?

There’s a whole category of platforms (Relevance AI, n8n, Lindy, various other low/no-code agent builders) that I’d put in a different bucket entirely. These are designed to let business users create lightweight automation without writing much or sometimes any code.

They can absolutely work for certain limited use cases. But they primarily exist for experimentation and getting an agent running quickly, not “last-mile embedding” into production systems with proper auth, governance, and compliance [8]. The enterprise infrastructure play is about taking agents that development teams have already built and making them safe to deploy at scale. This is a fundamentally different thing.

Why the AWS Analogy Breaks Down

Here’s where I keep coming back to AWS. For those old enough to remember, Amazon won by being radically neutral about what you ran on their infrastructure. They didn’t care if it was a modern microservices architecture or a legacy Perl script from 2003. The value was in the primitives (compute, storage, networking), being reliable, scalable, and pay-as-you-go. Everything else was your problem.

This created incredible growth because no technology choice was “wrong” for AWS. Migrations could be lifted and shifted without major re-architecture. They captured the long tail of weird enterprise workloads that nobody else wanted to support. The agent platforms being built today are fundamentally different. And a bit like your slightly racist aunt, they’re very opinionated.

AgentCore doesn’t just say, “here’s compute, run whatever agent framework you want.” It says, “here’s how memory should work, here’s how tools should integrate, here’s how policies should be enforced, here’s how observability should be structured.” The value proposition is in their specific abstractions, not neutral infrastructure. If you don’t use those abstractions, you’re basically just using EC2 with extra steps.

Why the Shift to Opinionated Platforms?

There are a few reasons:

Security requirements force it. With traditional compute, if your application gets compromised, that’s your problem within your “blast radius”. When agents have tool access and can take actions in external systems, the platform must ensure containment. You can’t offer “run whatever agent logic you want” without guardrails; the liability is simply too high.

The primitives aren’t settled. When AWS launched, everyone largely agreed on what “compute” and “storage” meant. Nobody yet agrees on what “agent memory” or “tool orchestration” should precisely look like. MCP is emerging as a standard for tool integration, but it’s still evolving quickly. Memory architectures vary wildly. Multi-agent coordination patterns are experimental, so platforms are making bets on specific patterns, hoping they become the standard. This is inherently opinionated.

Higher value capture. Neutral infrastructure commoditises quickly, becoming a race to the bottom on price. Opinionated platforms can charge more because they’re solving harder problems. If you’re just selling compute, you compete on price. If you’re selling “enterprise-ready agent deployment with compliance built in,” you capture more margin.

Lock-in by design. Once you’ve built around AgentCore’s memory service and gateway patterns, migration is expensive. Of course, as many enterprises have found, this is also true to an extent with AWS, particularly if you have exotic components in your enterprise architecture that aren’t widely supported elsewhere.

The Trust Problem This Creates

The “support anything” approach was what made AWS trustworthy as an infrastructure provider. Enterprises could adopt it knowing they weren’t betting on AWS’s opinions being correct, only on AWS's operational excellence.

The opinionated agent platform approach requires a different kind of trust. It requires the belief that AWS (or Microsoft, or Google) has figured out the right patterns for agent development and is willing to build around them.

That’s a harder sell when:

• The patterns are still evolving rapidly
• Different use cases might genuinely need different architectures
• The hyperscalers have obvious incentives to push you toward their own models (Nova for AWS, Azure OpenAI for Microsoft)

Yes, AgentCore supports external models like OpenAI and Anthropic [^9]. But the integration depth varies. The path of least resistance leads toward their ecosystem.

Could a Neutral Alternative Exist?

Theoretically, someone could build “EC2 for agents”, i.e., just isolated compute with no opinions. Run LangChain, CrewAI, AutoGen, your own custom framework, whatever. No prescribed patterns, just secure sandboxed execution.

The problem is that the hard aspects of agent deployment are exactly the things that require opinions:

• How do you enforce that an agent can’t exfiltrate data? You need a position on network egress controls, on what counts as sensitive data, and on whether the agent can write to external APIs.
• How do you audit what it did? This requires deciding what constitutes a step worth logging, how to capture tool calls, and what metadata matters.
• How do you manage credentials for tool access? OAuth flows, token refresh, and scope limitations all require specific patterns.
• How do you prevent prompt injection from untrusted sources? You need to decide where trust boundaries sit and how to sanitise retrieved content.

You can’t solve these without taking architectural positions. So the “neutral substrate” approach soon collapses into “you’re on your own”, which is exactly where most enterprises are today, and why some are struggling.

