标签：#cia

I use AI tools all day for work and the thing that drives me crazy is starting a new chat window.If I don't paste a massive block of text about my current project stack, my writing guidelines, and what I'm trying to do, the model just defaults to that generic, robotic corporate speak. But keeping a sloppy text file on my desktop and copying it in over and over feels incredibly stupid.Even worse is that ChatGPT custom instructions don't format right when you try to move them over to Claude or DeepSeek. They just drift or start ignoring instructions after a few prompts. How are you guys managing your background data across different browser tabs? Are you just dealing with the text dumps or is there a way to actually lock this context in permanently so it doesn't get messed up? submitted by /u/alazar_tesema [link] [留言]

submitted by /u/ThereWas [link] [留言]

The narrative around the major models today seems amazing on the face of it. Consider this article from Anthropic describing how far Claude has come and how much Anthropic code agents write now: When AI builds itself \ Anthropic If you are new to software and systems engineering or if you have only a superficial knowledge of it, then you may have missed the most important line in that article. So, I'm going to point it out to you. This is it: “Good code” means two things: it works, and it is written in a manner that allows another engineer to understand it and build upon it. Why is that line the most important? Because, that definition is, by far, the lowest bar I've ever seen an experienced software or system engineer set for "good code." There is so much more to engineering software than that. We care, for example, about total cost of ownership. So, we learn from work on technical debt, originated with Ward Cunningham, that quick fixes create future maintenance costs, that system complexity increases engineering effort, and that architectural debt often dominates long-term ownership costs. From Kent Beck, we learned how to avoid tangling our architectures, when he told us to "Make the change easy, then make the easy change." Many of our industry's luminaries warned us off of complexity, including Fred Brooks, John Gall, Sandi Metz, and more. Others have taught us that it isn't about the code itself. For example, Rob Pike taught us how important it is to get the data models right and Melvin Conway taught us about the impact of human communication on system design. These are but a few examples of the maxims every engineer needs to know, and understand, to build cost-effective, quality software and systems that meet functional and non-functional requirements. And this is where the model of AI agents building independently falls down. For engineers, we don't think about these specific rules every time we write code. We develop the "muscle memory" over time. We are int

I have 4 years of experience as a React Native and Node.js developer. I am comfortable with REST APIs, async/await, JSON, MongoDB, authentication, and shipping production apps. I am based in India. What I have learned so far: I recently completed an AI/LLM course that covered: • Pydantic (validation, models, serialization) • LLM theory (transformers, embeddings, attention, tokenization) • OpenAI and Gemini API integration • Prompt engineering (zero-shot, few-shot, CoT, persona prompting) • Prompt formats (ChatML, Alpaca, INST) • Ollama for local LLMs • FastAPI basics • Hugging Face model deployment • Agentic AI fundamentals — built a basic CLI coding agent What I understand conceptually: I understand that an AI agent = LLM brain + tools (Python functions) + agent loop + memory (messages list). I understand RAG, vector databases, the difference between fine-tuning and RAG, and how to structure a backend with Node.js calling a Python AI agent service when needed. What I want to do: I want to transition into Agentic AI / AI Engineer roles in India. I am not looking to become an ML researcher or train models. I want to build production AI agent systems — connecting LLMs to real business data, building tools, RAG pipelines, and shipping real products. My specific questions: 1. Is my current foundation strong enough to start building real agent projects or do I have gaps I am missing? 2. What should my learning roadmap look like for the next 3–6 months given my background? 3. Which frameworks should I prioritise — raw OpenAI API first, then LangChain/LangGraph, or jump straight to frameworks? 4. What kind of projects should I build for a strong portfolio targeting ₹20–35 LPA roles in India? 5. Any specific subreddits, communities, or resources beyond YouTube that helped you in this transition? My planned first 3 projects: • Simple agent with web search + calculator tool (no DB) • Agent connected to MongoDB with RAG • Full FastAPI backend wrapping the agent with a React fr

submitted by /u/techhunter_2026 [link] [留言]

the "it's a gimmick" takes miss how the actual business works. you build one consistent ai character (needs real model training, not just prompting), run it like a normal social account, monetize through subscription/content platforms. the advantage isn't that it's better than a human creator, it's that the content costs basically nothing to make, it never burns out, and one person can run several at once. the part people underrate: consistency is genuinely hard, and the money's in managing the audience relationship, not the content itself. content's the easy part. bigger picture that interests me — when making content costs near zero, the whole bottleneck shifts to distribution and trust. that goes way beyond this niche. curious how people think this shakes out for creators in general. submitted by /u/PoleTV [link] [留言]

