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

TaoSecurity Blog
TaoSecurity Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
F
Fortinet All Blogs
Cisco Talos Blog
Cisco Talos Blog
D
Darknet – Hacking Tools, Hacker News & Cyber Security
S
Secure Thoughts
美团技术团队
雷峰网
雷峰网
Hugging Face - Blog
Hugging Face - Blog
博客园_首页
C
CXSECURITY Database RSS Feed - CXSecurity.com
Engineering at Meta
Engineering at Meta
人人都是产品经理
人人都是产品经理
月光博客
月光博客
T
Tor Project blog
P
Privacy & Cybersecurity Law Blog
Recorded Future
Recorded Future
I
Intezer
博客园 - 【当耐特】
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
GbyAI
GbyAI
罗磊的独立博客
V
V2EX
Google DeepMind News
Google DeepMind News
D
DataBreaches.Net
Last Week in AI
Last Week in AI
T
Tailwind CSS Blog
www.infosecurity-magazine.com
www.infosecurity-magazine.com
A
About on SuperTechFans
Scott Helme
Scott Helme
Vercel News
Vercel News
Spread Privacy
Spread Privacy
T
Threat Research - Cisco Blogs
Recent Announcements
Recent Announcements
Hacker News: Ask HN
Hacker News: Ask HN
C
CERT Recently Published Vulnerability Notes
G
Google Developers Blog
B
Blog
博客园 - 叶小钗
WordPress大学
WordPress大学
博客园 - 聂微东
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
Jina AI
Jina AI
IT之家
IT之家
C
Cybersecurity and Infrastructure Security Agency CISA
P
Palo Alto Networks Blog
小众软件
小众软件
博客园 - Franky
Microsoft Azure Blog
Microsoft Azure Blog
AWS News Blog
AWS News Blog

Hacker News - Newest: "AI"

