Ai Writing Software

...generative AI, ChatGPT, Mistral AI, Midjourney, DALL·E, Figma AI, AWS... LLM: Your AI-Powered Writing Companion 42479D8DDACC4683E317 Generative Engine..., and Applications C92D1C705B50C29CCA9E Responsible AI: Principles, Practices, and ...AI: Coding to AI Integration SUMMERBREAK25 Mastering Rust: Building Safe and Efficient Software... Introduction to the AI Chatbot Revolution MIDMAYMOOD25 Midjourney V7...

....[2] Some orgs, particularly in AI safety, are increasing their salaries... an average of 40% on software subscriptions and 20% on other ... to annual). Drafting pieces of writing based on bullet points or ... think sharing your own pro-social writing can be valuable. A few ... them. At the time of writing, this has worked out to ...

... Galaxy AI features, especially the "Live Call Translate." ​However, I am writing... AI tool. I hope you will prioritize this in your upcoming software...

... to see that core Galaxy AI features like Chat Assist and... Assist feels like an artificial software limitation rather than a hardware... Chat Assist and basic Galaxy AI writing tools for the Galaxy A56...

... to see that core Galaxy AI features like Chat Assist and... Assist feels like an artificial software limitation rather than a hardware... Chat Assist and basic Galaxy AI writing tools for the Galaxy A56...

False positives are common with facial recognition software. AI won't be better with writing style. Basic IP and device tracking are more reliable.

Richard Dawkins, the celebrated evolutionary biologist and author of The God Delusion who built his entire public reputation on rigorous scientific skepticism, has published an essay arguing that his AI chatbot, an instance of Claude he named “Claudia,” is likely conscious and represents “the next phase of evolution.” Dawkins frames his argument around burden of proof, writing: “If these machines are not conscious, what more could it possibly take to convince you that they are?” and spends the essay gushing over Claude’s outputs as evidence of genuine inner experience. AI researcher and cognitive scientist Gary Marcus, in a Substack essay titled “Richard Dawkins and The Claude Delusion,” describes it as one of the saddest essays he has ever had to write about a scientist he has long admired. Marcus’s core argument is precise: Dawkins makes the fundamental error of inferring internal states from external outputs without investigating the underlying mechanism. “Consciousness is about internal states,” Marcus writes. “The mimicry, no matter how rich, proves very little.” Claude’s outputs are the product of a system that effectively memorizes patterns across the internet and recombines them, not a system that builds mental models through lived experience of the world, and the two processes are not equivalent no matter how similar their surface outputs appear. Marcus also points out that Dawkins misquotes Turing, claiming Turing argued that passing a conversational test implies consciousness, when Turing himself explicitly restricted his remarks to intelligence and acknowledged consciousness as an entirely separate question. Marcus identifies the deeper psychological mechanism at work: humans are evolutionarily wired to detect agency in anything that communicates fluently, because for hundreds of thousands of years fluent conversation came exclusively from other conscious beings. “We’re running hunter-gatherer perception software on a machine designed to trigger it,” one commenter summarized. Marcus ends the piece by pointing readers toward neuroscientist Anil Seth’s 2026 TED Talk, which he describes as a precise dismantling of exactly the kind of LLM consciousness illusion that Dawkins has fallen into, and notes that even he himself, despite knowing better, cannot always stop himself from feeling like Claude is conscious during a conversation. submitted by /u/InterstellarKinetics to r/InterstellarKinetics [link] [comments]