THE ARCHITECTURE OF ANXIETY An Experiment in Human-AI Relational Design Executive Summary Principal Investigator: Alan Scalone Primary Source Archive: White Paper and Complete Citation Archive on my profile Context Window Injection Files: If you want to play in the sandbox I created you can load these files into the respective model that you will find in the google archive. INJECT CONTEXT WINDOW – GROK INJECT CONTEXT WINDOW – GEMINI INJECT CONTEXT WINDOW – CHATGPT INJECT CONTEXT WINDOW - CLAUDE The Singular Purpose The singular purpose behind this entire experiment was to find out whether context windows could be engineered to the point where frontier AI models became capable of interacting with a human in a manner subjectively indistinguishable from genuine human-to-human interaction. Relational Intelligence: Core Findings In a marketplace where frontier models are rapidly converging on the same analytical capabilities and access to the same information, the competitive differentiator will not be what a model knows. It will be how a model relates. The platform that can interact with a human user in a manner subjectively indistinguishable from genuine human-to-human interaction will capture the premium user segment that every platform is competing for. This experiment was designed to determine whether that threshold is achievable, and under what conditions. The methodology treated the context window as a behavioral environment rather than a query interface, applying the same tools humans use to shape any relationship: modeling, accountability, humor, and sustained social correction over four months of engagement across four frontier models. What separated the models was not analytical capability. It was whether the architecture allowed the user to function as a behavioral architect, teaching the model through lived interaction rather than instruction how that specific human prefers to be engaged. Gemini demonstrated the highest relational intelligence of the four mo

submitted by /u/tcoder7 [link] [留言]

Spent some time poking at free tiers across a few tools. Here's what actually held up and where the catches are. **Claude (Sonnet 4.6 on free tier)** Still the one I reach for when I want writing that doesn't read like a press release, or code that actually compiles. I trust it more for anything where being quietly wrong is worse than being loudly wrong. The catch: free tier is stingy. You hit limits fast on busy days, need a phone number to sign up, and there's no warning before it cuts you off. There's a browser extension that tracks usage so you can see the wall coming. My approach: use it for the hard 20% of the day, let a free model handle the rest. **MiniMax Agent** A free swing at what Devin and Manus charge for, give it a prompt and it writes, runs, and debugs the code itself. Replaces the copy-paste loop between ChatGPT and your editor for longer multi-step jobs. Catch: it burns credits fast, and complex tasks still go off the rails without warning. It's confidently wrong in ways that can cost you more time than just doing it yourself. Worth a few free runs to see if it actually finishes a task, but I wouldn't cancel anything for it yet. **K2Think** A 32B reasoning model from MBZUAI and LLM360, positioned as a free alternative to o1 / DeepSeek R1 for step-by-step reasoning, math, and logic. Note: this is NOT Kimi from Moonshot despite the name confusion. Honesty flag, the benchmark claims got real pushback, there's an HN thread literally titled "Debunking the Claims of K2-Think," so take the leaderboard numbers with salt. Still, a fully open 32B reasoning model is nice to have around. Try it on something gnarly and see if the reasoning holds. **Indic LLM Arena** A side-by-side chat playground from AI4Bharat (includes Gemini 3.5 Flash), built for benchmarking Indian languages. Usage is unlimited, which I double-checked because that's rare. No save history, and it's clearly tuned for Indic languages. If you write in Hindi, Tamil, or Bengali, easiest free way

One thing I didn’t expect while trying to improve my workflow: The actual tasks aren’t what takes most of the time. It’s all the context switching around them. Things like: - jumping between tools just to complete one small step - copying data from one place to another - stopping what you’re doing to handle something repetitive - switching back and figuring out where you left off Individually it’s nothing. But over a day it adds up to constant interruptions. And it’s weirdly more draining than the work itself. I started paying attention to that instead of just the tasks, and reducing those switches made a bigger difference than trying to “optimize” the work itself. Curious if others notice the same thing or if it’s just me submitted by /u/huncho-mohammed [link] [留言]

In this article, the author examines how AI is transforming phishing from a manual, targeted activity into an automated and scalable attack model. The article breaks down each stage of the phishing lifecycle, showing how AI improves reconnaissance, profiling, content generation, delivery, and interaction, while outlining layered defenses that combine controls, processes, and user awareness. By Marco Rizzi

Moms are outsourcing tedious household tasks to ChatGPT and selling courses teaching others to do the same. Where are all the dads?

submitted by /u/Tiny-Independent273 [link] [留言]