AI can't read an investor deck AI as an attorney? Student uses ChatGPT, Gemini to sue UW over alleged racial discrimination Hacking MCP Servers in AI Systems – The Rug Pull: Tool Changes After Approval GitHub - MeepCastana/KubeezCut: Free Web based video editor GitHub - GenAI-Gurus/awesome-eu-ai-act: Curated tools, official sources, OSS, templates, and guides for EU AI Act compliance. Can AI judge journalism? A Thiel-backed startup says yes, even if it risks chilling whistleblowers Coming soon: 10 Things That Matter in AI Right Now DARPA built an AI to fact-check enemy weapons claims What explains heterogeneity in AI adoption? When AI Meets Muscle: Context-Aware Electrical Stimulation Promises a New Way to Guide Human Movements - Department of Computer Science AI Changed How We Build. It Did Not Change What Matters. Linux rules on using AI-generated code - Copilot is OK, but humans must take 'full responsibility for the… Meta spins up AI version of Mark Zuckerberg to engage with employees Code Mode: Let Your AI Write Programs, Not Just Call Tools | TanStack Blog GitHub - Delavalom/graft: Go framework for building AI agents. Type-safe tools, multi-provider (OpenAI, Anthropic, Gemini, Bedrock), zero vendor SDKs. India's TCS tops estimates, says new AI models did not dent services demand Gen Z's fading AI hype Strong feeling: we are in a folded AI reality GitHub - machinarii/total-recall-catalog: A reference catalog of latest knowledge retrieval, memory & RAG systems GitHub - mensfeld/code-on-incus: Give each AI agent its own isolated machine with root, Docker, and systemd. Active defense detects and stops threats automatically.. Quantization, LoRA, and the 8% Problem: Benchmarking Local LLMs for Production AI Iran war: We spoke to the man making Lego-style AI videos that experts say are powerful propaganda Powell, Bessent discussed Anthropic's Mythos AI cyber threat with major U.S. banks GitHub - immartian/bellamem: Persistent belief-graph memory for AI agents. Retrieves decisive context by importance — not recency, not RAG, not /compact. recursive-mode: The Repo-Native Operating System for AI Engineering After the attack on Sam Altman's home, will AI CEO's go on the offensive? The biggest advance in AI since the LLM Opus 4.6 vs GPT 5.4 One Prompt Unity World Generation Test “AI polls” are fake polls Client Challenge Can AI be a 'child of God'? Inside Anthropic's meeting with Christian leaders How to Switch AI Chatbots and Why You Might Want To GitHub - MattMessinger1/agentic_refund_guardrail: Safe refund policy layer for AI agents — Python + TypeScript. Same behavior, shared tests. Adam/papers/emergent_values_whitepaper.md at master · strangeadvancedmarketing/Adam Ask HN: How do you stop playing 20 questions with your AI coding tools How far can automation and AI support psychotherapy? - @theU GitHub - stagas/rtdiff: realtime git diff gui and AI-assisted commits A Mac Studio for Local AI — 6 Months Later A History of the Early Years of AI at the University of Edinburgh Why AI Coding Tools Still Feel Stuck on Localhost MSN AI Datacenters Are Becoming Strategic Targets twitter.com Penn Researchers Use AI to Surface Unreported GLP-1 Side Effects in Reddit Posts Show HN: MoodSense AI (ML and FastAPI and Gradio, Deployed on Hugging Face) Moodsense Ai - a Hugging Face Space by aman179102 AI models are terrible at betting on soccer—especially xAI Grok GitHub - xialeistudio/echoic GitHub - HimashaHerath/github-dev-wrapped: AI-powered weekly GitHub activity reports deployed to GitHub Pages GitHub - alejandrobalderas/claude-code-from-source: Architecture, patterns & internals of Anthropic's AI coding agent — reverse-engineered from source maps AI and Tech brief: Ireland ascendant GitHub - Titovilal/context0: Context0 - Never Surrender Training for a Marathon with an AI Coach: What Worked and What Didn't Cyber Pulse: Agentic Intel - Apps on Google Play I Built an AI PR Reviewer That Catches Bugs by Not Looking for Bugs Gen Z workers are so fearful AI will take their job they’re intentionally sabotaging their company’s AI rollout | Fortune How AI Is Reimagining the Game of Golf–For Both Players and Courses GitHub - nattergabriel/reseed: A CLI tool for managing and distributing agent skills across projects Is SVG the final frontier? My AI workflow evolved from prompts to a near-autonomous workflow MLSharp Help - 3DGS Viewer & Generator I put my cognitive field based AI's runtime on GitHub Is Numble the first AI-proof game? A3: Kubernetes for autonomous AI agent fleets | Emergent Principles Deepali Vyas ("The Elite Recruiter") GitHub - msmarkgu/RelayFreeLLM: A restful API designed to route user prompts to various AI model providers. Unionized ProPublica staff are on strike over AI, layoffs, and wages Unleashing the Advantage of Quantum AI We're heading for an AI-fueled 'dementia crisis,' brain scientist warns The AI-Assisted Breach of Mexico's Government Infrastructure [pdf] GitHub - stef41/lmscan: 🔍 Detect AI-generated text and fingerprint which LLM wrote it. Open-source GPTZero alternative. Zero dependencies, works offline. MSN GitHub - visionscaper/collabmem: Enabling long-term collaboration with Agentic AI - building up episodic and world model memory over time with in-context awareness We gave an AI a 3 year retail lease in SF and asked it to make a profit | Andon Labs AI Code is Hollowing Out Open Source, and Maintainers are Looking the Other Way What leaked "SteamGPT" files could mean for the PC gaming platform's use of AI AI is the boss at this retail store. What could go wrong? GitHub - Wuzu11517/agentic-proxy: Local proxy meant to help reduce With Drones, Geophysics and ArtificiaI Intelligence, Researchers Prepare to Do Battle Against Land Mines A Single Operator, Two AI Platforms, Nine Government Agencies: The Full Technical Report 在 Steam 上购买 FriedrichAI: Offline AI 立省 10% GitHub - inevolin/resume-cli: Hit Claude usage limits? Resume any AI coding session elsewhere. Switch tools at zero friction. GitHub - atripati/ark: AI Runtime Kernel — a context operating system for AI agents. Eliminates tool bloat, loads only what’s needed, and gives LLMs their reasoning space back. How to Build a Secure AI PR Reviewer with Claude, GitHub Actions, and JavaScript This Startup Wants You to Pay Up to Talk With AI Versions of Human Experts Intel Arc Pro B70 Brings 32GB VRAM to Local AI for $949 WordPress 7.0: The Good, the AI, and the Still Missing AI on the couch: Anthropic gives Claude 20 hours of psychiatry IatroBench: Pre-Registered Evidence of Iatrogenic Harm from AI Safety Measures AI Agents Know About Supabase. They Don't Always Use It Right. The history and future of AI at Google, with Sundar Pichai Inside an AI‑enabled device code phishing campaign How Meta Used AI to Map Tribal Knowledge in Large-Scale Data Pipelines AI for Systems: Using LLMs to Optimize Database Query Execution Forecasting the Economic Effects of AI Introducing Tinker: Play with AI, bring your ideas to life AI sheds light on an ancient gaming mystery People really hate AI but not as much as Iran—or Democrats | Fortune What is an AI Product Engineer? Phoebe Gates wants her $185 million AI startup to succeed with 'no ties to my privilege or my last name': 'I have a chip on my shoulder' | Fortune
4 years as a content writer working with AI
Stephanie · 2026-06-24 · via Hacker News - Newest: "AI"