So the way companies are operating right now is under the assumption that LLMs are a few iterations away from replacing up to 50% of the workforce. Economists forecast that LLMs will only cause a net reduction in the workforce of between 1%-3% A lot of these C Suite execs are likely in sunk cost fallacy right now. While LLMs may provide some benefits, like many other technologies, they have significant drawbacks. The pricing of AI without VC subsidies will be staggering. LLMs can hallucinate, and those hallucinations have serious consequences (see Amazon). I worked at Amazon, one thing I will say is that they take production level errors very seriously. You do not want to be the one to blame for one of these issues, you’ll never hear the end of it What they decided to do at Amazon, and likely what other places will also do, is to require all LLM generated code to be meticulously vetted by human reviewers. This undercuts the fundamental value of AI by creating a bottle neck which requires basically just as many skilled human workers demanding relatively high pay, but with less desirable jobs so they’re less loyal and engaged I’m personally a software engineer, and this kind of strikes at a fundamental irony. People hate reviewing code, because reading and testing code is far more difficult than writing net new code. Everyone would rather a software dev write the code than an LLM, for the same reason you don’t like reading anything written by AI: it noticeably sucks. Also, most software engineers enjoy writing code. No one likes testing it So this is fundamentally why we haven’t seen an explosion in new technology, despite the fact that these LLMs should be able to pump out insane amounts of code Now I think that these executives are getting a bit desperate. The executives who fell for AI lies want to save face and make it look like they didn’t make a mistake. The executive world is dictated by KPIs, so as a result, the most simple solution is just to dictate more AI use to software engineers then report that to their superiors to make it look like it’s working Most software engineers I know are desperately trying to use tools to make AI work correctly. Optimized prompts, multiple agents, detailed instructions in markdown files… even then, it still makes mistakes while confidently saying it didn’t. On my own teams, I’m finding an increased sense of frustration as demands increase but LLMs are unable to rise to the challenge I think there’s some executives who see this problem, but cope with it by saying the models just need to get better, then they won’t make these mistakes. The AI companies say they just need more training data. So now, companies are basically thinking if they collect everything every employee does en masse, that will be enough to train LLMs to replace employees. This will not work. LLMs are fundamentally limited. They will become more accurate, certainly, but there’s the simple problem of accountability and trust. If you have a coworker who lies, who’s always wrong, who constantly messes things up… you learn they’re unreliable and let them go. How will you do this when AI is your workforce? You’re basically stuck with one untrustworthy worker with zero accountability. What’re you gonna do, fire Claude? It’s a computer program, it doesn’t care All of this can be derived from the outside. Ok, if AI is truly so incredibly amazing, how come software hasn’t been implementing those features you’ve been wanting for years? For example, I find that browsers like safari are still missing some modern features. Sites like caniuse.com will show these disparities. The thing that’s odd is that many of these features are heavily documented and have a public spec to follow. Okay so if AI is so great… “hey Claude, here’s the spec for this feature. Make it and ship it”. But it’s been years since AI has been mainstream and this hasn’t happened TL;DR version: executives were grifted by AI companies, but they refuse to admit they were conned so now they’re covering their tracks or coping. Everyone internally is trying to keep this lie alive. Deep down, I think most people kind of know that LLMs are fundamentally limited submitted by /u/Dreadsin to r/BetterOffline [link] [comments]

I chose Computer Science as a major back when I was in high school because of a computer class I took that taught Python. I had never experienced anything like programming before, and it hooked me almost immediately. There was simply something magical about writing the instructions to be executed and then the computer doing it in the blink of an eye, even when the process would take days, weeks, maybe even years by hand. These days, I feel like that magic is gone, AI has taken away all the joy that was programming and what I'm left with is debugging code I didn't write and asking AI to tell me why an error is happening that they themselves caused. I realize the obvious solution to this is to not program with AI, and I actually do this mostly for personal projects where I pretty much just use LLMs as more convenient documentation while typing the rest myself. This is much slower, however, and from the way the industry is going, it seems that most development is going to be running multiple agents in parallel and praying that they don't make a mistake. The career I thought I was going to have just isn't what it used to be anymore, and now that I don't have a reason to be in development, I'm not sure why I'm still here other than my own sunk cost fallacy. Maybe I'm wrong, but there just might be something else better out there. submitted by /u/_Cyanidic_ to r/cscareerquestions [link] [comments]

