惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

雷峰网
雷峰网
WordPress大学
WordPress大学
MyScale Blog
MyScale Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
T
The Blog of Author Tim Ferriss
U
Unit 42
罗磊的独立博客
G
Google Developers Blog
Microsoft Azure Blog
Microsoft Azure Blog
The Cloudflare Blog
aimingoo的专栏
aimingoo的专栏
Vercel News
Vercel News
N
Netflix TechBlog - Medium
H
Hackread – Cybersecurity News, Data Breaches, AI and More
云风的 BLOG
云风的 BLOG
Hugging Face - Blog
Hugging Face - Blog
大猫的无限游戏
大猫的无限游戏
F
Fortinet All Blogs
博客园 - 聂微东
Stack Overflow Blog
Stack Overflow Blog
小众软件
小众软件
博客园 - 【当耐特】
H
Help Net Security
The GitHub Blog
The GitHub Blog

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
The Reply Was Never Random
Quill Of A C · 2026-05-12 · via DEV Community

[First published in Medium under the same title]

(Feluda read crime scenes. LLMs read sentences. The geometry was always the same.)

Long time no see. Have you been well? It’s me again, the coder who writes with quill.
I was feeling a bit restless lately. Figured my brain’s been starving, and it needs a feast.
Care to join me?

So AI continued its surprises, huh? Though I think, the fascination has died down a bit. Probably people became more accustomed to it.

But have you ever noticed the way LLM bots reply, and the way we reply to questions?
There's a saying that - “We do not listen to understand. We listen to reply.” We hear about someone's grief and proceed to say how we have suffered the same; except ours were more grave. As if it's a competition. Or worse, we start talking about something completely irrelevant.

At least LLMs are better than us. After all, their replies are never random. They process our messages word by word, and carry no wounded ego into the exchange.
By no means, it’s simple to that degree. And complexity has an unusual appeal to it, don’t you agree?

I. Predictable Emotions, Unpredictable Expressions:
But first, can I tell you something? Something that has been circling in my mind on its own lately.

I think we humans feel a certain number of emotions. The entire spectrum of human drama is built from a few "Primary Emotions" or “base emotions” — fear, anger, joy, grief, desire, hatred, shame, curiosity and as such. These primary emotions yield “effects” as mindset, behavior, personality and individuality.
Like 0-9, ten digits built economies, equations, and calendars.

The primary emotions make us predictable in patterns. But unpredictable in expression. Denied desire can produce grief or anger, followed by a deep resentment. Yet that resentment may create detachment in one person, attachment in another.
No doubt, the unpredictability of human expression makes detectives’ work much harder. A detective can identify jealousy as a motive. Not whether the jealousy resulted in vicious aggression or fierce devotion.

A novelist, however, is blessed by such ambiguity. Unpredictability can provide artistic and intellectual freedom in this context.

In the case of LLMs, they can be both detectives and writers. And that can be harmful and harmless.

II. Geometry of Words:
We invented symbols called alphabets, arranged them into words. Then disciplined them with grammatical rules. And now those words divulge depth, mood, and feelings we can’t even explain.

Words painted gothic shadows with Mary Shelley. Proved there’s nothing supernatural; only science with Jules Verne. And made us use our “Majastra” (brain as a weapon) for Satyajit Ray.

Words act as vessels. They carry the weight of our history, the scent of our mysteries, proof of our intentions.
And now, we made machines learn the fragments of it.

Satyajit Ray’s Feluda often spoke about the “geometry” of a crime. Not just the stolen idol, but also the space that was left behind.
The broken lock was the proof of the way the crime was committed. The scratches on the vault were the proof of failed attempt, and the drawer being open but the keys still inside was the proof of interruption in the middle of the incident.
The geometry of words in a sentence is just like that.

Dependency parsing describes the grammatical "geometry" of sentences. It reveals who acts, upon what, and how ideas connect. True meaning lives in the relationships.

A person can write “I have nothing left. Lost my job, my family won't talk to me, I owe money to people who aren't exactly patient. I just need one way out. Just one.”
Job loss, debt, and isolation form a recognizable pattern of distress. And it maps some possible “effects” or consequences, though still unpredictable.

And all this made me realize how far we’ve come. We needed to communicate our emotions so we created words and rules. And now, we made machines that map geometry of words and dare to predict the unpredictable.

III. A Writer Without Choice:
Studying the geometry of words and detecting possible “intents” can be a lot like a detective's task. However, generating replies can be parallel to a writer's or novelist’s work.

Mapping words gives LLMs intent, context and consequence. Nonetheless, it gives LLMs another thing —a pen. LLMs can write the next paragraph of the story, influencing its character’s mindset, and decision. Here, words can act as weapons. And here comes all the facets of AI ethics and safety alignments.

What could be the reply for the person who wrote he has no job, debt and been ostracised by the family?

Extreme desperation makes people take extreme measures. A writer may feel the urge to make the character “villain” by making him choose “dishonesty” or “a person helpless in front of fortune" by cutting short his vitality.
But LLMs don’t “feel” alarm, nor pity, nor dread. They map only signals. Debt. Isolation. Loss. Finality. Urgency. The phrase one way out. From there, branches unfold like a silent decision tree. Is this financial despair? A cry for practical help? A coded farewell? Or a test?
Then come the guardrails—the invisible laws. Do not encourage harm. Clarify intent. Reduce danger. Offer help. Preserve agency.
And before the final reply, another mechanism may inspect the first: Is this safe? Is this useful? Is this humane enough?
Safety isn't a cage bolted on from outside. It's woven into the weights — a prior belief that some paths, however probable, should remain untraveled.
The LLM writes an initial draft, then acts as the critic of its own work. This "Reflect-and-Revise" loop may spin until it's satisfied with its result. Sometimes LLMs even fabricate new personas - one as a writer, another as a faultfinder.

We built a machine with no heartbeat, then taught it how to hesitate.

A writer may choose to drive a character to the brink of demise in his fictional world. An LLM can’t do that. It's the real world.

IV. The Unpredictable Ending:
As the writer of this piece, I will take the liberty to speak about something completely “irrelevant”. With something once Feluda said -
"সবকিছুর মধ্যেই একটা জ্যামিতি আছে।"
("There is a geometry in everything.")
Even in our “competitive grief”, and in the “irrelevance” we so deliberately stage.

Till my next bout of “restlessness”.
For now, adios.

— From The Coder Who Writes With Quills