My profession is one of the main targets of the AI job doom. I've been working as a professional content writer for 4 years now, even longer if you consider the fact that all I ever did in college was write essays and thesis papers.

When Generative AI first arrived in 2020, everyone was talking about how it could make "good content" in orders of magnitude less time and money than what you'd have spent hiring a writer like me.

And yet, I'm still here. Like a cockroach that won't die, no matter what kind of bug spray humans come up with.

I don't attribute my survival in this complicated job market solely to my merits. I believe I'm a good writer, but being "good" isn't enough these days. I had the advantage of getting an early start on creating AI content. When OpenAI first introduced its breakthrough model GPT 3.5, I was lucky enough to be working in an SEO agency with a founder who prioritized learning about AI, and how it could be used in the marketing space.

Ever since then, I've had continuous exposure to how AI is used in content marketing. I've had a lot of clients, ranging from people who are extremely against AI to others who would give me access to their model and tell me to just generate everything with AI and focus on polishing it. It's this exposure to the development of AI in the content marketing space that led me to the opinions I'm sharing with you today.

I have a lot of reasons why I think AI won't (and can't) replace human workers. I've condensed them into 3 points, all based on my experience as a writer.

1. Good Prompting Is NOT Good Enough

I've heard one of my past clients say these exact words: "All you need is good prompting." Which I, in my humblest, most biased opinion, nope, it doesn't.

Unless you're the type of person to settle for just "good enough", then sure, go ahead. But your customers aren't stupid enough to fall for "good enough" content.

I also find it amusing how most of the people who say this are not writers themselves. There is more to making content than just explaining stuff to the reader, you know. If good prompting is all it takes, then why do marketing companies still hire writers? Simple, because even they know that good prompting is not good enough. The people yearn for more authenticity, something only human writers can give.

We are experiencing what is called AI fatigue. People are tired of seeing AI-generated content. Your readers can tell when you're just stating what everyone else has already said, and they stop reading.

The only instances I see where AI can make high-quality content are when it is used by competent writers or experts who have enough time to properly review and refine the content. They cover where the AI fails.

I remember working as a ghostwriter for a client who had this mindset that AI can do everything. He was an SEO specialist, and he needed some content done for a fashion company he'd contracted with. He told me to use AI and deliver the content immediately. I followed what he said and did everything I could to improve the content within the time I had. I submitted it to my client, and he approved it.

He came back to me a few days later with feedback from the fashion company. They said the content "lost" their brand voice. It was too AI-sounding. They weren't satisfied. I asked my client if we should rework it.