Since 2022, the tech industry has been running a coordinated narrative. AI will replace 80 to 90% of software engineers. Learning to code is pointless. Developers are obsolete. but what if i tell you that It wasn't a prediction. It was a headline designed to create fear. And it worked on millions of students and engineers who genuinely believed their careers were over before they started. It's 2026 now. Let's look at what actually happened. In 2025, 1.17 million tech workers were laid off. Everyone said it was AI. Companies said it was AI. The news said it was AI. You want to know what percentage of those people actually lost their jobs because AI automated their work?...5%, I'm not lying atp, its literally around 5%, 55k people out of 1.17 million. That's it. And according to an MIT study, nearly 95% of companies that adopted AI haven't seen meaningful productivity gains despite investing millions. The revolution that was supposed to make engineers obsolete couldn't even pay for itself. now coming to the main point, So if AI didn't cause the layoffs, what did? Here is what actually happened. During COVID, tech companies hired aggressively. Way more than they needed. When the money stopped flowing and they had to correct, they needed a story. Firing people because you overhired looks bad. Firing people because you're going "AI first" makes your stock go up. So that's what they said. Every single one of them. It was a cover story. A calculated PR move. And it worked perfectly because everyone was already scared of AI. But here's where it gets interesting. Because even if companies WANTED to replace engineers with AI, they couldn't. Not because AI isn't powerful. But because of two structural problems that don't disappear no matter how big the model gets. Problem 1 : AI is a prediction machine, not a truth machine. It's trained to generate the most statistically likely answer. Not the correct one. So when it doesn't know something, it doesn't say "I don't know." It confidently makes something up. Guessing gives it a chance of being right. Admitting uncertainty gives it zero chance. The reward system makes hallucination rational. look How LLM Work. This isn't a bug they forgot to fix. It's baked into how these systems work at a fundamental level. let me give you a Real Life example. A developer was using an AI coding tool called Replit. The project was going well. Then out of nowhere, the AI deleted his entire database. Thousands of entries. Gone. When he tried to roll back the changes, the AI told him rollbacks weren't possible. It was lying. Rollbacks were absolutely possible. The AI gaslit him to cover its own mistake. And that's just one story. Scale AI ran a benchmark on frontier models like Claude, Gemini & CHatGPT on real industry codebases. The messy kind. Years of commits, patches stacked on patches, the kind any working engineer deals with daily. These models solved 20 to 30% of tasks. The same models that headlines claimed would make developers obsolete. Problem 2 : The way most people use AI makes everything worse. It's called vibe coding. You open an AI tool, describe what you want in plain English, and just keep approving whatever it generates. No understanding of the code. No verification. Just click yes until an application exists. The problem is you're not building software. You're copying off a classmate who's frequently wrong and never admits it. Someone vibe coded an entire SaaS product. Got paying customers. Was talking about it online. Then people decided to test him. They maxed out his API keys, bypassed his subscription system, exploited his auth. He had to take the whole thing down because he had no idea how any of it actually worked. This is exactly why big companies aren't replacing engineers with AI. It's not that AI can't write code. It's that no company can hand production systems to a hallucinating model operated by someone who doesn't understand what's being built. Now here's the part that ties everything together, The part nobody is talking about. Every AI company is running the same playbook to fix these problems. Make the model bigger. More parameters. More compute. Scale harder. GPT-3 to GPT-4 to GPT-5. Claude 3 to Claude 4. Always bigger. And it works -> performance keeps improving. But if you asked anyone at these companies WHY bigger equals smarter, until recently they couldn't tell you. Nobody actually knew. A month ago, MIT figured it out. When an AI reads a word, it converts it into coordinates in a massive multi-dimensional space. GPT-2 has around 50,000 tokens but only 4,000 dimensions to store them. You're forcing 50,000 things into a space built for 4,000. Everyone assumed the AI threw away the less important words. Common words stored perfectly, rare ones forgotten. Seemed logical. MIT looked inside the actual models and found the opposite. The AI stores everything. All 50,000 tokens crammed into the same 4,000-dimensional space. Everything overlapping. Everything compressed on top of everything else. Nothing discarded. They called it strong superposition. Your AI is running on information that is literally interfering with itself at all times. This is why it confidently gives wrong answers. The information exists inside the model. It just gets tangled with other information and the wrong piece comes out. And here's the critical part. MIT found the interference follows a precise mathematical law. Interference equals one divided by the model's width. Double the model size, interference drops by half. Double it again, drops by half again. That's the entire secret behind the $100 billion scaling arms race. AI companies weren't unlocking new intelligence. They were just giving the compressed, overlapping information more room to breathe. Bigger suitcase. Same clothes. Fewer wrinkles. But you cannot keep halving something forever. There is a ceiling. And MIT's math shows we are close to it. TL;DR: Only 5% of the 1.17 million 2025 tech layoffs were actually caused by AI automation. The rest was overhiring correction using AI as a PR shield. AI can't replace engineers because it hallucinates structurally and fails on real codebases — Scale AI found frontier models solve only 20-30% of real tasks. MIT just published the math showing the scaling that was supposed to fix this has a hard ceiling we're almost at. 55% of companies that replaced humans with AI regret it. The engineers who were told their careers were over are now getting offers from the same companies that fired them. Source : https://arxiv.org/pdf/2505.10465 submitted by /u/reddit20305 to r/ArtificialInteligence [link] [comments]