The Vercel Analogy Might Be Closer

A better comparison might be Vercel or Netlify, platforms that have taken a strong position on how web applications should be built and deployed. They didn’t try to be neutral infrastructure. They said “here’s the right way to do this” (JAMstack, serverless functions, edge rendering, etc.) and made that path the easy one.

Developers adopted them not because they supported everything, but because they made the opinionated approach feel effortless. Similarly, the winning agent platforms will probably be ones that make secure, observable, compliant agent deployment the path of least resistance, even if that constrains what you can do.

Where Value Will Accrue

So, following my thought experiment to its conclusion, here’s how this could play out:

Hyperscaler platforms will capture the majority of enterprise spend. Companies with real compliance requirements and limited appetite for infrastructure complexity will pay the premium and accept the lock-in. AgentCore and Azure AI Foundry are the obvious choices depending on existing cloud commitments.

Framework-level tooling (LangChain, CrewAI, Strands, custom implementations) will serve teams who want control and are willing to own operational complexity. So fintechs with strong engineering cultures, AI-native startups, and research teams. A smaller segment but more technically sophisticated.

The middleware layer (i.e., observability, security, evaluation) has room for independent players. These tools can be platform-agnostic in ways that the core runtime can’t. LangSmith for debugging, Say Arize for monitoring, the security layer that Lakera occupied before Check Point acquired them [10]. This might be where the interesting startups emerge.

Consulting and integration services will capture significant revenue, helping enterprises navigate the transition. The technology is complex enough that most companies will want guidance.

The Timing Risk

It is a particularly difficult time for large companies to assess how much AI Agent infrastructure to be working on. Building on any of the current platforms now means betting on architectural patterns that might get superseded. MCP could evolve in a way that fundamentally breaks certain things. Memory architectures might standardise around different approaches. Multi-agent orchestration patterns are still largely unproven at scale.

For enterprises adopting these platforms early (and, contrary to the hype train, it is still very early) they may be building on foundations of sand that then shift in different directions. But there is also risk for enterprises in waiting and staying stuck in “prototype purgatory” while competitors ship production agents and capture market position.

There is no obviously correct answer. Which is probably why this space feels so chaotic. And of course, chaos is inherently interesting.

Pass the popcorn.

—

References

[1]: Lakera Q4 2025 threat data showed indirect prompt injection becoming more effective than direct techniques, with attackers increasingly targeting the data ingestion surfaces of agentic systems.

[2]: Gartner predicts one-third of agentic AI implementations will combine agents with different skills by 2027, with 40% of enterprise applications featuring task-specific AI agents by the end of 2026. Source: Gartner Press Release, August 2025

[3]: California AI regulations took effect January 2026, shifting AI regulation from policy documents to live, in-production behaviour requirements.

[4]: Amazon Bedrock AgentCore product page. Source: AWS Bedrock AgentCore

[5]: AgentCore Policy integrates with AgentCore Gateway to intercept tool calls in real time. Policies defined in natural language automatically convert to Cedar and execute deterministically outside the LLM reasoning loop. Source: AWS What’s New, December 2025

[6]: Azure AI Foundry provides 1,400+ business systems as MCP tools through Logic Apps connectors, with AI Gateway in API Management for policy enforcement. Source: Microsoft Tech Community, November 2025

[7]: PwC’s agent OS is cloud-agnostic, enabling deployment across AWS, Google Cloud, Microsoft Azure, Oracle Cloud Infrastructure, and Salesforce, as well as on-premises data centers. Source: PwC Newsroom

[8]: Visual agent builder platforms are designed for first-mile acceleration—getting an agent running fast—not last-mile embedding inside production products with user-scoped auth and governance. Source: Adopt.ai analysis of agent builder categories

[9]: AgentCore works with models on Amazon Bedrock as well as external models like OpenAI and Gemini. Source: Ernest Chiang’s technical analysis

[10]: Check Point acquired Lakera in September 2025 to build a unified AI security stack, integrating runtime guardrails and continuous red teaming into their existing security platform. Source: CSO Online, September 2025

[11]: Agentforce 2.0 embeds autonomous agents directly into Salesforce with self-healing workflows that automatically recover from errors and transparent human handoffs when escalation is needed. Source: Beam AI analysis of production agent platforms

Das neue Jahr,

stürmt herein,

eisig und weiß.

Schnee auf weißem Sand

und Tannenspitzen.

Der Wind pfeift um

die Häuser,

rüttelt an den Fenstern.

Die Wintersonne

grüßt mit kühlem

Schein.

Eiszeit.

Still und starr.

Auf den Spuren von ….