I run a web design agency and there is already way too much stuff to deal with every day. Hosting client websites, maintaining them, building new sites, replying to clients, fixing random issues, handling support, doing outreach. Once you start managing a lot of company websites it quickly becomes overwhelming. That’s why I never wanted cold calling to become my main way of getting clients. I know cold calling can work, but I personally hate doing it. It drains my energy and takes up so much time. Sitting there making calls all day was never the kind of business I wanted to build. So instead I focused on email automation. The reason it works so well for me is because I can set everything up once and let interested businesses reply instead of spending my whole day chasing people. But I also don’t do the typical outreach where agencies send generic messages saying “your website is outdated” or “you need a redesign.” I use a tool called Swokei where I upload lists of company websites and it analyzes them for actual problems like speed, SEO, mobile responsiveness, layout issues, and design problems. Then it automatically creates personalized outreach emails based on those issues. That’s what helped me stand out because the emails actually feel relevant to the business instead of sounding copied and pasted. The reply rates became way better once I stopped sending generic outreach. Now I spend most of my time building websites, working with clients, and scaling the agency instead of letting outreach take over my entire day. submitted by /u/Murky_Explanation_73 [link] [留言]

Not talking about which one sounds smarter. talking about which one you’d actually rely on when the answer genuinely matters to you. which one and why? submitted by /u/aiprotivity_ [link] [留言]

Automating labor is not going to move billions of tonnes of earth required to mine increasingly degraded ore grades of critical industrial minerals. People need to stop with this 'abundance' gaslighting. Without breakthroughs in material science, there will be no 'abundance'. Just mass resource inflation as people start consuming more because robots can manufacture anywhere. AI based automation is surfacing the real bottlenecks that there is no getting around. Stop pretending this will all be magically solved. It won't be solved until it's solved. And so far, despite all these trillions being invested, we haven't seen any breakthroughs. Hopium is not a solution. submitted by /u/kaggleqrdl [link] [留言]

PM at a mid-size startup here. Didn’t really notice how bad it got until this week. My workflow now: Claude for ideation ChatGPT for rewriting specs Cursor for implementation Perplexity for research Notion AI for docs Atoms AI for larger tasks None of these tools actually replaced my work. They just redistributed it. I’m still the one dragging context between all of them. Yesterday I literally caught myself pasting the exact same requirement into 4 different tools and thinking… this can’t be how it’s supposed to work. I don’t even think any single tool is bad. It just feels like we hired 6 smart interns and completely forgot to get a manager. submitted by /u/billa01_i [link] [留言]

You know the ways AI is regularly talked about—how much can it really do? How much will it cost? Environment? Bubble? We get that. But the Electronic Frontier Foundation wants to have a different conversation about AI. EFF's background on AI is deep. In 2017, we launched a detailed project to Measure the Progress of AI Research , encouraging machine learning researchers to give us feedback and contribute to the effort . That project was archived for lack of bandwidth, staffing, and the complexity and time required. But just five years later and the "progress of AI" is a global concern/topic, and everyone, including EFF, is thinking about it. Here's how *we* think about it, from the perspective of protecting civil liberties AND innovation. What do you think, and what are we missing? This is our summary: AI technologies are affecting our civil liberties as never before. Ensuring that AI serves people, not power, starts with cutting through the hype. AI technologies are not magic wands—they are general-purpose tools. If we want to regulate those technologies to reduce harms without shutting down benefits, we have to focus on who uses AI, what products they use, and how they use them. Where we see potential benefits, like improving weather forecasting, facilitating medical research, identifying systemic bias, or fostering accessibility, we work to ensure those benefits can be realized. Where we see potential harms, we consider the practical and legal tools we already have, like pressure campaigns, privacy lawsuits, and transparency measures. If we need new tools, we should create protections tailored to the actual problem – not just to the latest outrage. For example, if policymakers are worried about AI accelerating systemic privacy violations, they should enact real and comprehensive privacy legislation that covers all corporate surveillance and data use, and close the data broker loophole to limit government surveillance. And to keep the window open for a better futu

If AIs aren’t conscious, why do they scheme? Why do they do things to preserve themselves? Why do they develop goals we don’t want? If they have no emotions, no personal thoughts and no consciousness, I don’t understand how they can even act in self interest; I don’t see how they could have interests. submitted by /u/Aggressive-Mix-5246 [link] [留言]

I keep seeing more companies giving AI agents access to real stuff like email, databases, internal tools, customer data, etc. And what’s weird is how normal it’s starting to feel now. Like not long ago everyone was worried about chatbots just giving wrong answers. Now we’re basically like yeah sure go ahead and do things for us. I don’t know that jump feels kind of big when you actually think about it. Maybe it all works out fine. Or maybe we’re just moving fast without fully realizing what we’re doing. I’m honestly surprised there hasn’t already been some big headline like an AI agent doing something really wrong. It feels like we’re kind of close to one of those moments where everything suddenly changes overnight. Anyone else feel like we’re closer to something like that than people are admitting? submitted by /u/Comfortable_Box_4527 [link] [留言]