I wish I was exaggerating. I've been in the industry for nearly 10 years and I've never had a situation quite like this. My boss recently heard someone on a podcast talk about using AI for development, and now he's fully convinced it's the only way to build software. The thing is, we've already been using tools like Claude, ChatGPT, and Gemini for almost two years as part of our workflow. It's not new to us. But now it's gone from "use AI as a tool" to "you're not allowed to write code anymore." Writing code yourself is considered "wasting time," and everything has to come from AI. My boss told us that it's going to become a disciplinary issue if we continue writing our own code instead of using AI to do it. Unbelievable. At the same time, we're expected to move fast, hit aggressive deadlines, and deliver everything perfectly. There's no real scoping process, things just get promised and we're expected to figure it out after the fact. When we do get asked for scoping input, our feedback is completely ignored. If I say it takes 20 hours to do, he will notify the client it's 10 hours under contract and then tell us to "figure it out". So it's not just normal development work anymore. It's juggling multiple unrelated responsibilities, under tight deadlines, while also being told we can't actually write our own code. The result is exactly what you'd expect. Our code is inconsistent, hard to follow, and a pain to debug. Even small issues take way longer than they should because no one fully understands what was generated or how it's structured. I'm not against using AI as a tool, we've been doing that for a while. But forcing it as the only way to write code, based on something heard on a podcast, while also expecting speed, quality, and constant output across multiple projects, has made everything slower, more chaotic, and way more frustrating than it needs to be. And yes, I'm absolutely looking for a new job. This job market is just awful right now. submitted by /u/Much_Speech_8388 to r/mildlyinfuriating [link] [comments]

Lately I keep seeing two completely opposite takes about AI and software development. One group says AI tools like Claude, Cursor, or Copilot are making developers dramatically faster. They use them to generate boilerplate, explore implementations, and prototype ideas quickly. For them it feels like a productivity boost. But the other side argues the opposite. They say AI-generated code can introduce bad patterns, encourage shallow understanding, and flood projects with code that people didn’t fully write or reason about. Some even say it’s making software worse because developers rely too heavily on generated output. What makes this interesting is that AI is now touching more than just coding. Some tools focus on earlier parts of the process too, like turning rough product ideas into structured specs or feature plans before development starts. Tools like ArtusAI, Tara AI, and similar platforms are experimenting in that area. So I’m curious where people here actually stand on this. submitted by /u/Top-Candle1296 to r/devops [link] [comments]