Annette von Droste-Hülshoff reiste Mitte des 19. Jahrhunderts vom westfälischen Münster nach Meersburg am Bodensee und es war ein einziges Geschaukele in Kutschen, Eisenbahnen und Dampfschiffen.

In einem Brief an ihre Freundin Elise Rüdiger heisst es:

„Sie hatten mir alle Karten für Dampfboote und Eisenbahnen, sogar für den Omnibus bis Freyburg verschafft (diese Anstalten stehn miteinander in Berechnung) und zugleich ein Empfehlungsschreiben vom Direktor der Cölnischen Dampfschiffahrt, was an sämtliche Wagen- und Schiffkondukteure gerichtet, ihnen jede Rücksicht für mich auf die Seele band, so bin ich übergekommen fast so bequem wie in meinem Bette (d. h. bis Freyburg) — die Herrn Kondukteure führten mich immer gleich in den Pavillon, nahmen andern Kanapees ihre Kissen, um es mir bequem zu machen, versorgten mein Gepäck, banden mich den Marqueurs so eng aufs Gewissen, daß fast jede Viertelstunde einer kam, nachzusehen, ob ich etwas bedürfe, und wenn wir angekommen waren, ließen sie mein Gepäck gleich auf das morgige Dampfboot bringen und führten mich selbst an den Omnibus.

Auf der Eisenbahn ging es ebenso; ich bekam beide Male einen Waggon für mich allein, und fast bei jeder Station erschien ein Gesicht am Wagenschlage, um zu fragen, ob ich etwas bedürfe — und doch hat dies alles meine Reise nur unbedeutend vertheuert; die Kondukteure nahmen nichts und meine männlichen Wartfrauen waren am Rheine mit einem Gulden, weiterhin schon mit 30 Kreuzern, überglücklich.

Sie sehen, lieb Lies, ich bin wie in einem verschlossenen Kästchen gereist und habe (außer meinen lieben Wartfrauen) kein fremdes Gesicht gesehn, nicht mal in den Gasthöfen, wo ich mir gleich ein eigenes Zimmer geben ließ, wenn ich auch nur eine halbe Stunde blieb; so fühlte ich mich in Freyburg so wenig erschöpft, daß, statt (wie früher beschlossen) Extrapost zu nehmen, ich mich dem Eilwagen anzuvertrauen beschloss, obwohl er abends abging.

Meine Empfehlungen waren zu Ende, aber mein Glück verließ mich auch hier nicht, ich hatte bis Mitternacht einen Beiwagen ganz für mich allein, dann muste ich freylich in den allgemeinen Rumpelkasten, voll schnarchender Männer und Frauensleute, die brummend und ächzend zusammenrückten, als ich mich einschob; dann ging das Schnarchen wieder an, ich allein war wach bey dieser scheußlichen Bergfahrt und merkte allein, wie den Pferden die Knie oft fast einbrachen und der Wagen wirklich schon anfing rückwärts zu rollen. Mein Vis-a-Vis stieß mich unaufhörlich mit den Knien und die Köpfe meiner Nachbarn baumelten an mir herum. Doch gottlob nicht lange!

Es war noch stockfinster, als wir mit der Post nach Konstanz zusammentrafen, und siehe da, meine ganze Bagage kugelte und kletterte zum Wagen hinaus, und ich war wieder frey! frey! und machte mir ein schönes Lager aus Kissen und Mantel, auf dem ich es sehr leidlich aushalten konnte, bis nach Stockach, wo ich um zehn ankam, gleich Extrapost nahm und in Meersburg die Meinigen noch bey Tische traf.“

Gelesen, gesehen, gehört.

Der Lesestart ins Jahr: „Sommernachtstraum“ von Tanya Lieske. Eine Schulklasse führt das Stück von Shakespeare auf, und zeitgleich finden sich Lehrer:innen wie Schüler:innen in vergleichbaren persönlichen Geschichten wieder. Das Ende war ein wenig durcheinander. Ansonsten ein tolles Konzept!

“Versprich mir Morgen” handelt von den ersten Wochen und Monaten einer jungen Frau im Wohnheim eines Krankenhauses, in dem Auszubildende zusammen in einer WG leben. Die Herausforderungen des angehenden Berufs und die persönliche Entwicklung werden mit Detailkenntnis und spannend erzählt.

Das Hörbuch “The happiness blueprint”. Der Schauplatz ist ein Handwerksbetrieb in Schweden und kurzzeitig auch in London. Die Autorin lebt in beiden Ländern. Der Roman ist schön hyggelig.

“Ein gutes Jahr“: ein unterhaltsamer Film aus dem Jahr 2006, der Lust auf Frankreich macht!

#Winter #gelesen #gesehen #gehört #Möwenlyrik