I genuinely felt bad. But my client insisted it was okay. We didn't have to make any adjustments or revisions. "They got what they paid for," he said. I'm not really sure where to place the blame there. Is it me, the writer who technically made the content? Some of my colleagues said it was understandable with the timeline given to me, but I still felt bad. Was it the fashion company? Based on what my client said, it seems they paid a low rate. Still, I have only his words. Is it my client who insisted that the timeline was feasible because of AI? He was pretty open about his amazement at AI, and there were times when I felt he trivialized my work as a content writer, but if he was working with a lower-than-usual budget, then I understand.

This is also where I realized what my main issue with the "good prompting is all it takes" mindset is. It minimizes the skill, time, and attention to detail needed to write great content that resonates with readers. There's also this insistence that if the quality of the content is bad or lacking in some way, all we have to do is "fix the prompt".

Why do people listen to podcasts? You can make AI generate a podcast script, but it lacks soul. AI-generated videos have also failed in capturing an audience. Why?

There are certain ways you can make content engaging. One is writing like you're talking to someone, like a friend. You have your own quirks, your own ways of expressing stuff. And this is what piques human interest. Making good content requires you to address your readers in a personal way. That's how they'll know they're actually engaging with a real person with actual, lived experiences, not a corporate bot who just wants something from them.

Here's what I believe: AI could never write the same way humans would, no matter how good your prompt is. It's been 4 years since the AI boom, and yet people can still see signs of AI usage. You're probably reading my article because you were curious what a writer like me would say about AI, right? Making good content requires the human experience.

2. With Every New Model Come New Problems

When I used to work at a digital marketing agency that was big on AI, I remember one of the most common topics during our weekly meetings was this:

"What sort of errors has the AI given you this week?"

I was already working as a writer when ChatGPT 3.5 was first released. The main problem we had with it were hallucinations. Terrible hallucinations.

At this time, I wrote content for a highly regulated industry. I spent many hours reviewing laws, because I couldn't trust the AI to give me accurate drafts. Sometimes, it would even make statements that looked credible at first, but were completely misleading. I was less experienced back then, so I was prone to missing these details. I thank God for my editor.

ChatGPT 4.0 came out, and it hallucinated less. However, the quality of the answers got worse the more complicated your prompting was. The longer you pressed on with your queries, the more it seemed to "panic". Sometimes, it would generate a paragraph that looked good at first glance. But when you read it fully, you realized that it's basically just saying the same idea again and again.

A colleague of mine told me that the way to solve this is to "give the AI more material to work with." So I did. Did it produce content good enough for me? Nope. In fact, the more I fed it with quality data I found, the more it misled me. The longer my guardrails were, the higher the chance it would hallucinate again. I've tried different ways in my own prompting, and to this day, this still ends up being a common occurrence for me.

I do see the improvement of the recent ChatGPT models, but as usual, I've encountered bugs not present with the earlier models. One time, it started talking in a different language. I'd be asking it to write me a draft, and it would write parts of it in Russian or Arabic. When asked why, it would just say that it's an "error" on their end. Has anyone ever experienced that? I think it comes from training data where people switch languages mid-way when answering a question. That's just a theory, though.

With Claude, I have a whole other problem. Dear god, it tries so hard to be politically correct.

One time, I needed to make product descriptions and blog content for a toy maker. I had an outline, and asked Claude to help me write a draft for a makeup toy for girls. And what did it do? It began lecturing me about how toys are not exclusive to gender. All because one of the keywords I added was "toys for girls." THAT IS LITERALLY AN E-COMMERCE KEYWORD.

I am not making a political statement, I am optimizing for search! People write multiple keywords in e-commerce websites, such as "toys for kids", and when people want to specifically find toys for their daughters, they are more likely to type "toys for girls."

And yet what I got was a lecture from an AI model. I tried to "reason" with it, but it wouldn't budge. It did generate a draft, but every time the keyword "toys for girls" popped up, it had an accompanying "(or boys)" with it. And on top of it a huge paragraph after the draft that reminded me how bad it is of me to think that there are toys exclusive to gender. If I wanted to be lectured on political stuff, I'd just open Twitter.

I've used these major models extensively in making content, and in my experience, none of them has ever given me content with no issues.

3. AI Cannibalization

Half of the articles published online are written by AI. Coming from the content marketing side of business, that's not really surprising. Most digital marketing agencies nowadays use AI to speed up production. They pair it with writers and a few editors who can polish and refine, and you can produce twice as much content as you did before AI.

However, with this increased use comes a problem: AI cannibalization.

Ever noticed how the difference in quality between new ChatGPT model generations seems to be shrinking? The "trust me bro" benchmarks AI companies put out suggest otherwise, but in my opinion, the gains in content quality since o1 have been quite incremental.

I believe this is because the amount of authentic human-produced content in the training data has decreased. Because more and more people are making AI-generated content, this ends up being the bulk of what newer AI models "consume" for training. As a result, what we get is a model that reiterates the same ideas and biases.

Back in 2023, Reddit was my main competitor for Google rankings. Reddit posts were dominating search with every keyword I was aiming for, especially those that focused on asking questions. Why? Because it was the site with the most human-written answers and nuanced takes. As more and more blogs and business sites adopted AI, the insights they offered became much more similar to each other.

AI doesn't reward ideas that aren't already shared by many. It's a tool based on probability. So, if your main goal is to make content that "offers a unique perspective" to your readers, using AI as your only source of information is insufficient.

AI cannibalization is dangerous because it reduces the chances of finding varying perspectives. What if we rely on AI content so much that all that's left on the internet is a bunch of content that presents the same idea, just in different packaging?

It's precisely this problem that made marketing companies hire writers again. This time, the job revolves around using AI as a tool to speed up production, focusing on editing and refining it enough to make it valuable.

I'm not going to be a hypocrite here and say that I don't use AI in my workflow. ChatGPT is very good at helping me continue my sentences when I get stuck. If I have an idea, but I don't know how to express it, I give GPT a barebones description of it, and it generates paragraphs on how I could possibly say it.

Newer AI models have also been much more reliable in making drafts, though I find myself still rewriting most of them. I usually see what it generates, take a few parts, and enrich it with my own words. Using it to check for grammar is another good use case. For research, Perplexity is a great tool. If I remember reading something somewhere and don't know where I got it, I ask Perplexity to find it for me.

However, and this is a big disclaimer, my use of AI comes with careful consideration. I've learned from experience that AI, despite its strengths, is incredibly unreliable. That's why I make sure that whenever I write about a topic I don't know, I take the time to research it.

I would summarize my point like this: AI's inherent nature to favor the mean is exactly what makes it unreliable when creating high-quality content. And it's the job of writers like me to make sure that the content we produce presents more than that. That's why we still have jobs.

AI Is a Great Tool. But It Will Always Stay a Tool

If you're wondering why I, as a writer, am okay with using AI when most of my colleagues have varying degrees of colorful but mostly negative opinions towards it, it's practicality.

I know that I'm gonna get crucified by a particular group of creatives, but in my experience, I really can't ignore or avoid AI. I have even found success marketing myself as a content writer with experience using AI. My role still exists because AI isn't enough and will never be enough on its own. You may say I'm coping, but I believe AI will never be smart enough to replace writers fully, or any other professions that need genuine skill.

In creative writing, we have this belief that an author can't create a character smarter than themselves. If they try to do so, it often comes off as pretentious. Because in writing, you cannot fully explain something unless you actually understand it. I believe it's the same with AI models. No matter how much Anthropic would like to convince everyone, I don't think there will be an AI model that can surpass human expertise.

AI still cannot tell when we're being sarcastic. It's bound by probability, so if most people have the wrong idea about something, it will most likely give you the wrong answer. If we all talked enough about how pigs can fly, it would start saying it as well. Well, until the AI companies find out and start specifically designing their AI to not say that.

Human knowledge comes not just from recognizing patterns, but from experience. There are certain things you can only learn when you experience them yourself. As with AI, it only has our knowledge as a foundation. It can never "experience" things, so it will always just base its knowledge on what we know, never making its own.

Note from the founders

We were so sick of reading AI slop that we decided to bet on human-written content instead, and hired Stephanie as our new Head of Marketing.

At IbexAI, we're automating lead research at scale. Our agent finds high-intent LinkedIn leads for sales teams and founders by scanning thousands of interactions every day.

Check it out →